Surface salinity budget in oceanic simulation using data from TOGA COARE

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. C5, 3135, doi: /2001jc001013, 2003 Surface salinity budget in oceanic simulation using data from TOGA COARE Marcelo Dourado Program of Research Fellowships CNPq Brazil, Météo-France, Centre National de Recherches Météorologiques, Toulouse, France Guy Caniaux Météo-France, Centre National de Recherches Météorologiques, Toulouse, France Received 7 June 2001; revised 9 September 2002; accepted 28 January 2003; published 2 May [1] A three-dimensional regional model is used to investigate the role of different terms of the salinity budget in the western Pacific during TOGA COARE. The model is a version of the ocean general circulation model (OGCM) developed at the Laboratoire d Océanographie Dynamique et de Climatologie (LODYC) in Paris and includes open lateral boundaries and a 1.5-level-order turbulence closure scheme. The surface atmospheric forcing used, including water flux, comes from a combination of European Center for Medium-Range Weather Forecasts model output and estimates from bulk parameterization. The data set collected during the Intensive Observation Period enables the initialization, taking into account the lateral boundary conditions, and validating the model outputs. Different atmospheric weather conditions were experienced during the simulation of the variability of the mixed layer. During 25 days, air-sea fluxes were downward, that is precipitation exceeds evaporation, and increasing with time. In periods of weak winds (December 2 12), strong solar radiation, and shoaling of the oceanic mixed layer, the entrainment is negative and salt is removed from the oceanic mixed layer. Mixed layer currents are low and slightly divergent from the equator toward the south with a mean eastward component. Due to the zonal advection, there is a freshening tendency in the north of the domain. In periods of strong winds (December 21 27) and reduced solar radiation, the oceanic mixed layer is deepening and entrainment is positive and contributes to the salinity increase. The salinity storage term is quite inhomogeneous during the two periods as a consequence of the patchy pattern of advection. Moreover, most of its temporal and spatial variability is strongly correlated with the temporal and spatial variability of the advection term. Currents are largely converging toward the equator and create a band of saltier water in the surface layer. INDEX TERMS: 1610 Global Change: Atmosphere (0315, 0325); 1803 Hydrology: Anthropogenic effects; 1615 Global Change: Biogeochemical processes (4805); KEYWORDS: oceanic salinity budget, TOGA COARE, air-sea interaction, mesoscale modeling Citation: Dourado, M., and G. Caniaux, Surface salinity budget in oceanic simulation using data from TOGA COARE, J. Geophys. Res., 108(C5), 3135, doi: /2001jc001013, Introduction [2] The western equatorial Pacific is characterized by monthly sea surface temperature that exceeds 29 C, weak trade winds, deep convection, and net freshwater input for the ocean. This region plays an important role in climate variability through air-sea interaction [Webster and Lukas, 1992]. These calm conditions are sporadically disturbed by westerly wind bursts (WWB) along the equator which are supposed to play a major role in El Niño-Southern Oscillation (ENSO) events [Godfrey et al., 1998]. Copyright 2003 by the American Geophysical Union /03/2001JC [3] Tropical Ocean Global Atmospheric Coupled Ocean- Atmosphere Response Experiment (TOGA-COARE) program was conducted to describe and understand the principal processes responsible for the coupling of the ocean and the atmosphere in the western Pacific warm pool. The role played by salinity in this coupling, its variability at different time scales were defined as principal objectives of the oceanographical component of TOGA-COARE. [4] The intensive observation period (IOP) was conducted from November 1992 through February This period was chosen because westerly wind bursts have higher frequency of occurrence during these months [Cronin and McPhaden, 1997]. Three such bursts are apparent during the IOP, with westerlies in early November 1992 in 4-1

2 4-2 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE the IFA, strong westerlies near and east of the IFA in late December 1992 and in late January through February 1993 [Chen et al., 1996]. [5] Both oceanographic and meteorological observations were concentrated within the 150 km 150 km intensive flux array (IFA) centered at 2 S, 156 E [Huyer et al., 1997]. [6] Several studies have discussed the salinity and its variability. Delcroix and Henin [1991] used sea surface bucket measurements, obtained through a ship-of-opportunity program, to describe the sea surface salinity (SSS) field for the tropical Pacific during the period They found that at seasonal timescales, the maximum SSS changes occur in the ITCZ and SPCZ, in relation with local precipitation and eastward flowing North and South Equatorial Counter Currents. [7] The role of zonal advection was emphasized by Picaut et al. [1996]. The dominance of surface zonal advection in east-west migration of the warm pool is demonstrated with four different current data sets and three ocean models. The eastward advection of warm and less saline water from the western Pacific together with the westward advection of cold and more saline water from the central-eastern Pacific induces a convergence of water masses at the eastern edge of the warm pool and a welldefined salinity front. The location of this convergence is zonally displaced in association with El Niño-La Niña wind-driven surface current variations. [8] Ando and McPhaden [1997] examined the variation of the ocean surface layer hydrography on interannual timescales in the tropical Pacific Ocean using conductivity-temperature-depth measurements from 1976 to They demonstrated that associated with interannual variations in atmospheric forcing, there were distinct changes in mixed layer temperature, salinity, depth, and barrier layer thickness between normal, El Niño, and La Ninã time periods. [9] Vialard and Delecluse [1998a, 1998b] undertook a modeling study to investigate the effects of haline stratification on the low-frequency equilibrium of this region and the processes responsible for barrier layer formation in the Pacific Ocean during the decade. Their sensitivity experiments illustrate the important role of the haline stratification in the western Pacific. This stratification is the result of a balance between precipitation and entrainment of subsurface saltier water. It inhibits the downward penetration of turbulent kinetic energy. This results in a trapping of the WWB momentum in the surface layer, giving rise to strong fresh equatorial jets. [10] Using geostrophic surface current anomalies from Geosat sea level, Delcroix and Picaut [1998] found that zonal displacements of the eastern edge of the western equatorial Pacific fresh pool (SSS < 35) were dominated by interannual variations that are highly correlated with the Southern Oscillations Index of ENSO (SOI). [11] Hénin et al. [1998] investigated the variability of SSS with processed data collected from thermosalinographs embarked on merchant ships. The period of observation covered the El Niño and the moderate 1995 La Niña. They found that rainfall input acts as a source of freshwater responsible for the existence of a contrasted distribution of SSS. However, the main mechanism responsible for SSS variability is zonal advection that leads to convergence of the two water masses, resulting in a salinity front which shifts back and forth in the equatorial band. [12] Cronin and McPhaden [1998] studied the salt budget in TOGA-COARE area using 2.5 years of data (September 1991 through April 1994) in the equator (0, 156 E). They show that the excess of surface freshwater flux was balanced primarily by vertical mixing and by zonal advection. For timescales between a month and 2.5 years, surface salinity variability was dominated by zonal advection and only weakly correlated with precipitation. [13] Feng et al. [2000], using the R/V Wecoma IFA survey data, found that advection in the upper ocean cannot be neglected during the IOP. Zonal advection had a net freshening tendency, and meridional advection increased salinity in the surface layer. In order to explain the upperocean freshwater balance, 1D processes were generally not adequate especially during WWB periods [Feng et al., 2000]. [14] In this paper, a three dimensional regional model is used to investigated the time-space evolution of the salinity in the western Pacific warm pool in response to different weather conditions observed during the Intensive Observation Period (IOP). The present paper is the second part of the work by Dourado and Caniaux [2001] dedicated to the heat budget and specially focused the salinity budget. [15] This paper is organized as follows. In section 2, we describe the modeling approach, including the treatment of the model initialization procedure and the surface flux. Simulations with typical trade winds and typical westerly wind burst are performed. Comparisons between the model outputs, analyses and real data sets from COARE are presented in section 3. In section 4, the use of a specific form of the salinity budget is presented and the different physical processes active in the oceanic mixed layer (OML) discussed. Section 5 summarizes the most important results of this study. 2. Experiment Design 2.1. Numerical Model [16] In this section we describe in a summarized way the numerical model and the initialization procedure. For a more complete description the reader is invited to refer to Dourado and Caniaux [2001]. The model used for this study is a regional version derived from the OPA OGCM developed at the Laboratoire d Océanographie Dynamique et de Climatologie (LODYC). The basic assumptions and the set of discretized equations using tensorial formalism are described by Blanke and Delecluse [1993]. The main assumptions are: primitive equations, rigid lid approximation on the top, and a 1.5 turbulent closure scheme. The vertical mixing coefficients are based on the calculation of two turbulent length scales defined by Bougeault and Lacarrère [1989] Open Boundary [17] For the open boundary, a Newtonian damping [Davies, 1976] is applied to the prognostic variable X in a zone near the boundary, forcing it to relax toward a largescale data field ~X (i) following the t X ¼ DX ð Þ Ki ðþ X ~X ðiþ ; ð1þ

3 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE 4-3 where D(X) is the prognostic part of the model and K(i) isa damping coefficient. Both K(i) and ~X (i) vary with the distance i from the lateral boundary. Following Leslie et al. [1981], equation (1) is solved to find X t +1 by first estimating its value X p t +1 from the prognostic equation with the boundary relaxation term excluded. The final semi-implicit solution for X t +1 is found by time weighting as follows: where X tþ1 ¼ ½1 aðþ i ŠXp tþ1 þ aðþ~x i t ; ð2þ aðþ¼2k i ðþt i ½1 þ 2KðÞt i Š 1 : ð3þ [18] The relation is applied to the five outermost points of the simulation domain, a(i) varying from 0 to 1 inside the relaxation zone, and depends upon the time step (t). The specified values ~X (i) on the boundaries of the simulation domain may be held constant in time or may be allowed to evolve. [19] This procedure is robust enough to filter all wave disturbances on the inner domain boundary and may be used with time constant or time variable external fields [Caniaux and Planton, 1998]. The values ~X can be either derived from observations or from a larger domain model which includes the area of the present model. In the reported experiment a tendency is imposed on the ~X variable, at each point and level of the model. This tendency is obtained by linear interpolation between two analyses of the data set, calculated as reported in the next section. [20] This Newtonian damping is applied only to the temperature and salinity equations. Currents at the boundaries are deduced from the geostrophic approximation, which is justifiable even a few degrees away from the equator [Picaut et al., 1989]. In this configuration, the inertial oscillations of the mixed layer are smoothly damped close to boundary. For the barotropic stream function a tendency is added on the boundaries. This tendency is computed from the geostrophic currents associated with the initial and final analyses. Because of the linear interpolation this tendency is held constant throughout the simulation [Caniaux and Planton, 1998] Initialization Procedure [21] In the present study, the simulation domain extends from E to E and from 4.84 S to 1.24 S ( km 2 ) with a horizontal mesh of 0.1 in longitude and 0.08 in latitude. In order to have a good resolution in the mixed layer, the vertical levels are spaced 5 m in the top 40 m and are progressively stretched to 150 m at the bottom (580 m). In this configuration the modeled domain includes grid points and 29 levels. A time step of 15 min is used. [22] Initial temperature and salinity fields were determined by optimal interpolation from all available CTD and buoy data collected during the IOP. Three analyses have been done. The first analysis was time-centered on December 2, the second one on December 17, and the last one on December 27. These dates correspond to different weather conditions. Low wind and strong solar radiation persisted from December 2 to 17 and strong winds persisted from December 17 to 27. [23] The data set comes from nine ships, representing a total of 800 vertical profiles, and from the Woods Hole Oceanographic Institution (WHOI) IMET (1.75 S, 156 E) mooring with a sample interval of 15 min [Weller and Anderson, 1996]. Figure 1 shows the spatial distribution of CTD data set in December [24] For the first analysis (December 2), in order to fill some gaps not covered by data, we used as first guess temperature and salinity fields provided by numerical simulations of the tropical Pacific Ocean during the decade. These were performed with the OPA primitive equation OGCM at the LODYC (Trois Océan tropicaux (TOTEM)/OPA) [Maes et al., 1997]. The other analyses (December 17 and 27) use the previous analysis as first guess. Thus, we use the maximum of available information to make an initial state as realistic as possible. [25] The data were interpolated on each of the 29 levels of the model and then objectively analyzed [De Mey and Ménard, 1989]. These analyses were done using gaussian correlation functions with large correlation scales (300 km in space and 10 days in time) in order to cover the gaps. The analyses were centered at midnight (local time) because the diurnal variability of temperature could vary by more than 1 C over the course of the day Surface Fluxes [26] In this study we used the European Center for Medium Range Weather Forecasts (ECMWF) atmospheric parameters (air temperature, wind speed, pressure, humidity), the sea surface temperature of the model and a parameterization scheme to calculate the turbulent fluxes. [27] The analyses field from ECMWF provided gridded values of meteorological parameters. The surface meteorological parameters (pressure, air temperature, humidity, and wind speed) are instantaneous values. Precipitation, incoming longwave radiation, and incoming shortwave radiation represent cumulated values of 6 hours. Then the surface meteorological parameters are linearly interpolated in time, while the radiative fluxes and the precipitation used in the simulations are averaged values over the period of 6 hours. All these data are given on a coarser grid (0.5 ) than the ocean model. Thus, at each time step, the atmospheric parameters are horizontally interpolated to the grid of the present model. [28] The turbulent heat fluxes and stress are computed using the surface meteorological parameters of the ECMWF, the sea surface temperature of the ocean model, and the Fairall et al. [1996] bulk algorithm. The basic structure of this algorithm includes a different specification of the roughness/stress relationship to consider roughness due to gravity waves and molecular viscosity [Smith, 1988]. A gustiness coefficient accounts for the additional flux induced by boundary layer scale variability, and the profiles for the stability dependence of temperature, moisture, and momentum in very unstable conditions are changed to agree with the free convection of Panofsky and Dutton [1984]. Additionally, the model considers the contribution of the sensible heat carried by precipitation and the requirement that the net dry mass flux should be zero (the so-called Webb correction). Cool skin and warm layer effects on bulk sea temperature measurements are integrated into the algorithm [Fairall et al., 1996]. In order to take into account the effect of subgrid-scale convective motions on surface fluxes, the parameterization

4 4-4 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE Figure 1. TOGA COARE Intensive Flux Array and the spatial distribution of CTD data set collected by several ships for December 1992 (after Dourado and Caniaux [2001]). for the mesoscale enhancement proposed by Redelsperger et al. [2000] has been included. The outgoing longwave radiation is calculated using the Stephan-Boltzmann law. [29] Table 1 and Figure 2 show the temporal evolution of the surface salinity, precipitation rates (P), and evaporation (E) and the difference between the evaporation and precipitation for IMET buoy and the grid point of the model nearest to the buoy (1.72 S, 156 E). We have included also the precipitation data from CURRY et al. [1999] (hereinafter referred to as CURRY). They prepared a high-resolution data set for TOGA-COARE, with a spatial resolution of 50 km and temporal resolution of 3 hours. Their precipitation retrieval combines analysis of satellite microwave brightness with a statistical model using satellite observations of visible/infrared radiances (CURRY). However, these data do not cover the full model domain. [30] The difference E-P is generally negative and indicates an input of freshwater into the ocean (a factor, P/E, 5.8 for the IMET data set, 2.6 for ECMWF, and 3.2 for CURRY). However, the ECMWF precipitation is underestimated when compared to IMET and CURRY in both periods and mainly in the second one. Because evaporation is on average slightly overestimated, this results in a underestimation of the input flux of freshwater. The CURRY data reproduce in a more exact way the temporal variability of the precipitation. An improvement is also observed in the averaged values, even if the difference between the IMET and CURRY data is still significant. [31] Quantification of the freshwater flux is much more difficult than quantification of the heat flux. This is mainly due to the different space scales of the atmospheric input functions. Freshwater input is closely coupled with atmospheric convection cells, which are small and irregularly distributed in space [Tomczak, 1995]. This high degree of spatial and temporal variability makes reported rain rates extremely sensitive to the sampling method. Even when using relatively long periods, a good agreement among precipitation rates would not necessarily be expected [Zhang et al., 2000]. The IOP mean rain-rate estimates from different methods vary from 4.5 to 11.3 mm day 1 [Godfrey et al., 1998]. Moreover, precipitation rates from WHOI buoy are a combination of buoy and shipboard rain gauge measurements obtained several times per hour. However, according to Lin and Johnson [1996], the ECMWF time series does not match other rainfall estimates, because several surveys of COARE were not transmitted by the Global Telecommunication System (GTS) and were excluded from the assimilation scheme. These authors suggested, as other sources of deficiencies in the 6-hour forecast, cumulus and other parameterizations of the model. 3. Validation of the Model s Output [32] In this section, results of the simulations forced with the fluxes computed using the atmospheric parameters of the ECMWF and the Fairall et al. [1996] algorithm for

5 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE 4-5 Table 1. Comparison of the Temporal Averages (Standard Deviations) of the Atmospheric Variables of IMET Buoy, ECMWF and CURRY a Total First Period (December 2 17) Second Period (December 17 27) Precipitation (P) IMET 0.87(1.28) 0.57 (0.91) 1.34 (1.58) ECMWF 0.45(0.33) 0.35 (0.27) 0.59 (0.37) CURRY 0.55(0.93) 0.37 (0.64) 0.83 (1.2) Evaporation (E) IMET 0.15 (0.07) 0.13 (0.06) 0.17 (0.07) ECMWF 0.17 (0.04) 0.16 (0.04) 0.20 (0.04) CURRY 0.17 (0.08) 0.13 (0.07) 0.22 (0.07) Difference (E P) IMET 0.72 (1.25) 0.44 (0.89) 1.17 (1.55) ECMWF 0.28 (0.33) 0.19 (0.27) 0.39 (0.38) CURRY 0.38 (0.9) 0.23 (0.61) 0.61 (1.17) a Units are mm hr 1. periods of weak winds and WWB are shown. It is important to note that the analyses are not perfect, in particular in regions with little observations. [33] Figure 3 shows the sea salinity for the first level of the model (2.5 m). The salinity analysis indicates, on average, a reduction of 0.12 psu during the first period, and a slight average increase of 0.01 psu in the second. The model reproduces this variation in a very satisfactory way. The average reduction is 0.13 psu for the first period, and the average increase is 0.03 psu for the second. In addition, the existence of an average SE/NW salinity gradient, with saltier surface waters in the southeast corner of the analysis is quite well reproduced in the simulation. However, the model is slightly fresher than the analysis in the northwest part of simulation domain. The spatial coefficients of correlation are 0.94 and 0.93 for the first and second periods, respectively. Table 2 shows the horizontal average salinity (standard deviations), spatial coefficients of correlation, and root mean square error for the first level of the model (2.5 m) of the analyzed (final analysis minus initial analysis) and simulated salinity (final modeled field minus Figure 2. Time evolution of precipitation, evaporation, and evaporation minus precipitation (E-P) for period of simulation. WHOI is the Woods Hole Oceanographic Institution buoy (1.75 S, 156 E).

6 4-6 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE Figure 3. Comparison between the salinity (psu) for the first level of (top) the analyses and (bottom) the model (2.5 m). initial analysis) for the first (December 2 17) and second period (December 17 27). [34] Figure 4 shows vertical sections of the tendency of the analyzed (final analysis minus initial analysis) and simulated salinity (final modeled field minus initial analysis) for the first and second period at 156 E. For the first period, the analysis shows a general reduction of salinity between the surface and 300 m. At the surface the maximum reduction is between 3.5 S and 2 S with a maximum of 0.15 psu. In subsurface the maximum reduction ( 0.2 psu) occurs at 150 m between the equator and 2 S. Four regions show an increase of salinity during the period. The most important, approximately psu, is between 180 m and 270 m and between 3 S and 5 S. The model reproduces the general reduction of the salinity during the period. At the surface the maximum reduction, 0.3 psu, occurs between 2 S and 3 S, and its stronger intensity, when compared to the analysis, is associated with the eastward currents [Dourado and Caniaux, 2001]. At the subsurface, the model reproduces the reduction around 150 m between the equator and 2 S. However, the reduction is weaker than the analyses, 0.1 psu. In the model, six regions with increase of salinity can be observed. Two of them are not present in the analysis. The first one is between 3 S and 4 S and 50 and 150 m, and the second one is between the equator and 2 S and between 20 m and 120 m, approximately. The model also underestimates the increase of salinity observed in the analysis between 180 m and 270 m and between 3 S and 5 S. [35] During the second period the analysis shows at the surface an increase of the salinity between the equator and 2 S. Between 2 S and 5 S, there is a reduction of the salinity with a maximum, 0.1 psu, at 50 m. At subsurface a general increase of salinity is observed. Exceptions are between 3 S and 5 S and between 200 and 300 m, and between the equator and 2 S at 200 m and at 300 m. The model reproduces the increase of the salinity at the surface between the equator and 2 S. However, this increase matches the increase of salinity at the subsurface. The most important difference can be noted at surface between 3 S and 4 S. While the analysis shows a reduction, the model presents an increase of salinity. This difference is probably associated with the surface fluxes. The general increase of the salinity as well the three regions of reductions of salinity is simulated in a satisfactory way by the model. [36] The model reproduces relatively well the temporal evolution of salinity (Figure 5). However, the surface salinity of the model is underestimated in spite of the underestimation of precipitation (Figure 2). This underestimation does not come from a defect of initialization. One thus concludes that the model tends to underestimate

7 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE 4-7 Table 2. Comparison Between the Horizontal Average Salinity, in ( psu), for the First Level of the Model (2.5 m) for the Analyses and the Model a Analyses Model December 02 X (s) (0.13) correl. RMSE December 17 X (s) (0.14) (0.16) correl RMSE December 27 X (s) (0.12) (0.14) correl RMSE a Correl. is the correlation, and RMSE is the root mean square error. Standard deviations are given in parentheses. the surface salinity, either by process of horizontal advection, or by vertical mixing (turbulent fluxes, entrainment). There are two drops of SSS in the model, the first one on December 3 4 and second one on December [37] It is noticed that the response of salinity to precipitation is well marked in the data with peaks of reduction reaching 0.5 psu during several hours. Because the fresh water input from rainfall is mixing efficiently, these reductions do not change the general tendency of increase of observed salinity. These fast and intense fluctuations do not exist in the simulation because the ECMWF fluxes are averaged values of 6 hours and represent space averages spread out over the mesh of the model. [38] In Figure 6 a comparison between the observed current and the modeled is shown. Note the depths are different. In a general way the model is able to reproduce the observed current, but not all its variability. The model overestimates the zonal current in the period between 3 and 7, 18 and 22, and 23 and 27. Because eastward currents bring fresher water [Feng et al., 2000], this overestimation could explain the two drops observed in the salinity (Figure 5). Moreover, (E-P) is slightly negative in the model and can Figure 4. Vertical structure of the tendency of salinity according to latitude at 156 E for (top) analyses and (bottom) model.

8 4-8 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE Figure 5. Comparison between the salinity modeled at first level of the model (2.5 m) and observed at the WHOI buoy (2 m). also contribute to these drops. The eastward currents are also responsible for the fresh waters northwest of the domain in Figures 3 and Salinity Budget [39] The salinity budget in the mixed layer has been estimated for 25 days of the run. In a similar way to the heat budget [Dourado and Caniaux, 2001], the salinity budget can be written as follows: h@ S hsi ¼ where Z 0 hhui:rhsi r: ~U~Sdz hsi S ½ ð hþšw e ð hþ h fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} 2 1 þ ðe PÞS þ w fflfflfflfflffl{zfflfflfflfflffl} 0 S 0 ð hþ fflfflfflfflffl{zfflfflfflfflffl} 3 4 Z 0 þ A s r 2 Sdz hsi S ½ ð hþšr 2 h ; ð4þ h fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} 5 hzi ¼ 1 h Z 0 h z ¼ z hzi; where h is the depth of the mixed layer; z stands for salinity S and the horizontal current U; P is precipitation; E is evaporation; and A s is the horizontal salinity diffusion zdz coefficient. The vertical flow of material or entrainment rate across the surface z = h is w e ð h Þ ¼ þ Uð hþ:rh A sr 2 h: ð5þ [40] The left-hand side of equation (4) stands for the salinity storage. The terms on the right-hand side are (1) the horizontal salt advection by the depth-average current and by the deviations from this mean current; (2) the flux of salt carried by the mean flow across the surface z = h; (3) the surface fluxes; (4) the turbulent flux across the z = h surface; and (5) horizontal diffusion of salinity. [41] Here the depth of the oceanic mixed layer (h) is calculated from the vertical density profile [Sprintall and Tomczak, 1992], rðtð hþ; Sð hþþ ¼ rðsst 0:5 C; SSSÞ: ð6þ [42] Figure 7 shows the temporal evolution of each term of the salinity budget, cumulated in time and averaged over the whole domain of the model. The units are in psu m. The initial value being arbitrary, we chose to initialize each term of the budget to the zero value, for each of the two periods. Four periods (named P1, P2, P3, P4) were distinguished based on the variations of the heat storage [Dourado and Caniaux, 2001]. P1 and P3 represent period of weak winds, strong solar radiation and low precipitation. P2 and P4 represent periods of strong winds (during P4 there is a WWB), strong precipitation, and reduced solar radiation. These divisions were taken again here, although not coincident necessarily with divisions of the term of salinity storage.

9 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE 4-9 Figure 6. Comparison between the zonal and meridional current modeled at first level of the model (2.5 m) and observed at the WHOI buoy (7 m). The discontinuity indicates the beginning of the second period of simulation. [43] Table 3 gives the average values of each term of the budget according to various periods P1 to P4 in psu m s 1. During the period P1 (December 2 12), surface fluxes ( , with precipitation more significant than evaporation) and horizontal advection ( )are the prevalent terms of the budget. They represent a fresh water contribution for the mixed layer. In this period, there is a shoaling and restratification of the oceanic mixed layer due to the light winds and strong solar radiation. The storage term is subjected to diurnal fluctuation in the mixed layer, which is explained by the diurnal variability of the entrainment. Saltier water is removed from the mixed layer by this process ( ). Both vertical ( ) and horizontal ( ) diffusions are negligible. Consequently, the salt storage becomes largely negative in this period ( ). [44] The period P2 (December 12 17) is dominated by an increase in the surface wind and a deepening of the mixed layer (the average wind on the surface varies from 2 m/s during P1 to 5 m/s during P2 on IMET buoy). This deepening results in a contribution of saltier water into the mixed layer ( ) by entrainment. Vertical ( ) and horizontal ( ) diffusions also contribute to the increase of the salinity in the mixed layer. These terms are balanced by the horizontal advection ( ) and surface fluxes ( ). Finally, the storage is almost zero ( ) for this period. [45] The surface fluxes are largely dominant during the period P3 (December 17 21) ( ). Horizontal advection ( ), entrainment ( ), and vertical ( ) and horizontal ( ) diffusions are minor terms. Consequently, the average storage of salinity is [46] For the period of the strong westerly winds, P4 (December 21 27), precipitation is strong and the term of surface fluxes ( ) is the only one that contributes to the reduction of the salinity in the mixed layer. However, this term is completely compensated by the horizontal advection of salinity ( ). Consequently, storage ( ) increases by entrainment

10 4-10 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE Figure 7. Time evolution of the cumulated salt budget in m psu. ( ) and vertical ( ) and horizontal ( ) diffusions. Thus all the terms play a significant role in the salinity budget during the WWB. This conclusion had already been advanced for the heat budget [Dourado and Caniaux, 2001]. [47] Figure 7 shows that the term of salt storage follows the horizontal advection, i.e., they are negative between December 02 and 21, and positive between December 21 and 27. In periods of light winds (P1, P3), entrainment is weak and negative and its fluctuations reflect a significant diurnal variability of the mixed layer. This is particularly true during P1, when the mixed layer is not very thick. In periods of strong winds, entrainment becomes significant and brings saltier water in the mixed layer. The surface Table 3. Comparison Between the Terms of the Salinity Budget in m psu s Calculated With a Criterion of Depth of Mixed Layer Based on the Difference in Density and a Constant Depth h =50m Criteria Period P1 Dec Period P2 Dec Period P3 Dec Period P4 Dec Salt storage r(sst 0.5 C, SSS) h =50m Horiz. advection r(sst 0.5 C, SSS) h =50m Entrainment r(sst 0.5 C, SSS) h =50m (E - P)S r(sst 0.5 C, SSS) h =50m Vert. turb. flux r(sst 0.5 C, SSS) h = 50 m Horizontal diff r(sst 0.5 C, SSS) h =50m

11 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE 4-11 Figure 8. Synoptic charts of deviations from the spatial average for each term of the mixed layer salt equation for period P1. Units are m s 1 psu Contour intervals are 1 for (a) storage and (b) horizontal advection and 0.2 for (c) (E-P)S, (d) entrainment, (e) horizontal diffusion, and (f) turbulent flux. fluxes are negative for all periods (precipitation exceeds evaporation), which contributes to an input of fresh water in the mixed layer. The vertical diffusion at the depth of the OML is an order of magnitude weaker than the storage and surface fluxes for the period of weak winds, but it become significant and cannot be neglected in periods of strong winds. The horizontal diffusion is always negligible. [48] Two-dimensional plots of the deviations from the average for each term of the salinity budget are shown in Figures 8 and 9, which highlight the spatial variability of each term of the salinity budget. Note that the contour intervals are not the same. The average has been made over the time and the depth of the OML for two periods: the first one from December 2 to 12, P1, with maximum solar radiation input and increasing sea surface temperature, and second from December 21 to 27, P4, with large latent heat flux and decreasing SST. The salt budget is divided into six terms: salinity storage, horizontal advection, entrainment, surface fluxes (E-P), vertical turbulent flux, and horizontal diffusion. [49] These two plots show a strong horizontal local variability. Minimum, maximum, and variance values are given in Table 4. For the period P1, the mixed layer is thin and salinity decreases in any point of the domain. The storage and horizontal advection have very similar configurations. Negative values are in the north of the domain with a minimum near 2.5 S, E. In this area the zonal component of average current is most important and meandering jet appears. The geostrophic component is predominant (Figure 10). [50] As Feng et al. [2000] have shown and as has been pointed out in section 3, zonal advection brings fresher water. However, the model overestimates the advection in this period. The horizontal diffusion and entrainment also play an important role and contribute for these negative values with a minimum at 2.7 S, E and 2. S, E, respectively. There is no significant impact of the surface fluxes and turbulent flux. [51] At the time of P4, the local variability is greater than during P1. In this period a WWB is present. Precipitation associated with this WWB and its variability is strong. The ageostrophic component, representing Ekman convergence, becomes important. As the salinity gradient is rather meridional, this effect contributes to the greater variability in this period. The storage shows a positive pole at 3.5 S, 157 E and a negative pole near to 2 S, 157 E. At the positive maximum, the horizontal advection is predominant over the opposite effect of entrainment, horizontal diffusion, and turbulent flux. For the minimum, the horizontal advection

12 4-12 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE Figure 9. Synoptic charts of deviations from the spatial average for each term of the mixed layer salt equation for period P4. Units are m s 1 psu Contour intervals are 1 for (a) storage and (b) horizontal advection and 0.2 for (c) (E-P)S, (d) entrainment, (e) horizontal diffusion, and (f) turbulent flux. (negative), in addition to the surface flux, the horizontal diffusion, and the turbulent flux, are in opposite effect to the entrainment. The salt storage term is quite inhomogeneous during the two periods as a consequence of the patchy pattern of advection. These results show that it is the horizontal advection term that contributes most to the spatial variability of the storage term, and thus to the salinity field. The contribution of the E-P field to small scales in the salinity field is probably underestimated due to the smooth precipitation field used here Salinity Budget at Constant Depth [52] In this section we present the salt budget on a constant depth. The main objective of this section is to compare the results with those of Feng et al. [1998]. The method is a simple way to measure the salt redistribution due to the vertical movement [Stevenson and Niiler, 1983]. The 50 m depth was selected as a compromise in order to minimize the errors of the calculation of the advection and to maximize information in the mixed layer [Feng et al., 1998]. In this case, in equation t h = 0 and rh = 0, and entrainment is expressed as ENTRAIN M: ¼ ½hSi Sð hþšwð hþ: [53] The contribution of the vertical velocity in the entrainment term can be evaluated. Table 3 shows the temporal evolution of each term of the salt budget. [54] The entrainment becomes small except at the time of the WWB (P4). As the average depth of the OML was approximately 38 m during the WWB and salinity generally increases with depth, a difference hsi S( h) > 0 is expected. The deepening (shallowing) of the OML has a positive (negative) contribution to w( h). Thus w(h) < 0 during periods P1 and P2, and w( h) > 0 during P3 and P4. In the Table 4. Minimum, Maximum and Variance Estimated for the Terms of the Salinity Budget m 10 6a Period P1 Dec Period P4 Dec Min Max Variance Min Max Variance Salt Storage Horiz. advection Entrainment (E-P)S Vert. turb. flux Horizontal diff a Values are averaged over the whole domain.

13 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE 4-13 Figure 10. Horizontal current and its geostrophic and ageostrophic components at first level of the model (2.5 m) during periods P1 and P4. period P1, currents are slightly divergent and drive a shallowing OML (Figure 10). During P4, currents are converging toward the equator and, consequently, the OML is deepening. [55] Feng et al. [1998] have used the R/V Wecoma survey data (19 days, December 20 to January 7) to estimate the advection effects in response to the westerly wind burst during December 1992 at the center of the IFA region (at 2,156.2 E). Despite the different region and period, their results are in good agreement with period P4 (Table 5). Their results show an imbalance of 20% due to an overestimation of the rain rate or due to error in the vertical turbulent flux estimate. 5. Summary [56] A mesoscale ocean simulation has been performed to estimate the ability of a fine mesh primitive equation ocean model to analyze the evolution of the mixed layer in response to different weather conditions met during the TOGA COARE experiment. Much data were necessary for initialization, for taking into account the lateral boundary conditions, for forcing the model with realistic atmospheric flux, and for validating the model outputs. [57] Because ECMWF fluxes cannot properly reproduce short-term air-sea interaction [Zhang et al., 2000], the surface atmospheric forcing comes from a combination of ECMWF atmospheric parameters and estimates from a bulk parameterization. Precipitation data come from ECMWF and are cumulated values of 6 hours. These data are underestimated when compared with the WHOI buoy during the 25 days of simulation, mainly during the WWB. Other authors found similar discrepancies [Zhang et al., 2000; Lin and Johnson, 1996; Weller and Anderson, 1996; CURRY et al., 1999]. However, comparison of precipitation estimates is problematic because of the high degree of spatial and Table 5. Comparison Between the Terms of the Salinity Budget in m psu s Calculated With a Constant Depth = 50 m and the Feng et al. [1998] Results Feng et al. [1998] December 20 January 7 Model December Salt storage Advection a Entrainment 1.03 (E - P)S Vert. turb. flux Horizontal diff 0.0 Residual 1.4 a Horizontal advection only.

14 4-14 DOURADO AND CANIAUX: SALINITY BUDGET DURING TOGA COARE temporal variability [Zhang et al., 2000]. Thus, this underestimation must be taken with care. Despite this, the model is able to reproduce the main observed characteristics. The source of disagreement between the observations and the model is due to overestimation of the zonal current. The zonal current is predominant at the north of the domain and brings fresher water [Feng and al., 2000]. [58] The salinity budget was a valuable tool for analyzing which processes might be important in the mixed layer. Using such a mixed layer model allows us to take into account all the processes of the salinity budget equation, including advection, whose role is difficult to estimate from observations. [59] Different atmospheric weather conditions were experienced during the periods of simulation. Periods of weak winds and strong solar radiation with shoaling of the depth of the oceanic mixed layer during P1 and P3. Entrainment is weak and negative and salt is removed from the oceanic mixed layer. Its fluctuations reflect a significant diurnal variability of the mixed layer. This is particularly true during P1, when the mixed layer is not very thick. Periods of strong winds and reduced solar radiation are associated with deepening of the oceanic mixed layer (P2, P4). During these periods, entrainment is positive and contributes to increase salinity in the mixed layer. The surface fluxes are negative for all period of simulation and contributes to freshening the mixed layer. Even if P2 is similar to P4 in terms of atmospheric conditions, the difference in the budget strongly reflects the fact that P4 was longer than P2 and the meridional advection (Ekman convergence) took an important role to invert the sense of the storage. [60] Smyth et al. [1996] have studied the local response of the ocean to the WWB between December 20, 1992, and January 12, They observed that despite heavy precipitation, the fresh water input from rainfall is mixed efficiently and therefore has minimal effect on the salinity. In this case substantial changes are driven by horizontal advection. [61] Cronin and McPhaden [1998] found the effects of precipitation on local surface salinity are more apparent for timescales shorter than a month. But owing to a combination of mixing and advection, the precipitation-generated freshwater puddles were typically short-lived (3 hours). These freshwater puddles are observed in the Figure 2. They can locally change salinity by 0.5 psu, but they do not change the general tendency of salinity. During the calm wind period (P1), mixed layer currents are low and slightly divergent from the equator toward the south with a mean eastward component. Due to the zonal advection, there is a freshening tendency in the north of the domain. However, this seems overestimated in the model. During the westerly wind burst period, currents are largely converging toward the equator (a Yoshida jet appears in the northeast) and creates a band of saltier water extending from 156E, 3 S to E, 2.3 S. Feng et al. [2000] have found that advection in the upper ocean cannot be neglected during IOP. Zonal advection had a net freshening tendency while meridional advection increased the salinity in the surface layer. [62] Examining the spatial variability of each term of the salinity budget for periods of weak winds and westerly wind bursts, the similarity between the horizontal pattern of horizontal advection and the salinity tendency is evident. It is the horizontal advection term that contributes most to the spatial variability of the storage term, and thus the salinity field. The input of freshwater by precipitation occurs on the relatively small scales associated with convective cells. Thus the contribution of E-P to small scale in the salinity field is probably underestimated due to the smooth precipitation field. Moreover, the precipitation freshening during the convectively active periods is balanced by increased turbulent mixing. [63] Acknowledgments. The authors would like to thank the reviewers for their comments on the manuscript. We thank Youcef Amar for his technical assistance, Yves du Penhoat who provided the data used in this study, and Pascale Delecluse who supplied the LODYC ocean model and the guess fields. This work was supported by the Institut National des Sciences de l Univers. The first author acknowledges support of the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Proc /90-0 (nv) during graduate studies at Centre National de Recherches Météorologiques-Météo-France in Toulouse. References Ando, K., and M. J. 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