Development of a regional model for the North Indian Ocean

Ocean Modelling 75 (2014) 1–19

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Development of a regional model for the North Indian Ocean Hasibur Rahaman a,⇑, M. Ravichandran a, Debasis Sengupta b, Matthew J. Harrison c, Stephen M. Griffies c a

Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Hyderabad, India Center for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, India c National Oceanic and Atmospheric Administration (NOAA), Geophysical Fluid Dynamics Laboratory (GFDL), Princeton University Forrestal Campus, Princeton, USA b

a r t i c l e

i n f o

Article history: Received 31 July 2013 Received in revised form 27 November 2013 Accepted 26 December 2013 Available online 8 January 2014 Keywords: Indian Ocean Bay of Bengal Arabian Sea East India Coastal Current (EICC) West India Coastal Current (WICC)

a b s t r a c t We have developed a one-way nested Indian Ocean regional model. The model combines the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory’s (GFDL) Modular Ocean Model (MOM4p1) at global climate model resolution (nominally one degree), and a regional Indian Ocean MOM4p1 configuration with 25 km horizontal resolution and 1 m vertical resolution near the surface. Inter-annual global simulations with Coordinated Ocean-Ice Reference Experiments (COREII) surface forcing over years 1992–2005 provide surface boundary conditions. We show that relative to the global simulation, (i) biases in upper ocean temperature, salinity and mixed layer depth are reduced, (ii) sea surface height and upper ocean circulation are closer to observations, and (iii) improvements in model simulation can be attributed to refined resolution, more realistic topography and inclusion of seasonal river runoff. Notably, the surface salinity bias is reduced to less than 0.1 psu over the Bay of Bengal using relatively weak restoring to observations, and the model simulates the strong, shallow halocline often observed in the North Bay of Bengal. There is marked improvement in subsurface salinity and temperature, as well as mixed layer depth in the Bay of Bengal. Major seasonal signatures in observed sea surface height anomaly in the tropical Indian Ocean, including the coastal waveguide around the Indian peninsula, are simulated with great fidelity. The use of realistic topography and seasonal river runoff brings the three dimensional structure of the East India Coastal Current and West India Coastal Current much closer to observations. As a result, the incursion of low salinity Bay of Bengal water into the southeastern Arabian Sea is more realistic. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Interaction between the Indian Ocean and the tropical atmosphere plays an important role in climate variability on both regional and global scales (e.g., Vecchi and Harrison, 2002; Annamalai and Murtugudde, 2004; Schott et al., 2009). The tropical Indian Ocean and the land around its rim experience the most energetic component of the climate system, the Asian–Australian–African Monsoon. In recent years, the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) was established to help understand the role of Indian Ocean in monsoon variability (McPhaden et al., 2009) on intraseasonal to interannual time scales. The main thermocline is deeper than the mixed layer base over most of the Indian Ocean basin. This feature results in a near surface density structure unconstrained by large-scale ocean dynam⇑ Corresponding author. Address: Indian National Centre for Ocean Information Services, Ministry of Earth Sciences, Government of India, Ocean Valley, Pragathi Nagar (BO), Nizampet (SO), Hyderabad 500090, India. Tel.: +91 40 23886052/6000; fax: +91 40 23882910. E-mail address: [email protected] (H. Rahaman). 1463-5003/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ocemod.2013.12.005

ics, at least on relatively short time scales. Therefore, the nearsurface ocean is readily modulated by winds and buoyancy fluxes associated with intra-seasonal variability of the monsoon (Goswami et al., 2012), particularly over the North Indian Ocean. Beyond its potential role in monsoon variability, studies of the Indian Ocean Dipole or Zonal Mode (Saji et al., 1999; Webster et al., 1999; Saji et al., 2006) suggest an important climatic role of Indian Ocean SST both within the region and in other sectors of the globe. Schott et al. (2009) point out a number of deficiencies in ocean model simulations of the Indian Ocean on seasonal, intra-seasonal, and inter-annual time scales, largely attributed to the representation of ocean physical processes. His results provide a mandate for the present study, in which we pay particular attention to the model configuration requirements for better representation of the upper layers of the North Indian Ocean. The Bay of Bengal (BoB) receives excess fresh water (FW) from precipitation and river runoff that enables relatively strong haline stratification in the top 10–20 m (Sengupta et al., 2006). A paucity of ocean in situ data, and the spatial smoothing used to create gridded climatologies, result in only a weak version of the upper ocean haline stratification in such global products (e.g., Conkright et al., 1998) relative to that found in focused observation-based analyses

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(Sengupta et al., 2006; Shetye et al., 1993). Recent models have included realistic FW forcing, yet they have difficulty reproducing the observed features of the salinity distribution, especially in BoB and southeastern Arabian Sea (de Boyer Montégutet et al., 2007; Durand et al., 2007; Han et al., 2001; Han and McCreary, 2001; Kurian and Vinayachandran, 2006). Deficiencies in the parameterization of air–sea fluxes, ocean dynamics and mixing are potential contributors to the lack of model fidelity. A particularly tough challenge for an Indian Ocean model is to simulate this shallow haline stratification, and we focus on the realism of this feature in the regional simulations documented here. In the North Indian Ocean (NIO), particularly over BoB and the southeastern Arabian Sea the presence of freshwater on top of salty dense water leads to strong stratification within the surface layer. The warm ‘‘barrier’’ layer between the base of the mixed layer and the top of the seasonal or main thermocline (Godfrey and Lindstrom, 1989; Thadathil et al., 2007) inhibits entrainment cooling of the mixed layer. The impact of surface heat fluxes is confined to the shallow mixed layer, rapidly warming or cooling the ocean surface. A barrier layer often exists over the southeastern Arabian Sea (Shenoi et al., 2004), in the Bay of Bengal (Vinayachandran et al., 2002; Thadathil et al., 2007), and south of Indonesia (Qu and Meyers, 2005). A proper representation of the barrier layer requires relatively fine upper ocean vertical grid spacing, such as the 1 m chosen for our simulations. In addition to impacts from river runoff, Indian Ocean salinity distribution is also affected by the influx of relatively fresh Pacific water in the Indonesian Throughflow (ITF), and salty water from the Persian Gulf and Red Sea. In many regional Indian Ocean studies, the models are configured with closed (sponge) boundaries in the east and south of the domain (Kurian and Vinayachandran, 2006; Perigaud et al., 2003; Han et al., 2001). Closing these boundaries can result in unrealistic salinity distributions due to the absence of ITF impacts (Bray et al., 1997). For example, Zhang and Marotzke (1999) show an increase of SSS by 0.2–0.4 psu over entire Indian Ocean when the ITF is closed. We therefore pay particular attention in our study to the needs of prescribing realistic lateral boundary properties to help ensure a realistic salinity distribution within the Indian Ocean. Mesoscale eddies, narrow boundary currents, and flows through narrow passages are important elements of the Indian Ocean circulation. Along with the importance of the upper ocean haline stratification, these features require sufficiently fine horizontal and vertical resolution to properly represent. Coupling a global OGCM to a finer resolution regional model provides one means to make progress using limited computational resources while facilitating a wide suite of physical and biogeochemical processes. The use of such regional models has increased in recent years, in particular due to the development of operational oceanography. We take this approach in the present study. One-way nested regional models have been developed with great success for instance in the California Current region (Penven et al., 2006). The approach adopted in the present study to handling the open boundaries (OBCs) is less sophisticated but considered sufficient for the present work. Details of the model configuration (e.g., bathymetry, geography, forcing) determine the OBC method most suitable for a particular configuration (e.g., Herzfeld et al., 2011). We focus in this paper on the implementation of an open boundary formulation for a regional Indian Ocean model that is one-way nested into a coarser resolution global model. Notably, the global model and regional model use the same code (MOM4p1; Griffies, 2009) along with the same surface boundary forcing and matching topography at the open boundaries. We focus on the upper ocean temperature and salinity structure in the simulation, with special emphasis on the Bay of Bengal given its importance

for Indian Ocean circulation and variability. In Section 2, we describe the data used, model configuration, and the experiments performed. In Section 3 we present an analysis of the simulations and provide discussion. We offer summary and conclusions in Section 4.

2. Model configurations, data and experiments 2.1. Model description We make use of the Modular Ocean Model (MOM4p1; Griffies, 2009) for both the regional and global configurations used in this study. The global configuration follows that used for the earth system model documented by Dunne et al. (2012), which is an updated version of the configuration documented by Griffies et al. (2005), Delworth et al. (2006) and Gnanadesikan et al. (2006). We summarize here some of the key features of this configuration and note where it differs from the regional configuration. The vertical grid used in the global model has 50 vertical z⁄ coordinate levels, with 10 m grid spacing in the upper 220. The horizontal grid spacing is 1° with the meridional spacing refined to 1/3° within the equatorial waveguide. The model uses the KPP vertical mixing scheme to parameterize upper ocean boundary layer processes (Large et al., 1994). Rotated neutral diffusion of tracers (Griffies et al., 1998) and an eddy-induced advective transport (Gent and McWilliams, 1990; Griffies, 1998) are used to parameterize mesoscale eddy transport. A biharmonic lateral viscosity parameterization (Griffies and Hallberg, 2000) with western boundary enhancement is used in addition to a harmonic frictional operator. We include the Fox-Kemper et al. (2011) parameterization of sub-mesoscale mixed layer re-stratification. Shortwave penetration depth varies spatially and climatologically based on a global chlorophyll dataset (Manizza et al., 2005; Anderson et al., 2007). Topography is represented by the partial step formulation of Pacanowski and Gnanadesikan (1998). Tidal amplitudes in parts of the North Indian Ocean (NIO) can be large, and the shelf can be wide, as in the northern Bay of Bengal. An internal tidal mixing scheme (Simmons et al., 2004) parameterizes diapycnal mixing over rough topography where stratification exists at depth. Additionally, the frictional dissipation of barotropic tidal energy and subsequent diapycnal tracer mixing is parameterized separately as in Lee et al. (2006). The regional model configuration refines the grid from the nominal 1° in the global model to 0.25°  0.25° horizontal resolution. The vertical grid is also refined in the upper ocean (Fig. 1). We chose the regional model domain by considering dynamical as well as thermodynamical aspects of the North Indian Ocean as motivated by the study of Schott et al. (2009). In particular, the southern boundary at 30°S is chosen to include the Indian Ocean subduction zone (see Fig. 6 from Schott et al., 2009). Additionally, the earlier regional model study of Kurian and Vinayachandran (2006) uses the same domain used in the present study. The vertical physical parameterizations used in the regional model are identical to the global simulations (Table 1). In contrast, the mesoscale eddy parameterizations (neutral diffusion and eddy-induced advection) are disabled since the first baroclinic Rossby deformation radius is reasonably resolved for the model domain, although we do not presume 25 km fully resolves all mesoscale features. Horizontal harmonic and bi-harmonic eddy viscosities are retained but with reduced coefficients compared to the global simulation. The western and northern boundaries of the Indian Ocean region are bounded by land. For the eastern and southern boundaries, an active boundary condition is used for the regional model that requires the mass transport and elevation on the boundary to be known a priori (Flather, 1976). Monthly depth-

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(a)

(b)

0

0

500

20

1000

40

1500

60

2000

Depth (m)

Depth (m)

1 6 11 16 21 26 31 36 41 46 Model Levels

2500 3000 3500

1 6 11 16 21 26 31 36 41 46 Model Levels

80 100 120 140

4000 4500

160

5000

180

5500

200

Fig. 1. Model Vertical Levels blue (regional), purple (global) (a) full Ocean depth and (b) upper 200 m. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2. Regional model topography (m) from (a) Global model and (b) IOM-topo from Sindhu et al. (2007).

integrated transports normal to the eastern and southern boundaries, UHpn , and surface elevations, SSHp, are supplied from the (parent) global solution and interpolated to the open boundary faces. Outward normal velocities at the boundary are diagnosed at each barotropic time step such that,

UHn ¼ UHpn þ C g ðSSH  SSHp Þ

ð1Þ

where UHn and SSH are the interior model transport and surface elevation at the boundary and Cg is the surface gravity wave speed. Monthly averaged global model solutions at the boundaries are interpolated linearly in time and first-order conservatively in space to the regional model grid at each model tracer time step. Additionally, temperature and salinity are restored towards the global solution at the domain boundary with a relaxation timescale of about 1 day. Lateral harmonic and bi-harmonic viscosities are enhanced near the boundaries to prevent the build-up of energy at the grid scale. Regional model transports at the eastern boundary (ITF) and at 30°S are shown in Table 3. The regional model transports are in reasonable agreement with the global solution. 2.2. Initialization and surface forcing The global model was initialized from rest with climatological temperatures and salinities (World Ocean Atlas (WOA); Conkright et al., 1998). Coordinated Ocean-Ice Reference Experiments (CORE-

II v2) normal year forcing (NYF) (Large and Yeager, 2009) provided 6 hourly 10 m air temperature, specific humidity and wind vectors from which surface sensible and latent heat fluxes are derived using neutral drag coefficients (Large and Pond, 1982) based on a parameterized surface roughness (Large and Yeager, 2004). Incident shortwave radiation at the ocean’s surface is reflected with an albedo that increases where ice is present but otherwise has an approximate value of 0.07. The incoming longwave radiation at the ocean surface is balanced by outgoing longwave emissions according to the Stefan–Boltzmann relation. Shortwave and longwave are provided at daily intervals from CORE-II v2 and an idealized diurnal cycle in the shortwave radiation is computed by the model. Precipitation is provided at monthly resolution in CORE-II v2 and runoff is an annual climatology in the original version of CORE-II v1. This NYF data were used to force the global model for 200 years, at which point the temperatures and salinity in the upper few hundred meters of the ocean experienced negligible drift. Following the initial spin-up period, the model was forced with CORE-II v2 inter-annual data from 1991 through 2006. For the global simulations, model sea surface salinity (SSS) is relaxed towards climatological values with a relaxation timescale of 60 days for the 10 m thick top layer (167 mm/day). For all regional simulations, SSS is restored towards climatological values with the weaker relaxation of 60 days for the 1 m thick top layer (16.7 mm/day). Unless otherwise noted, all regional simulations use the CORE-II v2

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Fig. 3. Annual difference (observation minus model) plot of upper ocean salinity [0–30 m] in psu (a) Global, (b) IOM-ref, (c) IOM-topo and (d) IOM-DTrunoff.

Fig. 4. Annual difference of upper ocean salinity [0–30 m] in psu between WOA01 observation and IOM-DTrunoff simulation (a) with 5 m river mixing and (b) 40 m river mixing.

runoff dataset which has no seasonal dependence and is highly smoothed. Here, we will be evaluating a newer runoff dataset which contains monthly climatological values (Dai and Trenberth, 2002). 2.3. Description of experiments We consider three experiments using the regional model to explore sensitivity to Bay of Bengal topography and river runoff (see Table 2). The first experiment uses identical bathymetry and annual river runoff as in the global simulation (referred to as IOMref). The second experiment uses more realistic bathymetry from Sindhu et al. (2007) based on ETOPO5 with additional bathymetric

data for the northern Indian Ocean (IOM-topo). Fig. 2 shows the topography used for these experiments. Considerable differences exist near coastal regions, especially along east and west coast of India and north BoB. The ridge positions over the Arabian Sea (AS) and along 90°E are more accurately represented with this dataset. Shallower and more realistic topography over the northern BoB shelf area, and east and west coast of India will be shown to have a strong influence on boundary currents and salinity simulations. The third experiment uses the more accurate topography from the second experiment, along with the Dai and Trenberth (2002) seasonal river runoff instead of annual runoff used in the previous two experiments (IOM-DTrunoff). In the global simulation, runoff is uniformly mixed to a depth of 40 m at the river dis-

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Fig. 5. Seasonal upper ocean fresh water content (m) computed as per Sengupta et al. (2006) from WOA01 observations (a) DJF, (c) MAM, (e) JJA, (g) SON, and from IOMDTrunoff solutions (b) DJF, (d) MAM, (f) JJA, (h) SON. Reference salinity of 35 psu was used to estimate fresh water content in the upper 30 m. Negative FWC at any point signifies that mean salinity in the upper 30 m exceeds 35 (e.g., in the Arabian Sea).

charge locations. In the regional models, this depth is reduced to 5 m in order to capture the shallow observed halocline features in the northern BoB. This arbitrary river mixing depth is a proxy for more complicated estuarine dynamics absent in all of our simulations. We evaluated IOM-DTrunoff with 40 m river mixing instead of 5 m, with the change in river mixing depth having a negligible impact on metrics considered in this study.

2.4. Observational data To evaluate the model simulations, we used weekly sea surface height anomalies (SSHA) based on the combined TOPEX Poseidon (Jason-1) and ERS-1/ERS-2 (Envisat) satellite altimeter missions (Le Traon et al., 1998). The Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) gridded altimeter data

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(a)

(b)

Fig. 6. Sea surface temperature (°C) annual difference plot (WOA01 observation minus model) (a) Global (annual river runoff) and (b) IOM-DTrunoff.

Table 1 Summary of model resolution and subgrid scale parameterizations. Global

Regional Indian Ocean

Horz resolution Vert resolution Vertical mixing Lateral viscosity

1°  1° with 1/3°N/S in the tropics 50 levels w/10 m resolution to 220 m KPP with 1.e5 m2 s1 background and Tidal dissipation Laplacian + Biharmonic with Western boundary enhancement

Eddy parameterizations

Yes

0.25°  0.25° 50 levels w/4 levels in the upper 5 m Same as global Laplacian + Biharmonic with constant coefficients outside of the sponge regions No

Table 2 Description of experiments. Experiments performed

Exp name

Global 1992–2005 with initial condition from 200 year COREv2 spin-up and annual river runoff Regional 1992–2005 with global model bathymetry and annual river runoff Regional 1992–2005 with enhanced topography and annual river runoff Regional 1992–2005 with enhanced topography and Dai and Trenberth (2002) seasonal river runoff

Global IOM-ref

is based on the same in situ data used in the 1° analysis from NOAA/NODC, but in the gridding procedure the radius of influence of the data profiles on a grid cell is decreased by more than 50% (from 892 to 321 km for the first pass of the analysis). The resulting climatology is more revealing of the observed structure observed in the north BoB compared to the original 1° analysis.

IOM-topo IOMDTrunoff

3. Results 3.1. Upper ocean salinity

are constructed by interpolating along-track data onto a 1/3° Mercator grid. For comparisons to the model solutions, the SSHA data are linearly interpolated in space and time to the model grid. The model surface currents were compared with currents from the Ocean Surface Current Analysis (OSCAR), a satellite-based estimate. Altimetric data were combined with satellite winds, sea surface temperature and climatological in situ data to estimate 0–30 m average currents on a 1/3° Mercator grid (Bonjean and Lagerloef, 2002). The subsurface temperature and salinity were evaluated using the WOA01 0.25° gridded analysis (Boyer et al., 2005). This product

Surface salinity makes a major contribution to surface buoyancy in the tropical Indian Ocean, and influences the thermodynamic structure of the upper ocean (Durand et al., 2004; Vialard and Delecluse, 1998). Salinity in the NIO is strongly forced by river inflow in the BoB, the influx of low salinity Pacific water in the Indonesian Throughflow, and the flow of saltier water from the Persian Gulf and Red Sea. The BoB receives a large quantity of FW from rainfall and river discharge during the summer monsoon season (June– September). Surface salinity remains well below 33 psu, with values less than 20 psu in the North Bay during the post-monsoon, or autumn, season (Shetye et al., 1996; Varkey et al., 1996).

Table 3 Summary of experiment results. Units for evaporation, precipitation and transport are in Sv (106 m3 s1). Hydrographic biases were computed here using the WOA01 analysis of Boyer et al. (2005).

a b

Exp Name

Global

IOM-ref

IOM-topo

IOM-DTRunoff

Net Indian Ocean Evaporation (N Bay of Bengal) Net Indian Ocean Precipitation (N Bay of Bengal) Net Indian Ocean River Runoff (N Bay of Bengal) ITFa SST Bias/RMSb (N Bay of Bengal) SSS Bias/RMS (N Bay of Bengal) MLD Bias/RMS (N Bay of Bengal)

2.4 (0.07) 1.9 (0.10) 0.17 (0.06) 12.2 0.21/0.43 (0.3/0.52) 0.13/0.25 (0.22/0.85) 14.3/16.2 (16.3/17.5)

 2.3 (0.07) 1.8 (0.10) 0.22 (0.07) 11.0 0.16/0.44 (0.4/0.56) 0.1/0.27 (0.43/1.1) 14.1/17.2 (8.2/11.7)

2.4 (0.07) 1.9 (0.11) 0.24 (0.08) 11.8 0.15/0.46 (-0.33/0.58) 0.03/0.29 (0.16/1.1) 13.8/17 (8.6/12)

2.4 (0.07) 1.9 (0.11) 0.14 (0.06) 11.4 0.16/0.46 (0.33/0.57) 0.10/0.31 (0.06/1.5) 14.5/17.6 (9.7/12.8)

Indonesian Throughflow. Root Mean Square Error.

H. Rahaman et al. / Ocean Modelling 75 (2014) 1–19

The climatological annual mean upper ocean (0–30 m) salinity difference between observations and model simulations is shown in Fig. 3. Note that the global model has 3 levels in the upper 30 m, while the regional model has 12 levels (Fig. 1). The global simulation overestimates upper ocean salinity by about 1.5 psu in the Southeast Arabian Sea (SEAS), north BoB and eastern Indian Ocean (Fig. 3a). The large positive bias in the SEAS and BoB are reduced considerably in the IOM-ref regional solution using the global model bathymetry (Fig. 3b). Since the atmospheric forcing, bottom topography and vertical mixing parameterizations are the same in the global and IOM-ref experiments, the improvement from global to regional model solutions can be attributed to the finer horizontal and vertical resolution in IOM-ref as compared to global. Additional regional experiments with 40 m river mixing (instead of 5 m) did not show significant impact for this metric (Fig. 4). Salinity biases in the regional IOM-ref and IOM-topo simulations are further reduced in experiment IOM-DTrunoff using seasonally varying river runoff (Fig. 3d and Table 3). The total runoff for the entire model domain is 0.22 Sv and 0.14 Sv (6938 and 4415 km3/yr) and in IOM-ref and IOM-DTrunoff, respectively (Table 3). In the north BoB these values (north of 12°N) are 0.07 and 0.06 Sv (2207 and 1892 km3/yr), respectively. Note that seasonal river runoff from Dai and Trenberth (2002) is consistent with the data of Fekete et al. (2002). Recall that the salinity restoring is significantly weaker in the regional model simulations than the global simulation. We restore model salinity in the top layer to climatology with a relaxation time scale of 60 days in the global and regional simulations, but the top layer thickness is 10 m in the global and 1 m in the regional models, so the effective rate of restoring is a factor of 10 less in the regional models. Further, the mixing depth of river runoff is set to 5 m in the regional simulations, compared to 40 m in the global model. Fig. 5 shows the FW content (FWC) in the upper 30 m, computed as per Sengupta et al. (2006), from WOA01 and the regional simulation with seasonal runoff (IOM-DTrunoff). The model well captures large-scale patterns and lateral gradients of upper ocean FWC. A striking feature is the evidence of FW transport from the BoB along the east and west coasts of India in November–February. Many models do not adequately capture the seasonal transport of FW around India seen in the hydrographic observations (e.g., Shetye et al., 1991a,b, 1996). The improved FW transport in the regional models is due to the realistic simulation of the coastal circulation. Another striking feature of the regional models is that they capture the FW transport in the southern tropics from the eastern Indian Ocean to the Madagascar coast. This result is consistent with the finding of Sengupta et al. (2006), that FW from the BoB crosses the equator in the east, joins the Indonesian Throughflow, and moves west in the South Equatorial Current. During summer, relatively high salinity Arabian Sea water enters the southern BoB with the southwest monsoon current (Schott et al., 1994; Vinayachandran et al., 1999). Its effect can be seen (Fig. 5e and f) in the decrease of FWC by about 0.25 m in the south BoB. The narrow strip of high FWC is seen along the east coast of India in autumn (SON) and winter (DJF). The FWC pattern is consistent with surface currents (see Fig. 2 of Sengupta et al., 2006). Further, a hydrographic survey close to the east coast of India supports the model results (Shetye et al., 1996). The somewhat realistic seasonal variation of FWC in the regional model suggests that the model physical processes are reasonable in the upper ocean. 3.2. Sea surface temperature Fig. 6 shows the annual mean sea surface temperature (SST) difference between the global simulation and the IOM-DTrunoff simulations, and WOA01 observations. We do not use explicit

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relaxation to observed SST in any of our model runs. However, since air temperature is specified as part of the CORE-II surface boundary conditions, there is an effective restoring flux (Haney, 1971; Murtugudde and Busalacchi, 1999). There is a cool SST bias in the NIO in the global model, about 0.2–0.6 °C in the Arabian Sea and 0.8–1 °C in the BoB (Fig. 6a). The SST biases in the NIO are reduced considerably in the regional solutions with seasonal runoff (Fig. 6b) – for instance, SST bias in the BoB is under 0.4 °C, except in the coastal ocean in the east (Table 3). We find no significant difference in BoB annual mean SST between the three regional model simulations, although they differ from global simulations (figure not shown). Howden and Murtugudde (2001) suggest that BoB SST is not influenced by river runoff. Note that we cannot fully examine if SST has a dependence on river runoff since air temperature is specified in our experiments, so coupled air–sea feedbacks are not represented. During winter and spring (the pre-monsoon season), the IOM-DTrunoff simulation with seasonal river runoff simulates SST patterns and gradients with greater fidelity to observations (Fig. 7a–d), but the equatorial Indian ocean is warmer by about 0.5 °C (compared to the global simulation). During the summer monsoon season (JJA), the simulations exhibit a positive SST bias of about 1.5 °C in the Somali and Oman coastal regions, likely due to weak upwelling and entrainment cooling (Shenoi et al., 2002). Murtugudde and Busalacchi (1999) have shown that a large part of seasonal SST variability in the NIO is due to wind stress variability. Weak wind stress in the CORE-II dataset may be the reason for the warm model SST in the upwelling regions of the Arabian Sea. 3.3. Mixed layer depth Fig. 8 shows the annual mean mixed layer depth (MLD) difference between model solutions and WOA01. The MLD is calculated based on a density criterion – in both model and observations, MLD is defined as the shallowest depth where the potential density exceeds its surface (or 0.5 m level) value by 0.125 kg/m3. The global model MLD is up to 6–12 m deeper than observations in the north BoB and the SEAS (Fig. 8a). These are regions where near-surface salinity makes the largest contribution to density stratification and hence MLD (Kurian and Vinayachandran, 2006). In the previous section we have shown that the salinity simulation, particularly in the northern BoB and SEAS, is poor in the global solution as compared to the regional solutions. As expected, the simulation of MLD in the regional simulations improves considerably in the BoB and SEAS (Fig. 8b–d). The north BoB basin-average annual mean MLD bias improves considerably in the regional model as compared to global solutions (Table 3). The upper mixed layer in the Arabian Sea during the summer monsoon season is about 5 m too deep (Fig. 9), which is closer to observations than available model simulations (e.g., de Boyer Montégutet et al., 2007; McCreary et al., 2001). However, the MLD generally has a deep bias in all seasons. For example, the deep bias during the summer monsoon is nearly basin-wide in extent (Fig. 9f). 3.4. Upper ocean circulation and sea surface height The major current systems in the tropical Indian Ocean are better simulated in the regional simulations compared to the global simulation. These features include the highly seasonal Somali current and north Equatorial Current, as well as the South Equatorial Current, which does not undergo any seasonal variation in direction. In the transition periods between the summer and winter monsoons, i.e. in April–May and October–November, westerly winds force a swift, eastward jet at the equator (Wyrtki, 1973). The Wyrtki jets have an important role in the large-scale heat

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Fig. 7. Seasonal se surface temperature (°C) from WOA01 observations (a) DJF, (c) MAM, (e) JJA, (g) SON, and from IOM-DTrunoff model solutions (b) DJF, (d) MAM, (f) JJA, (h) SON.

and FW transport in the tropical Indian Ocean (see the reviews of Schott and McCreary, 2001; Schott et al., 2009). The regional model reproduces the structure of the jets, but appears to underestimate the speed of the fall jet in the far west, as compared to satellite-derived OSCAR currents (Fig. 10). The IOM-DTrunoff reproduces the basin-wide seasonal patterns of SSHA (Fig. 11), although the model underestimates the magnitude of anomalies. Seasonal averages of SSHA are computed using satellite and model data from the seven year period 1993–1999. We note the dominant Rossby wave-like structure of low SSHA

during winter and high SSHA in summer, and the seasonal reversal of the SSHA around the Indian peninsula in the altimeter data (Fig. 11a and e) and model simulation (Fig. 11b and f). Linearized models of tropical Indian Ocean dynamics, and more comprehensive models, have been extensively used to show how remotelyforced waves link the evolution of basin-scale SSHA and the dynamics around the Indian peninsula (e.g., McCreary et al., 1993, 1996; Shankar and Shetye, 1997; Schott and McCreary, 2001). The simulations of seasonal variations of sea level along the Indian coast are close to observations. The anti-cyclonic eddy

H. Rahaman et al. / Ocean Modelling 75 (2014) 1–19

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Fig. 8. Mixed layer depth (m) annual difference plot (WOA01 observation minus model) from (a) Global, (b) IOM-ref, (c) IOM-topo and (d) IOM-DTunoff.

in the southeastern Arabian Sea during December–March, or the Lakshadweep high (Bruce et al., 1994; Shankar and Shetye, 1997; Shenoi et al., 1999), plays an important role in temperature and salinity stratification in the region (Vinayachandran and Kurian, 2007). The Lakshadweep high is replaced by the Lakshadweep low in June–October (Shankar and Shetye, 1997). These features are simulated quite realistically in our regional models. 3.5. The Bay of Bengal 3.5.1. Subsurface salinity in the Bay of Bengal As mentioned before, large river runoff and precipitation minus evaporation results in strong haline stratification in the upper 10– 50 m of the north BoB (Harenduprakash and Mitra, 1988; Murty et al., 1992; Shetye et al., 1996). Upper ocean salinity stratification in this region is many fold greater than that in the western equatorial Pacific (Lukas and Lindstrom, 1991; Vialard and Delecluse, 1998), and accurate simulation of salinity (and density) stratification is a challenge. The problem is of substantial applied interest: net surface heat flux has sub-seasonal oscillations associated with the active-break cycle of the summer monsoon. Since the mixed layer is shallow, northern Bay of Bengal SST responds to net heat flux with large-amplitude SST oscillations (Sengupta and Ravichandran, 2001a), which then feedback on monsoon convection (e.g., Lau and Waliser, 2011;Vecchi and Harrison, 2002). In the active phase of the monsoon, low pressure systems and depressions born over the northern BoB move to the west and northwest, bringing rainfall to the dry regions of the Indian subcontinent (Gadgil, 2003). It is believed that the thermodynamic state of the upper ocean may explain the frequent formation of convective rain systems over the BoB (Bhat et al., 2001). We therefore detail the seasonal cycle of upper ocean salinity in the north BoB, and other related quantities. A total of about 1300 surface salinity observations from the north BoB (north of 13°N) go into the WOA01 product; in particu-

lar, there are few salinity observations within India’s Exclusive Economic Zone (EEZ) in the western BoB. Two regional model solutions (IOM-topo and IOM-DTrunoff) show a distinct, shallow halocline at 10–20 m depth (Fig. 12). The halocline is particularly strong after the summer monsoon season, when FW input to the BoB from river runoff and rainfall peak (Sengupta et al., 2006). The WOA01 atlas does not show a strong, shallow halocline. This absence is likely to be due to coarse vertical resolution – the atlases report data at standard depths (every 10 m from the surface to 50 m), whereas our model resolution is much finer (see Fig. 1). We note that individual salinity profiles from ship-borne conductivity, temperature and depth (CTD), with vertical resolution of order 1 m, commonly show a strong, shallow halocline between 5 and 20 m depth, particularly in the north BoB (Shetye et al., 1996; Sengupta et al., 2006). The two regional model simulations that use fine-scale topography show the strong near-surface haline stratification (Fig. 12), unlike the global simulation, or the regional simulation with the same topography as the global model (IOMref). As may be expected, the shallow salinity stratification is strong when seasonal river runoff is used in the regional model. In this simulation, the salinity difference between the surface and 30 m is about 2 psu during winter and fall, and about 1 psu in spring and summer. Salinity in the WOA01 atlas is generally higher than model salinity in the upper ocean, although the IOMDTrunoff experiment is closest to observations. One possible reason for the salinity offset is the near-absence of data in WOA01 from the Andaman Sea in the east, which receives freshwater from very heavy monsoon rainfall, and from the Irrawady and Salween rivers. 3.5.2. Seasonal cycle in the North Bay of Bengal The seasonal cycle of BoB SST has a semiannual component in observations (Shenoi et al., 2002; Vinayachandran and Shetye, 1991) and in model simulations (e.g., de Boyer Montégutet et al.,

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Fig. 9. Mixed Layer Depth (m) from WOA01 observations (a) DJF, (c) MAM, (e) JJA, (g) SON and from IOM-DTrunoff (b) DJF, (d) MAM, (f) JJA, (h) SON. The basin averaged MLD values are given in the upper left corner of each panel.

2007; Murtugudde and Busalacchi, 1999). In the present study, all model simulations show that north BoB SST has a significant semiannual component. The annual-mean SST bias in the global model is reduced by about 0.4 °C in the regional model simulations, which also have a more realistic seasonal cycle (Fig. 13a). There is no significant difference in SST between the different regional model simulations, indicating that the improvement relative to the global model is solely due to refined regional model resolution. However, the regional simulations have a nearly 1 °C cool SST bias in Janu-

ary–March. Near-surface air is dry at this time, and both latent heat flux and net surface heat flux are generally negative in the north BoB (i.e. the ocean loses heat; Yu et al., 2007; Praveen Kumar et al., 2011). We find that surface heat fluxes in the regional models are close to observational estimates. It is likely that the model’s cool bias is related to the absence of a warm subsurface layer. de Boyer Montégutet et al. (2007) have shown that the barrier layer in the north BoB warms in winter mainly due to penetrative shortwave radiation, and temperature inversions are common

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Fig. 10. Surface current (cm/s) April (left panel) and November (right panel) comparison with OSCAR observation for Global and IOM-DTrunoff.

(Thadathil et al., 2007). Surface buoyancy flux is negative, and models indicate that shallow surface-forced vertical mixing counteracts SST cooling (Rao and Sivakumar, 2000, 2003; Seo et al., 2009). As mentioned before, the northern BoB receives large quantities of FW from local precipitation and river discharge. Both rainfall and river discharge are highly seasonal – over 70% of river discharge from the four major rivers Ganga, Brahmaputra, Godavari and Irrawady, comes during June–October (Fekete et al., 2002; Papa et al., 2012). Almost 2 psu drop is seen from June to October in the sea surface salinity data and the regional model with seasonal river runoff (Fig. 13b). Our best simulation (IOM-DTrunoff) captures the seasonality well in terms of phase and magnitude, but has a fresh bias as compared to WOA01 observation (see Fig. 12). It is to be noted that WOA01 analysis may not well represent the northern BoB since data density is less over this region (see Section 3.5.1). As mentioned in Section 3.5.1, the low salinity values simulated in the model are close to the individual in situ profile data (Sengupta et al., 2006; Shetye et al., 1991b, 1993, 1996).

The time-varying MLD is a crucial property for the near-surface heat budget and SST evolution (Chen et al., 1994; Qiu et al., 2004). The seasonal cycle of BoB MLD has been studied from observations (Shenoi et al., 2002) and models (de Boyer Montégutet et al., 2007). The regional model simulations considered here reproduce the correct phase of the seasonal cycle – MLD is shallowest in March– April and September–October, as in the observations. Except in springtime, when BoB MLD is mainly determined by vertical gradients of temperature rather than salinity. MLD in global model is 16.3 m deeper than MLD estimated from the WOA01 atlas. However considerable improvement is seen in the regional model (Table 3). The depth of the 23 °C isotherm (D23) is a useful proxy for thermocline depth variability in the BoB (Girishkumar et al., 2011). Thermocline depth in the regional model is close to observations, with a seasonal cycle of 10–15 m amplitude. A shallow bias in the global and coarse topography regional (IOM-ref) simulations is removed in the IOM-topo and the IOM-DTrunoff simulations (Fig. 13d). The more realistic

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Fig. 11. Seasonal SSHA (cm) from Altimeter observations (a) DJF, (c) MAM, (e) JJA, (g) SON and from IOM-DTrunoff (b) DJF, (d) MAM, (f) JJA and (h) SON.

topography used in the IOM-topo and IOM-DTrunoff shows a deeper thermocline close to observation, which is much shallower using the global model topography. The improvement in D23 can therefore be attributed to the use of more realistic topography. We note that Howden and Murtugudde’s (2001) model experiments indicate that changes in upper ocean stratification and vertical mixing can lead to changes in thermocline depth in the BoB.

3.5.3. Seasonal variation of sea surface height in the Bay of Bengal The seasonal cycle of circulation in the upper ocean in the BoB is driven by wind stress over the BoB, as well as remote forcing from the equatorial Indian Ocean and FW from rivers and rain. The dynamics of the seasonal cycle of sea level is discussed in McCreary et al. (1996), and the influence of river runoff on sea level has been described by Han et al. (2001). Shankar (2000) notes that model deficiencies in the simulation of sea level along the Indian coast

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Fig. 12. Seasonal Salinity (psu) profile comparison from WOA01 Atlas data and model simulations averaged over North Bay of Bengal [80–100°E, 12–23°N].

(a)

(b)

(c)

(d)

Fig. 13. Seasonal cycle of (a) SST (°C), (b) SSS (psu), (c) MLD (m) and (d) D23 (m) over North Bay of Bengal [80–100°E, 12–23°N] from model simulations and WOA01 Atlas data.

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are partly related to poor simulation of the salinity seasonal cycle. The situation has improved somewhat in recent model studies that use a realistic seasonal cycle of river runoff (de Boyer Montégutet et al., 2007; Shankar et al., 2010). In this section we present an example of the assessment of model sea surface height anomalies against altimeter observations in the winter (Fig. 14), before discussing upper ocean circulation and the structure of western boundary currents in the pre-monsoon and post-monsoon seasons. In December, sea level along the east coast of India is 2–8 cm higher than in the open ocean (Rao et al., 2010) in the observations and in our regional simulation with seasonal river runoff (Fig. 14a

and j). Comparison with the regional simulation with annual-mean river runoff indicates that a 2–4 cm SSHA signal is associated with river water advected south by the East India Coastal Current (EICC). The SSHA signal associated with the EICC is broad and weak in the global simulation (Fig. 14d), as may be expected due to the coarse resolution. We note that many model simulations do not reproduce the observed winter peak along the east coast of India (see Shankar, 2000; Kurian and Vinayachandran, 2006). Additionally, the Lakshadweep High and the positive anomaly along the west coast of India are absent in the global simulation. These features are captured by the regional simulations, but are weaker than in

Fig. 14. Spatial distribution of SSHA (cm) during Dec from (a) Altimeter, (d) Global, (g) IOM-ref, (j) IOM-DTrunoff; during January from (b) Altimeter, (e) Global, (h) IOM-ref, (k) IOM-DTrunoff; during February from (c) Altimeter, (f) Global, (i) IOM-ref and (l) IOM-DTrunoff.

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the altimeter data. The IOM-DTrunoff experiment has a realistic depiction of the LH and the positive sea level anomaly along India’s west coast later in the season (Fig. 14k and l). Equatorially trapped Kelvin waves in the Indian Ocean generate coastal Kelvin waves, which propagate northward along the eastern boundary of the BoB. The coastal Kelvin waves radiate westward propagating Rossby waves, which influence the circulation of the BoB (McCreary et al., 1993; Iskandar et al., 2009). Girishkumar et al. (2011) uses in situ observations at 90°E, 8°N to show that barrier layer thickness is modulated by remotely forced downwelling and upwelling Rossby waves, thus influencing upper ocean thermodynamics (see also Thadathil et al., 2007). A coastally trapped upwelling Kelvin wave along the eastern boundary is prominent in January; by February the negative SSHA signal has moved all the way to the southern part of the western boundary of the Bay (Fig. 14a–c). Of the three regional simulations, the experiment with seasonal river runoff (IOM-DTrunoff) provides the most accurate simulation of the coastal Kelvin wave (Fig. 14j–l) and the large-scale pattern of SSHA in the northeast Indian Ocean. 3.5.4. Boundary currents in the Bay of Bengal Coastal currents around the Indian peninsula change direction with season (Shetye et al., 1990, 1991a,b, 1996; Schott and McCreary, 2001). Along the western boundary of the BoB, the EICC flows poleward in February–May, and equatorward during October– December. The current partially retroflects into the BoB (Vinayachandran et al., 2005), but a part flows around Sri Lanka and poleward along the west coast of India as the West India Coastal Current (WICC) all the way to 20°N. The regional model with seasonal runoff (IOM-DTrunoff) simulates the reversing coastal currents, including the WICC (Fig. 15). Comparison with available observations shows that the global solution has a weak and broad EICC, while the WICC is indistinct.

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The regional models with fine-scale topography (see below) simulate most of the observed features of the springtime poleward EICC (Shetye et al., 1993). The hydrographic observations of Babu et al. (2003) in March–April 1987 showed that the speed of the EICC increases from 0.4 m/s at 12°N to 0.7 m/s at 17.5°N, where the current leaves the coast and turns east to flow into the open ocean. In some years, two anti-cyclonic eddies are distinctly seen on the seaward side of the current. The regional model solutions have reasonable speeds, reaching 0.8 m/s in the north; there are two offshore anti-cyclonic eddies, and the current detaches from the coast at about 18°N, as in the observations. Recently, Durand et al. (2009) used along-track altimeter data to show that the width of the spring EICC is 100–150 km, which is also observed in the regional model solutions (Fig. 15c and d). The strength of the offshore eddies generated mainly by instability of the swift EICC (Kurien et al., 2010) is weak compared to observations (Babu et al., 2003). Although the western boundary currents are stronger in the regional simulations with fine topography (IOM-topo) as compared to coarse topography (IOM-ref), the peak speeds in the EICC may not be captured because horizontal resolution is not sufficiently fine. Seasonal river runoff can lead to changes in the eastern BoB circulation as well – the northward currents along the eastern boundary (Fig. 15) are absent in the global solution (figure not shown). Earlier studies have shown that apart from local and remote wind forcing, FW from rivers impacts sea level along the east coast of India, thereby influencing the EICC (Han et al., 2001; Han and Webster, 2002; Shankar, 2000). The realistic EICC in the regional models is associated with accurate simulation of sea level along the east coast of India (Fig. 14). During November–December, the equatorward EICC extends from 20°N (Shetye et al., 1996) to flow around Sri Lanka in the south. The model current speed in the north is about 40 cm/s, in agreement with estimates from altimeter (Durand et al., 2009). The current is narrow in the north, and its

Fig. 15. Spatial distribution of upper ocean (0–30 m) current (cm/s) for April from (a) IOM-ref (c) IOM-DTrunoff and November (b) IOM-ref (d) IOM-DTrunoff.

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strength and width increase to the south. Increased volume transport to the south is associated with recirculation at southern latitudes, consistent with hydrographic observations (Shcherbinin et al., 1979; Shetye et al., 1996). The inclusion of seasonal river discharge increases the speed of the EICC by about 10 cm/s and sea level by 10 cm during fall and winter (Fig. 15d, see Fig. 14j), consistent with previous model results (Han et al., 2001). Shetye et al. (1991a) demonstrated the existence of a poleward western boundary current along the west coast of India during December–January 1987–1988. The flow is broad in the south (near 10°N), and narrows to about 100 km in the north (near 20°N) mostly restricted to the continental slope. The flow pattern in the regional model simulations with realistic topography (IOM-DTrunoff) (Fig. 15d) is in basic agreement with observations, although the current may be too broad in the north. We note that previous model simulations have serious deficiencies in simulating the strength and spatial pattern of the WICC (e.g., Han et al., 2001; Kurian and Vinayachandran, 2006). These deficiencies could be due to the relatively smooth topography used in these model simulations. Fig. 16 shows the meridional component of velocity as a function of depth and longitude averaged over 10–14°N (where the coastline is oriented nearly north–south, see Fig. 15), in April and November. The main difference between the regional solution with

the coarse topography (IOM-ref) and more realistic topography (IOM-topo, IOM-DTrunoff) is that the core of the EICC is distinctly separated from the coast in the realistic topography simulations, both in spring (April) and October–December, consistent with hydrographic observations. For example, the observations of Shetye et al. (1996) suggest a peak southward speed exceeding 1 m/s at 10°N, with the EICC core separated from the coast by about 100 km. Furthermore, the data suggest northward flow in a narrow region (about 30–40 km) inshore of the fall/winter EICC. It is likely that our regional model does not have sufficiently fine horizontal resolution to capture the peak speeds in the EICC, as well as the northward flow inshore. The peak strength of the fall/winter EICC is enhanced due to seasonal river runoff, indicating a role for near surface salinity-dominated density gradients in intensifying the EICC. 4. Summary and conclusions In this study, we have examined a suite of regional model simulations using the Modular Ocean Model (MOM4p1) forced via the Coordinated Ocean-Ice Reference Experiments (CORE-II) protocol. We have shown that through incremental refinements in horizontal and vertical resolution, bathymetry, and seasonal runoff discharge, the model simulations become more consistent with

Fig. 16. Meridional current (cm/s) [10–14]°N during April (left panel) from (a) IOM-ref (b) IOM-topo (c) IOM-DTrunoff and during November (d) IOM-ref (e) IOM-topo (f) IOM-DTrunoff.

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observations. Despite being tightly coupled to 10 m air temperature in the CORE dataset, sea surface temperature simulations are generally improved, particularly in the Bay of Bengal. These improvements are attributable to inclusion of seasonal runoff and refined mixed layer grid resolution, each of which allows the model to better capture the strong surface halocline in this region. Many general circulation models of the Indian Ocean have deficiencies in the simulation of sea surface salinity (SSS). In particular, models often show positive SSS biases of 2–3 psu over the freshwater-dominated northern Bay of Bengal. In spite of very weak restoring to observed salinity, our regional model simulations achieve realistic SSS on annual and seasonal scales. In the IOM-DTRunoff experiment, which has a more realistic seasonal river discharge (compared to the other simulations with annual mean runoff), the mean bias is about 0.1 psu over the entire basin, and less than 0.1 psu in the North Bay of Bengal. The vertical structure of salinity in the North Bay of Bengal shows strong stratification at 10–20 m depth in the regional model, with the highest salinity gradient across the halocline reaching nearly 2 psu in September–November (Fig. 12), consistent with individual CTD profiles with order 1 m vertical resolution (Sengupta et al., 2006). Most present-day models are unable to capture this strong haline stratification. We note that fine vertical resolution is not sufficient to produce realistic salinity stratification in our simulations. In addition, we find that realistic topography is necessary to produce strong near-surface gradients, and the gradients are enhanced with realistic seasonal river runoff. The sensitivity of upper ocean salinity stratification to model resolution is marked not only in the Bay of Bengal, but also in contiguous regions influenced by Bay of Bengal rivers, such as the southeastern Arabian Sea. We find that model topography has a significant impact on boundary currents along the Indian coast. The use of realistic topography and seasonal runoff leads to major improvement in the simulation of the complex, seasonally-dependent East India Coastal Current and West India Coastal Current. The vertical structure of the coastal currents is significantly closer to observations (based on geostrophic estimates from hydrographic data) in simulations with fine resolution topography relative to simulations with coarse topography. When seasonally varying runoff is used to force the model, transport in the East India Coastal Current increases, and the amplitude of the seasonal cycle is much higher and more realistic. Seasonal variations of thermocline depth in the Bay of Bengal are close to observations in the regional simulations. Circulation in the Bay of Bengal is influenced by basin-scale waves, including equatorially trapped Kelvin and Rossby waves (McCreary et al., 1993; Schott et al., 2009). Sea surface height anomalies in the regional simulation with realistic topography and seasonal river runoff closely match satellite altimeter data across the tropical Indian Ocean, including the east and west coast of India (Fig. 14). The realistic simulation of near-surface salinity, large-scale circulation and transport of upper ocean fresh water makes the regional model a promising tool to study the hydrological cycle of the Indian Ocean. In conclusion, the Indian Ocean simulation in the regional model with realistic topography and seasonal river runoff exhibits a distinct improvement over earlier modeling studies, particularly in the northern Bay of Bengal. The model surface salinity and upper ocean FW features are quite accurate, even with a very weak surface salinity restoring. The distribution of upper ocean salinity and mixed layer depth are close to observations (especially compared to the North Indian Ocean hydrographic data). The realistic topography and salinity simulation lead to improvements in currents and SST. The representation of mixed layer physics, including thin low salinity layers and the barrier layers beneath, is possibly the most challenging aspect of modeling the Indian Ocean in climate models. These model results show an improvement in the

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seasonal MLD, current and SST as compared to global model configuration used in coupled climate simulations. This regional model study points to the importance of both fine horizontal and vertical grid resolution for accurate simulations of the North Indian Ocean. We conjecture that such accuracy is also important to properly represent the role of the Indian Ocean in the large-scale climate system. Acknowledgements The encouragement and facilities provided by the Director, Indian National Centre for Ocean Information Services (INCOIS) are gratefully acknowledged. The first author (HR) thank Indo-US Science and Technology Forum (IUSSTF) for the Indo-US research fellowship award and part of this work done whilst at National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory (NOAA/GFDL), Princeton, USA. The oceanice model forcing (CORE-IAF.v2) plus release notes and support code are available at http://data1.gfdl.noaa.gov/nomads/forms/ core/COREv2.html. We thank Dr. Shailesh Nayak, Secretary, MoES for his encouragement to complete this work. Thanks are also due to Dr. P.S. Swathi, CMMACS for his valuable comments. The comments from two anonymous reviewers greatly improved aspects of this paper, and we thank them for their candid feedback. Graphics are generated using Ferret. This is INCOIS contribution no. 173. References Anderson, W.G., Gnanadesikan, A., Hallberg, R., Dunne, J., Samuels, B.L., 2007. Impact of ocean color on the maintenance of the Pacific Cold Tongue. Geophys. Res. Lett. 34 (11), L11609. http://dx.doi.org/10.1029/2007GL030100. Annamalai, H., Murtugudde, R., 2004. Role of the Indian Ocean in regional climate variability. In: Earth’s Climate: The Ocean-Atmosphere Interaction. In: Wang, C., Xie, S.-P., Carton, J.A. (Eds.), . Geophysical Monograph Series, vol. 147. AGU, Washington, DC, pp. 213–246. Babu, M.T., Sarma, Y.V.B., Murty, V.S.N., Vethamony, P., 2003. On the circulation in the Bay of Bengal during northern spring inter-monsoon (March–April 1987). Deep-Sea Res. II 50, 855–865. Bhat, G.S., Gadgil, S., Harish Kumar, P.V., Kalsi, S.R., Madhusoodanan, P., Murty, V.S.N., Prasada Rao, C.V.K., Ramesh Babu, V., Rao, L.V.G., Rao, R.R., Ravichandran, M., Reddy, K.G., Sanjeeva Rao, P., Sengupta, D., Sikka, D.R., Swain, J., Vinayachandran, P.N., 2001. BOBMEX – the Bay of Bengal Monsoon experiment. Bull. Am. Meteorol. Soc. 82, 2217–2243. Bonjean, F., Lagerloef, G.S.E., 2002. Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. J. Phys. Oceanogr. 32, 2938–2954. Boyer, T., Levitus, S., Garcia, H., Locarnini, R., Stephens, C., Antonov, J., 2005. Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25 grid. Int. J. Climatol. 25 (7), 931–945. http:// dx.doi.org/10.1002/joc.1173. Bray, N.A., Wijffels, S.E., Chong, J.C., Fieux, M., Hautala, S., Meyers, G., Morawitz, W.M.L., 1997. Characteristics of the Indo-Pacific through ow in the eastern Indian Ocean. Geophys. Res. Lett. 24, 2569–2572. Bruce, J.G., Johnson, D.R., Kindle, J.C., 1994. Evidence for eddy formation in the eastern Arabian Sea during the northeast monsoon. J. Geophys. Res. 99 (C4), 7651–7664. Chen, D., Busalacchi, A.J., Rothstein, L.M., 1994. The roles of vertical mixing, solar radiation and wind stress in a model simulation of the sea surface temperature seasonal cycle in the tropical Pacific Ocean. J. Geophys. Res. 99, 20 345–20 359. Conkright, M.E., Levitus, S., O’Brien, T., Boyer, T.P., Antonov, J.I., Stephens, C., 1998. World Ocean Atlas 1998 CD–ROM Data Set Documentation, Technical Report 15, NODC, Silver Spring, MD. Dai, A., Trenberth, K.E., 2002. Estimates of freshwater discharge from continents: latitudinal and seasonal variations. J. Hydrometeorol. 3, 660–687. de Boyer Montégutet, C., Vialard, J., Shenoi, S.S.C., Shankar, D., Durand, F., Ethé, C., Madec, G., 2007. Simulated seasonal and interannual variability of mixed layer heat budget in the northern Indian Ocean. J. Clim. 20, 3249–3268. Delworth, T.L., Broccoli, A.J., Rosati, A., Stouffer, R.J., Balaji, V., Beesley, J.A., Cooke, W.F., Dixon, K.W., Dunne, J., Dunne, K.A., Durachta, J.W., Findell, K.L., Ginoux, P., Gnanadesikan, A., Gordon, C., Griffies, S.M., Gudgel, R., Harrison, M.J., Held, I.M., Hemler, R.S., Horowitz, L.W., Klein, S.A., Knutson, T.R., Kushner, P.J., Langenhorst, A.L., Lee, H.-C., Lin, S., Lu, L., Malyshev, S.L., Milly, P., Ramaswamy, V., Russell, J., Schwarzkopf, M.D., Shevliakova, E., Sirutis, J., Spelman, M., Stern, W.F., Winton, M., Wittenberg, A.T., Wyman, B., Zeng, F., Zhang, R., 2006. GFDL’s CM2 global coupled climate models – Part 1: Formulation and simulation characteristics. J. Clim. 19, 643–674.

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