JOURNAL OF GEOPHYSICAL RESEARCH: OCEANS, VOL. 118, 6123–6144, doi:10.1002/2013JC009196, 2013

A model for quantifying oceanic transport and mesoscale variability in the Coral Triangle of the Indonesian/Philippines Archipelago Frederic S. Castruccio,1 Enrique N. Curchitser,2,3 and Joan A. Kleypas1 Received 12 June 2013; revised 11 September 2013; accepted 7 October 2013; published 19 November 2013.

[1] The Indonesian Throughflow region (ITF) continues to pose significant research challenges with respect to its role in the global ocean circulation, the climate system, and the ecosystem sustainability in this region of maximum marine biodiversity. Complex geography and circulation features imply difficulties in both observational and numerical studies. In this work, results are presented from a newly developed high-resolution model for the Coral Triangle (CT) of the Indonesian/Philippines Archipelago specifically designed to address regional physical and ecological questions. Here, the model is used to quantify the transport through the various passages, sea surface temperature and mesoscale variability in the CT. Beyond extensive skill assessment exhibiting the model ability to represent many conspicuous features of the ITF, the high-resolution simulation is used to describe the mesoscale and submesoscale circulation through the application of Finite Size Lyapunov Exponents (FSLEs). The distribution of FSLEs is used to quantify the spatiotemporal variability in the regional mixing characteristics. The modeled seasonal and interannual variability of mixing suggests a link to large-scale climate signals such as ENSO and the Asian-Australian monsoon system. Citation: Castruccio, F. S., E. N. Curchitser, and J. A. Kleypas (2013), A model for quantifying oceanic transport and mesoscale variability in the Coral Triangle of the Indonesian/Philippines Archipelago, J. Geophys. Res. Oceans, 118, 6123–6144, doi:10.1002/2013JC009196.

1. Introduction [3] The oceanographic complexity of this region (Figure 1) presents major challenges to both field oceanog- [2] The Coral Triangle (CT) is a marine region that raphers and numerical modelers [Gordon and Kamenko- spans parts of Indonesia, Malaysia, Papua New Guinea, vich, 2010]. Within the CT, the Indonesian Archipelago the Philippines, the Solomon Islands, and Timor-Leste 2 (IA) represents a complex array of passages linking inter- (Figure 1). This region covers nearly 6 million km , which connected shelves, deep basins, shallow and deep sills, and is roughly three-quarters the land area of Australia and submerged ridges, that collectively provide a sea link encompasses portions of two biogeographic regions: the between two oceans [Gordon et al., 2003]. Known as the Indonesian-Philippines Region and the Far Southwestern Indonesian Throughflow (ITF), it is recognized as a key Pacific Region. Often referred to as the maritime continent, component of the global thermohaline circulation [Gordon this region is located at the confluence of tropical waters and Fine, 1996; Hirst and Godfrey, 1993; Wajsowicz and from the North and South Pacific and within the pathways Schneider, 2001]. It serves as the main return flow of upper of the inter-ocean exchange between the Pacific and Indian ocean warm waters from the tropical Pacific Ocean to the oceans. The maritime continent is recognized both as a key tropical Indian Ocean that balances the spreading of deep driver of atmospheric circulation due to its enormous abil- waters that form at high latitudes. Since water in the west- ity to transfer heat from the ocean to the atmosphere [Neale ern tropical Pacific is warmer and fresher than in the Indian and Slingo, 2003] and as a key checkpoint for the global Ocean, the ITF transport impacts the temperature and salin- thermohaline circulation [Gordon, 2005]. ity in the Pacific Ocean, Indian Ocean, and Indonesian seas and also affects the air-sea heat exchange patterns strongly influencing the Indo-Pacific climate [Song et al., 2007]. Observation-based estimates of the ITF transport are 15 Sv 6 3 21 1National Center for Atmospheric Research, Climate and Global (1 Sv 5 10 m s ). As the water is transported, its hydro- Dynamics Division, Boulder, Colorado, USA. logical characteristics are altered by heat and freshwater 2IMCS, Rutgers University, New Brunswick, New Jersey, USA. 3 inputs from the Indonesian seas and by strong vertical mix- DES, Rutgers University, New Brunswick, New Jersey, USA. ing. On a local scale, tides and winds, which are primarily Corresponding author: F. S. Castruccio, National Center for Atmos- monsoonal, are the dominant forcings but the large-scale pheric Research, Climate and Global Dynamics Division, P.O. Box 3000, pressure gradient between the Pacific and Indian oceans is Boulder, CO 80307, USA. ([email protected]) the main force driving the flow of Pacific water through the

VC 2013. American Geophysical Union. All Rights Reserved. Indonesian Archipelago into the Indian Ocean. As a result, 2169-9275/13/10.1002/2013JC009196 the structure and magnitude of the ITF varies on timescales

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Figure 1. Schematic of ocean circulation in the Coral Triangle region. The dashed orange line delineates the Coral Triangle following Veron et al. [2009]. Numbered passages are: (1) Makassar Strait, (2) Lifama- tola Strait, (3) Lombok Strait, (4) Ombai Strait, (5) Timor Passage, (6) Luzon Strait, (7) Karimata Strait, (8) Mindoro Strait, (9) Sibutu Strait, and (10) Torres Strait. Abbreviations are: NEC, North Equatorial Cur- rent; NECC, North Equatorial Countercurrent; SEC, South Equatorial Current; SECC, South Equatorial Countercurrent; ME, Mindinao Eddy; HE, Halmahera Eddy; and NGCC, New Guinea Coastal Current. from the interannual El Nino-Southern~ Oscillation (ENSO) oceanographic conditions are likely to vary spatially in signal to the semidiurnal tidal signals. response to climate change. Based on AVHRR Pathfinder [4] The CT region is also strongly influenced by the Sea Surface Temperature (SST) for 1985–2006, Penaflor~ South China Sea Throughflow (SCSTF). A recent study by et al. [2009] found that SST in the CT has increased an Qu et al. [2009] utilizing existing observations and results average of 0.2C per decade but with considerable variabil- from ocean GCMs showed that the SCSTF is a heat and ity across the region. freshwater conveyor, which may have an important influ- [6] The oceanographic complexity and large areal extent ence on the South China Sea (SCS) heat content, the path- of the CT, however, present challenges for understanding way and vertical structure of the ITF, and the heat and the roots of this spatial variability. Oceanographic models freshwater transport from the Pacific into the Indian Ocean. must consider the complex interactions between topogra- The interplay of the monsoon and the SCSTF and the phy, large-scale oceanic currents, surface heat fluxes, tidal resulting effect on the strength of the ITF are key to under- mixing, and wind-forced variations in thermocline depth of standing the regional climate variability and its implica- both the Indian and Pacific oceans (as reviewed by Qu tions on a global scale. et al. [2005]). In addition to the need to resolve the narrow [5] In addition to its importance in the global ocean and passages between the numerous islands of the CT, the climate variability, the CT region is also widely considered major factors that should be addressed to accurately simu- the apex of marine biodiversity for several major taxo- late ocean conditions in the CT are the wind field [Godfrey, nomic groups [Tittensor et al., 2010], and particularly for 1996], the tides [Ffield and Gordon, 1996; Koch-Larrouy zooxanthellate corals [Veron et al., 2009]. Over 120 million et al., 2007], and a proper treatment of boundary conditions people live in the CT and rely on its fisheries and coral that respects the mean flow currents from the Pacific to the reefs for food, income, and protection from storms. Conser- Indian Ocean [Sprintall et al., 2009]. vation in the CT has thus become a top priority of state [7] Several high-resolution modeling studies have been governments and international conservation efforts, with conducted in this region, but most have targeted particular the six Coral Triangle countries establishing the Coral Tri- subregions and/or specific processes. Robertson and Ffield angle Initiative (CTI) [Coral Triangle Secretariat, 2009] in [2008] used a regional high-resolution ocean model to sim- 2007. Conservation efforts recognize that because of the ulate the barotropic and baroclinic tides in the Indonesia CT’s oceanographic complexity, changes in SST and other seas and examine tide-induced mixing processes at the

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INSTANT mooring locations [Robertson, 2010] and the the ROMS computational kernel. ROMS makes use of very interaction and transfer of energy among tidal constituents accurate and efficient physical and numerical algorithms. In [Robertson, 2011]. Metzger et al. [2010] analyzed the path- particular, it utilizes consistent temporal averaging of the way of ITF by using a global high-resolution model driven barotropic mode to guarantee both exact conservation and by atmospheric forcing. Both of these high-resolution stud- constancy preservation properties for tracers and yields ies described the Indonesian seas using a single forcing, more accurate resolved barotropic processes while prevent- either tidal or atmospheric. Kartadikaria et al. [2011] ing aliasing of unresolved barotropic signals into the slow implemented a regional high-resolution ocean model for baroclinic motions. Accuracy of the mode splitting is further the Indonesian seas that combined the tidal and atmos- enhanced due to redefined barotropic pressure-gradient pheric forcings. Han et al. [2009] used a regional high- terms to account for the local variations in the density field resolution model to characterize seasonal surface ocean (i.e., the pressure-gradient truncation error that has previ- circulation and dynamics in the Philippine Archipelago ously plagued terrain-following coordinate models is greatly region during the Philippine Archipelago Experiment reduced) while maintaining the computational efficiency of (PhilEx). Several studies have used high-resolution model- a split model. ROMS has various options for advection ing in studies of the South China Sea [e.g., Metzger and schemes: second-order and forth-order centered differences; Hurlburt, 1996; Qu et al., 2005; Wang et al., 2009; Qu and third-order upstream biased. The vertical mixing et al., 2009; Xie et al., 2011], and another [Melet et al., schemes include several subgrid-scale parameterizations. 2010] used high-resolution modeling to describe thermo- The horizontal mixing of momentum and tracers can be cline circulation pathways in a the Solomon Sea. along vertical levels, geopotential surfaces, or isopycnal [8] Here we present results from a Regional Ocean Mod- surfaces. The vertical mixing parameterization in ROMS can eling System (ROMS) configuration for the entire CT (CT- be either by the local Generic Length Scale (GLS) closure ROMS), including both the ITF and the SCSTF, with a scheme by Umlauf and Burchard [2003], or the nonlocal, K- 5 km horizontal resolution. This resolution is appropriate profile boundary layer formulation by Large et al. [1994]. for capturing the complex ocean dynamic of the region while still permitting the long integrations needed for 2.2. CT-ROMS Configuration studying the dynamical response to rising temperature and [11] The CT-ROMS model domain spans the region its regional variability. The large-scale forcing is set from about 95E to 170E, and 25Sto25N (Figure 2). through accurate ocean boundary conditions from an assim- This is about 8350 km 3 5500 km, encompassing the entire ilation product; CT-ROMS also incorporates the two pri- CT as defined by Veron et al. [2009]. The horizontal grid mary forcings for the region at the local scale: tidal and resolution is 5 km on average, resulting in a 1280 3 640 atmospheric. The model explicitly solves the tides so that points grid. CT-ROMS uses 50 vertical levels in terrain- the mixing processes are not artificially parameterized (as following sigma-coordinates, weighted toward the surface in the study by Koch-Larrouy et al. [2007]). in order to better resolve the mixed layer. The vertical coor- [9] In this paper, we assess CT-ROMS model dynamics dinate transformations and stretching function of Shchepet- against the recently observed ocean dynamics in the area kin and McWilliams [2009] are used, so that the upper and use the resulting fields to quantify mesoscale variability. layers are closer to geopotential surfaces, which reduces A description of the model is provided (section 2), followed spurious advection in the ocean surface mixed layer as well by a description of important modeled circulation pathways as the errors in the pressure gradient. The model grid is and a comparison with the observed transports (section 3). slightly rotated relative to constant longitude/latitude lines We pay particular attention to the validation of the simulated in order to encompass the CT region while maximizing the tides (section 4), and to the comparison of simulated SSTs wet points ratio (i.e., the number of sea points over the total with satellite derived and in situ data (section 5). Finally, we number of points in the grid) and minimizing computation examine the surface mixing and the mesoscale turbulence over land points. The model bathymetry was interpolated simulated by the model, through application of the finite- from the global SRTM30_PLUS product, which has a raw size Lyapunov exponent (FSLE) method (section 6). bathymetric resolution of 30 s or roughly 1 km (Figure 2). In order to reduce the intrinsic error in the horizontal pres- 2. Model Description sure gradient associated with sigma-coordinates, the model bathymetry was smoothed using a two-step method com- 2.1. Regional Ocean Modeling System (ROMS) bining a Shapiro filter [Shapiro, 1975] and the direct itera- [10] The numerical simulations were performed with the tive technique proposed by Martinho and Batteen [2006]. Regional Ocean Modeling System (ROMS; http:// [12] CT-ROMS was integrated from June 2003 to the end www.myroms.org). ROMS is widely used for applications of 2006, to coincide with the observational period of the from the basin to coastal and estuarine scales [e.g., Curch- International Nusantara Stratification and Transport Program itser et al., 2005; Danielson et al., 2011; Haidvogel et al., (INSTANT) [Gordon et al., 2010]. The simulation was ini- 2000; Lemarie et al., 2012; Marchesiello et al., 2003, 2009; tialized using an initial condition interpolated on the CT- Warner et al., 2005a]. ROMS solves the incompressible, ROMS grid from the Simple Ocean Data Assimilation hydrostatic Boussinesq primitive equations in finite differ- (SODA) [Carton et al., 2000a, 2000b] retrospective analysis. ence form with a free-surface and within an Arakawa C-grid A short 6 month period was used to spin up the model which curvilinear horizontal coordinate system and a generalized is long enough for the upper ocean, our region of interest, to stretched terrain-following vertical coordinate system [Haid- reach a dynamical balance. All analyses were performed over vogel et al., 2008]. Shchepetkin and McWilliams [2003, the 2004–2006 period, coincident with the INSTANT obser- 2005, 2009] describe in detail the algorithms that comprise vations. INSTANT’s primary objective was to measure the

6125 ATUCOE L:MSSAEMDLN NTECRLTRIANGLE CORAL THE IN MODELING MESOSCALE AL.: ET CASTRUCCIO 6126

Figure 2. Bathymetry (in meters) and land-sea mask used by CT-ROMS. The four insets show blowups over the key ITF passages monitored dur- ing the INSTANT program. The same colorbar is used for all plots. CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Indonesian Throughflow (ITF) simultaneously across multi- studies [e.g., Metzger et al., 2010; Hurlburt et al., 2011; Du ple passages from the Pacific inflow at Makassar Strait and and Qu, 2010]. The circulation patterns simulated in CT- Lifamatola Passage to the Indian Ocean export channels of ROMS are in good agreement with these previous studies, Timor, Ombai, and Lombok (Figure 1) and thus capture the and the ability of the model to reproduce the most important ITF seasonal and annual cycle over a range of ENSO phases. features of the circulation is briefly described here. [13] SODA was also used to provide the model open boundary temperature, salinity, and velocity using a hybrid 3.1. Major Circulation Patterns of nudging and radiation approaches [Marchesiello et al., [18] Both the ITF and SCSTF are clearly identified fea- 2001]. The tidal forcing is naturally implemented at the tures of the near-surface current, which we define as the boundary by providing the tidal elevation and barotropic depth-averaged circulation between the surface and 250 m flows from the global model of ocean tides TPXO 7.2, depth (Figure 3). In the North Equatorial Pacific, the North which best fits in a least-squares sense the Laplace Tidal Equatorial Current (NEC) flows westward until it reaches Equations and along track averaged data from TOPEX/Pos- the Philippines Archipelago where it bifurcates into the eidon and Jason [Egbert and Erofeeva, 2002]. Because the northward-flowing Kuroshio Current and the southward- model domain is large, an astronomical tide-generating flowing Mindanao Current (MC) at around 13N, consistent potential is also added as a body force in the momentum with previous findings [Lukas et al., 1996; Qu and Lukas, equation to ensure correct tidal phasing. 2003]. [14] The surface forcing for the CT-ROMS model was [19] The MC flows southward along Mindanao Island derived from the Modern Era-Retrospective Analysis for until around 5N, where most of the flow turns eastward to Research and Applications (MERRA) reanalysis [Rienecker form the North Equatorial Counter Current (NECC). The et al., 2011]. MERRA is a NASA reanalysis for the satellite strong recirculation east of Mindanao Island is the Minda- era using the Goddard Earth Observing System Data Assimi- nao Eddy (ME). A significant portion of the MC water also lation System Version 5 (GEOS-5). MERRA provides an leaks into the Celebes Sea over the Sangihe Ridge. While extensive suite of global atmospheric fields with high tempo- some of this water recirculates and reexits the Celebes Sea, ral (hourly) and spatial (1=2 latitude 3 2=3 longitude) resolu- a large portion continues southward through the Makassar tions. Air temperatures, sea level pressure and specific Strait to form the main branch of the ITF. At the Dewakang humidity, daily short-wave and downwelling long-wave Sill in the southern Makassar Strait, it splits into a west radiation, and precipitation were used to compute air-sea branch directly exiting Flores Sea via Lombok Strait and heat and momentum fluxes using bulk formulae [Large and an east branch flowing along the north side of Lesser Sunda Yeager, 2009]. River discharge was implemented as a fresh Island into the Banda Sea. Also obvious in Figure 3 is the water flux using the global river flow and continental dis- inflow through the Halmahera Sea into the Banda Sea charges estimated by Dai and Trenberth [2002]. branching from the New Guinea Coastal Current (NGCC), [15] The vertical mixing in the interior layers was calcu- a northern branch of the South Equatorial Current (SEC). lated with the local generic two-equation turbulence clo- Water passes from the Banda Sea into the Indian Ocean via sure scheme (GLS) [Warner et al., 2005b]. The bottom two main passages, Ombai Strait and Timor Passage. stress was empirically parameterized with a spatially vari- [20] Most of the northward flowing Kuroshio Current able linear coefficient of friction based on total water col- bypasses Luzon Strait and continues along the continental umn depth. Sea surface salinity (SSS) was weakly restored slope, east of China. A small fraction intrudes the SCS, toward monthly observed SSS in order to prevent model most of which flows southward along the continental slope drift while allowing the model’s own variability in the sur- and crosses the entire basin, ultimately flowing through face salinity and deep circulation to develop. Karimata Strait into the Java Sea or through Mindoro Strait [16] Satellite-derived ocean color data have revealed the into the Sulu Sea and eventually reaching the Celebes Sea spatially complex structure of near-surface biooptical prop- to the south mostly through the shallow Sibutu Passage. erties in open-ocean frontal areas and in coastal waters [Ackleson, 2001]. In order to simulate the spatial variation 3.2. Mean Transports in light attenuation, we implemented a spatially varying [21] The simulated mean volume transport in Sverdrup water type in ROMS, following the five ocean water types (1 Sv 5 106 m3 s21) over the 2004–2006 period (Figure 4) introduced by Jerlov [1976]. Water types were approxi- is calculated for the full water column from sidewall to mated using a simple depth relationship, with deep open sidewall with negative transport defined as from the Pacific ocean cells having the clearest water, i.e., water Type I of Ocean to the Indian Ocean. INSTANT observational esti- Jerlov [1976], and shallow/coastal grid cells having the mates of the volume transport, where available, are pro- least transparent, i.e., water Type III. This is important, par- vided for comparison. ticularly in the tropics where the shortwave heat flux is [22] Following Sprintall et al. [2009], we define the total large, as a decrease (increase) of the attenuation depth will ITF transport as the sum of the three main outflow passages increase (decrease) the static stability in the surface water of Lombok Strait, Ombai Strait, and Timor Passage. The column and thus affect the surface layer temperature. simulated total ITF transport in CT-ROMS is 217.5 Sv over the 2004–2006 INSTANT time frame, which is in good agreement with the best estimate based on the 3. Circulation Pathways in CT-ROMS INSTANT moorings data: 215 Sv with values ranging [17] The complex circulation and transport pathways of from 210.7 to 218.7 Sv depending upon how the observa- this region have been described in previous observations tions were extrapolated to the surface and sidewalls [Sprin- [e.g., Gordon et al., 2010; Qu et al., 2009] and modeling tall et al., 2009]. The simulated Lombok Strait transport of

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Figure 3. Mean near-surface currents (depth averaged between the surface and 250 m depth, in ms21) simulated by CT ROMS over the 2004–2006 INSTANT period.

22.6 Sv (15% of the total ITF transport) is identical to the (50%), respectively, although the combined flow through observational estimate. However, the simulated transports these two passages agree well with observations. A detailed through Ombai Strait (29.8 Sv; 56% of the total ITF) and verification of the model bathymetry in the area, with par- Timor Passage (25.1 Sv; 29% of the total ITF) differ from ticular care regarding the sill depths upstream and down- INSTANT observations of 24.9 Sv (33%) and of 27.5 Sv stream of the two passages, did not reveal a bathymetric

Figure 4. Total mean volume transport (in Sverdrup, 1 Sv 5 106 m3 s21) simulated by CT-ROMS (value on the left) and observed by INSTANT (value on the right) over 2004–2006. Negative transport is toward the Indian Ocean. Simulated transport is calculated for the full water column from sidewall to sidewall. The red lines indicate the transects used to diagnose the transport in the model.

6128 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE cause for this discrepancy. Interestingly, Kartadikaria et al. overview of the previous published works for the area]. [2011] report a similar issue in their atmospheric-tidal Applying Godfrey’s [1989] Island Rule to the Luzon Strait forced model with positive meridional velocity at depth yielded an annual mean transport estimate (from the Pacific decreasing the transport toward the Indian Ocean for the into SCS) of approximately 4.2 Sv [Qu et al., 2000]. Other Timor Passage, and blamed a shear stress issue at the bot- observational and modeling studies arrived at a range of tom of the water column to explain this discrepancy. Arbic estimates from 0.5 to 10 Sv [e.g., Metzger and Hurlburt, et al. [2010] stressed the importance of an additional bot- 1996; Qu et al., 2000; Fang et al., 2003]. The mean value tom dissipative term for tidal models to parameterize topo- of all estimates is 4.4 Sv. The CT-ROMS simulated trans- graphic wave drag. Such parameterization is currently not port is close to this average with a transport estimated at implemented in ROMS. Instead, the bottom stress was 5.3 Sv. About 1.1 Sv of this Pacific water exits toward the empirically parameterized with a spatially variable linear north through Taiwan Strait and the remainder flows coefficient of friction based on total water column depth. It toward the south through Karimata Strait (0.7 Sv) and will be interesting to incorporate a parameterized topo- Mindoro Passage (3.5 Sv) and eventually reaches the graphic wave drag similar to the one described by Arbic Indian Ocean. The seasonal variability for all three straits is et al. [2010] into ROMS to see if the transport mismatch very large. Karimata Strait transport, for example, ranged between CT-ROMS and the INSTANT estimate for Timor from approximately 2 Sv southward during the Northeast Passage is indeed imputable to a shear stress issue at the monsoon (November to March) to 0.5 Sv northward during bottom of the water column. the Southwest monsoon (May to September) with a very [23] Transports through two other major passages were abrupt transition from one regime to another. monitored during the INSTANT program: Makassar Strait and Lifamatola Passage. In Makassar Strait, Gordon et al. 3.3. Makassar Strait Variability [2008] reported a transport of 211.6 Sv, which accounts [28] The model confirms observations that Makassar for 77% of the total ITF. A more recent estimate, using the Strait is the primary inflow passage for Pacific water, carry- same velocity data from the INSTANT moorings but a ing about three quarters of the total ITF transport. Two more accurate bathymetry suggests a net transport of INSTANT moorings were deployed for nearly 3 years on 212.7 Sv at Makassar for the 2004–2006 time frame [Sus- each side of the Labani Channel, a constriction in Makassar anto et al., 2012]. CT-ROMS simulated the Makassar Strait Strait (MAK-west: 251.900 S, 11827.300 E; MAK-east: transport at 213.1 Sv (75% of total ITF), which agrees 251.500 S, 11837.700 E). For comparison, two virtual well with these observations. moorings were deployed in CT-ROMS to record hourly [24] Transport through Lifamatola Passage was esti- model output at the same MAK-west and MAK-east loca- mated at 21.1 Sv [van Aken et al., 2009], while the CT- tions (Figure 5). The results are similar to plots based on ROMS simulated transport was 21.5 Sv. The strong the INSTANT observations of Gordon et al. [2008] and bottom-intensified overflow in the Seram Sea is well repre- Susanto et al. [2012] (not shown). The time-series section sented in the model with velocity close to the bottom of the of the along-axis current (Figure 5, top) exhibits thermo- 2000 m deep sill regularly surpassing 1 m s21. Note that cline intensification in agreement with the observations, the flow in the upper layer is northward (shown in Figure although the depth of the modeled maximum southward 3). current is somewhat shallower than the 120 m depth in the [25] Figure 4 also shows the transport simulated by CT- observations [Susanto et al., 2012]. The model accurately ROMS for some important straits not monitored during the captures the seasonal cycle with a deepening of the maxi- INSTANT program. For example, the model shows a sig- mum southward current during the northwest monsoon nificant transport of 23.2 Sv through the Halmahera Sea. (February to April) and a shallowing and intensification of This flow is surface intensified and carries water from the maximum southward current during the southeast mon- South Pacific origin into the Indonesian seas (Figure 3). soon (July to September). The signatures of semiannual [26] An alternate pathway to the ITF for exchange of Kelvin waves below 200 m depth propagating from the tropical waters between the Pacific and Indian oceans is Indian Ocean [Sprintall et al., 2000; Susanto et al., 2012] Torres Strait connecting the Gulf of Carpentaria to the con- and weakening the southward flow are clearly visible in tinental shelf of the Great Barrier Reef. Torres Strait is May and October. This feature is absent in May 2006, in very shallow (less than 10 m deep) with the presence of agreement with the observations [Gordon et al., 2008]. numerous islands and reefs. Based on in situ observations, During January to April 2006, weak La Nina~ condition pre- Wolanski et al. [1988] found strong tidal flow but very vailed and stands as a period of sustained throughflow, both weak mean flow through Torres Strait. The transport simu- in the model and the observed time sections. The model lated by CT-ROMS is 20.3 Sv, less than 2% of the ITF simulated a short period of reversal flow during the north- transport. west monsoon in the surface layer, also consistent with the [27] The South China Sea throughflow (SCSTF) is observations [Gordon et al., 2008]. During 2004, a series another important pathway that involves inflow of cold, of surface flow reversals occurred throughout the year. salty water through the deep Luzon Strait and outflow of [29] The temperature time section (Figure 5, middle) warm, fresh water through the shallow Karimata and Mind- shows a relatively weak variability over the 2004–2006 oro Straits [e.g., Qu et al., 2005; Fang et al., 2005; Yu period. This period is free of major ENSO events and the et al., 2007]. Many descriptive and quantitative studies weak variability is in agreement with the INSTANT obser- have focused on the westward intrusion of the Western vations [Susanto et al., 2012]. As seen in the observed time Pacific water into the SCS through the Luzon Strait [e.g., section [Susanto et al., 2012], the model simulates a deep- see Fang et al., 2005; Qu et al., 2009 for a comprehensive ening of the upper thermocline isotherms associated with

6129 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 5. Makassar Strait (top) along-channel velocity (in ms21), (middle) temperature (in C), and (bottom) salinity time sections from January 2004 to December 2006 simulated by CT-ROMS. The quantities represent an aver- age of virtual moorings at MAK-west and MAK-east loca- Figure 6. Power spectral density of the along channel tions deployed in CT-ROMS. A monthly low-pass filter has (170) velocity for the model (blue solid line) and for been applied before contouring. In the top plot, negative observations (red solid line) at (top) 150 m depth and (bot- velocities denote flow toward 170 along the Labani Chan- tom) 750 m depth at the MAK_west location. Peaks at the nel axis. dominant semidiurnal and diurnal component are well identified. The spectra are based on successive 180 day the weak La Nina~ condition in January to April 2006. subperiods during the 2004–2006 period with an overlap of Unlike the temperature, the salinity time section shows sig- 90 days. The 95% confidence intervals are indicated by the nificant seasonal variability. The low salinity values in the shading in the corresponding color. surface layer during the northwest monsoon associated with reversal flow (Figure 5, top) are consistent with the idea that the intrusion of fresh buoyant SCS water into the inertial subrange of three-dimensional isotropic turbu- Makassar Strait weakens the ITF surface flow and forces lence. This high energy at high frequencies is likely the the ITF to a deeper level (near the thermocline) [Qu et al., result of the bobbing up and down of the mooring, as Rob- 2009; Gordon et al., 2012]. ertson and Ffield [2008] and Susanto et al. [2012] reported [30] Figure 6 shows the results of a power-spectra analy- significant mooring blowdown at MAK-west by ocean cur- sis of the observed and simulated along-channel velocity rent and tides. Figure 6 confirms that tides are a dominant time series at MAK-west for a point within the thermocline forcing in the area and that the tidal current is dominated at 150 m depth and for a deeper point at 750 m depth. At by the diurnal (O1 and K1) and semidiurnal (M2 and S2) low frequencies, the model is in good agreement with the frequencies. observations both within the thermocline and at depth. The spectral analysis shows that velocity fluctuations peak at 4. Tides the dominant tidal frequencies, the diurnal (O1 and K1) and semidiurnal (M2 and S2), with the same intensity in the [31] The ITF is not only critical in transferring mass, model and the observations (Figure 6). Additional peaks heat, and salt between the Pacific and Indian oceans, it is are found at the higher harmonics of the diurnal and semi- also a region of strong water mass transformation. Several diurnal frequencies. The high frequencies contain less studies suggest that internal tides cause the intense mixing energy in the model than the observations. This is particu- required for this transformation in the ITF region [Schiller, larly true within the thermocline. The shape of the spec- 2004; Hatayama, 2004; Robertson and Ffield, 2005; trum based on the observed thermocline currents, however, Koch-Larrouy et al., 2007]. Tides are also a critical compo- suspiciously deviates from the characteristic 25/3 power nent of the local ocean dynamic, as they affect the flow law and the expected cascade of energy to smaller scales in through the straits by the generation of residual currents

6130 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE and by the density-driven flows resulting from tidal mixing. to 20 cm while passing through the Luzon Strait, which is Simulation of the tides in CT-ROMS is particularly chal- associated with strong tidal energy dissipation by the local lenging because of the region’s complex bathymetry and topography. However, the M2 tide is amplified in the because the interactions between the Pacific and Indian coastal regions with strong shoaling and narrowing effects, oceans tides within the Indonesian seas result in complex i.e., around the western and southern parts of the Malay barotropic and baroclinic tidal fields. Peninsula, south of the Indo-China Peninsula, west of Bor- [32] The simulated tides in CT-ROMS were compared neo, around Leizhou Peninsula and in Taiwan Strait. with the time-series observations of sea level and derived [36] The K1 diurnal tide, in contrast to the semidiurnal, constant harmonic tidal components from University of easily propagates from the Pacific to the Indian Ocean Hawaii Sea Level Center (http://ilikai.soest.hawaii.edu/uhslc/ through the Celebes Sea and the Sulawesi and Halmahera datai.html) (Figure 7). For clarity, only a 1 month period is seas. From the Celebes Sea, the wave passes through presented for each station, but the results are consistent Makassar Strait and either propagates into the Java Sea throughout the 3 year simulation. The correlations between where it encounters another K1 component coming from the observed and simulated time series are at least 80% for the SCS, or propagates into the Banda Sea where it encoun- all the stations and more than 90% for Bintulu, Cebu, Mala- ters the Pacific K1 tide coming through the Sulawesi and kal, Manila, Pohnpei, Puerto Princesa, and Sandakan. Halmahera seas. From the Banda Sea, this diurnal compo- [33] Harmonic analysis was used to compare the ampli- nent flows to the Indian Ocean. tude and phase of four dominant tidal constituents, i.e., M2, [37] Similar to the conditions of the M2 tide, relatively K1, S2, and O1, from the observed data and CT-ROMS sim- high amplitude of the K1 tide appears on the continental ulations (Table 1 and Figure 8). The error of the simulated shelf. But, unlike the M2 tide, the amplitude of K1 is mark- M2 constituent was greater than that of the other compo- edly increased in the SCS basin (about 0.4 m) after propa- nents (RMS difference 5 17 cm; Table 1). The reason for gating from the Pacific (about 0.2m) through the Luzon this is explained by several factors. M2 is the dominant Strait. Since the SCS is separated from Pacific Ocean forc- component in this region, small-scale coastal topography ing by Luzon Strait, and given that the phase and amplitude (seafloor slope, mouths of rivers and bays) can intensify the of the K1 tide in the SCS basin are nearly constant, the tide but are not resolved at the 5 km horizontal resolution amplified K1 is likely caused by the Helmholtz resonance of the model and semidiurnal tides are amplified where inside the SCS [Zu et al., 2008]. The K1 tide continues shelf resonance occurs, such as over the broad and shallow southward into the Java Sea where it encounters another K1 Australian North West Shelf, a region of strong barotropic component originating from the Pacific Ocean through and baroclinic tides. Port Darwin, for example, experiences Makassar Strait. The two components intersect to form a extremely large tidal amplitudes with a maximum tidal complicated system of large-amplitude, nearly amphi- range of 7.8 m. This is attributed to the semidiurnal tide dromic systems west of Borneo. amplification when entering Darwin Harbor. Because the [38] The distribution pattern, magnitude and phase of the 5 km resolution of CT-ROMS does not adequately resolve dominant semidiurnal and diurnal tide described above Darwin Harbor, the modeled M2 amplitude fails to capture (Figures 8 and 9) are generally similar to those found by the observed tide for this location. If Port Darwin is Ray et al. [2005], Robertson and Ffield [2008], and Zu removed from the computation, the RMS difference et al. [2008] and comprise a considerable range in tidal reduces to 8 cm for M2. The RMS error in amplitude for types. Diurnal, semidiurnal, and mixed tides are found components K1, S2, and O1 are 5 cm, 3 cm, and 3 cm throughout the Coral CT. In general, semidiurnal prevailing (excluding Port Darwin), respectively. The phasing of the tides are found in the Indonesian seas, as well as in the model tides is also in agreement with the observations with adjoining Pacific and Indian oceans. Diurnal tides prevail an RMS error of less than 20 degrees for three of the four in the SCS due to the Helmholtz resonance. Some regions dominant components (the M2 RMS error is 25 ; Table 1). such as Manila, Philippines, experience a purely diurnal [34] The horizontal distributions of semidiurnal (M2) and tide. Purely semidiurnal tides can be found in Malacca diurnal (K1) components computed from CT-ROMS are Strait or along the Northwest Australia shore, but mixed shown in Figure 9. The primary semidiurnal response is tide are more widely encountered ranging from mixed diur- dominated by the large M2 tide from the Indian Ocean, nal dominant (e.g., Bintulu, Malaysia) to mixed semidiur- with amplitudes larger than 1 m off northwest Australia. nal dominant (e.g., Guam). This wave is delayed slightly as it passes into the Banda and Flores seas, which are deep enough that high tide occurs almost simultaneously throughout both basins. The 5. Tidal Mixing and Upper Ocean Temperature M2 tide from the Indian Ocean also leaks into the Flores [39] Because of its deep cloud convection the CT is rec- Sea through Lombok Strait. Another component of the ognized as a primary energy source for the entire climate semidiurnal tides enters the Indonesian seas from the system. This energy is mainly supplied as latent heating, Pacific Ocean. The Indian and Pacific M2 tides meet in the released from the condensation of water vapor when clouds south of Makassar Strait and the Maluku and Halmahera and precipitation form due to cumulus convection. The seas. The Indian Ocean semidiurnal tide also propagates relationship between Sea Surface Temperature (SST) and slowly westward from the Flores Sea across the Java Sea. convective activity is highly sensitive and the local SST is [35] In the SCS, the M2 tide propagates mainly from the of major importance to atmospheric state not only over the Pacific into the SCS. M2 tide entering through the Luzon CT itself, but globally [Neale and Slingo, 2003]. Strait behaves as a decaying wave while propagating south- [40] The mechanisms that generate and maintain SST westward in the SCS. Its amplitude drops rapidly from 40 within the Indonesian seas are a consequence of the complex

6131 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 7. Water level (in meters) simulated by CT-ROMS (blue solid line) and tide gauge data (red solid line) for a 1 month period in 2004. The mean has been removed from all time series. The time period is not the same for all location. r is the correlation between the observed and simulated time series. Both the model and the data are in GMT time zone format.

Table 1. Amplitude and Phase RMS Error for the Dominant Tidal Constituents M2, K1, O1, and S2 Between CT-ROMS and the Observed Water Levela

M2 K1 O1 S2

Amplitude Phase Amplitude Phase Amplitude Phase Amplitude Phase

RMS Error 17 cm (8 cm) 25 5cm 15 3cm 18 8 cm (3 cm) 18 aTwelve tide gauges are used. The value inside parentheses for M2 and S2 is the value obtained when the Darwin tide gauge is excluded.

6132 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 8. Tidal chart for the four major tidal constituents (M2, K1, S2, and O1) driving the tidal forc- ing in the Indonesian seas. The blue arrows are for CT-ROMS and the red arrows are for the observa- tions. The length of the arrow is proportional to the tidal amplitude and the angle represents the tidal phase. The amplitude and phase of each tidal constituent were computed using a 6 month time series of the water level. topography and connectivity between the Pacific and Indian thermocline depth driven remotely by winds over the Pacific oceans. In addition to surface heat fluxes, intense tidal mix- and Indian oceans play a role in generating and maintaining ing of surface and thermocline waters, and variability in SST [Qu et al., 2005]. Koch-Larrouy et al. [2007], among

Figure 9. (left) Amplitude (in meters) and (right) Greenwich phase (in degrees) for (top) the M2 tidal constituent and(bottom) the K1 tidal constituent computed using CT-ROMS outputs. M2 and K1 are, respectively, the dominant semidiurnal and diurnal tidal constituents over the area.

6133 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

the Pacific subtropical water has been lost. Instead, the Pacific incoming water has been transformed into Indone- sian Throughflow water, a unique water mass with an almost uniform salinity below the 20C isotherm. Charac- terizing and quantifying those transformations using CT- ROMS is out of the scope of this paper but is clearly an interesting topic for subsequent work. [42] Figure 11 shows the mean sea surface temperature (MSST) and the standard deviation from both CT-ROMS and the Coral Reef Temperature Anomaly Database (CoR- TAD) [Selig et al., 2010]. CoRTAD was developed using data from the AVHRR Pathfinder Version 5.0 [Selig et al., 2010]. CoRTAD contains global, approximately 4 km reso- lution SST data on a weekly time scale from 1982 through 2010 and related thermal stress metrics, developed specifi- cally for coral reef ecosystem applications. This is a widely used data set for coral reef temperature analyses [e.g., Penaflor~ et al., 2009; McLeod et al., 2010] and because CT-ROMS will be used in subsequent work to investigate coral bleaching we evaluate CT-ROMS SST against the CoRTAD data. [43] Both the MSST and its variability from the CT-ROMS simulations agree well with the satellite observa- tions (Figure 11). Overall, the RMS errors between CoR- TAD and the model SSTs are very small with typical values less than 1C, although some areas close to shore have higher RMS errors between 1 and 2C (Figure 12). The bias Figure 10. TS diagrams for (left) CT-ROMS and (right) between the model SST and the satellite SST (not shown) is as low as 0.4C with a mean RMS error for the entire Levitus climatology, in the Indonesian seas entrances (red, North Pacific, and green, South Pacific) and in the Banda domain of 0.7 C. Sea (magenta). The color scale on each diagram represents [44] As with the surrounding western Pacific and eastern the depth (in meters). Isopycnic lines are overlaid. Indian oceans warm pools, the MSST is high and the SST variability is generally small. The relatively cold MSST over the Flores and the Banda seas observed in CoRTAD is others, have illustrated the importance of an appropriate tidal clearly identified in the model MSST illustrating the role of mixing parameterization in order for an OGCM to accu- tidally enhanced vertical mixing of surface warm waters rately represent the water masses and the SST in the area. In with colder waters from below, which results in a mean most OGCMs without tidal mixing [Gordon and Susanto, cooling of the SST over the Indonesian seas consistent with 1998; Schiller,2004;Koch-Larrouy et al., 2007] the water observations. In the SCS, similar to the satellite observa- masses remain almost unchanged during their journey tions, the simulated MSST is colder on the north and west through the Indonesian seas. This feature does not agree sides of the basin, reflecting the influence of the monsoonal with the observations and various tidal mixing parameteriza- winds. During the winter, a cyclonic gyre circulation in the tions have been introduced [e.g., Koch-Larrouy et al., 2007] central basin with a strong southward western boundary in order to artificially enhance the vertical mixing. In CT- current is generated by northeasterly monsoon winds. The ROMS, the barotropic tides are explicitly and accurately resulting cold advection along the western boundary leads resolved as described in section 4. As a result, baroclinicity to a cold tongue that is strongest from November to Febru- forms three dimensionally and internal tides are generated. ary [Liu et al., 2011; Varikoden et al., 2010]. In summer, The mixing associated with the tides in considerably as southwesterly monsoon winds prevail, an anticyclonic improved and more naturally generated without having to ocean eddy develops off the coast of South Vietnam, lead- tune the model diffusivity to artificially enhance the mixing ing to a cold filament located north of the wind speed maxi- like in the work by Koch-Larrouy et al. [2007]. mum and ocean upwelling off the coast, which helps to [41] Strong vertical mixing affects the modeled water cool the ocean surface [Xie et al., 2003]. masses as they flow along the ITF pathways (Figure 10). In [45] Areas with high temporal variability in SST high- the two entrance seas (North and South Pacific, red and light in particular those areas with strong seasonal upwell- green points), the TS diagrams display the typical structure ing, such as the southern coasts of Java and Flores Island, for Pacific water, with a salinity maximum associated with the southern coast of West Papua, the eastern Banda Sea, the Pacific subtropical water and a salinity minimum asso- and the south coast of Vietnam. The SST variability over ciated with the Pacific intermediate water. These patterns most of the Pacific Ocean (standard deviation of approxi- are similar to TS diagrams based on the Levitus climatol- mately 0.4C) is less in the simulations than in the satellite- ogy [Levitus et al., 1998] (Figure 10). By the time the water based observations (standard deviation of approximately reaches the Banda Sea (magenta point in Figure 10), the 0.6–0.8C). The reasons for this discrepancy are addressed two salinity maxima have been eroded, and the signature of below.

6134 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 11. (left) Mean SST (in C) and (right) standard deviation (in C) over the 2004–2006 period for (top) CT-ROMS and the CoRTAD satellite data. Note that the standard deviation is saturated at 2C. Values larger than 3C, both in the model and in the observations, are found in the SCS along the coast of China with the larger value (>4C) being found in the extreme north of the Gulf of Tonkin. Large value around and above 3C are also found near the coast in the Carpentaria Bay.

[46] Figure 13 shows the 3 year time series of SST for SST variability (Table 2). The mean SST for 12 locations TAO/TRITON moorings and for their model and CoRTAD (excluding the mooring at 0N, 137E where TAO/TRI- equivalents. The TAO/TRITON Array, designed for the TON data are only available for a small portion of the study of year-to-year climate variations related to ENSO 2004–2006 period) is 29.56C for the TAO/TRITON and [McPhaden et al., 1998], includes 13 mooring within the 29.32C for CoRTAD. The variability is also systemati- CT-ROMS domain. We only show the time series for three cally larger for CoRTAD (0.66C over the 12 locations) TAO/TRITON mooring locations but the results were than for the in situ TAO/TRITON data (0.42C). The cold equivalent for all locations within the CT-ROMS domain. bias (0.24C) and larger variability for CoRTAD suggest The modeled SSTs are in good agreement with the observa- that cloud contamination of the satellite data is the source tions, with no significant biases in the means, correlations for an overall negative temperature bias in AVHRR- above 0.8 and RMS differences smaller than 0.3C (Figure derived SST [Reynolds et al., 2002]. CT-ROMS performs 13). CoRTAD SSTs appear to have a cold bias and greater well when compared to TAO/TRITON with a mean SST of variability than the in situ TAO/TRITON observations. 29.56C and a standard deviation of 0.44C over the 12 One possible explanation for CT-ROMS warmer SST with locations (Table 2). smaller variability when compared to the CoRTAD data across the western equatorial Pacific area (Figure 11) could be cloud contamination in the CoRTAD data. The CT is 6. Mixing and Mesoscale Features characterized by high cloud cover and rainfall in response to [48] The CT is characterized by intense mesoscale and the ascending air over the western Pacific Warm Pool and submesoscale activity, such as eddies, fronts and shear cur- the Maritime Continent associated with the Walker Circula- rents, which are clearly visible in both high-resolution tion [Bjerknes, 1969]. observations of the ocean surface and in CT-ROMS SST [47] The distribution of observations included in the and SSH fields. The stirring of water masses with contrast- CoRTAD data set (Figure 14) illustrates the high cloud ing properties affects a large number of biophysical proc- cover over the area. The presence of clouds reduces the esses, including mixing, lateral and vertical transport, the number of the valid observations that can be made, result- structure of phytoplankton communities and the dispersal ing in large areas where observations are available for only of larvae and pollutants. It is also well recognized that mes- 50% of the time. The mean and standard deviation of SST oscale to submesoscale mixing plays a critical role in mod- values for the 13 TAO/TRITON locations, compared to ulating large-scale circulation [van Haren et al., 2004]. those from CT-ROMS and CoRTAD, show a small but per- [49] We describe here the mesoscale and submesoscale sistent cold bias in the CoRTAD SSTs, as well as greater circulation of CT-ROMS. Eddy kinetic Energy (EKE) is

6135 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 12. SST root mean square (RMS) errors (in C) against CoRTAD satellite SST averaged over the 2004–2006 period. often computed to characterize the mesoscale and eddy var- altimetry to evaluate the model ability to simulate meso- iability. While satellite altimetry has provided an unique scale features (Figure 15). The distribution and intensity of contribution to the observation of eddy variability, EKE simulated by CT-ROMS compare well with the satellite-derived EKE is not reliable within the 65 band assimilated products. Two active areas, respectively, asso- straddling the equator as the geostrophic approximation ciated with the unstable jet leaving the Vietnam coast and breaks down near the equator where the Coriolis force van- the Kuroshio intrusion are found in the SCS. Large EKE ishes. For that reason we use SODA, which assimilates values are also found in the Indonesian seas, with Makassar Strait, the Flores Sea, and the southern part of the Banda Sea being the more active regions. The Halmahera Eddy as well as the NGCC also show a strong signature in the EKE field. [50] In an attempt to further characterize the turbulent circulation in the model, we have also computed the Lagrangian coherent structures (LCSs) to quantify the turbulent circulation in the model. In a recent study, Harrison et al. [2013] show that the pathways of simu- lated Lagrangian particles are often organized into fila- ments between mesoscale eddies that correspond to attracting LCSs. These flow features are material curves that map filamentation and transport boundaries. Fluid particles straddling an LCS will either diverge (‘‘repel- ling’’) or converge (‘‘attracting’’) in forward time [Hal- ler and Yuan, 2000]. LCSs thus delineate the boundary between dynamically distinct regions of the flow field, effectively allowing us to visualize the skeleton of tur- bulent transport [e.g., Haller, 2002; d’Ovidio et al., 2004; Shadden et al., 2005]. The spatial organization of these structures has a large impact on the coastal envi- ronment not only because they influence the dispersion of tracers in the water but also because by separating dynamically distinct regions of the flow they can define fluid dynamical niches, which contribute to the structur- ing of marine ecosystems [d’Ovidio et al., 2010]. [51] Near LCSs, neighboring fluid parcels are strained differing amounts by the flow field. These differences in Figure 13. SST time series (in C) at three TAO/TRI- stretching rates allow the detection of LCSs by the Lyapu- TON locations (red dots on map) over the 2004–2006 nov exponent k, which quantifies the exponential rates of period for CT-ROMS (blue solid line), CoRTAD (green), divergence or convergence of initially infinitesimally close and TAO/TRITON moorings (red solid line). trajectories averaged over infinite time (the relative

6136 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 14. CoRTAD observational ratio for each point on the gridded product for the 2004–2006 period. The observational ratio is defined as the ratio between the number of weeks for which an SST observation is available over the total number of weeks for the period. Areas with small observational ratios identify regions with persistent and/or recurrent gaps in the CoRTAD satellite SST product.

dispersion), such that the distance d0 between two nearby of regional modeling like CT-ROMS where boundaries at particles changes in time as finite distances strongly constrain the circulation. To mea- sure the FSLE at a point x, a reference particle is started kt from x at time t, simultaneously with another particle at a dt5d0e (1) distance d0 from x. The time t required to reach a separation [52] In realistic situations the infinite time limit in the df is measured, so that the FSLE is defined as definition makes the Lyapunov exponent a quantity of lim- ÀÁ ited practical use. Instead, two of the most commonly used 1 df k x; t; d0; df 5 log (2) approximations are the finite-time Lyapunov exponents t d0 (FTLEs) [Haller and Yuan, 2000] and the finite-size Lya- punov exponents (FSLEs) [Aurell et al., 1997; Boffetta [53] The FSLE is inversely proportional to the time at et al., 2001]. FSLEs appear to be better suited in a context which two tracers reach a prescribed separation; large

Table 2. Mean and Standard Deviation (in C) for the 2004–2006 SST Time Series for TAO/TRITON In Situ Observations, CT-ROMS, and CoRTAD Satellite Observationsa

137E 147E 156E

Mean Standard Mean Standard Mean Standard

8N TAO 29.03 0.58 29.19 0.51 ROMS 28.95 0.55 29.19 0.37 CoRTAD 28.55 0.72 28.80 0.76 5N TAO 29.29 0.50 29.40 0.36 29.47 0.31 ROMS 29.02 0.57 29.41 0.32 29.59 0.28 CoRTAD 29.04 0.73 29.21 0.60 29.26 0.57 2N TAO 29.55 0.38 29.73 0.35 29.76 0.35 ROMS 29.20 0.58 29.74 0.39 29.94 0.36 CoRTAD 29.46 0.65 29.67 0.57 29.57 0.60 0N TAO 29.61 0.28 29.80 0.37 29.89 0.33 ROMS 29.32 0.55 29.72 0.47 30.03 0.34 CoRTAD 29.36 0.59 29.59 0.55 29.68 0.60 2S TAO 29.86 0.34 ROMS 30.04 0.40 CoRTAD 29.70 0.72 5S TAO 29.78 0.50 ROMS 29.83 0.50 CoRTAD 29.25 0.77 aTable shows all the TAO/TRITON mooring locations within the CT-ROMS domain.

6137 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 15. Map of Eddy Kinetic Energy (EKE, in cm2 s22) for (top) SODA and (bottom) CT-ROMS. SODA, which assimilates altimetry is used for validation because the EKE derived from altimetry is not reliable within the 65 band straddling the equator, as the geostrophic approximation breaks down near the equator where the Coriolis force vanishes.

values identify regions where the stretching induced by tures, we set df 5 0.6 , i.e., a separation of about 66 km. mesoscale and submesoscale activity is strong. Large FSLE The FSLE thus represents the inverse time scale for mixing values are typically organized in convoluted lines encir- fluid parcels over length scale df characteristic of the meso- cling submesoscale filaments. A line of local maxima of scale and submesoscale structures. The spatial distribution FSLEs (more precisely, a ridge) can be used to predict pas- of FSLE for a particular day of the simulation is shown in sive tracer fronts induced by horizontal advection and stir- Figure 15. Typical values are in the order of 0.120.6 ring. In practice, FSLEs provide a direct method for days21, corresponding to mixing times for mesoscale dis- characterizing the mixing activity and the coherent struc- tances of 1.7210 days. Several prominent features emerge tures that control transport at a given scale and can be used from the FSLEs analysis of the CT-ROMS simulation (Fig- to analyze dispersion processes in the ocean [e.g., d’Ovidio ure 15). First, the Indonesian seas and SCS emerge as et al., 2009; Hernandez-Carrasco et al., 2011]. regions of intense mesoscale and submesoscale activity [54] We estimated the Lyapunov exponents using the associated with strong horizontal mixing. Second, the surface velocity field from CT-ROMS. The FSLE depends FSLEs sharply identify well-known structures such as the on the choice of two length scales: the initial separation d0 Halmahera Eddy, which effectively isolates a large parcel and the final separation df. Here, we are interested in the of surface water from the surrounding ocean. Third, the spatial distribution of FSLEs and we naturally use d0 equal region includes large areas with weak mesoscale activity, to the grid spacing to calculate the FSLE, i.e., 0.02. Since such as the North West Australian Shelf and the Gulf of we are interested in mesoscale and submesoscale struc- Carpentaria.

6138 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 16. FSLE spatial distribution computed forward in time for the entire CT-ROMS domain for 21 14 September 2004, dx 5 d0 5 0.02, df5 0.6. Units for FSLEs are day . Mesoscale structures and vorti- ces can be clearly seen. Small values of FSLEs (low-dispersion rates) are found in the core of eddies. On the contrary, large values of the FSLEs can be found in the outer part of eddies, where the stretching of the fluid parcels is particularly important, and in lines indicating robust transport barriers.

[55] As observed in previous works, maximum values of particle trajectories, such lines strongly constrain and the distribution organize in lines or ridges that coincide organize the fluid motion in the surface layer and act as with repelling LCSs. Computing the FSLEs from trajectory transport barriers for passive particles and tracers advected integrations backward in time can also be used to approxi- by the large-scale flow [Shadden et al., 2005]. mate attracting LCSs. Figure 16 shows both the attracting [56] We also examined the seasonal variability of the and repelling LCSs for a zoom over the Indonesian seas. forward FSLEs for the 2004–2006 period (Figure 17). The Since LCSs are material lines that cannot be crossed by large FSLE values (short turbulence time scales) are the

Figure 17. Lagrangian coherent structures (LCSs) for 14 September 2004. Blue and red lines represent attracting and repelling LCSs, respectively. Repelling (attracting) LCSs coincide with ridges in the FSLE spatial distribution computed forward (backward) in time.

6139 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

Figure 18. Seasonal averages (for the 2004–2006 period) of the FSLEs for the entire CT-ROMS domain; spring 5 MAM; summer 5 JJA, fall 5 SON, and winter 5 DJF. FSLE units are day21, large values are equivalent to the shortest turbulence time scales.

signature of the strong divergence induced by the flow pat- of the Cenderawasih Bay (the large bay along the north terns. Several regions stand out as having large FSLE val- shore of western Papua New Guinea) almost year around ues. These include the Kuroshio Current (year round), and indicates that while particles to the north of the line will be the Flores and the western Banda seas. The mixing in the carried by the NGCC and continue westward, particles on Flores Sea is particularly intense during the winter when the south side of this line will remain in the bay and recir- winds of the Northeast Monsoon advect SCS surface water culate. Another example is the Halmahera Eddy, which is eastward into the Flores Sea through Karimata Strait, com- well developed during the northern summer monsoon when bining with the ITF flow and resulting in strong surface the South Pacific water from the NGCC curves back into currents. In Makassar Strait, winter is a period of weak the NECC. Interestingly, coral biogeographic studies also mixing, corresponding to its seasonal minimum transport. suggest that Cenderawasih Bay and the Halmahera region During the Southwest Monsoon (summer), however, a are rather isolated systems [Carpenter et al., 2011]. strong transport barrier develops in Makassar Strait and [59] The time series of the forward FSLEs averaged over persists during the fall season. This transport barrier the whole CT-ROMS domain for the 2004–2006 period appears to be associated with Southwest Monsoon wind (Figure 19) confirms the significant seasonal variability forcing. West of the barrier, the main surface current flows seen in Figure 18. In 2004, the two peaks of enhanced mix- westward from the Java Sea through the Karimata Strait ing coincide with the Australian monsoon (March) and the and eventually reaches the SCS. East of the barrier the ITF Western North Pacific-East Asian Monsoon (August and flows from Makassar Strait and turns eastward into the September). The absence of a well-defined peak in early Flores and Banda seas. 2005 may be due to the weak Australian monsoon in that [57] Other interesting patterns also emerge from Figure year, although the second peak in 2005 indicates the return 17. One example is found in the SCS. Monsoonal winds of vigorous mixing in phase with the Western North force the upper ocean circulation in the SCS, alternating Pacific-East Asian Monsoon. The second half of the time- from a strong basin-wide cyclonic gyre during the winter series appears to reflect the effects of a weak and short- Northeast Monsoon to a double gyre during summer South- lived La Nina~ which lasted from autumn 2005 to spring west Monsoon. The Ceram Sea, the eastern passage between 2006, followed by an El Nino~ phase, which lasted from fall the Halmahera and Banda seas, appears to be relatively quiet 2006 until early 2007. Although our time series is too short during the winter months but shows large FSLE values and to draw any definitive conclusions, Figure 18 does suggest intense mesoscale activity during the summer months. that mixing strength in the CT has a seasonal and interan- [58] Finally, persistently large FSLE values seem to nual variability that is linked to large-scale climate signals coincide with biogeographic provinces, illustrating their such as ENSO and/or the Asian-Australian monsoon sys- potential for identifying ‘‘invisible barriers’’ to larval dis- tem. We will pursue the investigation of those possible persal. The high FSLE region that extends across the mouth linkages in subsequent work using longer simulations.

6140 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE

ity maximum as seen in the TS diagram and acts to transfer cold water from below into the thermocline and the surface. The relatively cold MSST over the Flores and the Banda seas observed in both the CoRTAD and the modeled MSST is the signature of this upwelled water. These improve- ments in simulating ITF circulation encourage using the model to further characterize and quantify tidal mixing in the region, although such analyses are beyond the scope of this paper. [62] The comparison of simulated SSTs with those from the TAO-TRITON mooring and satellite-based SSTs illus- Figure 19. Time series of the FSLEs spatial average for trates the skills of the model as equal to or better than the the entire CT-ROMS domain over the 2004–2006 period. satellite product in the region, which is commonly ham- Unit is day21. pered by dense cloud cover. [63] To our knowledge, this is the first modeling study to characterize the rich mesoscale eddy activity in the region 7. Conclusions using Finite-size Lyapunov exponents (FLSEs). FSLEs [60] We present a model specifically designed to tackle were computed using the modeled velocity fields and using important questions regarding oceanographic circulation a final separation of df 5 0.6 in order to identify the coher- within the Coral Triangle and its relationship to climate ent structures that control transport and stirring at scales and marine ecosystems. Coral Triangle ROMS (CT- characteristic of the mesoscale and submesoscale struc- ROMS) includes the South China Sea, Indonesia, and Phil- tures. Spatial structures ranging from the small scales to the ippines, at a 5 km resolution. The primary forcings for the ones typical of mesoscale vortices were sharply identified region include the large-scale oceanic pressure gradients in the modeled flow. Our analysis shows that the CT is a that set the background inter-ocean exchange, atmospheric region of intense mixing that displays strong seasonal and drivers, and importantly, explicit tides. The high-resolution interannual variability. The seasonal average of the spatial atmospheric forcing combined with the region’s intricate distribution of FSLEs highlighted the regional differences topography, as well as interactions between the Pacific and in mixing activity as well as robust transport barriers in the Indian oceans tides within the Indonesian seas, result in a simulated flow. The seasonal transport barrier in Makassar complex barotropic and baroclinic ocean circulation. The Strait is one such example of a transport barrier visible model was integrated for the years 2004–2006, coincident through FSLE analysis that is not easily identified in other with the INSTANT observational period, and results were fields such as SST or SSH. A time series of the spatially evaluated against INSTANT and other observations. The average FSLEs allows quantifying the mesoscale activity simulated total ITF transport of 217.5 Sv over the 2004– and stirring in the CT at a given temporal window. 2006 period is in good agreement with the 215 6 3 Sv esti- Although the 3 year time series is too short to draw any mated ITF transport based on the INSTANT observations. definitive conclusions, it clearly suggests possible linkages The model is able to accurately reproduce the observed var- to large-scale climate signals such as ENSO and/or the iability in Makassar Strait, the main ITF branch, with a Asian-Australian monsoon. simulated mean transport of 13.1 Sv compared to the most [64] The repelling and attracting LCSs, which were iden- recent observational estimate of 12.7 Sv. The simulated tified using ridges in the FSLEs integrated forward and tides, with a correlation around 0.9, an RMS difference for backward in time, respectively, allow us to visualize the the four dominant tidal constituents (i.e., M2, K1, S2, and skeleton of the turbulent transport. Oceanic mesoscale and O1) of 5 cm for the amplitude and 19 for the phase, are submesoscale structures play a critical role in modulating also in agreement with the observations over the model large-scale circulation [van Haren et al., 2004]. Submeso- domain. The simulated SSTs are in agreement with both scale filaments have also been shown to have a structuring the CoRTAD satellite and the in situ TAO-TRITON moor- role on marine ecosystems [e.g., Bakun, 1996; Rossi et al., ing SSTs. The mean bias is as low as 0.4C with a mean 2008]. For example, d’Ovidio et al. [2009] found that the RMS error for the entire domain of 0.7C. When compared phytoplanktonic landscape is organized in submesoscale to the in situ TAO-TRITON mooring SSTs, the model patches (10–100 km) of dominant types separated by physi- shows a correlation of 0.8 or better, with no significant cal fronts induced by horizontal stirring. A recent study by biases in the means (bias of 0.03C) and RMS differences Harrison et al. [2013] also suggested that LCSs play an smaller than 0.3C. important role in pelagic transport of marine larvae and [61] By explicitly solving the tides, CT-ROMS is able to show that the pathways of the simulated larvae are often generate the mixing due to vigorous internal tides, which organized into filaments found between mesoscale eddies. were observed in both the simulated velocity and the sea While particle-tracking experiments are beyond the scope surface height fields (not shown). Internal tides are gener- of the present paper, the FSLE analysis illustrates its suit- ated through interactions of the barotropic tides with the ability for research on coral larval transport and connectiv- local topography consisting of a complex array of passages ity in the CT. linking interconnected shelves, deep basins, shallow and [65] The CT supports the highest marine biodiversity deep controlling sills and submerged ridges, which provide known, which is undoubtedly maintained by its complex a sea link between the two oceans. Vertical tidally oceanographic, topographic, and climatic complexity [Car- enhanced mixing erodes the Pacific subtropical water salin- penter et al., 2011]. With such complexity, it is unlikely

6141 CASTRUCCIO ET AL.: MESOSCALE MODELING IN THE CORAL TRIANGLE that the vulnerability of coral reefs and other marine eco- Coral Triangle Secretariat (2009), Coral Triangle Initiative on Coral Reefs, systems to climate change will be spatially uniform across Fisheries and Food Security (CTI-CFF) Regional Plan of Action, Coral Triangle Secr., Jakarta. the CT. This region is now the focus of multiple conserva- Curchitser,E.N.,D.B.Haidvogel,A.J.Hermann,E.L.Dobbins,T.M. tion efforts, in particular the Coral Triangle Initiative on Powell, and A. Kaplan (2005), Multi-scale modeling of the North Pacific Coral Reefs, Fisheries, and Food Security [Coral Triangle Ocean: Assessment and analysis of simulated basin-scale variability Secretariat, 2009], which deals with the multiple threats to (1996–2003), J. Geophys. Res., 110, C11021, doi:10.1029/2005JC002902. coastal and marine ecosystems and the people that depend d’Ovidio, F., V. Fernandez, E. Hernandez-Garcia, and C. Lopez (2004), Mixing structures in the Mediterranean Sea from finite-size Lyapunov on them. exponents, Geophys. Res. Lett., 31(17), L17203, doi:10.1029/ [66] The ability of CT-ROMS to simulate oceanographic 2004GL020328. circulation and SSTs will improve our ability to understand d’Ovidio, F., J. Isern-Fontanet, C. Lopez, E. Hernandez-Garcia, and E. how patterns of coral bleaching are linked to dominant Garcia-Ladon (2009), Comparison between Eulerian diagnostics and finite-size Lyapunov exponents computed from altimetry in the Algerian modes of variability, such as ENSO and the IOD, the inter- basin, Deep Sea Res., Part I, 56(1), 15–31, doi:10.1016/j.dsr.2008.07.014. play of the ITF and SCSTF, as well as climate change. An d’Ovidio, F., S. De Monte, S. Alvain, Y. Dandonneau, and M. Levy (2010), important component of the Coral Triangle Initiative is the Fluid dynamical niches of phytoplankton types, Proc. Natl. Acad. Sci. U. establishment of Marine Protected Area (MPA) networks S. A., 107(43), 18,366–18,370, doi:10.1073/pnas.1004620107. that plan for resilience to climate change through applica- Dai, A., and K. E. Trenberth (2002), Estimates of freshwater discharge from continents: Latitudinal and seasonal variations, J. Hydrometeorol., 3(6), tion of biophysical principles [Fernandes et al., 2012; 660–687, doi:10.1175/1525–7541(2002)003<0660:eofdfc>2.0.co;2. McLeod et al., 2009, 2012]. Several features of CT-ROMS Danielson, S., E. Curchitser, K. Hedstrom, T. Weingartner, and P. Stabeno render it particularly useful to such designs such as the (2011), On ocean and sea ice modes of variability in the Bering Sea, accurate simulation of SSTs and the ability to couple the J. Geophys. Res., 116, C12034, doi:10.1029/2011JC007389. Du, Y., and T. D. Qu (2010), Three inflow pathways of the Indonesian model with ecosystem models. The LCSs and FSLEs anal- throughflow as seen from the simple ocean data assimilation, Dyn. yses also illustrate the ability of CT-ROMS to resolve mes- Atmos. Oceans, 50(2), 233–256, doi:10.1016/j.dynatmoce.2010.04.001. oscale and submesoscale turbulence patterns that are Egbert, G. D., and S. Y. Erofeeva (2002), Efficient inverse modeling of bar- known to have large impacts on marine larval dispersal otropic ocean tides, J. Atmos. Oceanic Technol., 19(2), 183–204. [Harrison et al., 2013] and show that these patterns vary Fang,G.H.,Z.X.Wei,B.H.Choi,H.Wang,Y.Fang,andW.Li(2003),Inter- basin freshwater, heat and salt transport through the boundaries of the East both temporally and spatially across the CT. and South China Seas from a variable-grid global ocean circulation model, Sci. China Ser. D Earth Sci., 46(2), 149–161, doi:10.1360/03yd9014. Fang, G. H., D. Susanto, I. Soesilo, Q. A. Zheng, F. L. Qiao, and Z. X. Wei (2005), A note on the South China Sea shallow interocean circulation, [67] Acknowledgments. This work was funded by a National Science Adv. Atmos. Sci., 22(6), 946–954. Foundation grants OCE-1233430 and OCE-1234674. Additional support Fernandes, L., et al. (2012), Biophysical principles for designing resilient was provided by Rutgers University and by the National Center for Atmos- networks of marine protected areas to integrate fisheries, biodiversity and pheric Research, which is sponsored by NSF. Numerical integrations were climate change objectives in the Coral Triangle, report prepared by The performed with computational resources granted through the Janus super- Nature Conservancy for the Coral Triangle Support Partnership, 152 pp. computer (NSF-MRI grant CNS-0821794 and the University of Colorado Ffield, A., and A. L. Gordon (1996), Tidal mixing signatures in the Indone- Boulder, as a joint effort of the University of Colorado Boulder, the Uni- sian seas, J. Phys. 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