Background and motivation: A high-resolution regional model for the Coral Triangle Frederic Castruccio 1, Enrique Curchitser 1, Joanie Kleypas 2 1 Institute of Marine and Coastal Sciences, Rutgers University, 2 Integrated Science Program, National Center for Atmospheric Research The overarching goal of this project is to understand the patterns of vulnerability of coral reefs to bleaching in the Coral Triangle and how these patterns will change in response to climate forcing through the 21st Century. We examine not only the susceptibility of reef ecosystems to coral bleaching, but also the role of larval dispersal in their recovery. We focus on the Coral Triangle because (1) it is an oceanographically complex region that is likely to exhibit large regional differences in coral bleaching patterns and connectivity; and (2) it supports the highest biodiversity of any marine region. The Coral Triangle is a data-poor region but urgently in need of information to guide conservation efforts. High-resolution modeling provides a relatively inexpensive way to help fill the data gaps and understand oceanographic processes. We describe here an implementation of the Regional Ocean Model System (ROMS) that we have developed for the Coral Triangle. We present the initial results from this model that demonstrate its ability to simulate the ocean circulation in the Coral Triangle. The model: Circulation: Regional Ocean Modeling System (ROMS)  Free surface, hydrostatic, primitive equations  Curvilinear, C-grid, horizontal discretization  Terrain-following vertical grid  Bulk flux parameterization of air-sea fluxes  Generic Length Scale (GLS) vertical mixing parameterization (Warner et al., 2005) Current implementation (CT-ROMS):  ~5 km horizontal resolution (1280x640 points)  50 terrain-following levels in the vertical  Atmospheric forcing from Modern Era- Retrospective Analysis for Research and Applications (MERRA, Rienecker et al., 2011)  Boundaries and initial conditions from SODA (Simple Ocean Data Assimilation, Carton et al., 2000)  Tidal boundary conditions from global model of ocean tides TPXO 7.2 (Egbert and Erofeeva, 2002) Figure 3: ROMS Coral Triangle (CT) bathymetry. Figure 4: A. Mean near surface currents (depth averaged between 0 and 250 meters depth) simulated by CT ROMS over the INSTANT period ( ). B. Total mean volume transport in Sverdrup (10 6 m 3 s -1 ) simulated by CT-ROMS (value on the left) and observed by INSTANT (value on the right) over 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. Summary A regional high-resolution model of the Coral Triangle was developed to study the response of coral reef ecosystems to climate change. This is the first step toward developing a mechanistic understanding of how oceanographic changes in the Coral Triangle will affect future patterns of reef bleaching and and larval connectivity. The model was designed to resolve important processes such as flow through narrow straits, mesoscale variability and influence of tides. Model evaluation demonstrates significant skill in simulating transports through the major passages of the ITF. Future work will include a multi-decadal 20 th Century hindcast ( ) and down-scaled future projections of the NCAR-CESM for the region. Some results Figure 1: Major oceanographic features of the Coral Triangle Region (modified from [Du and Qu, 2010]). Additional labeled features include HE (Halamahera eddy), ME (Mindinao eddy), NEC (North Equatorial Current). The Coral Triangle:  Is a region of complex physical oceanography with an expansive and complex island/strait geometry  Encompasses the Indonesian Throughflow (ITF), a link between the tropical Pacific Ocean and the Indian Ocean  The ITF is also recognized as a factor in major climate phenomena such as El Niño- Southern Oscillation (ENSO), the Indian Ocean Dipole, or the Asian Monsoon Figure 2:. Top: global patterns of coral diversity, and Bottom: ecoregions of the Coral Triangle (from Veron et al. 2009). The 16 ecoregions are based on coral community composition; numbers in legend indicate number of coral species reported from the region.  Is the most biodiverse of any marine region, including many reefs that support high coral cover  Is in need of information to guide conservation efforts  Is also a data-poor region => necessitates the use of high-resolution regional modeling that can capture the spatial and temporal variability of oceanographic circulation in the region Acknowledgements: This work is partially funded by IMCS of Rutgers University and the Integrated Science Program of the NCAR. Computational resources were provided by NSF-MRI Grant CNS , MRI-Consortium: Acquisition of a Supercomputer by the Front Range Computing Consortium (FRCC), with additional support from the University of Colorado and NSF sponsorship of NCAR, as well as the Texas Advanced Computing Center (TACC). Figure 8: A. Map showing reef locations used to seed the Lagrangian floats in the model (based on the 2010 reef database of the World Conservation Monitoring Centre). Reefs have been sorted by ecoregions (Veron et al. 2009). Each ecoregion is identified by a color on the map; B. Connectivity matrix obtained for one release experiment. Larvae were released on April 1 st 2004 and tracked for 2 months. Release locations are on the y-axis and settlement location on the x-axis. Larvae can settle only after a pre-competency period which is function of temperature. After the pre-competency period, a larva can settle once it is advected within one grid cell of a reef. A variable mortality rate was applied in order to evaluate the percentage of larvae settling on a reef; C. and D. 2D dispersal kernel for the release and settlement locations. Larval positions were analyzed by means of PCA following Preisendorfer (1988).  Currents depicted in Fig. 1 are accurately captured by the model  Simulated total ITF transport (18.0 Sv) and variability are in agreement with the observational estimate (15.0 Sv) over the 3 years of INSTANT program (Sprintall et al., 2004).  Simulated flows between Ombai Strait and Timor Passage differ from the observations, although the combined flows of the two passages is in agreement with INSTANT.  Makassar carries 74% (77% for INSTANT) of the total ITF flow.  The model captures fine-scale variability and major oceanographic features such as upwelling south of Java; the cold tongue exiting the Molluca Sea; and many of the features identified in Fig. 1.  A comparison between CT-ROMS and tide gauges shows that the model is able to simulate the phase and amplitude of the principal tidal constituents.  The tidally enhanced mixing is responsible for eroding the salinity maximum found in the water masses advected from the Pacific Ocean.  Dispersal patterns coincide with the ecoregions defined in Fig. 2.  The connectivity matrix is consistent with the general idea that most coral larvae are retained locally.  Evidence of mixing between some ecoregions is also shown by the model. Background and motivation: Figure 5: A. M2 tidal constituent chart inferred from observed water level (WL) (red line) and from CT-ROMS WL (blue line). The length of the arrow is proportional to the tidal amplitude and the angle represents the tidal phase; B. Same as A but for K1 constituent; C. and D. show the WL time series for 2 locations, Cebu, Philippines, and Darwin, Australia, used to diagnose the tidal constituent amplitudes and phases. Figure 6: TS diagram for A. the North Pacific water and B. the Indonesian Sea outflow at Timor Chanel (bottom). Figure 7: A. CoRTAD weekly average sea surface temperature (SST) for August 17 th 2004, derived from Pathfinder V5.2 of NOAA/NODC satellite observations. Cloud cover is a persistent problem for remote SST measurements in this region; B. Corresponding CT-ROMS weekly average SST. C. and D. show SST time series at two TAO locations for the observations (red line) and for CT-ROMS (blue line). AB C D A B A C D B A B A B C D corr: 0.87 corr: 0.79