Salinity is a critical factor in understanding and predicting physical and biogeochemical processes in the coastal ocean where it varies considerably in time and space. In this paper, we introduce a Chesapeake Bay community implementation of the Regional Ocean Modeling System (ChesROMS) and use it to investigate the interannual variability of salinity in Chesapeake Bay. The ChesROMS implementation was evaluated by quantitatively comparing the model solutions with the observed variations in the Bay for a 15-year period (1991 to 2005). Temperature fields were most consistently well predicted, with a correlation of 0.99 and a root mean square error (RMSE) of 1.5°C for the period, with modeled salinity following closely with a correlation of 0.94 and RMSE of 2.5. Variability of salinity anomalies from climatology based on modeled salinity was examined using empirical orthogonal function analysis, which indicates the salinity distribution in the Bay is principally driven by river forcing. Wind forcing and tidal mixing were also important factors in determining the salinity stratification in the water column, especially during low flow conditions. The fairly strong correlation between river discharge anomaly in this region and the Pacific Decadal Oscillation suggests that the long-term salinity variability in the Bay is affected by largescale climate patterns. The detailed analyses of the role and importance of different forcing, including river runoff, atmospheric fluxes, and open ocean boundary conditions, are discussed in the context of the observed and modeled interannual variability.

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Restoration of ecologically important marine species and habitats is restricted by funding constraints and hindered by lack of information about trade-offs among restoration goals and the effectiveness of alternative restoration strategies. Because ecosystems provide diverse human and ecological benefits, achieving one restoration benefit may take place at the expense of other benefits. This poses challenges when attempting to allocate limited resources to optimally achieve multiple benefits, and when defining measures of restoration success. We present a restoration decision-support tool that links ecosystem prediction and human use in a flexible "optimization" framework that clarifies important restoration trade-offs, makes location-specific recommendations, predicts benefits, and quantifies the associated costs (in the form of lost opportunities). The tool is illustrated by examining restoration options related to the eastern oyster, Crassostrea virginica, which supported an historically important fishery in Chesapeake Bay and provides a range of ecosystem services such as removing seston, enhancing water clarity, and creating benthic habitat. We use an optimization approach to identify the locations where oyster restoration efforts are most likely to maximize one or more benefits such as reduction in seston, increase in light penetration, spawning stock enhancement, and harvest, subject to funding constraints and other limitations. This proof-of-concept Oyster Restoration Optimization model (ORO) incorporates predictions from three-dimensional water quality (nutrients-phytoplankton zooplankton-detritus [NPZD] with oyster filtration) and larval transport models; calculates size- and salinity-dependent growth, mortality, and fecundity of oysters; and includes economic costs of restoration efforts. Model results indicate that restoration of oysters in different regions of the Chesapeake Bay would maximize different suites of benefits due to interactions between the physical characteristics of a system and nonlinear biological processes. For example, restoration locations that maximize harvest are not the same as those that would maximize spawning stock enhancement. Although preliminary, the ORO model demonstrates that our understanding of circulation patterns, single-species population dynamics and their interactions with the ecosystem can be integrated into one quantitative framework that optimizes spending allocations and provides explicit advice along with testable predictions. The ORO model has strengths and constraints as a tool to support restoration efforts and ecosystem approaches to fisheries management.

[1] The shelf circulation off Ningaloo Reef near the North West Cape of Western Australia is driven by complex interactions between the southward flowing Leeuwin Current and wind-driven currents that episodically reverse the coastal flow toward the north. The presence of these northward (equatorward) wind-driven currents is thought to make this section of coast one of the few locations along Western Australia to experience periodic coastal upwelling. We used a combination of field observations and numerical modeling to investigate the summer circulation and upwelling dynamics along Ningaloo Reef. We analyzed current and temperature profiles from moorings at four sites across the shelf and used two Regional Ocean Modeling System (ROMS) sub-models: (1) a coarser model of northwestern Australia forced by a global ocean model and (2) a nested fine-scale model of the Ningaloo region. This nesting significantly improved model skill as it included the offshore mesoscale dynamics that strongly influenced the shelf circulation off Ningaloo. The field observations revealed several northward flow reversals, accompanied by cooling of the coastal waters adjacent to Ningaloo, which were associated with strong northward wind events. Analysis of the coastal heat budget revealed that cooling events were primarily driven by upwelling, whereas warming of coastal waters during relaxation events resulted mostly from along-shelf advection of warm water from the north. Due to the combined effects of its relatively steep (~1/50 slope) shelf and strong summer stratification, upwelled water was sourced from the interior of the water column, likely influencing the sources and fluxes of nutrients to Ningaloo Reef.