Climate Forcing and Salinity Variability in Chesapeake Bay, USA
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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 large-scale 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.
KeywordsChesapeake Bay Skill assessment Salinity variability EOF analysis Model validation Estuary Climate Spatiotemporal patterns
Funding for this study was provided by the National Oceanic and Atmospheric Administration’s (NOAA) Center for Sponsored Coastal Ocean Research’s Monitoring for Event Response for Harmful Algal Bloom (MERHAB) Program (NA05NOS4781222 and NA05NOS4781226 to PIs R. Hood and C. Brown, respectively) and the Center for Satellite Applications and Research. The authors thank Tom Gross for introducing ROMS to this project and helping with the initial model development and Frank Aikman and Edward Myers at NOAA/CSDL for generously supporting the completion of the model analyses and development of the manuscript. Murtugudde gratefully acknowledges the generous support of NOAA-CBFS grant and Divecha Center for Climate Change at IISc, Bangalore. This paper represents UMCES contribution no. 4510 and MERHAB publication no. 150.
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