Challenges associated with modeling low-oxygen waters in Chesapeake Bay: a multiple model comparison

As three-dimensional (3-D) aquatic ecosystem models are used more frequently for operational water quality forecasts and ecological management decisions, it is important to understand the relative strengths and limitations of existing 3-D models of varying spatial resolution and biogeochemical complexity. To this end, 2-year simulations of the Chesapeake Bay from eight hydrodynamic-oxygen models have been statistically compared to each other and to historical monitoring data. Results show that although models have difficulty resolving the variables typically thought to be the main drivers of dissolved oxygen variability (stratification, nutrients, and chlorophyll), all eight models have significant skill in reproducing the mean and seasonal variability of dissolved oxygen. In addition, models with constant net respiration rates independent of nutrient supply and temperature reproduced observed dissolved oxygen concentrations about as well as much more complex, nutrient-dependent biogeochemical models. This finding has significant ramifications for short-term hypoxia forecasts in the Chesapeake Bay, which may be possible with very simple oxygen parameterizations, in contrast to the more complex full biogeochemical models required for scenario-based forecasting. However, models have difficulty simulating correct density and oxygen mixed layer depths, which are important ecologically in terms of habitat compression. Observations indicate a much stronger correlation between the depths of the top of the pycnocline and oxycline than between their maximum vertical gradients, highlighting the importance of the mixing depth in defining the region of aerobic habitat in the Chesapeake Bay when low-oxygen bottom waters are present. Improvement in hypoxia simulations will thus depend more on the ability of models to reproduce the correct mean and variability of the depth of the physically driven surface mixed layer than the precise magnitude of the vertical density gradient

Climate Forcing and Salinity Variability in Chesapeake Bay, USA

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 degrees 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