An ensemble data assimilation scheme, Error Subspace Statistical Estimation (ESSE), is utilized to investigate the seasonal ecosystem dynamics of the Lagoon of Venice and provide guidance on the monitoring and management of the Lagoon, combining a rich data set with a physical-biogeochemical numerical estuary-coastal model. Novel stochastic ecosystem modeling components are developed to represent prior uncertainties in the Lagoon dynamics model, measurement model, and boundary forcing by rivers, open-sea inlets, and industrial discharges. The formulation and parameters of these additive and multiplicative stochastic error models are optimized based on data-model forecast misfits. The sensitivity to initial and boundary conditions is quantified and analyzed. Half-decay characteristic times are estimated for key ecosystem variables, and their spatial and temporal variability are studied. General results of our uncertainty analyses are that boundary forcing and internal mixing have a significant control on the Lagoon dynamics and that data assimilation is needed to reduce prior uncertainties. The error models are used in the ESSE scheme for ensemble uncertainty predictions and data assimilation, and an optimal ensemble dimension is estimated. Overall, higher prior uncertainties are predicted in the central and northern regions of the Lagoon. On the basis of the dominant singular vectors of the ESSE ensemble, the two major northern rivers are the biggest sources of dissolved inorganic nitrogen (DIN) uncertainty in the Lagoon. Other boundary sources such as the southern rivers and industrial discharges can dominate uncertainty modes on certain months. For dissolved inorganic phosphorus (DIP) and phytoplankton, dominant modes are also linked to external boundaries, but internal dynamics effects are more significant than those for DIN. Our posterior estimates of the seasonal biogeochemical fields and of their uncertainties in 2001 cover the whole Lagoon. They provide the means to describe the ecosystem and guide local environmental policies. Specifically, our findings and results based on these fields include the temporal and spatial variability of nutrient and plankton gradients in the Lagoon; dynamical connections among ecosystem fields and their variability; strengths, gradients and mechanisms of the plankton blooms in late spring, summer, and fall; reductions of uncertainties by data assimilation and thus a quantification of data impacts and data needs; and, finally, an assessment of the water quality in the Lagoon in light of the local environmental legislation.

Data-driven forecasts and simulations for Massachusetts Bay based on in situ observations collected during August – September 1998 and on coupled four-dimensional (4-D) physical and biogeochemical models are carried out, evaluated, and studied. The real-time forecasting and adaptive sampling took place from August 17 to October 5, 1998. Simultaneous synoptic physical and biogeochemical data sets were obtained over a range of scales. For the real-time forecasts, the physical model was initialized using hydrographic data from August 1998 and the new biogeochemical model using historical data. The models were forced with real-time meteorological fields and the physical data were assimilated. The resulting interdisciplinary forecasts were robust and the Bay-scale biogeochemical variability was qualitatively well represented. For the postcruise simulations, the August – September 1998 biogeochemical data are utilized. Extensive comparisons of the coupled model fields with data allowed significant improvements of the biogeochemical model. All physical and biogeochemical data are assimilated using an optimal interpolation scheme. Within this scheme, an approximate biogeochemical balance and dynamical adjustments are utilized to derive the non-observed ecosystem variables from the observed ones. Several processes occurring in the lower trophic levels of Massachusetts Bay during the summer – autumn period over different spatial and temporal scales are described. The coupled dynamics is found to be more vigorous and diverse than previously thought to be the case in this period. For the biogeochemical dynamics, multiscale patchiness occurs. The locations of the patches are mainly defined by physical processes, but their strengths are mainly controlled by biogeochemical processes. The fluxes of nutrients into the euphotic zone are episodic and induced in part by atmospheric forcing. The quasi-weekly passage of storms gradually deepened the mixed layer and often altered the Bay-scale circulation and induced internal submesoscale variability. The physical variability increased the transfer of biogeochemical materials between the surface and deeper layers and modulated the biological processes.

The Littoral Ocean Observing and Predicting System (LOOPS) concept is that of a generic, versatile and portable system, applicable to multidisciplinary, multiscale generic coastal processes. The LOOPS advanced systems concept consists of: a modular, scalable structure for linking, with feedbacks, models, observational networks and data assimilation and adaptive sampling algorithms; and an efficient and robust, integrated and distributed, system software architecture and infrastructure. LOOPS applications include scientific research, coastal zone management and rapid environmental assessment for naval and civilian emergency operations. The LOOPS design is the scientific and technical conceptual basis of an interdisciplinary national littoral laboratory system. The LOOPS partners include: J.G. Bellingham (MBARI), C. Chryssostomidis (MIT), T.D. Dickey (UCSB), E. Levine (NUWC), N. Patrikalakis (MIT), D.L. Porter (JHU/APL), B.J. Rothschild (Umass-Dartmouth), H. Schmidt (MIT), K. Sherman (NMFS), D.V. Holliday (Marconi Aerospace) and D.K. Atwood (Raytheon). LOOPS objectives and accomplishments are summarized in the final section of this note.