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Multiscale Physical and Biological Dynamics in the Philippines Archipelago: Predictions and Processes

Lermusiaux, P.F.J., P.J. Haley, Jr., W.G. Leslie, A. Agarwal, O. Logutov and L.J. Burton, 2011. Multiscale Physical and Biological Dynamics in the Philippines Archipelago: Predictions and Processes. Oceanography. PhilEx Issue, 24(1), 70-89, doi:10.5670/oceanog.2011.05.

The Philippine Archipelago is remarkable because of its complex geometry, with multiple islands and passages, and its multiscale dynamics, from the large-scale open-ocean and atmospheric forcing, to the strong tides and internal waves in narrow straits and at steep shelfbreaks. We employ our multiresolution modeling system to predict and study multiscale dynamics in the region, without the use of any synoptic in situ data, so as to evaluate modeling capabilities when only sparse remotely sensed sea surface height is available for assimilation. We focus on the February to March 2009 period, compare our simulation results to ocean observations, and utilize our simulations to quantify and discover oceanic features in the region. The findings include: the physical drivers for the biogeochemical features; the diverse circulation features in each sub-sea and their variations on multiple scales; the flow fields within the major straits and their variability; the transports to and from the Sulu Sea and the corresponding balances; and finally, the multiscale mechanisms involved in the formation of the deep Sulu Sea water.

Lagoon of Venice ecosystem: Seasonal dynamics and environmental guidance with uncertainty analyses and error subspace data assimilation

Cossarini, G., P.F.J. Lermusiaux, and C. Solidoro, 2009. Lagoon of Venice ecosystem: Seasonal dynamics and environmental guidance with uncertainty analyses and error subspace data assimilation, J. Geophys. Res., 114, C06026, doi:10.1029/2008JC005080.

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.

Coupled physical and biogeochemical data driven simulations of Massachusetts Bay in late summer: real-time and post-cruise data assimilation

Besiktepe, S.T., P.F.J. Lermusiaux and A.R. Robinson, 2003. Coupled physical and biogeochemical data driven simulations of Massachusetts Bay in late summer: real-time and post-cruise data assimilation. Special issue on "The use of data assimilation in coupled hydrodynamic, ecological and bio-geo-chemical models of the oceans", M. Gregoire, P. Brasseur and P.F.J. Lermusiaux (Eds.), Journal of Marine Systems, 40, 171-212.

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 development and demonstration of an advanced fisheries management information system

Robinson, A.R., B.J. Rothschild, W.G. Leslie, J.J. Bisagni, M.F. Borges, W.S. Brown, D. Cai, P. Fortier, A. Gangopadhyay, P.J. Haley, Jr., H.S. Kim, L. Lanerolle, P.F.J. Lermusiaux, C.J. Lozano, M.G. Miller, G. Strout and M.A. Sundermeyer, 2001. The development and demonstration of an advanced fisheries management information system. Proc. of the 17th Conference on Interactive Information and Processing Systems for Meteorology, Oceanography and Hydrology, Albuquerque, New Mexico. American Meteorological Society, 186-190.

Fishery management regulates size and species-specific fishing mortality to optimize biological production from the fish populations and economic production from the fishery. Fishery management is similar to management in industries and in natural resources where the goals of management are intended to optimize outputs relative to inputs. However, the management of fish populations is among the most difficult. The difficulties arise because (a) the dynamics of the natural production system are extremely complicated; involving an infinitude of variables and interacting natural systems and (b) the size-and species-specific fishing mortality (i.e. system control) is difficult to measure, calibrate, and deploy. Despite the difficulties, it is believed that significant advances can be made by employing a fishery management system that involves knowing the short-term (daily to weekly) variability in the structures of environmental and fish fields. We need new information systems that bring together existing critical technologies and thereby place fishery management in a total-systems feedback-control context. Such a system would monitor the state of the structure of all stocks simultaneously in near real-time, be adaptive to the evolving fishery and consider the effects of the environment and economics. To do this the system would need to (a) employ new in situ and remote sensors in innovative ways, (b) develop new data streams to support the development of new information, (c) employ modern modeling, information and knowledge-base technology to process the diverse information and (d) generate management advice and fishing strategies that would optimize the production of fish.

The Advanced Fisheries Management Information System (AFMIS), built through a collaboration of Harvard University and the Center for Marine Science and Technology at the University of Massachusetts at Dartmouth, is intended to apply state-of-the-art multidisciplinary and computational capabilities to operational fisheries management. The system development concept is aimed toward: 1) utilizing information on the “state” of ocean physics, biology, and chemistry; the assessment of spatially-resolved fish-stock population dynamics and the temporal-spatial deployment of fishing effort to be used in fishing and in the operational management of fish stocks; and, 2) forecasting and understanding physical and biological conditions leading to recruitment variability. Systems components are being developed in the context of using the Harvard Ocean Prediction System to support or otherwise interact with the: 1) synthesis and analysis of very large data sets; 2) building of a multidisciplinary multiscale model (coupled ocean physics/N-P-Z/fish dynamics/management models) appropriate for the northwest Atlantic shelf, particularly Georges Bank and Massachusetts Bay; 3) the application and development of data assimilation techniques; and, 4) with an emphasis on the incorporation of remotely sensed data into the data stream.

AFMIS is designed to model a large region of the northwest Atlantic (NWA) as the deep ocean influences the slope and shelves. Several smaller domains, including the Gulf of Maine (GOM) and Georges Bank (GB) are nested within this larger domain (Figure 1). This provides a capability to zoom into these domains with higher resolution while maintaining the essential physics which are coupled to the larger domain. AFMIS will be maintained by the assimilation of a variety of real time data. Specifically this includes sea surface temperature (SST), color (SSC), and height (SSH) obtained from several space-based remote sensors (AVHRR, SeaWiFS and Topex/Poseidon). The assimilation of the variety of real-time remotely sensed data supported by in situ data will allow nowcasting and forecasting over significant periods of time.

A real-time demonstration of concept (RTDOC) nowcasting and forecasting exercise to demonstrate important aspects of the AFMIS concept by producing real time coupled forecasts of physical fields, biological and chemical fields, and fish abundance fields took place in March-May 2000. The RTDOC was designed to verify the physics, to validate the biology and chemistry but only to demonstrate the concept of forecasting the fish fields, since the fish dynamical models are at a very early stage of development. In addition, it demonstrated the integrated system concept and the implication for future coupling of a management model. This note reports on the RTDOC.


Real-time Forecasting of the Multidisciplinary Coastal Ocean with the Littoral Ocean Observing and Predicting System (LOOPS)

Robinson, A.R. and the LOOPS Group, 1999. Real-time Forecasting of the Multidisciplinary Coastal Ocean with the Littoral Ocean Observing and Predicting System (LOOPS). Preprint Volume of the Third Conference on Coastal Atmospheric and Oceanic Prediction and Processes, 3-5 November 1999, New Orleans, LA, American Meteorological Society, Boston, MA.

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.