Ecosystem Forecasting

Simulation models coupling physics to biological processes in the ocean are central to achieving CINAR goals. 

Ocean physical models have reached a high level of sophistication; the physical relationships are canonical, and modern computational technology for fluid mechanics has advanced steadily for two generations or more.  The complexity of biological processes in the ocean presents enormous difficulties beyond physics. There is a recognizable mode of operation wherein ‘complete’ physics is coupled to reduced-complexity biology; and simulations are typically chosen to match field problems and available data. The upshot of this situation is a great diversity in what is possible in ‘replicating observations’, and even more importantly, in assimilating them into simulations and creating forecast systems.

The CINAR modeling strategy will directly address the consequences of this situation. The biological problems are of immediate human concern, and there is a sense that skillful simulations are feasible. Yet what is meant by “skillful” and “simulation” is typically very different depending on the target problem. A scholarly basis for skill assessment is prerequisite for sound regulatory action and public advisement. CINAR offers an exceptional opportunity for progress on this topic by virtue of a regional effort where state-of-the art ecological models are applied to practical problems.

Coupled physical-biological models, adapted and validated for the NES LME, are integrative tools that link CI themes and facilitate both analysis and prediction. Temporal scales of interest span hours to days to decades; spatial scales range from local to basin in scope. This breadth necessitates a nested modeling approach (Fig. 3), by which open boundaries of the LME and its subdomains are fed by information from larger-scale models and observations from “upstream.”  Coupled modeling will facilitate analysis of climate variability and human-induced impacts on habitat, range, and species distribution, evaluation of closing areas to harvesting or proposed designations of marine protected areas, and the downscaling of climate change scenarios to explore regional and ecosystem impacts. We propose continued development and application of predictive models of four basic types: 1) aggregate, lower-trophic-level ecosystem models of the “NPZ” genre; 2) zooplankton population dynamics; 3) larval fish recruitment and population connectivity; and 4) species-specific models of biological hazards such as harmful algal blooms (HABs) and pathogens.  In each area, we will define quantitative metrics by which model performance will be evaluated.  A key outgrowth of such skill assessment will be use of these models to define the observational needs required to drive forecast systems with specified levels of accuracy. Together, these steps will provide information needed to assess feasibility of transitioning such ecological forecast systems into operational use.

Research proposed within this theme is highly relevant to NOAA’s goals, as an ecosystem approach to management (EAM) requires integrated, multidisciplinary models and coordination across research activities to provide a sound basis for decision making. The application of existing operational models, transitioning models from research to operations, and developing new models for estuarine, coastal and oceanic waters will be essential for NOAA to achieve its mission of providing products, services, and information that sustain marine ecosystems and fisheries, and mitigate coastal hazards. These models include physical, biological, chemical, ecological, and socioeconomic processes that, when suitably parameterized, can forecast a variety of ecosystem and human-related properties supporting support of policy development and management. CINAR partners have extensive experience in these areas, and are uniquely poised to assist NOAA managers. 

Funded Ecosystem Forecasting Abstracts