A sequence of Bayesian Hierarchical Models (BHM) will be developed to synthesize coastal ecosystem dynamics and responses to climate change across focus regions bounding the North Pacific Ocean. BHM is a unified probabilistic modeling approach that updates uncertain distributional knowledge about process models and parameters in the presence of multi-platform observations. Summary measures of the resulting "posterior" distributions provide realistic quantitative estimates of central tendencies and uncertainties. The investigators will develop our process model distributions after the North Pacific Ecosystem Model for Understanding Regional Oceanography (NEMURO). So, a significant outcome of the research will be quantitative understanding and comparisons of the relative uncertainties of NEMURO state variables and parameters, region-by-region across the North Pacific. A three-step BHM development plan will address pan-regional syntheses, climate change impacts, and ecosystem management tool concepts, over a three-year schedule. The initial BHM development will be a relocatable, time-dependent, one-dimensional (vertical) model intended to summarize ecosystem dynamics for different regimes (shelf, slope, upwelling loci, boundary current extensions, etc.) within the coastal regions of interest. Data and insights from multi-disciplinary observational programs and deterministic model implementations in coastal regions of the North Pacific will be fully exploited. In addition to emphasizing field observations, the BHM methodology will incorporate deterministic model output (e.g. the Regional Ocean Modeling System or ROMS) as data, providing a rigorous and complete synthesis of the state of understanding for coastal ocean ecosystems of the North Pacific. The investigators will focus on data and models in the Eastern Pacific from parts of the US GLOBEC program (i.e. California Current System, CCS; and Coastal Gulf of Alaska, CGOA) and in the Western Pacific (WPAC) from the North Pacific Marine Science Organization (PICES). The 1D BHM will also be implemented in climate-scale calculations to document and compare climate change impacts within and across North Pacific coastal ocean ecosystems, and to quantify uncertainties in these comparisons. The ultimate BHM implementation will be in three dimensions, accounting for mesoscale ocean dynamical impacts on the coastal ecosystem regions, and demonstrating potential ecosystem management advantages of the BHM approach.
The intellectual merit of this research derives from a novel extension of probabilistic modeling methods (i.e. BHM) to synthesize disparate observations and deterministic model simulations from coastal regions on eastern and western boundaries of a major ocean basin. Application of BHM in Biological Oceanography represents a transformative research step and introduces a new paradigm. The research proposed here combines the strengths of deterministic and probabilistic models to obtain uncertainty estimates for state variables and parameters of a modern lower-trophic level ocean ecosystem model. A broader impact of the research will be the training of postdoctoral and graduate students (in statistics and oceanography) in this new synergy of ocean modeling approaches. As ecosystem managers and scientists learn to utilize state and parameter information in probability distributions, uncertain parts of the ecosystem model can be targeted for more intensive observations and/or more sophisticated parameterizations.
