Collaborative Research: Estimating Ecosystem Model Uncertainties in Pan-Regional Syntheses and Climate Change Impacts on Coastal Domains of the North Pacific Ocean

合作研究：估计泛区域综合中的生态系统模型不确定性和气候变化对北太平洋沿海地区的影响

• 批准号: 0814934
• 负责人: Christopher Wikle
• 金额: $ 23.5万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0814934&HistoricalAwards=false
• 关键词: Collaborative; Research; Estimating; Ecosystem; Model

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.

将开发一系列贝叶斯分层模型（BHM），以综合北太平洋边界重点区域的沿海生态系统动态和对气候变化的响应。 BHM 是一种统一的概率建模方法，可在多平台观测的情况下更新有关过程模型和参数的不确定分布知识。由此产生的“后验”分布的汇总测量提供了集中趋势和不确定性的实际定量估计。研究人员将根据了解区域海洋学的北太平洋生态系统模型（NEMURO）开发我们的过程模型分布。因此，这项研究的一个重要成果将是对北太平洋各地区 NEMURO 状态变量和参数的相对不确定性进行定量理解和比较。分三步走的 BHM 发展计划将在三年内解决泛区域综合、气候变化影响和生态系统管理工具概念。最初的 BHM 开发将是一个可重定位、依赖时间的一维（垂直）模型，旨在总结感兴趣的沿海区域内不同状况（陆架、斜坡、上升流位点、边界流延伸等）的生态系统动态。来自多学科观测计划和北太平洋沿海地区确定性模型实施的数据和见解将得到充分利用。除了强调实地观测外，BHM 方法还将纳入确定性模型输出（例如区域海洋建模系统或 ROMS）作为数据，提供对北太平洋沿海海洋生态系统了解状态的严格和完整的综合。研究人员将重点研究来自美国 GLOBEC 计划部分内容（即加州洋流系统，CCS；和阿拉斯加沿海湾，CGOA）的东太平洋数据和模型以及来自北太平洋海洋科学组织的西太平洋（WPAC）数据和模型（图片）。一维 BHM 还将在气候尺度计算中实施，以记录和比较北太平洋沿海海洋生态系统内部和整个气候变化的影响，并量化这些比较中的不确定性。最终的 BHM 实施将在三个维度上进行，解释中尺度海洋动力对沿海生态系统区域的影响，并展示 BHM 方法的潜在生态系统管理优势。这项研究的智力价值源于概率建模方法（即 BHM）的新颖扩展，以综合主要海洋盆地东部和西部边界沿海地区的不同观测结果和确定性模型模拟。 BHM 在生物海洋学中的应用代表了变革性的研究步骤，并引入了新的范式。这里提出的研究结合了确定性模型和概率模型的优势，以获得现代低营养级海洋生态系统模型的状态变量和参数的不确定性估计。该研究的更广泛影响将是对博士后和研究生（统计学和海洋学）进行海洋建模方法新协同的培训。随着生态系统管理者和科学家学会利用概率分布中的状态和参数信息，可以针对生态系统模型的不确定部分进行更深入的观察和/或更复杂的参数化。