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: 0814749
• 负责人: Andrew Moore
• 金额: $ 18万
• 依托单位: University of California-Santa Cruz
• 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=0814749&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），以综合沿着北太平洋界限的沿海气候变化的沿海生态系统动力学和对气候变化的反应。 BHM是一种统一的概率建模方法，它在存在多平台观测值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”分布的摘要度量提供了对中心趋势和不确定性的现实定量估计。研究人员将在了解区域海洋学（Nemuro）的北太平洋生态系统模型之后开发我们的过程模型分布。因此，这项研究的重大结果将是对Nemuro状态变量和参数的相对不确定性的定量理解和比较。三步的BHM开发计划将在三年的时间表中解决泛区域合成，气候变化影响和生态系统管理工具概念。最初的BHM开发将是一个可重新定位的，依赖时间的，一维（垂直）模型，旨在总结沿海地区沿海地区内不同政权（架子，坡度，上升地点，边界电流扩展等）的生态系统动力学。北太平洋沿海地区的多学科观察计划和确定性模型实施的数据和见解将得到充分利用。除了强调现场观察外，BHM方法还将将确定性模型输出（例如区域海洋建模系统或ROM）纳入数据，从而提供了对北太平洋沿海海洋生态系统的严格综合理解状态。研究人员将专注于美国环球计划部分地区（即加利福尼亚州当前系统，CCS； Alaska，CGOA沿海湾）和北太平洋海洋科学组织（PICES）的西太平洋（WPAC）的数据和模型。 1D BHM还将在气候规模计算中实施，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，并在这些比较中量化不确定性。最终的BHM实施将分为三个维度，考虑到沿海生态系统区域的中尺度海洋动态影响，并证明了BHM方法的潜在生态系统管理优势。该研究的智力优点源于概率建模方法的新颖范围（即，沿海）的延伸（即，沿海）的范围延伸（即，沿沿海）的范围，并确定了分散的观察，并确定了分散的观察。主要海盆的边界。 BHM在生物海洋学中的应用代表了一个变革性的研究步骤，并引入了新的范式。这里提出的研究结合了确定性和概率模型的优势，以获得现代低营养水平海洋生态系统模型的状态变量和参数的不确定性估计。这项研究的更广泛影响将是在这种海洋建模方法的新协同作用中培训博士后和研究生（统计和海洋学）。随着生态系统经理和科学家学会在概率分布中利用状态和参数信息，生态系统模型的不确定部分可以针对更密集的观察和/或更复杂的参数化。