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层次模型，以综合北太平洋各重点区域的沿海生态系统动态和对气候变化的反应。BHM是一种统一的概率建模方法，在多平台观测的情况下更新关于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对中心趋势和不确定性的现实定量估计。研究人员将在北太平洋生态系统模型理解区域海洋学（根室）之后开发我们的过程模型分布。因此，研究的一个重要成果将是对北太平洋各区域根室状态变量和参数的相对不确定性的定量理解和比较。BHM的三步发展计划将在三年时间内解决泛区域综合、气候变化影响和生态系统管理工具概念。最初的BHM开发将是一个可重新定位的、随时间变化的、一维（垂直）模型，旨在总结不同制度（大陆架、斜坡、上升流地点、边界流延伸等）的生态系统动态。在沿海地区的利益。将充分利用来自北太平洋沿海地区多学科观测计划和确定性模型实施的数据和见解。除了强调实地观测外，BHM方法还将把确定性模型输出（例如区域海洋模拟系统）作为数据，对北太平洋沿海海洋生态系统的了解状况进行严格和完整的综合。调查人员将重点关注美国GLOBEC计划部分（即加州海流系统，CCS;阿拉斯加沿海海湾，CGOA）和北太平洋海洋科学组织（PICES）西太平洋（WPAC）的数据和模型。1D 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之间的气候变化影响，并量化这些比较中的不确定性。BHM的最终实施将在三个方面，考虑到中尺度海洋动力学对沿海生态系统区域的影响，并展示了BHM方法潜在的生态系统管理优势。本研究的智力价值来自于概率建模方法的新扩展（即BHM）综合不同的观测和确定性模式模拟从沿海地区的东部和西部边界的一个主要的海洋盆地。BHM在生物海洋学中的应用代表了一个变革性的研究步骤，并引入了一个新的范式。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作用的培训。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，生态系统模型的不确定部分可以针对更密集的观测和/或更复杂的参数化。