Methods of Uncertainty Analysis of Complex Non-Linear Components in Integrated Systems Models

PI建议测试一种方法，用于对具有不连续状态的复杂、高度非线性模型进行不确定性分析，并应用这些技术更好地表征温室气体强迫可能导致大规模地球系统高度非线性响应的可能性。PI将采用两种方法：（i）蒙特卡洛方法，其中需要运行数千次给定的复杂模型，以及（ii）确定性等效建模方法（DEMM）。DEMM将应用于海洋-大气耦合模型，即马歇尔（麻省理工学院）的海洋环流模型（OGCM），耦合到大气的二维表示（综合全球系统模型或IGSM）。OGCM是一个复杂的模型，表现出非线性行为，包括关闭的温盐循环（THC）。IGSM包括排放预测和政策分析模型，该模型预测经济活动、能源使用和相应于特定气候变化的人为温室气体排放;大气成分和气候预测的耦合模型，其中包括以戈达德空间研究所开发的大气GCM为基础的二维陆地-海洋模型;以及模拟生态系统响应的陆地生态系统模型，即，碳和氮通量和水库规模对气候和CO2变化的响应。这项工作很重要，因为它将为量化气候模型的不确定性提供有用的见解。研究结果将对政策制定者和参与气候建模的科学家产生兴趣。 该奖项由NSF的综合评估方法和模型（MMIA）倡议资助。

The PIs propose to test a methodology for conducting uncertainty analysis of complex, highly non-linear models with discontinuous states and apply the techniques to better characterize the likelihood that greenhouse gas forcing could cause a highly non-linear response in large-scale earth systems. The PIs will utilize two approaches: (i) a Monte Carlo approach in which thousands of runs of a given complex model are required, and (ii) the Deterministic Equivalent Modeling Method (DEMM). DEMM will be applied to a coupled ocean-atmosphere model, viz the ocean general circulation model (OGCM) of Marshall (MIT) coupled to a two-dimensional representation of the atmosphere (the Integrated Global System Model or IGSM). The OGCM is a complex model that exhibits nonlinear behavior, including the shutdown of the thermohaline circulation (THC). The IGSM includes the Emissions Prediction and Policy Analysis (ESSA) model that predicts economic activity, energy use and resulting anthropogenic greenhouse gas emissions in response to given climatic changes; a coupled model of atmospheric composition and climate prediction that includes a two-dimensional land-ocean model based on the atmospheric GCM developed at Goddard Institute for Space Studies; and the Terrestrial Ecosystem Model that simulates the ecosystem response, i.e., carbon and nitrogen fluxes and reservoir sizes in response to changes of climate and CO2. The work is important because it will yield useful insights into the quantification of climate model uncertainties. The results will be of interest to policy makers as well as scientists involved in climate modeling. The award is funded under NSF's Methods and Models for Integrated Assessment (MMIA) initiative.