A Feasibility Study for Understanding Climate Uncertainty with an Ocean Focus

气候模型被广泛用于了解气候变化和制定应对气候变化的政策，量化海洋部分的不确定性对于了解海洋对地球气候的重要性至关重要。理解海洋模型的参数空间的非线性所表现出的环流将导致改进的模拟海洋和它的不足之处的代表性。智力优势：复杂的气候模式使用参数来代表各种物理过程的各个方面。由于缺乏用于表示模型物理学的参数的知识，输出中存在不确定性。通过复杂和非线性模型传播不确定性的最佳方法是使用大的运行集合。这项可行性研究将展示一种方法，使用一个相对较小的合奏模拟加上一个仿真器，以检查在一个模型中的海洋环流参数规格的不确定性的影响。将探讨气候模式海洋部分的参数空间，重点是了解与一套海洋度量（例如输送、纬向海洋环流强度、海平面、区域和全球SST）有关的不确定性。首先，将使用共同体气候系统模式参数空间的海洋模式，以粗分辨率（3°）对100个（如果参数集减少，则更少）的完整集合进行100年实验。然后，模型输出将用于开发一套基于海洋的度量标准，以检查模型参数空间的不确定性。最后，这些指标将用于创建和探索更高分辨率（1°）下的10次运行的集合，方法是映射两个参数空间之间的差异，以推断1°下的不确定性。该方法被称为计算机代码输出的统计分析或计算机实验的设计和分析，而不是经典的蒙特卡罗集成方法。这些方法允许使用小得多的集合，即，100次而不是1000次。在设计的实验中，需要最少数量的模拟来跨越参数空间。模拟器或替代品是对原始气候模型的快速统计近似。因此，仿真器可以用来，而不是复杂的模型，有效地跨越参数空间，以估计概率分布，并解决有关参数设置和度量不确定性的问题。更广泛的影响：这项研究的资金将有更广泛的影响。调查员是物理海洋学和统计与数学界代表性不足的群体的成员。因此，她参加两个社区的会议将增加其多样性。该提案的多学科方面将允许思想的相互交流。此外，该提案将产生一个前所未有的数据集，供研究人员和本提案范围以外的项目使用，用于分析各种海洋气候和统计调查。了解气候模式的不确定性和对未来气候可能的估计对于公众接受气候预测至关重要。与天气预报一样，公众和政策制定者能够更好地了解与结果（在这种情况下，气候变化）相关的风险，并在已知不确定性时信任信息。这项研究将为公众和政策制定者提供信息，并为外部研究人员提供确定类似规模问题的不确定性的方法。

Climate models are used extensively for understanding climate change and for developing policies to address it. Quantifying the uncertainty in the ocean component is important to understanding the importance of the oceans to the Earth's climate. Understanding the non-linearity of the ocean model's parameter space as exhibited by the resulting circulation will lead to improved representation of thesimulated ocean and its deficiencies. Intellectual Merit: Complex climate models make use of parameters to represent aspects of various physical processes. There is uncertainty in the outputs from the lack of knowledge of the parameters used to represent the model physics. The best method of propagating the uncertainty through the complex and non-linear models is to use large ensembles of runs. This feasibility study will demonstrate a method using a relatively small ensemble of simulations coupled with an emulator to examine the effect of uncertainty in parameter specification on ocean circulation in a model. The parameter space of the ocean component of a climate model will be explored, focusing on understanding the uncertainties associated with a set of ocean metrics (e.g. transports, strength of the meridional ocean circulation, sea level, regional and global SST). First, a 100-year experiment using the ocean model of Community Climate System Model parameter space, will be conducted for a full ensemble of 100 (fewer if for a reduced set of parameters) at coarse resolution (3°). Then, the model output will be used to develop a set of ocean based metrics to examine the model's uncertainty of its parameter space. Finally, these metrics will be used to create and explore an ensemble of 10 runs at a higher resolution (1°) by mapping differences between two parameter spaces to make inferences about uncertainties at 1°. The proposed method is known as Statistical Analysis of Computer Code Output or Design and Analysis of Computer Experiments, rather than the classical method of Monte Carlo ensembles. These methods allow the use of much smaller ensembles, i.e., order 100 rather than order of a 1000 runs. A minimum number of simulations are required to span the parameter space in a designed experiment. An emulator, or surrogate, is a fast statistical approximation to the original climate model. As such, the emulator can be used, instead of the complex model, to efficiently span the parameter space to estimate the probability distribution and to address questions relating to parameter setting and metric uncertainty.Broader Impacts: The funding of this research will have broader impacts. The investigator is a member of an underrepresented group within both the Physical Oceanography and the Statistical and Mathematical communities. As such, her participation in meetings of both communities will increase their diversity. The multi-disciplinary aspect of the proposal will allow for the cross fertilization of ideas. Further, this proposal would produce an unprecedented dataset that would be available to researchers and projects beyond the scope of this proposal for analyses of various ocean-climate and statistical investigations. Understanding the uncertainty in a climate model and the estimates of what the future climate might look like is critical to the public's acceptance of climate predictions. As with weather forecasts, the public and policy makers are better able to understand the risk associated with an outcome (in this case, climate change), as well as trust the information, when the uncertainty is known. This research will give the public and policy makers the information and to external researchers, the methods for determining uncertainties of similarly sized problems.