Collaborative Research: Understanding Multidecadal Changes in the Instrumental Mean Sea Level Record

全球海平面在上个世纪稳步上升，人们担心海平面将在下个世纪加速上升。早期发现海平面加速是适应努力所必需的，这取决于对多年代代性海平面变化的更好理解。本研究利用海平面数据、海洋和气候模式、大气再分析、垂直陆地运动和分层贝叶斯数据同化方法对多年来海平面变化进行了详细调查。本研究的一个成果将是基于分层贝叶斯模型方法的增强型增值海平面数据集，它将补充现有的由测高仪、潮汐仪和海洋环流模型估算的海平面。这些来自贝叶斯模型解的海平面场将被免费公开访问和使用。由于它们对不确定性的估计，这些领域将适合用于世纪时间尺度上的状态估计工作。这个项目是学术界和工业界之间的合作，也将支持一个早期的职业科学家。该项目的主要目标包括：重建两个世纪以来的区域地图和全球海平面时间序列；阐明全球多年代际平均海平面变化的内外部强迫作用诊断静态和动态过程对多年代际区域海平面变化的影响；以及评估气候模式的准确性。研究结果将填补基本知识空白，提高对海洋环流和气候变化的认识。为了增加对海洋环流和气候变化的基本认识，并应对与海平面数据在空间和时间上的稀疏相关的困难，将对现有的数据集（测高、测潮、垂直陆地运动）采用分层贝叶斯模型，对过去两个世纪的海平面行为给出基于观测的约束。基于层次贝叶斯模式解的海洋动力学基础探索将阐明多年代际海平面变化的物理驱动力和时空尺度，并为气候模式的评估提供依据。

Global sea levels have risen steadily over the last century and there is concern that sea level rise will accelerate over the next century. Early detection of sea level acceleration, necessary for adaptation efforts, depends on an improved understanding of multidecadal sea level changes. This study consists of a detailed investigation of multidecadal sea level changes using sea level data, ocean and climate models, atmospheric reanalyses, vertical land motion, and a hierarchical Bayesian data-assimilation approach. An outcome of this study will be an enhanced, value-added sea level dataset based on the hierarchical Bayesian model approach, which will complement the available sea level estimates from altimetry, tide gauges, and ocean circulation models. These sea level fields from the Bayesian model solution will be made freely and publicly accessible and available. With their estimates of uncertainty, these fields would be suited for use in state estimation efforts on century time scales. This project is a partnership between academia and industry and would also support an early career scientist.The main objectives of this project include: reconstructing regional maps and global time series of sea level going back two centuries; elucidating roles of internal versus external forcing of global mean multidecadal sea level change; diagnosing the impacts of static and dynamic processes on multidecadal regional sea level changes; and evaluating the veracity of climate models. The outcomes will fill basic knowledge gaps, improving understanding of ocean circulation and climate change. To increase basic knowledge of ocean circulation and climate change, and to cope with difficulties related to the sparseness of sea level data in space and time, a hierarchical Bayesian model will be brought to bear on extant datasets (altimetry, tide gauge, vertical land motion), giving observation-based constraints on sea level behavior over the last two centuries. Exploration of the underlying ocean dynamics based on hierarchical Bayesian model solutions will elucidate the physical drivers and space-time scales of multidecadal sea level changes and provide a basis to evaluate climate models.