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，人们担心海平面在下个世纪将加速上升。及早发现海平面加速是适应工作所必需的，这取决于对海平面数十年变化的更好理解。这项研究包括使用海平面数据、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几十年来海平面变化的详细调查。这项研究的结果将是一个基于分层贝叶斯模型方法的增强的、增值的海平面数据集，它将补充来自测高、验潮仪和海洋环流模型的现有海平面估计。这些来自贝叶斯模型解决方案的海平面区域将被自由和公开地访问和使用。由于它们对不确定性的估计，这些领域将适合用于百年时间尺度的状态估计工作。该项目是学术界和产业界的伙伴关系，也将支持一名早期职业科学家。该项目的主要目标包括：重建两个世纪前的区域地图和全球海平面时间序列；阐明全球平均数十年海平面变化的内部和外部强迫的作用；诊断静态和动态过程对数十年来区域海平面变化的影响；以及评估气候模型的准确性。这些成果将填补基本知识空白，提高对海洋环流和气候变化的理解。为了增加海洋环流和气候变化的基本知识，并解决与海平面数据在空间和时间上稀疏有关的困难，将在现有的数据集(测高、验潮仪、垂直陆地运动)上引入分层贝叶斯模型，对过去两个世纪的海平面行为给予基于观测的约束。基于分层贝叶斯模型解的海洋动力学探索将阐明海平面数十年变化的物理驱动因素和时空尺度，并为评估气候模型提供基础。

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.