Understanding Global Patterns of Sea Level Rise and Ocean Heat Redistribution (Project C2)

C2项目将解决对我们理解气候变化至关重要的问题：海平面上升的区域速率是多少？区域海平面上升与海平面极端变化和海况变化有何关系？卫星对海面的观测在多大程度上符合海洋热变化(OHC)的模拟？从卫星观测可以得出哪些热量再分配模式？我们将探索RU中的协同作用，以更好地利用大量的卫星数据和海洋建模。将针对这个问题量身定做新的和有效的几何表示和聚集数据的方法(人工智能方法以前也被引入到海洋遥感中，但这些方法大多是从图像分析中转移出来的，往往没有适当地考虑问题的几何性质)。因此，我们决定用新的基于人工智能的方法解决四个特殊问题：(1)对海面高度(SSH)和有效波高(SWH)的雷达回波进行一致性分析，(2)结合和分析SSH的可变性，(3)利用稀疏数据重建多十年的SSH，以及(4)将SSH的雷达观测数据转换为OHC。这些问题遵循海平面研究的分析链，但它们可以独立解决。

Project C2 will address questions that are fundamental to our understanding of climate change: What are theregional rates of sea level rise? How is regional sea level rise related to changes in sea level extremes andto changes in sea state? To what extent do satellite observations of the sea surface fit to modelling of ocean heat change (OHC)? Which patterns of heat redistribution can be derived from satellite observations? Wewill explore the synergy in the RU to better exploit the vast archive of satellite data and ocean modelling.New and efficient methods for geometric representation and clustering data will be tailored to the problem (AI methods have been introduced in ocean remote sensing before, but these were mostly transferredfrom image analysis and often did not properly account for the geometrical nature of the problems). Thus,within this RU, we decided to address four particular problems with novel AI-based methods: (1) Consistent analysis of radar echoes for sea surface height (SSH) and significant wave height (SWH), (2) Combining and analysing SSH variability, (3) Reconstructing multi-decadal SSH with sparse data, and (4) inverting radar observations of SSH into OHC. These problems follow the analysis chain in sea level research, but they can be addressed independently.