Satellite altimeter observations retrieved since 1993 show that the global
mean sea level is rising at an unprecedented rate (3.4mm/year). With almost
three decades of observations, we can now investigate the contributions of
anthropogenic climate-change signals such as greenhouse gases, aerosols, and
biomass burning in this rising sea level. We use machine learning (ML) to
investigate future patterns of sea level change. To understand the extent of
contributions from the climate-change signals, and to help in forecasting sea
level change in the future, we turn to climate model simulations. This work
presents a machine learning framework that exploits both satellite observations
and climate model simulations to generate sea level rise projections at a
2-degree resolution spatial grid, 30 years into the future. We train fully
connected neural networks (FCNNs) to predict altimeter values through a
non-linear fusion of the climate model hindcasts (for 1993-2019). The learned
FCNNs are then applied to future climate model projections to predict future
sea level patterns. We propose segmenting our spatial dataset into meaningful
clusters and show that clustering helps to improve predictions of our ML model.

自 1993 年以来，卫星测高仪观测表明全球平均海平面正以空前的速度上升（每年 3.4 毫米）。我们利用机器学习（ML）技术研究了海平面变化的未来模式，以了解气候变化信号如温室气体、气溶胶和生物质燃烧在海平面上升中的贡献，并用于未来海平面变化的预测。我们提出了一种利用卫星观测和气候模型模拟相结合的机器学习框架，以 30 年为期限进行 2 度分辨率的海平面上升预测。我们通过训练全连接神经网络（FCNNs）进行非线性融合，预测测高仪数值（1993-2019 年）。然后将学习到的 FCNNs 应用于未来的气候模型预测，以预测未来的海平面模式。我们提出将空间数据集分段成有意义的聚类，并显示聚类有助于改进我们的机器学习模型的预测能力。