Sea surface height observations provided by satellite altimetry since 1993
show a rising rate (3.4 mm/year) for global mean sea level. While on average,
sea level has risen 10 cm over the last 30 years, there is considerable
regional variation in the sea level change. Through this work, we predict sea
level trends 30 years into the future at a 2-degree spatial resolution and
investigate the future patterns of the sea level change. We show the potential
of machine learning (ML) in this challenging application of long-term sea level
forecasting over the global ocean. Our approach incorporates sea level data
from both altimeter observations and climate model simulations. We develop a
supervised learning framework using fully connected neural networks (FCNNs)
that can predict the sea level trend based on climate model projections.
Alongside this, our method provides uncertainty estimates associated with the
ML prediction. We also show the effectiveness of partitioning our spatial
dataset and learning a dedicated ML model for each segmented region. We compare
two partitioning strategies: one achieved using domain knowledge, and the other
employing spectral clustering. Our results demonstrate that segmenting the
spatial dataset with spectral clustering improves the ML predictions.

利用卫星测高测量，对全球平均海平面上升速率进行预测，并研究未来海平面变化的模式。我们展示了机器学习在长期海平面预测中的潜力，结合测高观测和气候模型模拟数据，利用全连接神经网络框架预测海平面趋势，并提供与该预测相关的不确定性评估。我们同时展示了分区数据集和为每个分割区域学习独立机器学习模型的有效性，并比较了两种分割策略的结果。