The ability to understand causality from data is one of the major milestones of human-level
intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships …

This survey paper covers the breadth and depth of time-series and spatiotemporal causality
methods, and their applications in Earth Science. More specifically, the paper presents an …

Highly accurate and interpretable forecasting of non-stationary long-time series is a
challenge. It is often difficult for existing models to capture the trends of non-stationary time …

Spatial interference (SI) occurs when the treatment at one location affects the outcomes at
other locations. Accounting for spatial interference in spatiotemporal settings poses further …

The Arctic is a region with unique climate features where warming has been almost three
times as fast as the rest of the world. The warming of the Arctic, also known as Arctic …

The relentless retreat of sea ice in polar regions, driven by the impacts of climate change,
demands innovative solutions to predict and navigate these transforming environments …

The Earth's polar regions are undergoing rapid and unprecedented changes, with the extent
and thickness of sea ice diminishing due to climate change. As a consequence, accurate …