This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

In this paper, we performed an analysis of the 500 most relevant scientific articles published
since 2018, concerning machine learning methods in the field of climate and numerical …

Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …

Could computer simulation models drive our ambitions to sustainability in urban and non-
urban environments? Digital twins, defined here as real-time, virtual replicas of physical and …

Current global climate models struggle to represent precipitation and related extreme
events, with serious implications for the physical evidence base to support climate actions. A …

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-
relevant scales is critical to mitigating climate change and ensuring sustainable food …

Today's floating-point arithmetic landscape is broader than ever. While scientific computing
has traditionally used single precision and double precision floating-point arithmetics, half …

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of
environmental factors. The constraints that modulate the evaporation from plant leaves (or …

Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …