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 …

Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …

Improved knowledge of glacial-to-interglacial global temperature change yields Charney
(fast-feedback) equilibrium climate sensitivity 1.2±0.3° C (2σ) per W/m2, which is 4.8° C±1.2° …

Numerical simulation of fluids plays an essential role in modeling many physical
phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well …

The representation of nonlinear subgrid processes, especially clouds, has been a major
source of uncertainty in climate models for decades. Cloud-resolving models better …

Extreme weather amplified by climate change is causing increasingly devastating impacts
across the globe. The current use of physics-based numerical weather prediction (NWP) …

Neural networks can emulate nonlinear physical systems with high accuracy, yet they may
produce physically inconsistent results when violating fundamental constraints. Here, we …

Since its discovery in the early 1970s, the crucial role of the Madden‐Julian Oscillation
(MJO) in the global hydrological cycle and its tremendous influence on high‐impact climate …

Global climate models represent small-scale processes such as convection using subgrid
models known as parameterizations, and these parameterizations contribute substantially to …

Representing unresolved moist convection in coarse‐scale climate models remains one of
the main bottlenecks of current climate simulations. Many of the biases present with …