We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …

Existing machine learning models of weather variability are not formulated to enable
assessment of their response to varying external boundary conditions such as sea surface …

Abstract Machine learning (ML)‐based parameterizations have been developed for Earth
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …

Coarse‐grid weather and climate models rely particularly on parameterizations of cloud
fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target …

Understanding the role of dynamic, thermodynamic, and cloud microphysical parameters
governing the occurrence and magnitude of convective precipitation at subgrid scales is …

Operational weather forecasting models have advanced for decades on both the explicit
numerical solvers and the empirical physical parameterization schemes. However, the …

Deep-learning-based convection schemes have garnered significant attention for their
notable improvements in simulating precipitation distribution and tropical convection in Earth …

The radiative forcing of carbon dioxide (CO2) at the top-of-atmosphere (TOA) plays a central
role in quantifying climate change and its global-mean value is a key aspect of radiative …