Quantum mechanics

Scale (ratio)

Engineering

Aerospace engineering

Statistics

Atmospheric model

Cartography

Precipitation

Range (aeronautics)

Computer science

Climate model

Radiative transfer

Grid

Geology

Oceanography

Physics

Climate change

Parametrization (atmospheric modeling)

Mathematics

Geography

Environmental science

Geometry

General Circulation Model

Meteorology

Replicate

Nature Communications

Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising alternative approach is to use machine learning to build new parameterizations directly from high-resolution model output. However, parameterizations learned from three-dimensional model output have not yet been successfully used for simulations of climate. Here we use a random forest to learn a parameterization of subgrid processes from output of a three-dimensional high-resolution atmospheric model. Integrating this parameterization into the atmospheric model leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation. The parameterization obeys physical constraints and captures important statistics such as precipitation extremes. The ability to learn from a fully three-dimensional simulation presents an opportunity for learning parameterizations from the wide range of global high-resolution simulations that are now emerging.

Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions