A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics‐Based Component
Abstract
It is shown that a recently developed hybrid modeling approach that combines machine learning (ML) with an atmospheric global circulation model (AGCM) can serve as a basis for capturing atmospheric processes not captured by the AGCM. This power of the approach is illustrated by three examples from a decades‐long climate simulation experiment. The first example demonstrates that the hybrid model can produce sudden stratospheric warming, a dynamical process of nature not resolved by the low resolution AGCM component of the hybrid model. The second and third example show that introducing 6‐hr cumulative precipitation and sea surface temperature (SST) as ML‐based prognostic variables improves the precipitation climatology and leads to a realistic ENSO signal in the SST and atmospheric surface pressure.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 26, 2023
- Source ID
- 10.1029/2022gl102649
Entities
People
- Alexander Wikner
- Brian R. Hunt
- Edward Ott
- István Szunyogh
- Troy Arcomano
Organizations
- Argonne National Laboratory
- Defense Advanced Research Projects Agency
- University of Maryland