A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics‐Based Component

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...

Full description

Saved in:
Bibliographic Details
Published in:Geophysical research letters Vol. 50; no. 8
Main Authors: Arcomano, Troy, Szunyogh, Istvan, Wikner, Alexander, Hunt, Brian R., Ott, Edward
Format: Journal Article
Language:English
Published: Washington John Wiley & Sons, Inc 28.04.2023
Wiley
Subjects:
Atmospheric circulation
Atmospheric models
Atmospheric processes
Climate
Climate models
Climatology
El Nino
El Nino phenomena
El Nino-Southern Oscillation event
La Nina
Learning algorithms
Machine learning
Modelling
numerical weather prediction
Physics
Precipitation
Pressure
Resolution
Sea surface
Sea surface temperature
Simulation
Southern Oscillation
Stratospheric warming
Surface pressure
Surface temperature
ISSN:0094-8276, 1944-8007
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. Plain Language Summary This paper introduces and tests schemes for efficiently enabling significant expansion of the utility and scope of a recently introduced hybrid modeling technique that combines machine learning with an atmospheric global circulation model (AGCM). Simulation experiments are carried out with an implementation of the approach on a low resolution simplified AGCM. An examination of the simulated atmospheric circulation suggests that the hybrid model can capture dynamical process not captured by the AGCM. Moreover, the addition of precipitation and sea surface temperature (SST) as machine learning predicted physical quantities to the model improves the precipitation climatology and leads to a realistic El Niño‐La Niña signal in the SST and atmospheric surface pressure. Key Points A hybrid system combining an atmospheric global circulation model (AGCM) with a machine‐learning component can capture processes not captured by the AGCM Machine learning provides a flexible framework to introduce additional prognostic variables into the hybrid model The prototype hybrid model tested in the paper is stable and has a realistic climate in decades‐long simulation experiments
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0094-8276
1944-8007
DOI:10.1029/2022GL102649