Articles | Volume 15, issue 4
Geosci. Model Dev., 15, 1677–1687, 2022
https://doi.org/10.5194/gmd-15-1677-2022
Geosci. Model Dev., 15, 1677–1687, 2022
https://doi.org/10.5194/gmd-15-1677-2022

Development and technical paper 25 Feb 2022

Development and technical paper | 25 Feb 2022

A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1

Lu Shen et al.

Data sets

Replication Data for: A machine learning-guided and accurate algorithm to halve the computational cost of atmospheric chemistry in Earth System models Lu Shen https://doi.org/10.7910/DVN/KASQOC

Model code and software

geoschem/geos-chem: GEOS-Chem 12.0.0 release (12.0.0) The International GEOS-Chem User Community https://doi.org/10.5281/zenodo.1343547

Replication Data for: A machine learning-guided and accurate algorithm to halve the computational cost of atmospheric chemistry in Earth System models Lu Shen https://doi.org/10.7910/DVN/KASQOC

geoschem/geos-chem: GEOS-Chem 12.9.1 (12.9.1) The International GEOS-Chem User Community https://doi.org/10.5281/zenodo.3950473

Short summary
The high computational cost of chemical integration is a long-standing limitation in global atmospheric chemistry models. Here we present an adaptive and efficient algorithm that can reduce the computational time of atmospheric chemistry by 50 % and maintain the error below 2 % for important species, inspired by machine learning clustering techniques and traditional asymptotic analysis ideas.