Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 4
Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1677-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1677-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 4
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, Daniel J. Jacob, Mauricio Santillana, Kelvin Bates, Jiawei Zhuang, and Wei Chen

Supplement

https://doi.org/10.5194/gmd-15-1677-2022-supplement

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

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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.
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