Preprints
https://doi.org/10.5194/gmd-2020-425
https://doi.org/10.5194/gmd-2020-425

Submitted as: development and technical paper 16 Feb 2021

Submitted as: development and technical paper | 16 Feb 2021

Review status: this preprint is currently under review for the journal GMD.

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

Lu Shen1, Daniel J. Jacob1, Mauricio Santillana2,3, Kelvin Bates1, Jiawei Zhuang1, and Wei Chen4 Lu Shen et al.
  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
  • 2Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA
  • 3Department of Pediatrics, Harvard Medical School, Boston, MA, USA
  • 4Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA

Abstract. Atmospheric composition plays a crucial role in determining the evolution of the atmosphere, but the high computational cost has been the major barrier to include atmospheric chemistry into Earth system models. Here we present an adaptive and efficient algorithm that can remove this barrier. Our approach is inspired by unsupervised machine learning clustering techniques and traditional asymptotic analysis ideas. We first partition species into 13 blocks, using a novel machine learning approach that analyzes the species network structures and their production and loss rates. Building on these blocks, we pre-select 20 submechanisms, as defined by unique assemblages of the species blocks, and then pick locally on the fly which submechanism to use based on local chemical conditions. In each submechanism, we isolate slow species and unimportant reactions from the coupled system. Application to a global 3-D model shows that we can cut the computational costs of the chemical integration by 50 % with accuracy losses smaller than 1 % that do not propagate in time. Tests show that this algorithm is highly chemically coherent making it easily portable to new models without compromising its performance. Our algorithm will significantly ease the computational bottleneck and will facilitate the development of next generation of earth system models.

Lu Shen et al.

Status: open (until 13 Apr 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

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 Shen et al. https://doi.org/10.7910/DVN/KASQOC

Model code and software

GEOS-Chem code GEOS-Chem Team https://doi.org/10.5281/zenodo.1343547

Lu Shen et al.

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