Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1677-2022
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, Daniel J. Jacob, Mauricio Santillana, Kelvin Bates, Jiawei Zhuang, and Wei Chen

Related authors

High-resolution US methane emissions inferred from an inversion of 2019 TROPOMI satellite data: contributions from individual states, urban areas, and landfills
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024,https://doi.org/10.5194/acp-24-5069-2024, 2024
Short summary
Continuous weekly monitoring of methane emissions from the Permian Basin by inversion of TROPOMI satellite observations
Daniel J. Varon, Daniel J. Jacob, Benjamin Hmiel, Ritesh Gautam, David R. Lyon, Mark Omara, Melissa Sulprizio, Lu Shen, Drew Pendergrass, Hannah Nesser, Zhen Qu, Zachary R. Barkley, Natasha L. Miles, Scott J. Richardson, Kenneth J. Davis, Sudhanshu Pandey, Xiao Lu, Alba Lorente, Tobias Borsdorff, Joannes D. Maasakkers, and Ilse Aben
Atmos. Chem. Phys., 23, 7503–7520, https://doi.org/10.5194/acp-23-7503-2023,https://doi.org/10.5194/acp-23-7503-2023, 2023
Short summary
Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023,https://doi.org/10.5194/acp-23-5945-2023, 2023
Short summary
Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins
Lu Shen, Ritesh Gautam, Mark Omara, Daniel Zavala-Araiza, Joannes D. Maasakkers, Tia R. Scarpelli, Alba Lorente, David Lyon, Jianxiong Sheng, Daniel J. Varon, Hannah Nesser, Zhen Qu, Xiao Lu, Melissa P. Sulprizio, Steven P. Hamburg, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022,https://doi.org/10.5194/acp-22-11203-2022, 2022
Short summary
Methane emissions from China: a high-resolution inversion of TROPOMI satellite observations
Zichong Chen, Daniel J. Jacob, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Elise Penn, and Xueying Yu
Atmos. Chem. Phys., 22, 10809–10826, https://doi.org/10.5194/acp-22-10809-2022,https://doi.org/10.5194/acp-22-10809-2022, 2022
Short summary

Related subject area

Atmospheric sciences
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024,https://doi.org/10.5194/gmd-17-5023-2024, 2024
Short summary
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
Nathan P. Arnold
Geosci. Model Dev., 17, 5041–5056, https://doi.org/10.5194/gmd-17-5041-2024,https://doi.org/10.5194/gmd-17-5041-2024, 2024
Short summary
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024,https://doi.org/10.5194/gmd-17-4983-2024, 2024
Short summary
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024,https://doi.org/10.5194/gmd-17-4961-2024, 2024
Short summary
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024,https://doi.org/10.5194/gmd-17-4891-2024, 2024
Short summary

Cited articles

Bates, K. H. and Jacob, D. J.: A new model mechanism for atmospheric oxidation of isoprene: global effects on oxidants, nitrogen oxides, organic products, and secondary organic aerosol, Atmos. Chem. Phys., 19, 9613–9640, https://doi.org/10.5194/acp-19-9613-2019, 2019. 
Brasseur, G. P. and Jacob, D. J.: Modeling of atmospheric chemistry, Cambridge University Press, 2017. 
Brown-Steiner, B., Selin, N. E., Prinn, R., Tilmes, S., Emmons, L., Lamarque, J.-F., and Cameron-Smith, P.: Evaluating simplified chemical mechanisms within present-day simulations of the Community Earth System Model version 1.2 with CAM4 (CESM1.2 CAM-chem): MOZART-4 vs. Reduced Hydrocarbon vs. Super-Fast chemistry, Geosci. Model Dev., 11, 4155–4174, https://doi.org/10.5194/gmd-11-4155-2018, 2018. 
Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G. R.: The kinetic preprocessor KPP – a software environment for solving chemical kinetics, Comput. Chem. Eng., 26, 1567–1579, 2002. 
Eastham, S. D., Weisenstein, D. K., and Barrett, S. R. H.: Development and evaluation of the unified tropospheric– stratospheric chemistry extension (UCX) for the global chemistry-transport model GEOS-Chem, Atmos. Environ., 89, 52–63, https://doi.org/10.1016/j.atmosenv.2014.02.001, 2014. 
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.