Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2569-2020
https://doi.org/10.5194/gmd-13-2569-2020
Development and technical paper
 | 
03 Jun 2020
Development and technical paper |  | 03 Jun 2020

Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0

Sam J. Silva, Colette L. Heald, and Alex B. Guenther

Related authors

Technical note: AQMEII4 Activity 1: evaluation of wet and dry deposition schemes as an integral part of regional-scale air quality models
Stefano Galmarini, Paul Makar, Olivia E. Clifton, Christian Hogrefe, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Tim Butler, Jason Ducker, Johannes Flemming, Alma Hodzic, Christopher D. Holmes, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Juan Luis Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Sam Silva, and Ralf Wolke
Atmos. Chem. Phys., 21, 15663–15697, https://doi.org/10.5194/acp-21-15663-2021,https://doi.org/10.5194/acp-21-15663-2021, 2021
Short summary
Physically regularized machine learning emulators of aerosol activation
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021,https://doi.org/10.5194/gmd-14-3067-2021, 2021
Short summary
Importance of dry deposition parameterization choice in global simulations of surface ozone
Anthony Y. H. Wong, Jeffrey A. Geddes, Amos P. K. Tai, and Sam J. Silva
Atmos. Chem. Phys., 19, 14365–14385, https://doi.org/10.5194/acp-19-14365-2019,https://doi.org/10.5194/acp-19-14365-2019, 2019
Short summary
Impacts of current and projected oil palm plantation expansion on air quality over Southeast Asia
Sam J. Silva, Colette L. Heald, Jeffrey A. Geddes, Kemen G. Austin, Prasad S. Kasibhatla, and Miriam E. Marlier
Atmos. Chem. Phys., 16, 10621–10635, https://doi.org/10.5194/acp-16-10621-2016,https://doi.org/10.5194/acp-16-10621-2016, 2016
Short summary
Land cover change impacts on atmospheric chemistry: simulating projected large-scale tree mortality in the United States
Jeffrey A. Geddes, Colette L. Heald, Sam J. Silva, and Randall V. Martin
Atmos. Chem. Phys., 16, 2323–2340, https://doi.org/10.5194/acp-16-2323-2016,https://doi.org/10.5194/acp-16-2323-2016, 2016
Short summary

Related subject area

Atmospheric sciences
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024,https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024,https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024,https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024,https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary

Cited articles

Ashworth, K., Chung, S. H., Griffin, R. J., Chen, J., Forkel, R., Bryan, A. M., and Steiner, A. L.: FORest Canopy Atmosphere Transfer (FORCAsT) 1.0: a 1-D model of biosphere–atmosphere chemical exchange, Geosci. Model Dev., 8, 3765–3784, https://doi.org/10.5194/gmd-8-3765-2015, 2015. 
Ashworth, K., Chung, S. H., McKinney, K. A., Liu, Y., Munger, J. W., Martin, S. T., and Steiner, A. L.: Modelling bidirectional fluxes of methanol and acetaldehyde with the FORCAsT canopy exchange model, Atmos. Chem. Phys., 16, 15461–15484, https://doi.org/10.5194/acp-16-15461-2016, 2016. 
Baldocchi, D. D., Hicks, B. B., and Camara, P.: A canopy stomatal resistance model for gaseous deposition to vegetated surfaces, Atmos. Environ., 21, 91–101, https://doi.org/10.1016/0004-6981(87)90274-5, 1987. 
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. 
Chen W. H., Guenther A. B., Wang X. M., Chen Y. H., Gu D. S., Chang M., Zhou S. Z., Wu L. L., and Zhang Y. Q.: Regional to Global Biogenic Isoprene Emission Responses to Changes in Vegetation From 2000 to 2015, J. Geophys. Res.-Atmos., 123, 3757–3771, https://doi.org/10.1002/2017JD027934, 2018. 
Download
Short summary
Simulating the influence of the biosphere on atmospheric chemistry has traditionally been computationally intensive. We describe a surrogate canopy physics model parameterized using a statistical learning technique and specifically designed for use in large-scale chemical transport models. Our surrogate model reproduces a more detailed model to within 10 % without a large computational demand, improving the process representation of biosphere–atmosphere exchange.