Articles | Volume 13, issue 6
Geosci. Model Dev., 13, 2569–2585, 2020
https://doi.org/10.5194/gmd-13-2569-2020
Geosci. Model Dev., 13, 2569–2585, 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 et al.

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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. 
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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.