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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-334
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-334
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 28 Jan 2020

Submitted as: development and technical paper | 28 Jan 2020

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This preprint is currently under review for the journal GMD.

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. Silva1,a, Colette L. Heald1, and Alex B. Guenther2 Sam J. Silva et al.
  • 1Department of Civil and Environmental Engineering, Massachusetts Institute of 5Technology, Cambridge, MA, USA
  • 2Department of Earth System Science, University of California Irvine, Irvine, CA, USA
  • anow at: Pacific Northwest National Laboratory,Richland, WA, USA

Abstract. Biosphere-atmosphere interactions strongly influence the chemical composition of the atmosphere. Simulating these interactions at a detailed process-based level has traditionally been computationally intensive and resource prohibitive, commonly due to complexities in calculating radiation and light at the leaf level within plant canopies. Here we describe a surrogate canopy physics model based on the MEGAN3 detailed canopy model parameterized using a statistical learning technique. This surrogate canopy model is designed specifically to rapidly calculate leaf-level temperature and photosynthetically active radiative (PAR) for use in large-scale chemical transport models (CTMs). Our surrogate model can reproduce the dominant spatiotemporal variability of the more detailed MEGAN3 canopy model to within 10 % across the globe. Implementation of this surrogate model into the GEOS-Chem CTM leads to small local changes in ozone dry deposition velocities of less than 5 %, and larger local changes in isoprene emissions of up to ∼40 %, though annual global isoprene emissions remain largely consistent (within 5 %). These changes to surface-atmosphere exchange lead to modest changes in surface ozone concentrations of ± 1 ppbv. The use of this surrogate canopy model drives emissions of isoprene and concentrations of surface ozone closer to observationally constrained values, without any noticeable impact on computational demand. Additionally, this surrogate model allows for the further development and implementation of leaf-level emission factors in the calculation of biogenic emissions in the GEOS-Chem CTM. Though not the focus of this work, this ultimately enables a complete implementation of the MEGAN3 emissions framework within GEOS-Chem, which produces 570 Tg yr−1 of isoprene in 2012.

Sam J. Silva et al.

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Sam J. Silva et al.

Model code and software

Code for GEOS-Chem Canopy Model S. J. Silva, C. L. Heald, and A. B. Guenther https://doi.org/10.5281/zenodo.3614062

Sam J. Silva et al.

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Latest update: 28 Feb 2020
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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, designed specifically 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 process representation of biosphere-atmosphere exchange.
Simulating the influence of the biosphere on atmospheric chemistry has traditionally been...
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