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Published January 3, 2022 | Published
Journal Article Open

On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes

Abstract

Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote-sensing-based products provide an alternative way to verify or constrain land models given their global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global-scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using the recently developed land model by the Climate Modeling Alliance, CliMA Land. Our model results suggested that (1) when using the same model inputs, model-predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences between canopy complexity levels increased compared to the scenario of a uniform canopy; and (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs and (2) using a complex canopy model with angular distribution and a hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.

Additional Information

© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 11 August 2021 – Discussion started: 12 August 2021; Revised: 2 November 2021 – Accepted: 22 November 2021 – Published: 3 January 2022. We gratefully acknowledge the generous support of Eric and Wendy Schmidt (by recommendation of the Schmidt Futures) and the Heising-Simons Foundation. This research has been supported by the National Aeronautics and Space Administration (grant nos. 80NSSC18K0895 and 80NSSC21K1712). Code and data availability: We coded our model and did the analysis using Julia (version 1.6.2), and the current version of the CliMA Land model is available from the project website at https://github.com/CliMA/Land under the Apache License 2.0. The exact version of the model used to produce the results employed in this paper is archived on CaltechDATA (https://doi.org/10.22002/D1.2316, Wang and Frankenberh, 2021). Author contributions: YW and CF designed and conducted the research, performed the general data analysis, and wrote the paper. The contact author has declared that neither they nor their co-author has any competing interests. Review statement: This paper was edited by Martin De Kauwe and reviewed by Xiangzhong Luo and one anonymous referee.

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Created:
August 22, 2023
Modified:
October 23, 2023