Preprints
https://doi.org/10.5194/bg-2021-214
https://doi.org/10.5194/bg-2021-214

  12 Aug 2021

12 Aug 2021

Review status: this preprint is currently under review for the journal BG.

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

Yujie Wang1 and Christian Frankenberg1,2 Yujie Wang and Christian Frankenberg
  • 1Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California 91125, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA

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 its 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 big-leaf canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using a recently developed Land model by the Climate Modeling Alliance. 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 among canopy complexity levels increased compared to the scenario of a uniform canopy; (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 complex canopy model with angular distribution and hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.

Yujie Wang and Christian Frankenberg

Status: open (until 02 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-214', Anonymous Referee #1, 21 Sep 2021 reply

Yujie Wang and Christian Frankenberg

Yujie Wang and Christian Frankenberg

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Short summary
Modeling vegetation canopy is important in predicting whether the land remain a carbon sink to mitigate climate change in the near future. Vegetation canopy model complexity, however, impacts the model predicted carbon and water fluxes as well as canopy fluorescence, even if the same suite of model inputs are used. Given the biases caused my canopy model complexity, we recommend not misusing parameters inverted using different models or assumptions.
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