Submitted as: model description paper 01 Jun 2021

Submitted as: model description paper | 01 Jun 2021

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

Testing stomatal models at stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0.1)

Yujie Wang1, Philipp Köhler1, Liyin He1, Russell Doughty1, Renato K. Braghiere2,3, Jeffrey D. Wood4, and Christian Frankenberg1 Yujie Wang et al.
  • 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
  • 3Joint Institute for Regional Earth System Science and Engineering, University of California at Los Angeles, Los Angeles, California 90095, USA
  • 4School of Natural Resources, University of Missouri, Columbia, Missouri 65211, USA

Abstract. At the leaf level, stomata control the exchange of water and carbon across the air-leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, it has not been evaluated how well the optimization models perform at larger scales. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves or the entire canopy. As a result, they are unable to evaluate the impact of vertical gradients within the canopy, or directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth System model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (c. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1 : 1 comparison (slope increases from c. 0.34 to 0.91 for the gymnosperm forest, and from c. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth System modeling in general.

Yujie Wang et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-154', Martin De Kauwe, 10 Jun 2021 reply
  • RC1: 'Comment on gmd-2021-154', Anonymous Referee #1, 23 Jul 2021 reply

Yujie Wang et al.

Model code and software

Test CliMA Land model with flux tower and TROPOMI SIF datasets Yujie Wang

Yujie Wang et al.


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Short summary
We presented our first step of testing a new land model as part of a new Earth System Model. Our model links plant hydraulics, stomatal optimization theory, and a comprehensive canopy radiation scheme. We compared model predicted carbon and water fluxes to flux tower observations and model predicted sun-induced chlorophyll fluorescence to satellite retrievals. Our model quantitatively predicted the carbon and water fluxes, and canopy fluorescence yield.