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

Abstract

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, how well the optimization models perform at larger scales has not been extensively evaluated. 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 nor the entire canopy. As a result, they are unable to 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 (ca. 14.2 % and 21.8 % improvement for the gymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from ca. 0.34 to 0.91 for the gymnosperm forest and from ca. 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.

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

“…We used the CliMA Land model (v0.1) to evaluate how canopy model complexity impacts the simulated carbon, water, and SIF fluxes. The CliMA Land model mechanistically addresses soil-plant-air continuum processes and is able to simulate canopy carbon and water fluxes as well as SIF simultaneously (Wang et al, 2021b). CliMA Land model code and documentation are freely and publicly available at https: //github.com/CliMA/Land (last access: 15 November 2021).…”

“…For IJKX, we simulated the canopy radiative transfer using the CliMA Land-adapted SCOPE model (Soil Canopy Observation, Photochemistry and Energy fluxes; SCOPE v1.7; Yang et al, 2017). The adaptations included that carotenoid absorption was accounted for as APAR (Wang et al, 2021b) and that canopy clumping was addressed using a clumping index (Braghiere et al, 2021). At layer i, the shaded-leaf fraction (relative to total leaf area in the canopy) is p sh,i , and the sunlit-leaf fraction (relative to total leaf area in the canopy) is p sl,i (incl, azi) ("incl" is the inclination angle, and "azi" is the azimuth angle).…”

“…where E is the leaf transpiration rate and E crit is the critical transpiration rate of the leaf beyond which leaf hydraulic conductance drops below 0.1 % of the maximum (see Sperry et al, 2016, andWang et al, 2021b, for more details of E crit ).…”

“…Prescribing leaf temperature and soil water potential allowed us to tease apart the difference caused by canopy complexity from that caused by environmental and physiological differences. See Wang et al (2021b) for the model setup details for US-MOz. In addition to the observations that were used to set up the CliMA Land model (Wang et al, 2021b), we further applied vertical V cmax profiles in the simulations (note that V cmax changed in the sunlit and shaded fractions with time for 2X and stayed constant for the other four complexity levels).…”

“…Ideally, LSMs can be constrained using raw reflection and fluorescence spectra. This, nevertheless, requires the LSMs to move from broadband canopy radiation to a hyperspectral representation and from sunlit and shaded fractions to leaf angular distributions (such as the land model developed by Climate Modeling Alliance, CliMA Land; Wang et al, 2021b). This way, the LSM can be directly coupled to remotely sensed canopy spectra (e.g., Shiklomanov et al, 2021) rather than to reprocessed datasets using often incompatible assumptions.…”