Preprints
https://doi.org/10.5194/gmd-2022-149
https://doi.org/10.5194/gmd-2022-149
Submitted as: model evaluation paper
08 Jun 2022
Submitted as: model evaluation paper | 08 Jun 2022
Status: this preprint is currently under review for the journal GMD.

Evaluating the vegetation-atmosphere coupling strength of ORCHIDEE land surface model (v7266)

Yuan Zhang1,2, Devaraju Narayanappa1, Philippe Ciais1, Wei Li3, Daniel Goll1, Nicolas Vuichard1, Martin G. De Kauwe4, Laurent Li2, and Fabienne Maignan1 Yuan Zhang et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement (LSCE), IPSL, CEA/CNRS/UVSQ, Gif sur Yvette, France
  • 2Laboratoire de Météorologie Dynamique, IPSL, Sorbonne Université/CNRS, Paris, France
  • 3Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
  • 4School of Biological Sciences, University of Bristol, Bristol, BS8 1TQ, UK

Abstract. Plant transpiration dominates terrestrial latent heat fluxes (LE) and plays a central role in regulating the water cycle and land surface energy budget. However, currently Earth system models (ESM) disagree strongly on the amount of transpiration, and thus LE, leading to large uncertainties in simulating future climate. Thus it is crucial to correctly represent the mechanisms controlling the transpiration in models. At the leaf-scale, transpiration is controlled by stomatal regulation, and at the canopy-scale, through turbulence, which is a function of canopy structure and wind. The coupling of vegetation to the atmosphere can be characterized by a coefficient Ω. A value of Ω → 0 implies a strong coupling of vegetation and the atmosphere, leaving a dominant role to stomatal conductance in regulating water (H2O) and carbon dioxide (CO2) fluxes, while Ω → 1 implies a complete decoupling of leaves from the atmosphere, that is, the transfer of H2O and CO2 is limited by aerodynamic transport. In this study, we investigated how well the land surface model ORCHIDEE (v7266), simulates the coupling of vegetation to the atmosphere by using empirical daily estimates of Ω derived from flux measurements from 106 FLUXNET sites. Our results show that ORCHIDEE generally captures the Ω in forest vegetation types (0.27±0.10) compared with observation (0.26±0.09), but underestimates Ω in grasslands and croplands (0.26±0.16 for model, 0.33±0.17 for observation). The good model performance in forests is due to compensation of biases in surface conductance (Gs) and aerodynamic conductance (Ga). Calibration of key parameters controlling the dependence of the stomatal conductance to the water vapor deficit (VPD) improves the simulated Gs, and Ω estimates in grasslands and croplands (0.30±0.21). To assess the underlying controls of Ω, we applied random forest (RF) models to both simulated and observation-based Ω. We found that large observed Ω are associated with periods of low wind speed, high temperature, low VPD and related to sites with large leaf area index (LAI) and/or short vegetation. The RF models applied to ORCHIDEE output generally agree with this pattern. However, we found the ORCHIDEE underestimated the sensitivity of Ω to VPD when VPD is high, overestimated the impact of LAI on Ω, and did not correctly simulate the temperature dependence of Ω when temperature is high. Our results highlight the importance of observational constraints on simulating the vegetation-atmosphere coupling strength, which can help improve predictive accuracy of water fluxes in Earth system models.

Yuan Zhang et al.

Status: open (extended)

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Yuan Zhang et al.

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Short summary
There remains few study to examine if current models correctly represented the complex processes of transpiration. Here we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes. We also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.