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Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robertson, Eddy; Kautz, Markus; Lawrence, Peter; San Martin, Lorea Garcia; Anthoni, Peter; Ciais, Philippe; Pongratz, Julia ORCID: 0000-0003-0372-3960; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro (2018): Evaluating the Interplay Between Biophysical Processes and Leaf Area Changes in Land Surface Models. In: Journal of Advances in Modeling Earth Systems, Vol. 10, No. 5: pp. 1102-1126
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Abstract

Land Surface Models (LSMs) are essential to reproduce biophysical processes modulated by vegetation and to predict the future evolution of the land-climate system. To assess the performance of an ensemble of LSMs (JSBACH, JULES, ORCHIDEE, CLM, and LPJ-GUESS) a consistent set of land surface energy fluxes and leaf area index (LAI) has been generated. Relationships of interannual variations of modeled surface fluxes and LAI changes have been analyzed at global scale across climatological gradients and compared with those obtained from satellite-based products. Model-specific strengths and deficiencies were diagnosed for tree and grass biomes. Results show that the responses of grasses are generally well represented in models with respect to the observed interplay between turbulent fluxes and LAI, increasing the confidence on how the LAI-dependent partition of net radiation into latent and sensible heat are simulated. On the contrary, modeled forest responses are characterized by systematic bias in the relation between the year-to-year variability in LAI and net radiation in cold and temperate climates, ultimately affecting the amount of absorbed radiation due to LAI-related effects on surface albedo. In addition, for tree biomes, the relationships between LAI and turbulent fluxes appear to contradict the experimental evidences. The dominance of the transpiration-driven over the observed albedo-driven effects might suggest that LSMs have the incorrect balance of these two processes. Such mismatches shed light on the limitations of our current understanding and process representation of the vegetation control on the surface energy balance and help to identify critical areas for model improvement.