Abstract

Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, and CO2 fluxes. This is achieved by scaling stomatal conductance, g(w), determined from physiological models developed for leaves. Tra... Show moreEarth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, and CO2 fluxes. This is achieved by scaling stomatal conductance, g(w), determined from physiological models developed for leaves. Traditionally, models for g(w) have been semi-empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to model g(w) in LSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization-type g(w) model, termed BB and MED, respectively. Overall, we find no difference between the two models when used to simulate g(w) from photosynthesis data, or leaf gas exchange from a coupled photosynthesis-conductance model, or gross primary productivity and evapotranspiration for a FLUXNET tower site with the CLM5 community LSM. Field measurements reveal that the key fitted parameters for BB and MED, g(1B) and g(1M,) exhibit strong species specificity in magnitude and sensitivity to CO2, and CLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high-CO2 scenarios. Further, we show that g(1B) and g(1M) can be determined from mean c(i)/c(a) (ratio of leaf intercellular to ambient CO2 concentration). Applying this relationship with c(i)/c(a) values derived from a leaf delta C-13 database, we obtain a global distribution of g(1B) and g(1M), and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of the BB and MED models in LSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types. Show less