Main content area

Comparing optimal and empirical stomatal conductance models for application in Earth system models

Franks, Peter J., Bonan, Gordon B., Berry, Joseph A., Lombardozzi, Danica L., Holbrook, N. Michele, Herold, Nicholas, Oleson, Keith W.
Global change biology 2018 v.24 no.12 pp. 5708-5723
Earth system science, atmospheric precipitation, canopy, carbon, carbon dioxide, databases, ecosystems, energy, evapotranspiration, forests, gas exchange, geographical distribution, gross primary productivity, leaves, methodology, models, photosynthesis, stable isotopes, stomatal conductance
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, gw, determined from physiological models developed for leaves. Traditionally, models for gw have been semi‐empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to model gw 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 gw model, termed BB and MED, respectively. Overall, we find no difference between the two models when used to simulate gw 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, g₁B and g₁M, 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 g₁B and g₁M can be determined from mean cᵢ/cₐ (ratio of leaf intercellular to ambient CO₂ concentration). Applying this relationship with cᵢ/cₐ values derived from a leaf δ¹³C database, we obtain a global distribution of g₁B and g₁M, 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.