Improving Canopy Processes In The Community Land Model Version 4 (CLM4) Using Global Flux Fields Empirically Inferred From FLUXNET Data

The Community Land Model version 4 (CLM4) overestimates gross primary production (GPP) compared with data-driven estimates and other process models. We use global, spatially gridded GPP and latent heat flux upscaled from the FLUXNET network of eddy covariance towers to evaluate and improve canopy processes in CLM4. We investigate differences in GPP and latent heat flux arising from model parameterizations (termed model structural error) and from uncertainty in the photosynthetic parameter V_cmax (termed model parameter uncertainty). Model structural errors entail radiative transfer, leaf photosynthesis and stomatal conductance, and canopy scaling of leaf processes. Model structural revisions reduce global GPP over the period 1982–2004 from 165 Pg C yr⁻¹ to 130 Pg C yr⁻¹, and global evapotranspiration decreases from 68,000 km³ yr⁻¹ to 65,000 km³ yr⁻¹, within the uncertainty of FLUXNET-based estimates. Colimitation of photosynthesis is a cause of the improvements, as are revisions to photosynthetic parameters and their temperature dependency. Improvements are seen in all regions and seasonally over the course of the year. Similar improvements occur in latent heat flux. Uncertainty in V_cmax produces effects of comparable magnitude as model structural errors, but of offsetting sign. This suggests that model structural errors can be compensated by parameter adjustment, and this may explain the lack of consensus in values for V_cmax used in terrestrial biosphere models. Our analyses show that despite inherent uncertainties global flux fields empirically inferred from FLUXNET data are a valuable tool to guide terrestrial biosphere model development and evaluation.