An improved vegetation emissivity scheme for land surface modeling and its impact on snow cover simulations

This study developed an improved vegetation emissivity scheme for the Community Land Model (CLM) version 4.5 to more accurately simulate the effects of vegetation emissivity on snow processes in the Northern Hemisphere over winter and spring. The original scheme of vegetation emissivity in CLM produced an unreasonably low vegetation emissivity with a minimum value of around 0.70 in the cold season. Thus, we developed a new vegetation emissivity scheme based on maximum emissivity and leaf and stem area indices of vegetation, which can simulate vegetation emissivity more realistically than the original scheme. Our simulations were driven by the Climatic Research Unit-National Centers for Environmental Prediction (CRU-NCEP) reanalysis data. Results show that CLM with the new scheme produces stronger longwave radiation to the ground surface and generates more solid water drips off vegetation over winter and spring than with the original scheme. Such changes improve snow cover fraction (SCF) simulations for the middle and high latitudes in North America, central Eurasia, and the eastern Tibetan Plateau. About 200 and 350 thousand km² with the SCF changes show a better SCF simulation with the new scheme over winter and spring, respectively. However, increased errors were found in SCF simulations with the new scheme, and further analysis indicates that such errors may be related to biases in the CLM forcing variables from the CRU-NCEP reanalysis data as compared with those from in situ observations. Moreover, the new emissivity scheme decreases total upward longwave radiation and increases surface net radiation and turbulent fluxes. Overall, the improved vegetation emissivity scheme in this study provides an effective tool to generate better understanding of the effects of vegetation on snow at regional scales and gives strong insight into improved land surface process modeling.