Evaluation of a global vegetation model using time series of satellite vegetation indices

Abstract

Atmospheric CO2 drives most of the greenhouse effect increase. Onemajor uncertainty on the future rate of increase of CO2 in theatmosphere is the impact of the anticipated climate change on thevegetation. Dynamic Global Vegetation Models (DGVM) are used to address thisquestion. ORCHIDEE is such a DGVM that has proven useful for climate changestudies. However, there is no objective and methodological way to accuratelyassess each new available version on the global scale. In this paper, wesubmit a methodological evaluation of ORCHIDEE by correlatingsatellite-derived Vegetation Index time series against those of the modeledFraction of absorbed Photosynthetically Active Radiation (FPAR). A perfectcorrelation between the two is not expected, however an improvement of themodel should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated,using our methodology. In the first one, a new phenology version in ORCHIDEEis shown to bring a significant impact on the simulated annual cycles, inparticular for C3 Grasses and C3 Crops. In the second case study, we compare the simulationswhen using two different weather fields to drive ORCHIDEE. The ERA-Interimforcing leads to a better description of the FPAR interannual anomalies thanthe simulation forced by a mixed CRU-NCEP dataset. This work shows that longtime series of satellite observations, despite their uncertainties, canidentify weaknesses in global vegetation models, a necessary first step toimproving them. © Author(s) 2011.