Using Satellite Data Assimilation to Infer Global Soil Moisture Status and Vegetation Feedback to Climate

Abstract

The importance of land surface and vegetation characteristics for climate has long been hypothesisized and is reflected by increasingly sophisticated land surface schemes used in climate models. However, accurate parameterisation of land surface processes is still hampered by the complexity of the processes, and by data availability at the global scale required for general circulation models. It is, therefore, desirable to utilise additional data sources for land surface models, of which satellite data appear to be the most promising in terms of availability and spatial and temporal coverage. Here, monthly satellite-derived fields of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are assimilated into a land surface and vegetation model, the Biosphere Energy-Transfer Hydrology scheme (Bethy). Assimilation offers the advantage that uncertainties of both the satellite-derived fAPAR and model parameters can be accounted for. Since fAPAR can also be predicted by the model, this information is not discarded as in other approaches where satellite data are used as forcing. During assimilation, a number of model parameters are adjusted until a cost function reaches its minimum. This cost function is defined by the squared deviation between monthly model-simulated and satellite-derived fAPAR as well as between initial and adjusted model parameters, both normalised by their assumed error variances. One of the adjusted parameters, the maximum plant-available soil moisture, is used in a subsequent sensitivity study with the Echam-4 climate model. The results show that changes in this parameter as a result of satellite data assimilation can lead to significant changes in simulated soil moisture and 2m air temperature over large parts of the tropics, where soil water storage is usually underestimated in climate and vegetation models. A comparison of Bethy simulations with soil water measurements from Amazonia supports this finding, and also shows that using fAPAR as forcing would have lead to inconsistencies between the carbon balance, predicting a strong decrease in fAPAR at negative carbon gains, and the value of fAPAR prescribed from the satellite data. The study aims at demonstrating the potential of assimilating satellite data into land surface models, as well as the significance of vegetation for the land surface climate. It is further intended to indicate a methodology for the assimilation of satellite data into general circulation models that include an interactive, i.e. climate-responsive, vegetation component.