Extending the Global Sensitivity Analysis of the SimSphere model in the Context of its Future Exploitation by the Scientific Community

In today’s changing climate, the development of robust, accurate and globally applicable models is imperative for a wider understanding of Earths terrestrial biosphere. Moreover, an understanding of the representation, sensitivity and coherence of such models are vital for the operationalization of any physical based model. A Global Sensitivity Analysis (GSA) was conducted on the SimSphere land biosphere model in which a meta-modelling method adopting Bayesian theory was implemented. Initially, effects of assuming uniform probability distribution functions (PDFs) for the model inputs when examining sensitivity of key quantities simulated by SimSphere at different output times were examined. The development of topographic model input parameters (e.g. slope, aspect, and elevation) were derived within a Geographic Information System (GIS) before implementation within the model. The effect of time of the simulation on the sensitivity of previously examined outputs was also analysed. Results showed that simulated outputs were significantly influenced by changes in topographic input parameters, Fractional Vegetation Cover, vegetation height and surface moisture availability, in agreement with previous studies. Time of model output simulation had a significant influence on the absolute values of the output variance decomposition, but it did not change the relative importance of each input parameter. SA results of the newly modelled outputs allowed identification of the most responsive model inputs and interactions. Our study presents an important step forward in SimSphere verification given the increasing interest in its use both as an independent modelling or educational tool. Furthermore, this study is very timely, given on-going efforts towards the development of operational products based on the synergy of SimSphere with EO data. In this context, results also provide additional support for the potential applicability of the assimilation of spatial analysis data derived from GIS and EO data into an accurate modelling framework.