SimSphere model sensitivity analysis towards establishing its use for deriving key parameters characterising land surface interactions
Abstract. Being able to accurately estimate parameters characterising land surface interactions is currently a key scientific priority due to their central role in the Earth's global energy and water cycle. To this end, some approaches have been based on utilising the synergies between land surface models and Earth observation (EO) data to retrieve relevant parameters. One such model is SimSphere, the use of which is currently expanding, either as a stand-alone application or synergistically with EO data. The present study aimed at exploring the effect of changing the atmospheric sounding profile on the sensitivity of key variables predicted by this model assuming different probability distribution functions (PDFs) for its inputs/outputs. To satisfy this objective and to ensure consistency and comparability to analogous studies conducted previously on the model, a sophisticated, cutting-edge sensitivity analysis (SA) method adopting Bayesian theory was implemented on SimSphere. Our results did not show dramatic changes in the nature or ranking of influential model inputs in comparison to previous studies. Model outputs examined using SA were sensitive to a small number of the inputs; a significant amount of first-order interactions between the inputs was also found, suggesting strong model coherence. Results showed that the assumption of different PDFs for the model inputs/outputs did not have an important bearing on mapping the most responsive model inputs and interactions, but only the absolute SA measures. This study extends our understanding of SimSphere's structure and further establishes its coherence and correspondence to that of a natural system's behaviour. Consequently, the present work represents a significant step forward in the global efforts on SimSphere verification, especially those focusing on the development of global operational products from the model synergy with EO data.