Baatz, R.; Sullivan, P. L.; Li, L.; Weintraub, S. R.; Loescher, H. W.; Mirtl, M.; Groffman, P. M.; Wall, D. H.; Young, M.; White, T.; Wen, H.; Zacharias, S.; Kühn, I.; Tang, J.; Gaillardet, J.; Braud, I.; Flores, A. N.; Kumar, P.; Lin, H.; Ghezzehei, T.; Jones, J.; Gholz, H. L.; Vereecken, H.; Van Looy, K. 2018. Steering operational synergies in terrestrial observation networks: opportunity for advancing Earth system dynamics modelling. Earth System Dynamics. 9(2): 593-609. doi: 10.5194/esd-9-593-2018
Advancing our understanding of Earth system dynamics (ESD) depends on the development of models and other analytical tools that apply physical, biological, and chemical data. This ambition to increase understanding and develop models of ESD based on site observations was the stimulus for creating the networks of Long-Term Ecological Research (LTER), Critical Zone Observatories (CZOs), and others. We organized a survey, the results of which identified pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that represent ESD processes, and provide insights for improvement in both data collection and model integration.
From this survey overview of data applications in the context of LTER and CZO research, we identified three challenges: (1) widen application of terrestrial observation network data in Earth system modelling, (2) develop integrated Earth system models that incorporate process representation and data of multiple disciplines, and (3) identify complementarity in measured variables and spatial extent, and promoting synergies in the existing observational networks. These challenges lead to perspectives and recommendations for an improved dialogue between the observation networks and the ESD modelling community, including co-location of sites in the existing networks and further formalizing these recommendations among these communities. Developing these synergies will enable cross-site and cross-network comparison and synthesis studies, which will help produce insights around organizing principles, classifications, and general rules of coupling processes with environmental conditions.