how observations help improve model/understanding Observations of these carbon-water couplings have been made from the leaf scale to the ecosystem and global scales. Depending on the scale, research has focused on different aspects, such as the coupling between photosynthesis and transpiration, ecosystem water use efficiency’s control over changes in gross primary productivity, and the links between atmospheric CO₂ growth rates and terrestrial water storage. Despite these efforts, there remain clear discrepancies in modeling the processes linking the water and carbon cycles, resulting in mismatches with observations and uncertainties in past and future simulations. model data integration and their challenges The models representing vegetation-water-carbon interactions vary significantly in process formulation, complexity, and parameterization, often dictated by the spatial scale of model development and application. As the spatial scale increases, model complexity typically decreases, requiring simplification that introduces empirical model parameters, often under-constrained. The rapid growth of satellite Earth observations, observational networks, and observation-based data presents unprecedented opportunities to enhance the representation and understanding of key carbon-water cycle processes in models. However, many terrestrial biogeochemical models remain too rigid for flexible model structures and too demanding for model-data-integration experiments that leverage observational data constraints.
References: Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195-204, 10.1038/s41586-019-0912-1, 2019. Reichstein, M., Mahecha, M. D., Ciais, P., Seneviratne, S. I., Blyth, E. M., Carvalhais, N., and Luo, Y.: Elk–testing climate–carbon cycle models: a case for pattern–oriented system analysis, iLEAPS Newsletter, 11, 14-21, 2011. Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y. P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341-1359, 10.5194/bg-6-1341-2009, 2009. Reichstein, M. and Beer, C.: Soil respiration across scales: The importance of a model-data integration framework for data interpretation, Journal of Plant Nutrition and Soil Science, 171, 344-354, 10.1002/jpln.200700075, 2008.