Technical Abstract: Many process-based models of carbon (C) and nitrogen (N) cycles have been developed for terrestrial ecosystems, including forest ecosystems. Existing models are sufficiently well advanced to help decision makers develop sustainable management policies and planning of terrestrial ecosystems, as they make realistic predictions when used appropriately. However, decision makers must be aware of their limitations by having the opportunity to evaluate the uncertainty associated with process-based models. The variation in scale of issues currently being addressed by modelling efforts makes the evaluation of uncertainty a daunting task. Despite the fact that the importance of uncertainty issues is increasingly recognized, sensitivity and uncertainty analyses still remain rarely used in C and N cycling process-based models of terrestrial ecosystems. The uncertainty in model outputs may be caused by different factors, including measurement errors that affect parameter estimates, model structure, and lack of knowledge of the variation inherent within and between components of natural systems. Although uncertainty and sensitivity analyses have been used in other fields related to natural resources (e.g., hydrology), there still remain many research needs in these types of analyses for application to terrestrial ecosystem models that address C and N cycling. In particular, the application context may differ considerably, as the majority of terrestrial ecosystem models usually contain complex nonlinear mathematical expressions. This workshop was intended to address uncertainty issues associated with C and N cycle process-based models developed for terrestrial ecosystems with particular emphasis on forest ecosystems. Several topics were discussed in the workshop, including the comparison of different numerical parameter derivation methods to estimate confidence intervals, error propagation, statistical robustness, uncertainty analysis methods in relation to the decision making process and risk assessment, and the role of uncertainty and sensitivity analyses to prioritize research efforts. The selected papers in this special issue were developed with the overall objective of identifying research needs and suggesting future directions for research on uncertainty.