W15: Dealing with uncertainty and sensitivity issues in process-based models of carbon and nitrogen cycles in northern forest ecosystems

Several process-based models on the carbon and nitrogen cycles have been developped for forest ecosystems. These models have made significant contributions to better understand the processes that govern both cycles. Thus, there is increasing pressure from policy makers to use process-based models to predict the effects of global change, as it is believed that this model type can best deal with the mathematical representation of the dynamics of complex processes in forest ecosystems. However, the modelling efforts have focused on model development. Less attention has been given to uncertainty and sensitivity issues. It is important to address both issues to determine the extent to which the natural variability in forest ecosytems affects the precision of the predictions and the degree to which predictions are affected by the amplitude of change in initial conditions.

Several subjects can be discussed in this workshop, including:

1. Comparison of different parameter derivation methods to estimate confidence intervals.
2. Error propagation methods in complex models.
3. Statistical robustness of models.
4. Methods of uncertainty analysis in model predictions with respect to the decision making process and risk assessment.
5. What are the links between sensitivity analysis and priorization of research efforts.

Organizers

Click here to go to the associated Session 1.

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Position Paper
G.R. Larocque, J.S. Bhatti, A.M. Gordon, N. Luckai, J. Liu, S. Liu, P. A. Arp, C.-F. Zhang, A. Komarov, P. Grabarnik, M. Wattenbach, C. Peng, Jianfeng Sun, T. White	Dealing with uncertainty and sensitivity issues in process-based models of carbon and nitrogen cycles in northern forest ecosystems
Participants
Changhui Peng, Xiaolu Zhou, Jianfeng Sun	Modelling carbon exchange of Canadian boreal forests: Model development, validation, and sensitivity analysis
Paul Arp, Jagtar Bhatti, Chengfu Zhang	Performance evaluation of organic matter decomposition models based on carbon, nitrogen and matter dynamics in litterbags
Shuguang Liu, Jinxun Liu, Zhengpeng Li, Thomas R. Loveland, Mingshi Chen, Larry L. Tieszen	Upscaling Terrestrial Carbon Dynamics From Sites to Regions With Uncertainty Measures: The GEMS Experience
Martin Wattenbach, Pia Gottschalk, Pete Smith	A framework for assessing uncertainty in ecosystem models
Alexander Komarov, Pavel Grabarnik, Alexey Mikhaylov, Oleg Chertov Presented in Session 1	Improving the Use of Data for Quantifying Uncertainty in Parameters and Predictions of Forest Dynamic Models by Bayesian Approach.
Guy R. Larocque, David Parè, Robert Boutin, Valèrie Lacerte Presented in Session 1	Using the Monte Carlo method to quantify uncertainty in predictions of a soil carbon cycle model in balsam fir (Abies balsamea (L.) Mill.) and black spruce (Picea mariana (Mill.) B.S.P.) forest ecosystems in the boreal forest.
Thomas White, Werner Kurz, Graham Stinson Presented in Session 1	Quantifying uncertainties associated with estimates of greenhouse gas emissions and removals from Canada's managed forests.
Jinxun Liu, Shuguang Liu, Linda S. Heath, Thomas R. Loveland, Larry L. Tieszen Presented in Session 1	Impacts of Forest Disturbances on the Carbon Cycle in the Laurentian Plains and Hills of the United States.