A framework for assessing uncertainty in ecosystem models

In addition to their use as research tools, ecosystem models have been used more frequently in the last two decades to support policy decisions and inform stakeholder consultations. Models have been central to the work of the Intergovernmental Panel of Climate Change (IPCC) and the International Geosphere-Biosphere Programme (IGBP). The usefulness of results from model simulations for any purpose is determined by their quality like the uncertainty accompanying model outputs. In model evaluation, however, a broad variety of different approaches to define uncertainty still exists and these have not, so far, been standardized. In contrast, field research has already defined standard uncertainties. Here, we define uncertainty based on statistical methods like standard deviation of a number of independent measurements as type A uncertainty, and define uncertainty based on scientific judgement as type B uncertainty. We are proposing three further categories of model uncertainty. Baseline uncertainties that originate from type A and B uncertainties in measurements used to determine inputs to the model are termed type C uncertainties. Further uncertainty arises from the scenarios constructed to run the model, which cannot be defined precisely. This category of uncertainty named type D uncertainty includes that element of future scenarios that cannot be predicted. Uncertainty also arises from not knowing precisely the true value of internal parameters of the model equations; this is referred to as type E uncertainty. Here we propose an experimental framework for harmonisation of uncertainty and sensitivity analyses of ecosystem models. The heuristic framework is based on standardised protocols for a general ecosystem model interface. The interface is part of an experimental client-server environment, which will allow common access to model experiment results for the research community, stakeholders and decision makers.