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 and the uncertainty accompanying model outputs. In model evaluation, however, a broad variety of different approaches to define uncertainty still exists and these have not been standardized so far. 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 uncertianty. We are proposing three other 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 incorporates type C uncertainty but 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 refered to as type E uncertainty. Here we propose a framework for expressing the quality of model outputs in terms of a quantification of type C uncertainty for descriptive model uses and type D uncertainty for predictive model uses, each with associated type A and B uncertainty ranges. Internal parameter uncertainty (type E) should be treated separately as it refers to model structure itself, and which we assume as valid when assessing uncertainty due to other factors.