Abstract
A model represents the way in which information about the world is captured in a form that can be manipulated for application to new situations. However, quantification of `model error' presents formidable challenges. Various inverse problems in carbon cycle modelling are presented as illustrations of the issues. A `maximum-entropy' representation of carbon cycle response is used to explore techniques for non-parametric estimation of carbon cycle uncertainty.

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