The application of complex land surface models to long-term estimation of water, energy, and CO2 exchange suffers from possible static parameterization of inherently time-varying properties. This paper presents a method by which structural (i.e., spatial) patterns in model output errors can be identified and associated with errors in a single parameter or set of interacting parameters. We focus on CO2 concentration profiles as the observed quantity containing spatial information on model performance. The core of the method relies on the empirical orthogonal function (EOF) analysis of an ensemble of model error profiles produced by synthetically inducing parameter biases. EOF analyses of the error profiles associated with photosynthetic capacity, stomatal conductance, radiation interception, soil respiration, and turbulent mixing parameters concentrated greater than 94% of the error pattern variability in the first two EOFs, producing very compact (two member) basis sets. Application of the EOFs to the identification of parameter error sources was performed with three sets of synthetic tests. The method was broadly successful at identifying the primary error source when one or two error sources were present, using single-parameter basis sets. Two-parameter basis sets were able to identify strongly interacting parameter errors (i.e., errors of approximately equal magnitude). Environmental classification of photosynthetically active radiation and wind speed regimes was effective at untangling error influences for the tightly coupled photosynthetic, radiation, and stomatal conductance parameters. Data analysis of measured concentration profiles was used to derive the environmental classes.