To increase understanding of mercury cycling, a seasonal mass balance model was developed to predict mercury concentrations in lakes and fish. Results indicate that seasonality in mercury cycling is significant and is important for a northern latitude lake. Models, when validated, have the potential to be used as an alternative to measurements; models are relatively inexpensive and are not as time intensive. Previously published mercury models have neglected to perform a thorough validation. Model validation allows for regulators to be able to make more informed, confident decisions when using models in water quality management. It is critical to quantify uncertainty; models are often over-parameterized and constrained by few measurements. As an approach, the Markov Chain Monte Carlo (MCMC) Bayesian method was used for uncertainty analysis. The uncertainty analysis provided a better means for calibration, helpful insight on the distribution of model parameter values, and the uncertainty in model predictions.