The uncertainty in carbon emissions from fire was estimated for the boreal region of Alaska over the 50 years of recorded wildfire. Building on previous work where carbon emissions were estimated using a geographic information systems-based model, the uncertainty attached to the different parameters of the basic equation was assessed and propagated through the equation using Monte Carlo simulation. The result is a distribution of possible values for total carbon and three carbon-based gases (CO2, CO, and CH4) that provides a measure of the uncertainty in the output estimates. Additionally, the relative impact of each input parameter on the output uncertainty has been quantified (sensitivity analysis). Assumptions were made in building the uncertainty model regarding the shape of the distribution of each model parameter since this information is unavailable. Because of the lack of information on the precision of input parameter estimates, a range of possible spread values for the probability distributions, as defined by the coefficient of variation (CV; standard deviation/mean), was considered. Using the best guess values for input CVs, the resulting estimate of total annual carbon emission can be as high as 10.6 TgC or as low as 1.1 TgC, a CV of 24%. Lowering the input CVs to 5% results in an output CV of 4.2% for total carbon emissions. For the three carbon-based gases the CV of simulated carbon distributions for the best guess scenario ranges from 23 to 27%. The sensitivity analysis reveals that ground-layer fraction consumed, ¿g, is the most important parameter in terms of output uncertainty. The results of this work emphasize that current estimates of carbon emission from biomass burning are not well constrained because input data sets are incomplete and lack adequate error information. Furthermore, we conclude that although burn area estimates are improving, more effort is needed in quantifying fuel and consumption variables at fire sites if accurate estimates of carbon emissions from fire are to be made.