Sublimation is important to the water cycle of cold, snow dominated ecosystems, many of which have been recently disturbed. In high elevation spruce-fir forests of western North America spruce beetle outbreaks have killed trees, reduced the canopy, and altered the processes that control sublimation. This publication includes the data, source code used for statistical analyses, and Bayesian posterior distributions used in Frank et al. 2018 (in review). That study evaluates two hypotheses in these ecosystems: (1) the dominant source for sublimation is canopy intercepted snow and (2) the loss of canopy following a beetle disturbance leads to less total sublimation. To incorporate uncertainty hierarchically across multiple data sources and address phenomenological parsimony, Bayesian statistics were used to analyze seventeen years (2000-2016) of winter eddy covariance flux data at the Glacier Lakes Ecosystem Experiments Site (GLEES) AmeriFlux sites where a spruce beetle outbreak caused 75-85% basal area mortality. This data publication includes micrometeorological data from the GLEES AmeriFlux sites for water years 2000-2016, water vapor stable isotope concentrations for water years 2014-2016, MODIS leaf-area index around the AmeriFlux scaffold from 2000-2015, and subcanopy four-component radiation measurements from 2014-2016 as well as modeled predictor variables for heat flux into the snowpack, energy storage in the canopy, leaf area index, and Beer’s law canopy extinction coefficient. This archive includes source code that analyzes sublimation data using a hierarchical Bayesian statistical model based on these data and modeled predictor variables. Finally, the posterior distributions for process parameters and derived quantities resulting from the Bayesian statistical analyses are included.