Hundreds of gas and particle phase organic acids were measured in a rural ponderosa pine forest in Colorado, USA, during the Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen – Rocky Mountain Biogenic Aerosol Study (BEACHON-RoMBAS). A recently developed Micro-Orifice Volatilization Impactor High-Resolution Time-of-Flight Chemical Ionization Mass Spectrometer (MOVI-HRToF-CIMS) using acetate (CH3C(O)O-) as the reagent ion was used to selectively ionize and detect acids semi-continuously from 20–30 August 2011, with a measurement time resolution of ~1.5 h. At this site 98% of the organic acid mass is estimated to be in the gas-phase, with only ~2% in the particle phase. We investigated gas/particle partitioning, quantified as the fraction in the particle phase (Fp), of C1–C18 alkanoic acids, six known terpenoic acids and total bulk organic acids. Data were compared to the absorptive partitioning model and suggest that bulk organic acids at this site follow absorptive partitioning to the organic aerosol mass. The rapid response (<1–2 h) of partitioning to temperature changes for bulk acids suggests that kinetic limitations to equilibrium are minor, which is in contrast to conclusions of some recent laboratory and field studies, possibly due to lack of very low ambient relative humidities at this site. Time trends for partitioning of individual and groups of acids were mostly captured by the model, with varying degrees of absolute agreement. Species with predicted substantial fractions in both the gas and particle phases show better absolute agreement, while species with very low predicted fractions in one phase often show agreement on trends, but poor absolute agreement, potentially due to thermal decomposition, inlet adsorption, or other issues. Based on measurement-model comparison we conclude that species carbon number and oxygen content, together with ambient temperature control the volatility of organic acids and are good predictors for partitioning. Partitioning of bulk acids is more consistent with model predictions for hydroxyacids, hydroperoxyacids, or polyacids, and less so for ketoacids.