The continuing effort to accurately estimate global radiative forcing has long been hampered by a degree of uncertainty in the tropospheric aerosol contribution. Reducing uncertainty in natural aerosol processes, which form the baseline of the aerosol budget, thus becomes a fundamental task. Appropriate representation of aerosols in the marine boundary layer (MBL) is essential to achieving the uncertainty reduction goal and providing reliable information on offsets to global warming by long lived greenhouse gases. We have developed an International Ocean Model Benchmarking (IOMB) package to analyze marine biogeochemical variables and processes, and the package was employed in this initial study to evaluate surface ocean concentrations and sea-air fluxes of dimethylsulfide (DMS), which we used as a first representative for biological volatile organic compounds (BVOCs). Performance for each model was scored based on how well it captured the spatial distribution and temporal variability in high quality observational data. Results show that model-data biases increased as DMS entered the MBL, but unfortunately more than three-quarters of CMIP5 participants do not have a dynamic representation of this unique source of natural sulfur. When it is present, models tend to overpredict DMS surface concentrations in the productive region of the eastern tropical Pacific by almost a factor of two and the sea-air fluxes by a factor of three. Sea-air transfer of the sulfur gas is fairly consistent over the historical period, but inter-model deviation in the global flux is about 5 μmol m-2 d-1 under RCP8.5. Overall, the development of a systematic model-data benchmarking package will inform improvement of subgrid-scale parameterizations and climate model developments. This presentation will highlight the need for an objective benchmarking package that automates systematic analyses of marine biogeochemical processes. Such a validation tool helps to identify possible areas of discrepancy with high uncertainties within representations of biogeochemical processes in climate models, therefore providing strategies to improve next generation Earth System Models (ESMs).