Black Carbon (BC) aerosols play an important role in the climate system directly by absorbing light, and semi-directly by heating of the atmosphere. They also have an indirect effect on cloud properties, while deposition of BC onto snow has the potential to reduce surface albedo and increase melting. Global chemistry-climate models are often used to estimate the climate effects of BC, but the impacts do not always agree among these models. This project will provide an accurate, generalizable statistical model for the conversion of aerosol light absorption measurements to BC mass loadings for both filter-based absorption photometers and remote sensing techniques that will enable improved evaluations of predicted BC concentrations in chemistry-climate models. The team will achieve this goal through two objectives: (1) Improve upon our existing models and explore alternative models to reduce prediction biases across a range of atmospheric environments; (2) Apply the best statistical model to remote sensing data to evaluate its applicability for use in chemistry-climate model evaluation.