Abstract. One of the challenges of understanding atmospheric organic aerosol (OA) stems from its complex composition. Mass spectrometry is commonly used to characterize the compositional variability of OA. Clustering of a mass spectral data set helps identify components that exhibit similar behavior or have similar properties, facilitating understanding of sources and processes that govern compositional variability. Here, we developed a novel clustering algorithm, Noise-Sorted Scanning Clustering (NSSC), and apply it to thermal desorption measurements from the Filter Inlet for Gases and AEROsols coupled to a chemical ionization mass spectrometer (FIGAERO CIMS). NSSC provides a robust, reproducible analysis of the FIGAERO temperature-dependent mass spectral data. The NSSC allows for determination of thermal profiles for compositionally distinct clusters, increasing the accessibility and enhancing the interpretation of FIGAERO data. Applications of NSSC to several laboratory biogenic secondary organic aerosol (BSOA) systems demonstrate the ability of NSSC to distinguish different types of thermal behaviors for the components comprising the particles along with the relative mass contributions and chemical properties (e.g. average molecular formula) of each cluster. For each of the systems examined, more than 80 % of the total mass is clustered into 9–13 clusters. Comparison of the average thermograms of the clusters between systems indicate some commonalty in terms of the thermal properties of different BSOA, although with some system-specific behavior. Application of NSSC to sets of experiments in which one experimental parameter, such as the concentration of NO, is varied demonstrates the potential for clustering to elucidate the chemical factors that drive changes in the thermal properties of OA. Further quantitative interpretation of the clustered thermograms followed by clustering will allow for more comprehensive understanding of the thermochemical properties of OA.