Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their life cycles

Mineral dust aerosol is important in the Earth system, and the correct representation of its size distribution is fundamental for shaping the current state and evolution of the climate. Despite many observational dust size data that are available in the literature, using this body of information to properly guide the development and validation of climate models and remote sensing retrievals remains challenging. In this study we collect, evaluate, harmonize, and synthesize 58 size distribution data from the past 50 years of in situ field observations with the aim of providing a consistent dataset to the community for use in constraining the representation of dust size across its life cycle. Four levels (LEVs) of data treatment are defined, going from original data (LEV0), data interpolated and normalized on a standardized diameter grid (LEV1), and data in which original particle diameters are converted to a common geometrical definition under both spherical (LEV2a) and aspherical (LEV2b) assumptions. Size distributions are classified as emission or source (SOURCE, 4 d of transport; N=10). The harmonized dataset shows consistent features suggesting the conservation of airborne particles with time and a decrease in the main coarse-mode diameter from a value on the order of 10 µm (in volume) for SOURCE dust to a value on the order of 1–2 µm for LRT conditions. An additional mode becomes evident below 0.4 µm for MRT and LRT dust. Data for the three levels (LEV1, LEV2a, and LEV2b) and the three categories (SOURCE, MRT, and LRT), together with statistical metrics (mean, median, 25th and 75th percentiles, and standard deviation), are available as follows: SOURCE (https://doi.org/10.57932/58dbe908-9394-4504-9099-74a3e77140e9, Formenti and Di Biagio, 2023a); MRT (https://doi.org/10.57932/31f2adf7-74fb-48e8-a3ef-059f663c47f1, Formenti and Di Biagio, 2023b); LRT (https://doi.org/10.57932/17dc781c-3e9d-4908-85b5-5c99e68e8f79, Formenti and Di Biagio, 2023c).