EPCAPE-PT-LANL

Coastal cities provide a distinctive platform for examining the interplay between aerosols and clouds, as well as the impact of human-induced particulates on cloud properties. In support of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) objectives, we are conducting the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) to profile the aerosol radiative properties and aerosol interactions in marine stratocumulus clouds over the Eastern Pacific. EPCAPE-PT-LANL bolsters the primary EPCAPE aims by carrying out novel observations of the: distribution of vapor-phase transition between aerosols and cloud droplets; influence of black carbon on aerosol-cloud interactions; and the repercussions of cloud processing on aerosol optical properties.

Our main tool in this investigation is a ground-based counterflow virtual impactor that separates cloud droplets from interstitial particles. This enables us to discern the segregation between interstitial and cloud droplets within a cloud. We are probing how the gas-phase organics are selectively absorbed into cloud droplets, which relies on the solubility and oxidation condition of the organic vapors. To measure the distribution of organic aerosols and volatile organic compounds, we will use LANL’s proton-transfer reaction time-of-flight mass spectrometer (PTR-TOF) to measure the gas-particle distribution of organic molecules with a CHARON (CHemical Analysis of aeRosol ON-line) inlet. The soot-particle aerosol mass spectrometer (SP-AMS) from LANL will examine the size-resolved composition of both residual (post-droplet evaporation) and interstitial aerosols, identifying if any aqueous processing is in effect. Lastly, we will investigate whether the characteristics of black carbon (BC) coatings critically affect the aerosol's activation capacity, measuring the hygroscopicity parameter (κ) at both RH < 100% and RH > 100%. Studying these components of aerosol processing is expected to improve our comprehension of aerosol-cloud interactions, thus improving our ability to model and predict cloud formation and atmospheric chemistry.

Timeline