(593j) Probability of the Growth of Ultrafine Atmospheric Aerosol to Cloud Condensation Nuclei

The magnitude of the impact of atmospheric aerosols on the radiative properties of clouds and climate is highly uncertain. Changes in atmospheric aerosol concentrations due to human activity changes the number of particles on which cloud droplets may form (cloud condensation nuclei, CCN). In general, many particles emitted from combustion sources and recently nucleated in the atmosphere are too small to act as CCN and it is not well known how much these processes contribute to the CCN concentrations. Therefore, the ability for new particles that are smaller than CCN sizes to grow to become CCN must be understood in order to understand changes in the global CCN budget and cloud radiative properties. We have assessed the probability that these ultrafine particles (< 100 nm dry diameter) will grow to become CCN depending on their environment. There are competing physics that cause the particle to either grow to larger sizes or be removed from the particle budget. Growth is caused by condensation of non-volatile and semi-volatile gases as well as coagulation with smaller particles. Removal is caused by coagulation with larger particles and deposition. The timescales of these processes change as the particle grows to CCN sizes such that the probability of ultrafine growth cannot be calculated through a simple comparison of these timescales. We have developed the Probability of Ultrafine-aerosol Growth (PUG) model that determines how these timescales evolve as the particle grows calculating the probability that the particles will reach intermediate sizes on the way to growing to become a CCN. We use PUG in conjunction with results from a box model and the general circulation model, GISS II-prime, with online size resolved aerosol microphysics, TOMAS, to evaluate the probability of ultrafine particles growing to CCN globally. The PUG model is used to determine how emissions and nucleation events may contribute to CCN concentrations in different tropospheric regions. Furthermore, we use the PUG model to evaluate the uncertainty in CCN predictions due to uncertainty in new particle emissions and nucleation.