Spring and summer contrast in new particle formation over nine forest areas in North America
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CitationYu, F. Luo, G. Pryor, S C. Pillai, P R. Lee, S H. Ortega, J. Schwab, J J. Hallar, A G. Leaitch, W R. Aneja, V P. Smith, J N. Walker, J T. Hogrefe, O. Demerjian, K L. (2015). Spring and summer contrast in new particle formation over nine forest areas in North America. Atmospheric Chemistry and Physics, 15 (24) , 13993-14003. 10.5194/acp-15-13993-2015.
Recent laboratory chamber studies indicate a significant role for highly oxidized low-volatility organics in new particle formation (NPF), but the actual role of these highly oxidized low-volatility organics in atmospheric NPF remains uncertain. Here, particle size distributions (PSDs) measured in nine forest areas in North America are used to characterize the occurrence and intensity of NPF and to evaluate model simulations using an empirical formulation in which formation rate is a function of the concentrations of sulfuric acid and low-volatility organics from alpha-pinene oxidation (Nucl-Org), and using an ion-mediated nucleation mechanism (excluding organics) (Nucl-IMN). On average, NPF occurred on ~ 70 % of days during March for the four forest sites with springtime PSD measurements, while NPF occurred on only ~ 10 % of days in July for all nine forest sites. Both Nucl-Org and Nucl-IMN schemes capture the observed high frequency of NPF in spring, but the Nucl-Org scheme significantly overpredicts while the Nucl-IMN scheme slightly underpredicts NPF and particle number concentrations in summer. Statistical analyses of observed and simulated ultrafine particle number concentrations and frequency of NPF events indicate that the scheme without organics agrees better overall with observations. The two schemes predict quite different nucleation rates (including their spatial patterns), concentrations of cloud condensation nuclei, and aerosol first indirect radiative forcing in North America, highlighting the need to reduce NPF uncertainties in regional and global earth system models.