RESUMO
Aerosol-cloud interactions remain uncertain in assessing climate change. While anthropogenic activities produce copious aerosol nanoparticles smaller than 10 nanometers, they are too small to act as efficient cloud condensation nuclei (CCN). The mechanisms responsible for particle growth to CCN-relevant sizes are poorly understood. Here, we present aircraft observations of rapid growth of anthropogenic nanoparticles downwind of an isolated metropolis in the Amazon rainforest. Model analysis reveals that the sustained particle growth to CCN sizes is predominantly caused by particle-phase diffusion-limited partitioning of semivolatile oxidation products of biogenic hydrocarbons. Cloud-resolving numerical simulations show that the enhanced CCN concentrations in the urban plume substantially alter the formation of shallow convective clouds, suppress precipitation, and enhance the transition to deep convective clouds. The proposed nanoparticle growth mechanism, expressly enabled by the abundantly formed semivolatile organics, suggests an appreciable impact of anthropogenic aerosols on cloud life cycle in previously unpolluted forests of the world.
RESUMO
The Atmospheric Radiation Measurement program Raman lidar was upgraded in 2004 with a new data system that provides simultaneous measurements of both the photomultiplier analog output voltage and photon counts. We describe recent improvements to the algorithm used to merge these two signals into a single signal with improved dynamic range. The effect of modifications to the algorithm are evaluated by comparing profiles of water vapor mixing ratio from the lidar with radiosonde measurements over a six month period. The modifications that were implemented resulted in a reduction of the mean bias in the daytime water vapor mixing ratio from a 3% dry bias to well within 1%. This improvement was obtained by ignoring the temporal variation of the glue coefficients and using only the nighttime average glue coefficients throughout the entire diurnal cycle.