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Relationships between giant sea salt particles and clouds inferred from...

Dadashazar, H., Z. Wang, E. Crosbie, M. Brunke, X. Zeng, H. Jonsson, R. K. Woods, R. C. Flagan, J. H. Seinfeld, and A. Sorooshian (2017), Relationships between giant sea salt particles and clouds inferred from aircraft physicochemical data, J. Geophys. Res., 122, 3421-3434, doi:10.1002/2016JD026019.

This study uses airborne data from multiple field campaigns off the California coast to determine the extent to which a size distribution parameter and a cloud water chemical measurement can capture the effect of giant cloud condensation nuclei (GCCN), specifically sea salt, on marine stratocumulus cloud properties. The two GCCN proxy variables, near-surface particle number concentration for diameters >5 μm and cloud water chloride concentration, are significantly correlated (95% confidence) with each other, and both exhibit expected relationships with other parameters (e.g., surface wind) that typically coincide with sea salt emissions. Factors influencing the relationship between these two GCCN proxy measurements include precipitation rate (R) and the standard deviation of the subcloud vertical velocity owing likely to scavenging effects and improved mixing/transport of sea salt to cloud base, respectively. When comparing 12 pairs of high and low chloride cloud cases (at fixed liquid water path and cloud drop number concentration), the average drop spectra for high chloride cases exhibit enhanced drop number at diameters exceeding 20 μm, especially above 30 μm. In addition, high chloride cases coincide with enhanced mean columnar R and negative values of precipitation susceptibility. The difference in drop effective radius between high and low chloride conditions decreases with height in cloud, suggesting that some GCCN-produced raindrops precipitate before reaching cloud tops. The sign of cloud responses (i.e., R) to perturbations in giant sea salt particle concentration, as evaluated from Modern Era Retrospective Analysis for Research and Applications version 2 reanalysis data, is consistent with the aircraft data.

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Modeling Analysis and Prediction Program (MAP)