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List of Abstracts 67P-Chemistry Climate.17 ID:4299 15:35Correlations between fine particulate matter (PM2.5) and meteorological variables in the UnitedStates: implications for the sensitivity of PM2.5 to climate changeAmos Tai, Loretta Mickley, Daniel JacobHarvard UniversityContact: tai@seas.harvard.eduWe applied a multiple linear regression (MLR) model to quantify the relationships of total PM2.5 and itscomponents with meteorological variables using an 11-year (1998-2008) observational record over the scaleof the contiguous US. The data were deseasonalized and detrended to focus on synoptic-scale correlations.We find that daily variation in meteorology as described by the MLR model can explain up to 50% of PM2.5variability, with temperature and precipitation being the most significant predictors. Temperature ispositively correlated with sulfate, organic carbon (OC) and elemental carbon (EC) almost everywhere. Thenitrate-temperature correlation is mostly negative but is positive in California and the northern US. Relativehumidity is positively correlated with sulfate and nitrate, but negatively with OC and EC. Precipitation isstrongly negatively correlated with all PM2.5 components. We find that PM2.5 concentrations are onaverage 2.6 µg m-3 higher on stagnant vs. non-stagnant days, and more than 5 µg m-3 higher in some areas.Extrapolation of current stagnation trends suggests that summer mean PM2.5 could increase by up to 1 µgm-3 by 2050 in some areas as a result of increased stagnation. Our observed climatological correlationsprovide a test for chemical transport models (CTMs) used to simulate the sensitivity of PM2.5 to climatechange. They point to the importance of representing the temperature dependence of agricultural, biogenicand fire emissions in CTMs. Application to the GEOS-Chem CTM will be presented.P-Chemistry Climate.18 ID:4552 15:35Aerosol Hygroscopicity Distribution and Mixing State Determined by Cloud CondensationNuclei(CCN) MeasurementsHang Su 1 , Diana Rose 1 , Ya Fang Cheng 2 , Sachin Gunthe 1 , Ulrich Pöschl 11 Max Planck Institute for Chemistry, Germany2 Leibniz Institute for Tropspheric Research, GermanyContact: hang.g.su@gmail.comThis paper presents, firstly the concept of hygroscopicity distribution and its application in the analysis ofcloud condensation nuclei (CCN) measurement data. The cumulative particle hygroscopicity distributionfunction H(κ) is defined as the number fraction of particles with a hygroscopicity parameter, κ, smaller thana certain value of κ. Since the measured CCN (at supersaturation S) can be considered as those particles withκ larger than a certain value, the CCN efficiency spectra (activation curve) can be easily converted to H(κ)distributions. Unlike studies calculating only one hygroscopicity parameter from a CCN activation curve, theconcept of H(κ) shows the usefulness of all points on the activation curve. Modeling studies of threeassumed H(κ) distributions are used to illustrate the new concept H(κ) and how it is related to the sizeresolvedCCN measurements. Secondly, we discuss the aerosol mixing state information that can be obtainedfrom the shape of H(κ). A case study is performed based on the CCN measurements during theCAREBEIJING 2006 campaign. In the campaign-averaged H(κ) distribution, most particles (>80%) lie in amode with a geometric mean κ around 0.2-0.4, and an increasing trend in the mean κ is found as particle sizeincreases. There seems to be another less hygroscopic mode but the κ resolution (depending on the sizeresolution) in the campaign is not high enough to interpret it. It is also clear that H(κ) is not a monodispersedistribution (implying an internal mixture of the aerosols). The dispersion parameter σg,κ, which is thegeometric standard deviation of H(κ), can be used as an indicator for the aerosol mixing state. The indicatorσg,κ shows good agreement with the soot mixing state measured by a volatility tandem differential mobilityiCACGP-IGAC 2010 12 July, 2010