the Los Angeles air basin well inland from the coast. The increase of nitrate largely occurred in inland areas where nitric acid was produced downwind of urban centers with large NO x emissions. For conditions unfavorable for ammonium nitrate formation (e.g., high temperature, low RH, low NH 3 ), nitrate may still form inseaspray particles through replacement reactions (e.g., NaCl(p) + HNO 3 (g) → NaNO 3 (p) + HCl(g)). Since
The third update to theaerosol module adds a new treat- ment of gas-to-particle mass transfer for coarse particles and updates thein-line treatment of sea-salt emissions. In ear- lier CMAQ model versions, the coarse particle mode was treated as chemically inert, with a fixed geometric standard deviation. Emission fluxes from the coastal surf zone were set equal to those from the open ocean. These simplifica- tions hindered our ability to simulate aerosol composition in coastal urban areas and nutrient deposition to sensitive ecosystems (Nolte et al., 2008a). The new coarse-particle treatment in CMAQv4.7 allows sulfuric acid to condense on the coarse mode and allows semi-volatile inorganic species (ammonia, nitric acid, and hydrochloric acid) to condense and evaporate from the coarse mode. The water content of coarse particles is now determined by equilibrium with am- bient RH and the size distribution of coarse particles is al- lowed to broaden and narrow as a result of microphysical processes. As in previous CMAQ model versions, the fine particle modes are assumed to reach equilibrium with the gas phase instantaneously. In contrast, dynamic mass trans- fer is simulated for the coarse mode because large particles are often out of equilibrium with the gas phase (Meng and Seinfeld, 1996). As a result, important aerosol processes such as the replacement of chloride by NO − 3 in mixed ma- rine/urban air masses can now be simulated. In conjunc- tion with this update, the CMAQ input file OCEAN 1, has been enhanced to better allocate the fractions of each grid
Theversion used in this study is CHIMERE 2009 with specific modifications described inthe following paragraphs of this section. Theaerosolmodel species are the primary particle material (PPM), secondary inorganic aerosol (SIA; sulfate, nitrate and ammonium) based on the ISORROPIA thermodynamic equilibrium model (Nenes et al., 1998), sec- ondary organic aerosol (SOA, whose formation is repre- sented according to a single-step oxidation of the relevant an- thropogenic and biogenic precursors), sea salt and dust (non- African mineral dust is not included). The particles’ size dis- tribution ranges from 39 nm to 10 µm and the particles are distributed into eight bins (0.039, 0.078, 0.156, 0.312, 0.625, 1.25, 2.5, 5 and 10 µm). Vertically, the domain is divided in eight hybrid-sigma layers from the ground to 500 hPa. Gas phase tropospheric chemistry is represented using the reduced MELCHIOR chemical mechanism (120 reactions and 44 gaseous species) and the dry and wet depositions are taken into account. For the study, a nested domain (328 × 416 grid boxes) that covers most of Europe from 10.43750 ◦ W
Time series of simulated hourly natural pollutant concentra- tions for 2002, when averaged over the entire modeling do- main, provide insight into the joint behavior of emissions and secondary pollutants. Surface layer mixing ratios of selected gas species and aerosol concentrations were aver- aged for each hour and then a 24-h smoothing filter applied to suppress diurnal noise. Model output for 29 December 2001 through 10 January 2002 was dropped from the anal- ysis due to chemical spin-up issues. The simulation ended at 00:00 UTC on 1 January 2003 making 31 December in- complete (based on local time). Therefore, all 2002 results are presented for 354 days. Note that all time series plots in- clude “background” contributions from pollutants advected into the domain from the boundaries.
Hennigan, C. J., Miracolo, M. A., Engelhart, G. J., May, A. A., Presto, A. A., Lee, T., Sullivan, A. P., McMeeking, G. R., Coe, H., Wold, C. E., Hao, W.-M., Gilman, J. B., Kuster, W. C., de Gouw, J., Schichtel, B. A., Collett Jr., J. L., Kreidenweis, S. M., and Robinson, A. L.: Chemical and physical transformations of organic aerosol from the photo-oxidation of open biomass burning emissionsin an environmental chamber, Atmos. Chem. Phys., 11, 7669–
Massoli, P., Onasch, T. B., Cappa, C. D., Nuamaan, I., Hakala, J., Hayden, K., Li, S. M., Sueper, D. T., Bates, T. S., Quinn, P. K., Jayne, J. T., and Worsnop, D. R.: Characterization of black carbon-containing particles from soot particle aerosol mass spectrometer (SP-AMS) measurements on the R/V Atlantis during CalNex 2010, J. Geophys. Res., submitted, 2014. Matsumoto, K. and Tanaka, H.: Formation and dissociation of atmospheric particulate nitrate
physical bases for this outcome and points towards practical applications, such as the retrieval of aerosol production from satellite radiometric data. Some of the main uncer- tainties and shortcomings that are out of the scope of the current work but which must be addressed in any transition of this technique to operational use are: (i) imperfec- tions of aerosol production estimate methods (see Sect. 6.2), (ii) limited sample size
When constrained using the same chamber data, the BaseM (traditional two-product model that does not resolve multi- generational oxidation) and SOM models predict roughly the same SOA mass concentrations and spatial distribution for regional air pollution episodes inthe SoCAB and the east- ern USA. This suggests that the chamber data used to con- strain the BaseM and SOM parameterizations presumably already includes a majority of the SOA mass that would be attributable to multi-generational oxidation. The extent to which multi-generational oxidation influences the produc- tion of SOA in a given chamber experiment depends on both the volatility and reactivity of the first-generation products and the timescale of the experiment (Wilson et al., 2012). If SOA formation is dominated by first-generation prod- ucts, then explicit accounting for multi-generational ageing will not be important. Alternatively, if most SOA is formed from second-generation products with little direct contribu- tion from first-generation products, then a static represen- tation (such as with the2-product model) might be suffi- cient even when multi-generational ageing is, in fact, dom- inant. But if SOA formation is balanced between contribu- tions from first, second and later generation products, then the extent to which a static representation will capture thein- fluence of multi-generational ageing may be highly variable and sensitive to the experimental conditions and number of oxidation lifetimes. Consequently, the appropriateness of ex- trapolating static model parameterizations to longer (global atmospheric) timescales remains unclear. The results pre- sented here indicate that the2-product model does capture the influence of multi-generational ageing as part of the pa- rameterization in terms of mass concentration, at least for the regional episodes considered, but it is also apparent that the simulated SOA properties (e.g., volatility) and the explicit contributions of various SOA types are not fully captured by such simple models.
Abstract. TheCommunity Earth System Model (CESM1), maintained by the United States National Centre for At- mospheric Research (NCAR) is connected with the Modu- lar Earth Submodel System (MESSy). For the MESSy user community, this offers many new possibilities. The option to use theCommunity Atmosphere Model (CAM) atmospheric dynamical cores, especially the state-of-the-art spectral ele- ment (SE) core, as an alternative to the ECHAM5 spectral transform dynamical core will provide scientific and com- putational advances for atmospheric chemistry and climate modelling with MESSy. The well-established finite volume core from CESM1(CAM) is also made available. This of- fers the possibility to compare three different atmospheric dynamical cores within MESSy. Additionally, the CESM1 land, river, sea ice, glaciers and ocean component models can be used in CESM1/MESSy simulations, allowing the use of MESSy as a comprehensive Earth system model (ESM). For CESM1/MESSy set-ups, the MESSy process and diagnostic submodels for atmospheric physics and chemistry are used together with one of the CESM1(CAM) dynamical cores; the generic (infrastructure) submodels support the atmospheric model component. The other CESM1 component models, as well as the coupling between them, use the original CESM1 infrastructure code and libraries; moreover, in future devel- opments these can also be replaced by the MESSy frame- work. Here, we describe the structure and capabilities of CESM1/MESSy, document the code changes in CESM1 and
chard, 1964; Gershey, 1983a; Hoffman and Duce, 1974, 1976; Oppo et al., 1999; Keene et al., 2007; Facchini et al., 2008; Russell et al., 2010). The exact mechanism for such large organic mass fraction (and roughly by 2 to 3 or- ders of magnitude organic enrichment relative to subsurface waters) of submicron SSA is not well defined. It is thought that when ocean bubbles generated by the entrainment of air due to wave action rise to the surface, the surface active ma- terial inthe bulk water aggregates to the walls of the bub- bles. When these bubbles reach the water surface after hav- ing been enriched in organics relative to the bulk sea wa- ter, they burst and eject the organics absorbed on their sur- face into the atmosphere along with dissolved inorganic con- stituents of seawater (Blanchard, 1964). The amount of or- ganics absorbed on the bubble surface is thought to be mainly controlled by the abundance of dissolved and particulate or- ganic matter of the subsurface water (broadly characterized as lipids, amino and fatty acids, mono- and poly-saccharides, humic substances, and phytoplankton cell fragments) (Ben- ner et al., 1992; Millero, 2006). However, not all the organic material brought to the surface gets aerosolized. A signifi- cant amount of biogenic organic matter can accumulate at theair-sea interface, forming an organic film (the “sea surface microlayer”, SML) (Blanchard, 1964; Gershey et al., 1983b; Liss and Duce, 1997). Bubble-mediated processes also are not the only mechanism for forming SML. Transparent ex- opolymer particles (TEP) formed from dissolved exudates re- leased by phytoplankton and bacteria are positively buoyant and able to ascend the water column (Alldredge et al., 1993; Azetsu-Scott and Passow, 2004). These gel-like clumps are mostly polysaccharide, negatively charged, very sticky parti- cles ranging in size from ∼2 to ∼200 µm and present in high concentrations in most sea and freshwaters (Azetsu-Scott and Passow, 2004). The ascending TEP can initiate the forma- tion of natural biofilms on surfaces even under calm condi- tions. Overall, a number of water column processes (convec- tion, mobile biota, biota attached to buoyant particles, burst- ing bubbles, buoyant TEPs, diffusion and wave motion) can regulate the accumulation and reduction of material in SML (Wurl and Obbard, 2004; Cunliffe et al., 2011). Past stud- ies have shown that the SML can have a strong influence on the bubble-bursting process at theair-sea interface and sub- micron marine aerosol production and chemical composition (e.g., Ellison et al., 1999; O’Dowd et al., 2004).
SEMBLES Project. The simulations were performed on the NEC SX-6 supercomputer of the German High Performance Computing Centre for Climate- and Earth System Research in Ham- burg. Review comments by S. Kinne and D. Banse greatly improved this manuscript. We would also like to thank I. Fischer-Bruns for helpful discussions and M. Werner (MPI-Biogeochemistry, Jena) for his support with the dust source. The continuous support of our colleagues L. Korn-
trievals/pixels with geometric cloud cover greater than 20 % and poor quality data flags (flag = −1) were removed. The product uses the algorithm of Braak (2010) to identify OMI pixels aﬀected by row anomalies and sets the data flags to −1. We also filter these out in this study. Even though OMI has an approximate 13:00 LT London overpass, we used all OMI retrievals inthe domain between 11:00 and 15:00 LT to get more exten-
other parts of thesea (Fig. 7), while the evaporation has somewhat smoother varia- tion over the basin. (ii) The predominant southward winds inthe MCB and SCB are favourable for drifting surface water off the eastern coast, thus producing typical coastal upwelling of cold and saline sub-surface waters along this coast. All of the above fac- tors support asymmetrical distribution of salinity, yielding high density waters on the
Acknowledgements. We thank the captains and crews of R/V L’Atalante and R/V Poseidon for their excellent support during the cruises. Also, we thank A. Freing for inspiring discussions about the N 2 O mixed layer source and A. K¨ortzinger, B. Fiedler, T. Tanhua, and M. Glessmer for their support during the field work. We would like to thank two anonymous referees for their constructive comments that helped to improve the manuscript. Financial support for this study was provided by DFG grants DE 1369/1-1 and DE 1369/3-1 (JS and MD) and BMBF grant SOPRAN FKZ 03F0462A (AK). QuikScat data are produced by Remote Sensing Systems and sponsored by the NASA Ocean Vector Winds Science Team. Data are available at www.remss.com.
decision was based on theoretical and statistical accounts. The content of item 8 (“I am not as productive at work because I am losing sleep over traumatic experiences of a person I [help]”) seems to describe a symptom associated with Compassion Fatigue. In support for this, Modification Indices (MI) also indicated a correlation between item 8 and the Compassion Fatigue latent variable. Regarding item 11, an examination of its content suggested that this item seems to more appropriately reflect Burnout as it expresses a sense of continuous ‘wear and tear,’ and not so much a consequence of secondary exposure to a traumatic event. Modification Indices also indicated a correlation between item 11 and the Burnout latent variable. Model fit for the final model is presented in Table 2, and indicates that this model presents the best fit to the data, when compared to Model 1 and Model2. This model is composed by 19 items, with 9 items on the Compassion Satisfaction subscale, 5 items on the Compassion Fatigue subscale, and 5 items on the Burnout subscale.
12 The IDW is a method often used to interpolate data from airquality, given its simplicity (Brigs et al.; Keuken et al.; Lindley and Walch). To predict a value for any unmeasured location, this method uses the measured values surrounding the prediction location. Closest values have more influence on the predicted value than those farther away, hence the name inverse distance weighted. The surface calculated depends on the selection of a power value and the neighbourhood search strategy. IDW is an exact interpolator, where the maximum and minimum values inthe interpolated surface can only occur at sample points. The output surface is sensitive to clustering and the presence of outliers. IDW assumes that the surface is being driven by the local variation, which can be captured through the neighbourhood.
Reference city data collection includes information on ben- eﬁt prices and other geographic factors. Some information on beneﬁt prices, such as electricity and natural gas, were easily obtained and their application is clearly deﬁned by customer ser- vice areas. However, the beneﬁt price for rainfall interception was not readily available and required hydrologic calculations to esti- mate the volume of urban runoff. Furthermore, Lisbon’s sizable capital investment in improvement of its stormwater management infrastructure inﬂated the control cost value. The resulting bene- ﬁt price, which was about ﬁve times greater than the next highest price, may be anomalous. Given the high level of uncertainty in runoff estimation and Lisbon’s unique capital improvement invest- ment, other cities inthe region should be cautious before adopting this beneﬁt price. Other geographic data, such as building construc- tion types, numbers and types of properties opposite street trees, saturation rates for air conditioning equipment, and utility fuel mixes were easily obtained and may be suitable for use by other cities inthe region that are conducting similar analyses.
practical albeit inherently limited and narrowed alternative. We therefore focus on the role of freshwater in some of the Miocene abrupt (on geological time scale) changes in climate around 22 and 15 Ma. The results, as much of paleoclimate modeling, should thus be viewed as just one of the possibilities within an ensemble of probable scenarios. Elaborating further, we stress that freshwater control of ocean circulation is a pow-
Inthe European context over a 9-year period (2002-2011), emissions of major air pollutants have decreased, thereby improving airquality throughout the European region. Some of these pollutants show reductions with some variations, since several parts of Europe face transport of intercontinental pollutants by air and also present polluting sources of different types and weights. For particulates (PM10), there was a decrease between 20% and 44% of emissions, a decrease that was accompanied by other pollutants such as: ozone (14% - 65%); nitrogen oxides (approximately 27%); sulfur oxides (about 50% reduction); and carbon monoxide, which decreased by about 35% in a maximum daily period of eight hours . Although there are reductions in primary pollutant emissions, the corresponding concentrations of pollutants are not always in tune, i.e. due to the complexity of the chemical relationships between pollutants and the atmosphere, certain pollutants, such as secondary pollutants, may not decreasing trends inemissions of their precursor pollutants .
concentrations are on the same order of magnitude as those found in industrialized regions inthe northern hemisphere. Although in Brazil, as in many other developing countries, gold mining activities have been considered as the main sources of mercury emission, our results showed that other anthropogenic sources, such as fossil fuel combustion, can also significantly enhance atmospheric mercury concentrations. Nevertheless, probably due to the limited quantity of our data, it was not possible to point to a main mercury emission source, and our study also shows the need for further research about soil emission. Our study points out the need for more monitoring campaigns and also the need for assessment of anthropogenic mercury emission sources in Brazilian industrial regions. Our data also show that emissions from the most industria- lized Brazilian regions, and probably from similar regions in other countries of the southern hemisphere, should be assessed and integrated into the global anthropogenic mercury emission assessment.