An increase in water temperature affects the production of gas bubbles in the water. These gas bubbles produce aerosols when rising and bursting on the ocean surface. Thorpe et al. (1992) concluded that the temperature-related decrease in gas solubility and the temperature-related increase in molec- ular diffusivity cancel out and that the net effect of a tem- perature increase is a decrease in bubble concentrations due to a reduction in viscosity. However, a water-temperature- independent change in oxygen saturations in the water occurs due to a change in photosynthesis rates. Since altered envi- ronmental conditions caused by climate change may impact the flora and fauna in the Arctic (Wassmann and Reigstad, 2011; Tremblay et al., 2011), a future change in oxygen sat- uration is likely to take place. Z´abori et al. (2012a) did not observe a change in PMA number production with a change in oxygen saturation between 72 and 83 % for ArcticOcean conditions. Hultin et al. (2011) observed for Baltic Sea water an anti-correlation between particle production and oxygen saturations in the range 90 to 100 %. Thus, it is not possible to derive a clear conclusion on the role of oxygen saturation on PMA production.
SSA are released to the atmosphere by air bubbles burst- ing on the ocean surface. For a natural ocean environment, air bubbles are generated from air entrainment during wave breaking (O’Dowd et al., 1997). Many different parameters influence the development of a bubble. Depending on the bubble diameter and the level of gas saturation in the water, bubbles tend to grow or dissolve (Slauenwhite and Johnson, 1999). The rise velocity of a bubble depends on the viscos- ity and the water density, but also varies with bubble size (Leifer et al., 2000). Coalescence between bubbles is thought to be inhibited or even prevented by ions in seawater (Slauen- white and Johnson, 1999). The complexity of aerosol pro- duction due to bubble bursting has resulted in many different formulations of the SSA source functions, based on differ- ent methods, and a large uncertainty in the production fluxes (de Leeuw et al., 2011; Lewis and Schwartz, 2004). Parame- terizations have been developed based on experimental stud- ies relating parameters which influence ocean bubble forma- tion to sea spray aerosol emissions. Sea spray production is highly dependent on wind speed, but water temperature (T w ),
Two diastereoisomeric 2-methyltetrols (2-methylthreitol and 2-methylerythritol) were first identified as oxidation products of isoprene in the Amazonian forest aerosols (Claeys et al., 2004). Since then, these organic marker com- pounds have been detected in ambient aerosols from different locations in the world (Hallquist et al., 2009 and references therein). Concentrations of 2-methylerythritol are generally 1.5–2.5 times more abundant than that of 2-methylthreitol in ambient aerosols (Claeys et al., 2004; Cahill et al., 2006; Fu et al., 2010). Interestingly, rather lower ratios of 2- methylerythritol to 2-methylthreitol (down to 0.76) were ob- served in the summertimemarine aerosols over the ArcticOcean, ranging from 0.76–2.1 (Fig. 7). Such a variation in- dicates that the formation processes and/or the sources of the two isomers varied with time and location; this also indicates that one of the isomers may have a larger preference to at- mospheric oxidative aging (i.e., heterogeneous reaction with hydroxyl radicals) during long-range transport. Another pos- sibility is that such a difference may be due to one isomeric epoxide preferentially produced in the gas phase oxidation of isoprene (Paulot et al., 2009). In addition, Nozi`ere et al. (2011) recently reported that 2-methyltetrols could be of biogenic origin at a certain level. A good correlation between 2-methyltetrols and sugar compounds was also reported by
discharges (Zhang et al., 2012) or increments in precipitation due to enhanced lo- cal evaporation due to less SIE (Bintanja and Selten, 2014), (ii) radiative, particularly trough rises in cloud cover and water vapour (Kapsch et al., 2013), (iii) dynamical, namely more unusual summer storms crossing the Arctic (Simmonds and Rudeva, 2012). Most likely these different mechanisms coexist to a certain extent and are not
Abstract. Water column data of carbon and carbon relevant hydrographic and hydrochemical parameters from 188 previously non-publicly available cruises in the Arctic, Atlantic, and Southern Ocean have been retrieved and merged into a new database: CARINA (CARbon IN the Atlantic). The data have been subject to rigorous quality control (QC) in order to ensure highest possible quality and consistency. The data for most of the parameters included were examined in order to quantify systematic biases in the reported values, i.e. secondary quality control. Significant biases have been corrected for in the data products, i.e. the three merged files with measured, calculated and interpolated values for each of the three CARINA regions; the Arctic Mediterranean Seas (AMS), the Atlantic (ATL) and the Southern Ocean (SO). With the adjustments the CARINA database is consistent both internally as well as with GLODAP (Key et al., 2004) and is suitable for accurate assessments of, for example, oceanic carbon inventories and uptake rates and for model validation. The Arctic Mediterranean Seas include the ArcticOceanand the Nordic Seas, and the quality control was carried out separately in these two areas. This contribution provides an overview of the CARINA data from the Nordic Seas and summarises the findings of the QC of the salinity data. One cruise had salinity data that were of questionable quality, and these have been removed from the data product. An evaluation of the consistency of the quality controlled salinity data suggests that they are consistent to at least ±0.005.
variability in cloud droplet size that can be explained by changes in aerosol concen- tration. We want to analyse data daily when the specific conditions are present (see Sect. 3.3) and divide data into small bins of Liquid Water Path (LWP) to approximate the conditions in each bin to a constant LWP, as postulated by Twomey. Due to daily data analysis we will always have a smaller sample than in the case of data aggregated from
The MAECHAM5-HAM-SALSA simulations were carried out with a free running setup without nudging. Thus the dynamical feedback resulting from the additional heat- ing from increased stratospheric sulfate load was taken into account. On the other hand, not running the model in the nudged mode means that the online emissions of, e.g., sea salt and mineral dust that are sensitive to wind speed at 10 m height, can dif-
For clarity, the annual cycles are also shown in the Supplement (Fig. S12). From there it is clear that Gual Pahari SSA is lowest during monsoon season and highest during winter. In Mukteshwar, on the other hand, SSA does not seem to vary with month. There are a few explanations for the Gual Pahari SSA cycle. First of all, highly absorb- ing black carbon may have higher concentrations in the monsoon season, because wet
to sea-salt particles with dry diameters of ∼ 72 nm, i.e., accumulation mode where ambient measurements show the largest increase in CCN number associated with bi- ologically active regions (Bigg, 2007; Yoon et al., 2007; Rinaldi et al., 2010), treatment of marine POA emissions only as internal mixture is likely to underestimate their cloud microphysical and radiative effects. The large discrepancy in yearly-mean surface CCN
data files; one each for the Atlantic Ocean, Arctic Mediterranean Seas and Southern Ocean regions. These files contain all the CARINA data and also include: 1) interpo- lated values for nutrients, oxygen and salinity if those data were missing and the inter- polation could be made according to certain criteria, as described in Key et al. (2009); 2) calculated carbon parameters; e.g. if total dissolved inorganic carbon (TCO 2 ) and
The ineffectiveness of primary production as an indicator of fisheries yield at a global scale is consistent with theoretical arguments supporting a more nuanced and complex relationship between the two quantities. Ryther  for example, argues that shifts in the size structure of the phytoplankton community to larger phytoplankton and increasing consumer gross growth efficiencies in more productive ecosystems should result in greater fisheries production per unit of primary production. The importance of particle export fluxes, or the fraction of primary production exported from pelagic foodwebs via sinking particles, can vary in complex ways with planktonic foodweb, water column structure, temperature, and ecosystem disturbance [19,20,21,22, 23,24]. These flux rates can also strongly influence trophic transfer within ecosystems [25,26,27]. In this paper, we assess primary productivity and a collection of additional variables as predictors of fisheries yields for 52 of the 64 of the globally-distributed Large Marine Ecosystems. The additional variables include simple geographic, physical, and biological variables that are readily observed (latitude, temperature, chlorophyll concentration) as well as derived quantities which may more accurately indicate differences in the export of energy from the planktonic ecosystem to fisheries across ecosystems on a global scale (e.g., particle export fluxes and estimated mesozooplankton production).
the Arctic pose a threat for human pre- and neonatal neurological development (Stef- fen et al., 2008). Arcticmarine mammals such as beluga whales frequently contain total Hg levels well above Canadian Federal Consumption Guidelines (Lockhart et al., 2005). Again, the fate of surplus mercury deposited to the Arctic basin during polar spring is largely indefinite with reference to transport and transformation. This issue
sist from the winter buildup that occurs under conditions of low photochemical loss. Summertime forcing could also be significant, particularly when agricultural or boreal forest fire emissions increase ozone levels in the Arctic. The values shown in Table 1 for summertime are based on a standard climatology for present day biomass burning emissions (including forest fires) (Shindell et al., 2006). As such, they do not capture
340 4 and thus also longer than the corresponding period during the MIS 2 deglaciation, i.e. Heinrich 341 event 1. Just prior to the arrival of the subpolar waters, the Iberian margin experienced increased 342 paleoproductivity with Portugal Current persistence during MIS 12 and MIS 2 (Amore et al., 2012; 343 Palumbo et al., 2013a, b). However, comparing the paleoproductivity records during the two 344 glacials, reveals some differences in the Portugal Current dynamics as indicated by the order of 345 magnitude difference in the values of the paleoproductivity proxies (Fig. 3). The Portugal Current 346 and associated upwelling regime were more intense during the last glacial maximum than during 347 late MIS 12. Nevertheless, SST values (Fig. 2) were quite comparable during both periods (values 348 close to 15°C), suggesting that the main difference cannot be associated with a response of 349 coccolithophores to different temperature ranges but more likely to higher nutrient availability 350 during MIS 2 than MIS 12, which, at the studied site, is nowadays caused by stronger westerly 351 winds and upwelling intensity (Ríos et al., 1992; Fiúza et al., 1998; Pérez et al., 2001; Coelho et al., 352 2002; Peliz et al., 2005; Relvas et al., 2007). In fact, the PCA reveals that during both intervals, i.e. 353 19-13.5 kyr BP (Fig. 6A) and 430-425 kyr (Fig. 6B), the paleoproductivity increase occurred during 354 warming phases, whereas the subpolar waters were clearly characterized by low SST and less 355 adequate conditions for coccolithophore proliferation.
The qualitative variables were expressed as absolute and relative frequencies and quantitative variables as medians, medium and standard error (SE), with the confidence interval (CI) of 95%. To compare proportions, was applied the Chi- square test, as needed. To compare attribute average scores, according to the care model, it was used the Kruskal-Wallis test and then it was used the Post Hoc Dunn test to identify which of the pairs of groups differ, being “a” different from “b” and both different from “c”. For all the statistical analysis, it was considered a significance level of 5% (p < 0.05).
Froidevaux, L., Jiang, Y. B., Lambert, A., Livesey, N. J., Read, W. G., Waters, J. W., Browell, E. V., Hair, J. W., Avery, M. A., McGee, T. J., Twigg, L. W., Sumnicht, G. K., Jucks, K. W., Mar- gitan, J. J., Sen, B., Stachnik, R. A., Toon, G. C., Bernath, P. F., Boone, C. D., Walker, K. A., Filipiak, M. J., Harwood, R. S., Fuller, R. A., Manney, G. L., Schwartz, M. J., Daffer, W. H., Drouin, B. J., Cofield, R. E., Cuddy, D. T., Jarnot, R. F., Knosp, B. W., Perun, V. S., Snyder, W. V., Stek, P. C., Thurstans, R. P., and Wagner, P. A.: Validation of Aura Microwave Limb Sounder stratospheric ozone measurements, J. Geophys. Res., 113, D15S20, doi:10.1029/2007JD008771, 2008.
Atmospheric marine aerosols consist of primaryaerosol, which comprises organic material and sea-salt and is pro- duced on the ocean surface by bubble-bursting and tearing from breaking waves (Anguelova and Webster, 1999), and secondary aerosol, formed by non sea-salt sulphate and or- ganic species originated via gas-to-particle conversion pro- cesses such as homogeneous nucleation and condensation onto pre-existing particles (O’Dowd et al., 1997). Chemi- cal analysis of atmospheric sea-spray particles collected in field experiments has provided evidence for the presence of significant concentrations of organic matter in the submicron size range (Hoffman and Duce, 1977; Novakov et al., 1997; Middlebrook et al., 1998; Putaud et al., 2000; Cavalli et al., 2004). The contribution of the organic fraction in the ma- rine aerosol has been found to be seasonal, accounting for up to 63% of the submicrometre aerosol dry mass, with an in- creasing enrichment with decreasing particle size (O’Dowd et al., 2004; Yoon et al., 2007). Studies on North East At- lantic marineaerosol conducted over long periods (O’Dowd et al., 2004; Yoon et al., 2007) have shown that the shape of the submicron sea spray distribution presents a seasonal pat- tern, characterized by an increase of the accumulation and Aitken modes modal sizes from winter to summer. Although this finding has been attributed to an effect produced by pri-
Subsequently the AODs increased steadily to reach a peak value (0.4 at 500 nm) by 30 April to 2 May period and then dropped off. The rapid decrease off Kochi is mainly due to the decreasing source impact. Kochi is one of the busiest ports on the west coast (after Mumbai) and is also a rapidly growing urban centre. The city also has several small and medium scale industries, beside a large oil refinery and a fertilizer
posed on the measured DMS g time series, as well as the GEOS-Chem sea salt (a ma- rine tracer) and methyl ethyl ketone and carbon monoxide (MEK and CO, biomass burning tracers) mixing ratios. Figure 4b shows the main land cover types in the re- gion. Panel c in Fig. 4 shows examples of potential emissions sensitivity plots gener- ated using FLEXPART-WRF that indicate regions the air has passed over before being
Table 1. Overview of C-130 flight legs, and the earlier BAe-146 flight legs. SC: subcloud leg; CB: leg around cumulus cloud base level; C: leg near the center of the stratocumulus cloud layer; AC: above cloud leg in the free-troposphere above the marine inversion; S: sawtooth run from above inversion to base of cloud. The mean run altitude z is given. Directions are given as from POC to overcast (P→O) or vice-versa. Times are in UTC (LT+3 h). Run lengths L in the three different regions are given in kilometers.