Research ArticleCLIMATE SCIENCE

Natural aerosols explain seasonal and spatial patterns of Southern Ocean cloud albedo

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Science Advances  17 Jul 2015:
Vol. 1, no. 6, e1500157
DOI: 10.1126/sciadv.1500157
  • Fig. 1 Elevated mean September to April cloud droplet concentrations over the SO are associated with regions of high Chl-a (indicating the presence of phytoplankton biomass).

    Spring-summer-autumn (September to April) mean values of Nd are shown as a function of SeaWiFS Chl-a (for 5° latitude × 15° longitude boxes from 35° to 55°S). Three comparisons are shown: Nd from 2007 only using retrievals SZA <65° versus Chl-a from 2007 (red); Nd from 2007 (only retrievals with SZA <65°) versus a climatology of Chl-a from 2001 to 2009 (black); and Nd from each year from 2001 to 2009 (all retrievals) versus Chl-a from each year from 2001 to 2009 (brown). The Spearman rank correlations are 0.76, 0.63, and 0.57 ± 0.04, respectively, where the mean and SD of the latter is calculated from the individual correlations from each year from 2001 to 2009.

  • Fig. 2 Monthly mean Nd correlates with OMF and SO4 concentration.

    Data points are given as gray dots. Box and whisker plots show binned means, boxes show interquartile range, whiskers show twice interquartile range, and outliers are shown as colored circles. Left: Nd as a function of OMF. Box and whisker plots are shown for three levels of SO4 concentration. Higher SO4 surface concentrations (orange) are associated with higher Nd for the same OMF. Right: Nd as a function of SO4 surface concentration. Box and whisker plots are shown for three different bins of OMF, where higher OMF (orange) is associated with higher Nd for a given SO4 concentration. All data are averaged monthly and over 5° × 15° boxes. R2 values are shown for linear regressions between Nd and the respective predictor variable.

  • Fig. 3 Comparison of the fractional contributions to CCN from SS as estimated from this study and as modeled in the global aerosol model GLOMAP (7).

    The percentage contribution of SS to sulfate and SS CCN is estimated in this study as the constant term in Eq. 1 divided by the sum of the sulfate and constant terms from Eq. 1. The winter and summertime contributions of SS to CCN modeled by GLOMAP are shown in the figure with triangles, and the dashed connecting lines are provided as guides to the eye. The SS contributions from this study (oceans only) are shown aggregated into summer, fall, winter, and spring seasons and into 35° to 45°S and 45° to 55°S.

  • Fig. 4 The model captures significant spatial and seasonal structure in the SO Nd.

    (A) One year of Nd observations averaged from September through April (when observations are available for all months at all latitudes). (B) Predicted Nd from the regression model. (C and D) Contributions from OMF and SO4 (note the different color scale). The means and spatial SDs are shown above each panel. The correlation between modeled and observed September-April mean Nd is r = 0.71, and the correlation between the September-April mean OMF and SO4 contributions to Nd is r = 0.41. (E) Mean ocean Chl-a observed by SeaWiFS. (F) Time series of the observed Nd, modeled Nd, and the contributions of OMF, SO4, their sum, and the constant term to Nd. Time series data are monthly averages over all locations where Nd is observed.

  • Fig. 5 OMF and SO4 significantly affect SO cloud albedo.

    (A to C) Contours show the zonal mean change in top-of-atmosphere (TOA) net shortwave radiation (RSW) due to modification of Nd by (A) SO4 and OMF, (B) OMF alone, and (C) SO4 alone. (D) The annual mean contribution from OMF, SO4, and the combination of OMF and SO4. In (A) to (C), filled contours show the analytical estimate of the change in RSW based on observed cloud properties, and dashed white lines show the change in RSW calculated by an offline radiative transfer model using simulated cloud properties. Contour intervals are 1 W m−2 for both. Hatched white lines show the observed zonal mean sea ice extent (in percent). (D) Annual mean changes in RSW from the analytical estimate (solid lines) and the offline radiative transfer model (crosses).

  • Table 1 The variability in Nd explained by each predictor.

    R2 is given in percent for both unweighted calculations and a robust calculation using bi-square weights, using a bias-corrected Nd data set. Spatial R2 values are calculated by applying the same regression procedure and using bi-square weights, but using time-averaged values of bias-corrected Nd for the period September through April. Additional notes following asterisks give the R2 values calculated using a 9-year monthly climatology of MODIS Nd including retrievals at all SZAs. Uncertainties are the SDs of estimates of R2 from 10-fold cross-validation, repeated 10 times.

    R2 (%)R2 (robust, %)Spatial R2
    OMF24 ± 535 ± 10*(14 ± 4, 16 ± 4)33 ± 22
    SO445 ± 748 ± 7*(20 ± 6, 23 ± 7)48 ± 24
    OMF + SO449 ± 553 ± 6*(22 ± 6, 28 ± 7)53 ± 22
    OMF + SO4 + SS + wind49 ± 653 ± 557 ± 20
    OMF + SO4 + SS49 ± 653 ± 758 ± 22
    SS11 ± 49 ± 416 ± 17
    SST4 ± 33 ± 213 ± 13
    Wind14 ± 413 ± 318 ± 18
    SS + wind14 ± 413 ± 414 ± 13
    SST + wind + SS16 ± 517 ± 416 ± 13
    Chl-a20 ± 735 ± 1355 ± 24
    DMS15 ± 619 ± 612 ± 11

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/1/6/e1500157/DC1

    Fig. S1. The AeroCom scenario B and PRE SO4 column density (black) across eight models (MPI-HAM excluded due to unavailability of SO4 column density) and difference in SO4 column density (μg/m2) between preindustrial and present day (red).

    Fig. S2. The AeroCom scenario B and PRE SO4 surface concentration from LSCE, GISS, LOA, and UIO-GCM.

    Fig. S3. The fractional change in SO4 surface concentration between the present-day and preindustrial period from AeroCom scenario B and PRE SO4 surface concentration from LSCE, GISS, LOA, and UIO-GCM.

    Fig. S4. Tenfold cross-validation performed using only POP/BEC-modeled chlorophyll-consistent quantities.

    Fig. S5. Contribution of the labile “lipid-like” group and total contribution of the semi-labile “polysaccharide-like” and “protein-like” groups to the total Nd from the regression model.

    Fig. S6. The 10-fold cross-validation repeated using wind speed from the Multi-Platform Ocean Surface Wind Velocity L3.5, SST from the NOAA OI data product, SS aerosol concentration from AeroCom, and DMS (50).

    Fig. S7. Scatter plot of lipid-like OMF in SSA as calculated by B14 as a function of POP chlorophyll.

    Fig. S8. Tenfold cross-validation performed using only SeaWiFS consistent Chl-a.

    Fig. S9. Tenfold cross-validation repeated using wind and SS concentration in addition to OMB14 and SO4 concentration.

    Fig. S10. As in fig. S1, but averaged over 5° latitude × 5° longitude bins.

    Table S1. Correlation coefficients in space and time using labile and semi-labile fields from B14.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. The AeroCom scenario B and PRE SO4 column density (black) across eight models (MPI-HAM excluded due to unavailability of SO4 column density) and difference in SO4 column density (μg/m2) between preindustrial and present day (red).
    • Fig. S2. The AeroCom scenario B and PRE SO4 surface concentration from LSCE, GISS, LOA, and UIO-GCM.
    • Fig. S3. The fractional change in SO4 surface concentration between the presentday and preindustrial period from AeroCom scenario B and PRE SO4 surface concentration from LSCE, GISS, LOA, and UIO-GCM.
    • Fig. S4. Tenfold cross-validation performed using only POP/BEC-modeled chlorophyll-consistent quantities.
    • Fig. S5. Contribution of the labile “lipid-like” group and total contribution of the semi-labile “polysaccharide-like” and “protein-like” groups to the total Nd from the regression model.
    • Fig. S6. The 10-fold cross-validation repeated using wind speed from the Multi-Platform Ocean Surface Wind Velocity L3.5, SST from the NOAA OI data product, SS aerosol concentration from AeroCom, and DMS (50).
    • Fig. S7. Scatter plot of lipid-like OMF in SSA as calculated by B14 as a function of POP chlorophyll.
    • Fig. S8. Tenfold cross-validation performed using only SeaWiFS consistent Chla.
    • Fig. S9. Tenfold cross-validation repeated using wind and SS concentration in addition to OMB14 and SO4 concentration.
    • Fig. S10. As in fig. S1, but averaged over 5° latitude × 5° longitude bins.
    • Table S1. Correlation coefficients in space and time using labile and semi-labile fields from B14.

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