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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 7, Iss. 11 — Oct. 31, 2012
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Particulate optical scattering coefficients along an Atlantic Meridional Transect

G. Dall’Olmo, E. Boss, M.J. Behrenfeld, and T.K. Westberry  »View Author Affiliations


Optics Express, Vol. 20, Issue 19, pp. 21532-21551 (2012)
http://dx.doi.org/10.1364/OE.20.021532


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Abstract

The particulate optical backscattering coefficient (bbp) is a fundamental optical property that allows monitoring of marine suspended particles both in situ and from space. Backscattering measurements in the open ocean are still scarce, however, especially in oligotrophic regions. Consequently, uncertainties remain in bbp parameterizations as well as in satellite estimates of bbp. In an effort to reduce these uncertainties, we present and analyze a dataset collected in surface waters during the 19th Atlantic Meridional Transect. Results show that the relationship between particulate beam-attenuation coefficient (cp) and chlorophyll-a concentration was consistent with published bio-optical models. In contrast, the particulate backscattering per unit of chlorophyll-a and per unit of cp were higher than in previous studies employing the same sampling methodology. These anomalies could be due to a bias smaller than the current uncertainties in bbp. If that was the case, then the AMT19 dataset would confirm that bbp:cp is remarkably constant over the surface open ocean. A second-order decoupling between bbp and cp was, however, evident in the spectral slopes of these coefficients, as well as during diel cycles. Overall, these results emphasize the current difficulties in obtaining accurate bbp measurements in the oligotrophic ocean and suggest that, to first order, bbp and cp are coupled in the surface open ocean, but they are also affected by other geographical and temporal variations.

© 2012 OSA

1. Introduction

The following inherent optical properties are typically employed to characterize the scattering of light by marine particles: the particulate backscattering, scattering, and beam-attenuation coefficients (bbp, bp, and cp, respectively). Note that bp and cp are approximately equal in clear waters, due to the low influence of particulate absorption (ap) on cp [7

7. H. Loisel and A. Morel, “Light scattering and chlorophyll concentration in case 1 waters: A reexamination,” Limnol. Oceanogr. 43, 847–858 (1998). [CrossRef]

, 8

8. T. K. Westberry, G. Dall’Olmo, E. Boss, M. J. Behrenfeld, and T. Moutin, “Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments,” Opt. Express 18, 15419–15425 (2010). [CrossRef]

].

The particulate optical scattering is thought to be generated by particles in the phytoplankton size range (0.5–20 μm, e.g., ref. [14

14. H. Pak, D. A. Kiefer, and J. C. Kitchen, “Meridional variations in the concentration of chlorophyll and microparticles in the north Pacific ocean,” Deep Sea Res. Part A 35, 1151–1171 (1988). [CrossRef]

, 15

15. D. Stramski and D. Kiefer, “Light scattering by microorganisms in the open ocean,” Progr. Oceanogr. 28, 343–383 (1991). [CrossRef]

]). The particle size domain responsible for bbp, on the other hand, remains uncertain. Some studies suggest that bbp is governed by submicron detrital particles and minerals [15

15. D. Stramski and D. Kiefer, “Light scattering by microorganisms in the open ocean,” Progr. Oceanogr. 28, 343–383 (1991). [CrossRef]

18

18. D. Stramski, A. Bricaud, and A. Morel, “Modeling the inherent optical properties of the ocean based on the detailed composition of the planktonic community,” Appl. Opt. 40, 2929–2945 (2001). [CrossRef]

], while others have shown the importance of larger particles (including phytoplankton cells) in generating the observed bbp variability [12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

, 19

19. M. S. Quinby-Hunt, A. J. Hunt, K. Lofftus, and D. Shapiro, “Polarized-light scattering studies of marine chlorella,” Limnol. Oceanogr. 34, 1587–1600 (1989). [CrossRef]

, 20

20. J. C. Kitchen and J. R. V. Zaneveld, “A three-layered sphere model of the optical properties of phytoplankton,” Limnol. Oceanogr. 37, 1680–1690 (1992). [CrossRef]

].

The particulate backscattering efficiency (bbp:bp) is less dependent on the absolute concentration of particles than either property alone and is largely governed by particle size, composition, and morphology [22

22. O. Ulloa, S. Sathyendranath, and T. Platt, “Effect of the particle-size distribution on the backscattering ratio in seawater,” Appl. Opt. 33, 7070–7077 (1994). [CrossRef]

, 23

23. M. S. Twardowski, E. Boss, J. B. Macdonald, W. S. Pegau, A. H. Barnard, and J. R. V. Zaneveld, “A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters,” J. Geophys. Res.-Oceans 106, 14129–14142 (2001). [CrossRef]

]. Highly refractive particles, such as the minerals contained in atmospheric dust or coccolithophorids, are expected to be associated with high bbp:bp [22

22. O. Ulloa, S. Sathyendranath, and T. Platt, “Effect of the particle-size distribution on the backscattering ratio in seawater,” Appl. Opt. 33, 7070–7077 (1994). [CrossRef]

24

24. A. L. Whitmire, E. Boss, T. J. Cowles, and W. S. Pegau, “Spectral variability of the particulate backscattering ratio,” Opt. Express 15, 7019–7031 (2007). [CrossRef]

]. On the other hand, bbp:bp for a given particle composition should, to first order, increase when small particles become relatively more abundant than large particles [22

22. O. Ulloa, S. Sathyendranath, and T. Platt, “Effect of the particle-size distribution on the backscattering ratio in seawater,” Appl. Opt. 33, 7070–7077 (1994). [CrossRef]

,23

23. M. S. Twardowski, E. Boss, J. B. Macdonald, W. S. Pegau, A. H. Barnard, and J. R. V. Zaneveld, “A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters,” J. Geophys. Res.-Oceans 106, 14129–14142 (2001). [CrossRef]

]. However, the uncertainties on the sources of bp and bbp as well as potential limitations in current optical models of oceanic particles may limit the interpretation of the bbp:bp ratio.

Experimental studies have demonstrated a relatively large range of variation for bbp:bp (i.e., approximately 0.2%–2%, refs. [8

8. T. K. Westberry, G. Dall’Olmo, E. Boss, M. J. Behrenfeld, and T. Moutin, “Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments,” Opt. Express 18, 15419–15425 (2010). [CrossRef]

, 12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

, 13

13. D. Antoine, D. A. Siegel, T. Kostadinov, S. Maritorena, N. B. Nelson, B. Gentili, V. Vellucci, and N. Guillocheau, “Variability in optical particle backscattering in contrasting bio-optical oceanic regimes,” Limnol. Oceanogr. 56, 955–973 (2011). [CrossRef]

, 24

24. A. L. Whitmire, E. Boss, T. J. Cowles, and W. S. Pegau, “Spectral variability of the particulate backscattering ratio,” Opt. Express 15, 7019–7031 (2007). [CrossRef]

26

26. D. Stramski, “Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern south Pacific and eastern Atlantic oceans (vol 5 pg 171, 2008),” Biogeosci. 5, 595–595 (2008). [CrossRef]

]), in part due to difficulties in accurately determining bbp in clear oligotrophic waters. Nevertheless, a surprisingly coherent value for bbp:bp of around 1% was recently reported in diverse surface open-ocean waters, using data from a series of cruises where scattering measurements were conducted using a consistent methodology [8

8. T. K. Westberry, G. Dall’Olmo, E. Boss, M. J. Behrenfeld, and T. Moutin, “Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments,” Opt. Express 18, 15419–15425 (2010). [CrossRef]

]. Although other studies have also measured similar backscattering ratios [24

24. A. L. Whitmire, E. Boss, T. J. Cowles, and W. S. Pegau, “Spectral variability of the particulate backscattering ratio,” Opt. Express 15, 7019–7031 (2007). [CrossRef]

], this finding suggests that methodology may be an important source of uncertainty in bbp and bp measurements, especially in clear oligotrophic waters.

Loisel et al. (2006) inverted space-based ocean color measurements to estimate the spectral slope of bbp [32

32. H. Loisel, J. M. Nicolas, A. Sciandra, D. Stramski, and A. Poteau, “Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean,” J. Geophys. Res.-Oceans 111, C09024 (2006). [CrossRef]

]. These authors found that γbbp exhibited positive values (relatively more small particles) in oligotrophic waters and negative values (relatively more large particles) in more eutrophic waters. Building on this insight, Kostadinov and collaborators [33

33. T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Retrieval of the particle size distribution from satellite ocean color observations,” J. Geophys. Res.-Oceans 114, C09015 (2009). [CrossRef]

] developed an inversion algorithm to relate γbbp to the slope of the particle size distribution and produced global maps of the abundances of three different size classes of phytoplankton [34

34. T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Global variability of phytoplankton functional types from space: assessment via the particle size distribution,” Biogeosci. 7, 3239–3257 (2010). [CrossRef]

].

2. Methods

Data were collected on board the RRS James Cook from October 13th to December 1st 2009 covering a meridional transect approximately from 45°N to 40°S (Fig. 1).

Fig. 1 Locations of CTD casts during AMT19 indicating the cruise track. Background colors represent the bathimetry.

2.1. Flow through optical measurements

Optical measurements were conducted on seawater from the ship’s clean flow-through system pumped from a depth of about 5 m. The methodology described in ref. [12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

] was followed, including one-minute data binning and the use of a 0.2μm-cartridge filter (Cole Parmer) through which the water supply was diverted every hour for ten minutes to provide a baseline for particulate absorption and attenuation measurements.

2.1.1. bbp

Table 1. Scaling factors for the BB349 and BB499 meters. “Final values” are those used to process the data presented in this study. Absolute uncertainties are standard error of the means of the AMT19 determinations. All values, except relative uncertainties, are in units of sr−1 counts−1 × 10−6. Number in parentheses are percent changes from the WetLabs (07/2009) calibration.

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Three dark counts determinations were carried out during the cruise (yeardays 293, 313, and 326) by covering the detectors with black tape and submerging the instrument in water. Results from these independent replicate measurements indicated that the dark counts varied at most by 2 counts in the blue and green channels, but as much as 6 counts in the red channel (Table 2). In addition, the dark counts determined during the cruise differed considerably from those provided by the manufacturer.

Table 2. Dark counts provided by manufacturer and measured during AMT19. Uncertainties represent half the central 68th percentile of the measurements conducted during AMT19. All units are counts.

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Average scaling factors, S, and dark counts, D, measured during the cruise were employed in the processing of the BB349 data. The contribution to bbp by reflections from the internal walls of the flow-through chamber, bb,wall, was determined in the laboratory (following the procedure described in ref. [12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

]) after the cruise and found to be (3.70 ± 0.83) × 10−4 m−1 and (3.14 ± 0.52) × 10−4 m−1 for the blue and green channels, respectively. Calculation of the particulate backscattering coefficient was conducted as in ref. [12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

], after subtracting the contribution of pure sea water, βsw (differences in temperature and salinity were accounted for using data from the ship’s underway CTD system; [36

36. X. Zhang, L. Hu, and M.-X. He, “Scattering by pure seawater: Effect of salinity,” Opt. Express 17, 5698–5710 (2009). [CrossRef]

,37

37. X. Zhang and L. Hu, “Estimating scattering of pure water from density fluctuation of the refractive index,” Opt. Express 17, 1671–1678 (2009). [CrossRef]

]) and using a χp factor of 1.1 to relate the volume scattering function at 117° to bbp [38

38. E. Boss and W. S. Pegau, “Relationship of light scattering at an angle in the backward direction to the backscattering coefficient,” Appl. Opt. 40, 5503–5507 (2001). [CrossRef]

]:
bbp=2πχp[S(CD)βsw]bb,wall
(3)
where C are the digital counts recorded by the instrument. Finally, the particulate bbp of water that passed through a 0.2 μm filter (hereafter bb02) was estimated from the BB349 measurements conducted each hour on the 0.2μm-filtered seawater (details of these calculations can be found in reference [12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

]).

To understand how the uncertainty in dark counts affects the resulting bbp values, the entire BB349 dataset was reprocessed using the dark counts supplied by the manufacturer. Resulting bbp values were then compared to those derived using dark counts measured during the cruise. Relative differences ranged from 5% to 25% (not shown), with largest values in the clearest waters. This analysis demonstrates the importance of employing field-based determinations of dark counts for achieving maximally accurate bbp estimates in oligotrophic regions [25

25. M. S. Twardowski, H. Claustre, S. A. Freeman, D. Stramski, and Y. Huot, “Optical backscattering properties of the “clearest” natural waters,” Biogeosci. 4, 1041–1058 (2007). [CrossRef]

].

The combined uncertainty in bbp due to the uncertainties in all its input parameters (Tables 3, 4) was computed using the standard law of propagation of uncertainty [39

39. BIPM and ISO, Guide to the Expression of Uncertainty in Measurement (International Organization for Standardization, Geneve, Switzerland, 1995).

] and assuming uncorrelated uncertainties. The median absolute uncertainties were 3.3 × 10−4 m−1 and 2.5 × 10−4 m−1, for the blue and green channels, respectively. Relative uncertainties in bbp ranged approximately from 20% in eutrophic waters to 40% in oligotrophic waters.

Table 3. Uncertainties used to compute the combined experimental uncertainty of bbp as a function of wavelength. Units of absolute uncertainties are the same as those reported in the text for the corresponding variables.

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Table 4. Uncertainty budget for bbp based on the values presented in Table 3 and on all backscattering measurements collected during the cruise in flow-through mode (BB349). Numbers represent the median values of the squared percent contributions, σrel2 (unitless), and the median absolute contributions, σ (m−1), by each input variable to the combined experimental uncertainty in bbp as a function of wavelength.

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Given that bbp was accurately determined only at two wavelengths, γbbp was derived as γbbp = −log[bbp(470)/bbp(526)]/log(470/526). These γbbp values should be treated with caution, as they may be affected by uncertainties due to the limited number of wavelengths employed in the computation.

2.1.2. cp, ap, and bp

Spectrally-resolved particulate beam-attenuation, cp, and absorption, ap, measurements were conducted with WET Labs ACs (hyperspectral between 400 and 750 nm, with a spectral resolution of 5 nm and a band pass of 15 nm) and AC9 (nine wavelengths between 412 and 715 nm, with a band pass of 10 nm) absorption and attenuation meters. The ACs was used from the beginning of the cruise, but after 13 days (i.e., yearday 299) the lamp of the attenuation channel (i.e., C-channel) failed. To ensure continuous measurements of spectral cp after the ACs failure, the AC9 meter was added to the flow-through system, without removing the ACs. cp and ap for the ACs were computed after subtraction of baseline signals derived from the 0.2μm-filtered water following established protocols [12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

, 40

40. W. H. Slade, E. Boss, G. Dall’Olmo, M. R. Langner, J. Loftin, M. J. Behrenfeld, C. Roesler, and T. K. Westberry, “Underway and moored methods for improving accuracy in measurement of spectral particulate absorption and attenuation,” J. Atmos. Ocean. Tech. 27, 1733–1746 (2010). [CrossRef]

]. ap for the AC9 meter was computed by subtracting the 0.2μm-filtered signal and applying a scattering correction [41

41. J. R. V. Zaneveld, J. C. Kitchen, and C. C. Moore, “Scattering error correction of reflecting tube absorption meters,” in Ocean Optics XII2258, S. Ackelson, ed. (SPIE, 1994), 44–55.

]. Particulate scattering coefficients, bp, were derived as the difference between cp and ap. AC9 measurements were linearly interpolated to derive cp and bp at 470 and 526 nm. The spectral slope of cp, γcp, was estimated by fitting Eq. (1) to cp spectra.

2.1.3. Chl

Discrete water samples (2–4 liters) were collected from the flow-through system during the cruise, filtered onto Whatman GF/F filters and immediately stored in liquid nitrogen. Phytoplankton pigments were determined in the laboratory after the cruise by high performance liquid chromatography (HPLC) analysis [42

42. L. Van Heukelem and C. S. Thomas, “Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments,” J. Chromatogr. A 910, 31–49 (2001). [CrossRef]

]. Total chlorophyll-a concentration (TChl-a) was calculated by summing the contributions of monovinylchl-a, divinyl-chl-a (DivChl-a), and chloro-phyllide a.

The concentration of chlorophyll-a (Chl) was also estimated from the ap line height around 676 nm as: Chl = [ap(676) – 39/65ap(650) – 26/65ap(715)]/0.014, ref. [43

43. E. S. Boss, R. Collier, G. Larson, K. Fennel, and W. S. Pegau, “Measurements of spectral optical properties and their relation to biogeochemical variables and processes in crater lake, Crater Lake National Park, OR,” Hydrobiologia 574, 149–159 (2007). [CrossRef]

]. While the AC9 instrument outputs measurements at 650, 676, and 715 nm, the ACs does not. Therefore, the ACs data were linearly interpolated to estimate ap values at 650, 676, and 714 nm.

As mentioned above, two different instruments (i.e., ACs and AC9) were employed to measure ap and estimate Chl during the cruise. Therefore, an intercalibration was required to make Chl estimated from the ACs ap data comparable to the Chl derived from the AC9 ap data. Simultaneous measurements of ap by the ACs and AC9 instruments were, however, available only after the failure of the ACs C-channel, used for the scattering correction required to derive the ap data. Thus, although simultaneous measurements of ap from the two instruments were available, the ap spectra derived from the ACs instrument could not be scattering corrected and thus were, in principle, not comparable to the AC9 ap spectra. The sensitivity of the ap-based Chl on the scattering correction was therefore investigated by exploiting the ACs ap measurements collected at the beginning of the cruise, when the ACs C-channel was still functioning. The first 13 days of ACs ap data were reprocessed without applying any scattering and residual temperature corrections and Chl was derived from these newly processed ap spectra. The ratio between Chl derived from the ACs with and without the scattering correction was statistically indistinguishable from one (median ± σ68 = 1.03 ± 0.08, where σ68 is half the central 68th percentile range and is equivalent to one standard deviation, if the data are normally distributed). This result indicates that Chl can be computed from the ACs ap spectra, even if a scattering correction is not implemented. We therefore compared the Chl values estimated from the simultaneous AC9 and ACs ap data collected after the failure of the ACs C-lamp, even though no scattering correction could be applied to the ACs ap data. The ratio of these ACs-to-AC9 Chl was found to be 0.69± 0.08 (median ± σ68), indicating that the Chl derived from the ACs was about 30% lower than that derived from the AC9. This difference is likely due to the “band pass” of the AC9 that is narrower (10 nm) than that of the ACs (15 nm). The median ACs-to-AC9 Chl ratio was finally employed to correct the Chl derived from the AC9 data.

Figure 2 compares coincident HPLC-derived TChl-a and optically-determined Chl (derived from both AC9 and ACs instruments) and shows that the optically-determined Chl was underestimated by about 10% (in median) and had a precision (σ68 of relative residuals) of about 10%. The 10% bias was removed from Chl for the rest of the analysis.

Fig. 2 Comparison between Chl estimated from the AC9 and ACs instruments and HPLC derived TChl. The dashed line is the 1:1 line. N = 109. δr and σr are robust estimates of relative bias (median of the relative residuals) and precision (σ68 of the relative residuals), respectively.

2.2. Measurements of bbp from profiling package

An independent WetLabs ECO-BB3 backscattering meter (S/N 499, hereafter BB499) was installed on a profiling package that was deployed daily. The instrument was equipped with spectral channels at 470, 526, and 595 nm.

A calibration based on beads was not completed for this instrument at the beginning of the cruise. However, an intercalibration between flow-through and profiling meters was conducted at the beginning of the cruise (Oct. 16th–18th, 2009). This inter comparison consisted of collecting coincident data with the two meters by temporarily installing the profiling meter in a flow-through chamber similar to that where the flow-through meter was installed. Scaling factors, S499i, for the profiling BB499 meter were derived as S499i=S349(C499iD499)/(C349iD349), where each variable depends on wavelength, S349 is the scaling factor derived for the flow-through instrument as the average of the two cruise calibrations, C349i and C499i are the coincident raw counts recorded by the two instruments, D349 and D499 are the corresponding dark counts, and the 〈〉 brackets indicate that the median value was taken. In addition, a standard calibration was completed towards the end of the cruise using a dilution series of NIST-traceable 2-μm polystyrene beads, Thermo Scientific). The relative differences between the scaling factors derived from the intercalibration and standard calibration and those provided by the factory are presented in Table 1. Dark counts were also measured during the cruise and showed significant deviations from those provided by the manufacturer (Table 2). However, these D499 value were not determined with the BB499 instrument installed on the profiling package and thus could be biased. Data from the 595-nm channel were excluded from the rest of the analysis because of the unexplained large change (23 counts) in its dark counts. The bbp values and corresponding uncertainties derived from the profiling instrument (BB499) were computed following the same methodology employed for the flow-through instrument (BB349) (for in-water measurements no correction for wall effects was needed).

Median bbp data were extracted from each upcast profile between 4 and 10 meters to match the depth of the ship’s water intake. A comparison of coincident bbp values determined by the two independently-calibrated and deployed instruments indicated biases of −16% and −13% and precisions of 7% and 6% for the 470 and 526 nm channels, respectively (Fig. 3). The above biases may be due to uncertainties in dark counts, that were determined with the instrument connected to an electrical system (i.e., power supply and cables) different from the one used to collect measurements.

Fig. 3 Comparison between bbp measurements collected at two different wavelengths by the instruments mounted on the flow-through and profiling systems. Error bars represent the combined uncertainties in the bbp estimates. Dashed lines are the 1:1 lines. Note the logarithmic scales on all axes. δ and δr are robust estimates of the absolute and relative bias, respectively. σ and σr are estimates (σ68 of residuals) of the absolute and relative precision, respectively. N = 24.

3. Results

Fig. 4 Time series of Chl, bbp(526) and bp(526), sea surface temperature (SST), salinity (from which a constant value of 10 psu was subtracted for plotting purposes, SSS-10, psu) and potential density anomaly (σθ, kg m−3).
Fig. 5 Bivariate histograms showing the relationships between bbp(526) and bp(526) versus Chl. Solid and dashed lines are the models presented in refs. [12] and [10], respectively. The inset presents the bbp(526) vs. Chl bivariate histogram, but in linear scale (bbp is multiplied by 1000). The colorbar identifies the number of data points in each bivariate bin.

The particulate backscattering ratio, bbp:bp, varied latitudinally, with maximum values (∼ 0.02) in oligotrophic regions and minimum values in the eutrophic waters (< 0.01, Fig. 6(a)). An important fraction of this variability (∼ 30%) appeared to be due to variations occurring at the diel scale. The chlorophyll-specific particulate scattering and backscattering coefficients ( bp* and bbp*, respectively) varied by over an order of magnitude along the transect with maximum values in the most oligotrophic regions and also showed strong diel cycles (Fig. 6(b)). Finally, the spectral slopes of bbp and cp (γbbp and γcp, respectively) were inversely correlated, with γcp displaying higher values in productive regions and lower values in the most oligotrophic waters (Fig. 6(c)). Strong variations of γcp were observed at the end of the transect in the most productive region sampled. While γcp appeared to be affected by diel cycles, γbbp did not.

Fig. 6 Time series of bbp:bp at 526 nm, chl-specific bp(526) and bbp(526) ( bp* and bbp*, respectively; m2 mg−1) and the spectral slopes of cp (γcp) and bbp (γbbp).

Particulate absorption contributed, as expected, only a relatively small fraction of cp (data not shown): the largest values of ap:cp were found at 440 nm (8 – 16%, 95th percentile range), while minimum values were found for wavelengths smaller than 532 nm (2 – 6%). The ratio of particulate backscattering to beam-attenuation at 526 nm, bbp:cp, ranged from less than 0.002 to 0.020, with a median value (± σ68) of 0.0130 ± 0.0032 (Fig. 7, solid line). bbp:cp values measured during AMT19 were skewed towards higher values than those of previously published datasets [8

8. T. K. Westberry, G. Dall’Olmo, E. Boss, M. J. Behrenfeld, and T. Moutin, “Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments,” Opt. Express 18, 15419–15425 (2010). [CrossRef]

].

Fig. 7 Normalized frequency distribution of bbp:cp measured during AMT19 (solid line) and of (bbpbb02):cp (dashed line). All measurements are at 526 nm. The shaded area represents the mean value of the normalized frequency distributions of the datasets presented in ref. [8].

Fig. 8 Time series of (a) bb02 with error bars indicating 95% confidence intervals and (b) bb02:bbp with error bars representing standard errors plotted at discrete locations for clarity. Black dashed and red solid vertical lines indicate times when the flow-through system was cleaned and when the 0.2μm filter was replaced, respectively.

4. Discussion

4.1. bbp:Chl, bp:Chl

4.2. bbp:cp

4.3. bb02

The backscattering measured on sample water that passed through the 0.2-μm filter was found to be higher than zero, although not significantly at the 95% confidence level, and contributed about 10% of the bbp signal at 526 nm (Fig. 8). Previously, bb02(526) was found to be indistinguishable from zero (at the 95% confidence level) in the Equatorial Pacific [12

12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

] and in the Mediterranean Sea (Dall’Olmo G., unpublished data), or significantly higher than zero, but negligible with respect to the bulk bbp, in the North Atlantic and North Pacific (Westberry T.K., unpublished data).

Another potential explanation for the relatively high values of bb02 could be the presence of fine Saharan dust particles in the water. Dust deposition, however, is estimated to be one order of magnitude more intense in the northern than in the southern Atlantic (e.g., ref. [54

54. Y. P. Shao, K. H. Wyrwoll, A. Chappell, J. P. Huang, Z. H. Lin, G. H. McTainsh, M. Mikami, T. Y. Tanaka, X. L. Wang, and S. Yoon, “Dust cycle: An emerging core theme in earth system science,” Aeolian Res. 2, 181–204 (2011). [CrossRef]

]). If atmospheric dust was responsible for the relatively elevated bb02 values, one would expect bb02 to be higher in the north than in the south Atlantic. Figure 8, however, does not show important differences in bb02 values between the northern and southern parts of the transect, suggesting that atmospheric dust is unlikely to be the cause of the elevated bb02.

4.4. Uncertainties in bbp and bb02

Overall our results and the above considerations emphasize the difficulties in obtaining accurate particulate backscattering measurements in the open ocean with the current instrumentation. This limitation is likely biasing the development and validation of open-ocean remote-sensing algorithms and it is hampering progress in the understanding of the sources of bbp and in the application of bbp measurements to the study of ocean biology and biogeochemistry.

4.5. Spectral slopes of cp and bbp

Fig. 9 Subset of time series showing diel cycles in each variable. Filled circles in the bottom plot are TChl-a estimates from discrete HPLC measurements. Values for γbbp were median filtered (window size of 120 minutes) to remove noise.

During AMT19, bbp spectra followed expected patterns [32

32. H. Loisel, J. M. Nicolas, A. Sciandra, D. Stramski, and A. Poteau, “Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean,” J. Geophys. Res.-Oceans 111, C09024 (2006). [CrossRef]

, 33

33. T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Retrieval of the particle size distribution from satellite ocean color observations,” J. Geophys. Res.-Oceans 114, C09015 (2009). [CrossRef]

]: γbbp increased from the most eutrophic regions, where large cells are abundant, to the most oligotrophic regions that are dominated by small cells (Fig. 6). In contrast, γcp was maximal in eutrophic regions and minimal in oligotrophic waters (Fig. 6). To account for this anomalous behavior in γcp, one or more of the above hypotheses must be invalid. Indeed, deviations from the power law approximation of the PSD are common in the open ocean, both in oligotrophic [32

32. H. Loisel, J. M. Nicolas, A. Sciandra, D. Stramski, and A. Poteau, “Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean,” J. Geophys. Res.-Oceans 111, C09024 (2006). [CrossRef]

] and productive regions and have been shown to cause significant perturbations to γcp in coastal waters [56

56. R. Astoreca, D. Doxaran, K. Ruddick, V. Rousseau, and C. Lancelot, “Influence of suspended particle concentration, composition and size on the variability of inherent optical properties of the southern North Sea,” Cont. Shelf Res. 35, 117–128 (2012). [CrossRef]

]. In addition, the acceptance angles of the AC9 and ACs transmissometers (0.93°) are known to reduce the instrument sensitivity to particles larger than about 10–20 μm [31

31. E. Boss, W. H. Slade, M. Behrenfeld, and G. Dall’Olmo, “Acceptance angle effects on the beam attenuation in the ocean,” Opt. Express 17, 1535–1550 (2009). [CrossRef]

].

During diel cycles, on the other hand, γcp followed expected dynamics, increasing during the day and decreasing at night (Figs. 6 and 9). These patterns have been previously observed [40

40. W. H. Slade, E. Boss, G. Dall’Olmo, M. R. Langner, J. Loftin, M. J. Behrenfeld, C. Roesler, and T. K. Westberry, “Underway and moored methods for improving accuracy in measurement of spectral particulate absorption and attenuation,” J. Atmos. Ocean. Tech. 27, 1733–1746 (2010). [CrossRef]

,57

57. K. Oubelkheir and A. Sciandra, “Diel variations in particle stocks in the oligotrophic waters of the Ionian Sea (Mediterranean),” J. Mar. Syst. 74, 364–371 (2008). [CrossRef]

,58

58. G. Dall’Olmo, E. Boss, M. J. Behrenfeld, T. K. Westberry, C. Courties, L. Prieur, M. Pujo-Pay, N. Hardman-Mountford, and T. Moutin, “Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient,” Biogeosci. 8, 3423–3439 (2011). [CrossRef]

] and are thought to be caused by the increase in size of synchronous cell populations during the day and their decrease at night following cell division [58

58. G. Dall’Olmo, E. Boss, M. J. Behrenfeld, T. K. Westberry, C. Courties, L. Prieur, M. Pujo-Pay, N. Hardman-Mountford, and T. Moutin, “Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient,” Biogeosci. 8, 3423–3439 (2011). [CrossRef]

, 59

59. M. D. DuRand and R. J. Olson, “Contributions of phytoplankton light scattering and cell concentration changes to diel variations in beam attenuation in the equatorial pacific from flow cytometric measurements of pico-, ultra-and nanoplankton,” Deep-Sea Res. Part II 43, 891–906 (1996). [CrossRef]

].

4.6. Diel variability

5. Conclusions

6. Appendix: Calibration of WET Labs ECO-BB3 meters

Acknowledgments

The authors would like to thank the captain and crew members of the RSS James Cook. C. Gallienne is thanked for his help in deploying the profiling package. J. Sullivan at WET Labs and an anonymous reviewer are thanked for their comments on an earlier draft of this manuscript. G.D.O. was funded by NASA grant NNX09AK30G and by the UK National Centre for Earth Observations. This study was supported by the UK Natural Environment Research Council National Capability funding to Plymouth Marine Laboratory and the National Oceanography Centre, Southampton. This is contribution number 218 of the AMT programme.

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E. Boss, D. Swift, L. Taylor, P. Brickley, R. Zaneveld, S. Riser, M. J. Perry, and P. G. Strutton, “Observations of pigment and particle distributions in the western north atlantic from an autonomous float and ocean color satellite,” Limnol. Oceanogr. 53, 2112–2122 (2008). [CrossRef]

4.

N. Briggs, M. J. Perry, I. Cetinic, C. Lee, E. D’Asaro, A. M. Gray, and E. Rehm, “High-resolution observations of aggregate flux during a sub-polar north atlantic spring bloom,” Deep Sea Res. Part I 58, 1031–1039 (2011). [CrossRef]

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8.

T. K. Westberry, G. Dall’Olmo, E. Boss, M. J. Behrenfeld, and T. Moutin, “Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments,” Opt. Express 18, 15419–15425 (2010). [CrossRef]

9.

A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters: a reappraisal,” J. Geophys. Res.-Oceans 106, 7163–7180 (2001). [CrossRef]

10.

Y. Huot, A. Morel, M. S. Twardowski, D. Stramski, and R. A. Reynolds, “Particle optical backscattering along a chlorophyll gradient in the upper layer of the eastern south pacific ocean,” Biogeosci. 5, 495–507 (2008). [CrossRef]

11.

M. J. Behrenfeld and E. Boss, “The beam attenuation to chlorophyll ratio: an optical index of phytoplankton physiology in the surface ocean?” Deep-Sea Res. Part I 50, 1537–1549 (2003). [CrossRef]

12.

G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci. 6, 947–967 (2009). [CrossRef]

13.

D. Antoine, D. A. Siegel, T. Kostadinov, S. Maritorena, N. B. Nelson, B. Gentili, V. Vellucci, and N. Guillocheau, “Variability in optical particle backscattering in contrasting bio-optical oceanic regimes,” Limnol. Oceanogr. 56, 955–973 (2011). [CrossRef]

14.

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15.

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16.

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17.

Y. Ahn, A. Bricaud, and A. Morel, “Light backscattering efficiency and related properties of some phytoplankters,” Deep-Sea Res. Part A 38, 1835–1855 (1992). [CrossRef]

18.

D. Stramski, A. Bricaud, and A. Morel, “Modeling the inherent optical properties of the ocean based on the detailed composition of the planktonic community,” Appl. Opt. 40, 2929–2945 (2001). [CrossRef]

19.

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J. C. Kitchen and J. R. V. Zaneveld, “A three-layered sphere model of the optical properties of phytoplankton,” Limnol. Oceanogr. 37, 1680–1690 (1992). [CrossRef]

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M. J. Behrenfeld, E. Boss, D. A. Siegel, and D. M. Shea, “Carbon-based ocean productivity and phytoplankton physiology from space,” Global Biogeochem. Cy. 19, 1–14, doi: [CrossRef] (2005).

22.

O. Ulloa, S. Sathyendranath, and T. Platt, “Effect of the particle-size distribution on the backscattering ratio in seawater,” Appl. Opt. 33, 7070–7077 (1994). [CrossRef]

23.

M. S. Twardowski, E. Boss, J. B. Macdonald, W. S. Pegau, A. H. Barnard, and J. R. V. Zaneveld, “A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters,” J. Geophys. Res.-Oceans 106, 14129–14142 (2001). [CrossRef]

24.

A. L. Whitmire, E. Boss, T. J. Cowles, and W. S. Pegau, “Spectral variability of the particulate backscattering ratio,” Opt. Express 15, 7019–7031 (2007). [CrossRef]

25.

M. S. Twardowski, H. Claustre, S. A. Freeman, D. Stramski, and Y. Huot, “Optical backscattering properties of the “clearest” natural waters,” Biogeosci. 4, 1041–1058 (2007). [CrossRef]

26.

D. Stramski, “Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern south Pacific and eastern Atlantic oceans (vol 5 pg 171, 2008),” Biogeosci. 5, 595–595 (2008). [CrossRef]

27.

F. Nencioli, G. Chang, M. Twardowski, and T. D. Dickey, “Optical characterization of an eddy-induced diatom bloom west of the island of Hawaii,” Biogeosci. 7, 151–162 (2010). [CrossRef]

28.

A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water),” J. Geophys. Res.-Oceans 93, 10749–10768 (1988). [CrossRef]

29.

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30.

E. Boss, M. S. Twardowski, and S. Herring, “Shape of the particulate beam attenuation spectrum and its inversion to obtain the shape of the particulate size distribution,” Appl. Opt. 40, 4885–4893 (2001). [CrossRef]

31.

E. Boss, W. H. Slade, M. Behrenfeld, and G. Dall’Olmo, “Acceptance angle effects on the beam attenuation in the ocean,” Opt. Express 17, 1535–1550 (2009). [CrossRef]

32.

H. Loisel, J. M. Nicolas, A. Sciandra, D. Stramski, and A. Poteau, “Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean,” J. Geophys. Res.-Oceans 111, C09024 (2006). [CrossRef]

33.

T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Retrieval of the particle size distribution from satellite ocean color observations,” J. Geophys. Res.-Oceans 114, C09015 (2009). [CrossRef]

34.

T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Global variability of phytoplankton functional types from space: assessment via the particle size distribution,” Biogeosci. 7, 3239–3257 (2010). [CrossRef]

35.

J.M. Sullivan, C.C. Moore, M.S. Twardowski, and J.R.V. Zaneveld, “Measuring optical backscattering in water” in “Light Scattering Reviews, Volume 7: Radiative transfer and optical properties of atmosphere and underlying surface” (Praxis Publishing Ltd, in press).

36.

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37.

X. Zhang and L. Hu, “Estimating scattering of pure water from density fluctuation of the refractive index,” Opt. Express 17, 1671–1678 (2009). [CrossRef]

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40.

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57.

K. Oubelkheir and A. Sciandra, “Diel variations in particle stocks in the oligotrophic waters of the Ionian Sea (Mediterranean),” J. Mar. Syst. 74, 364–371 (2008). [CrossRef]

58.

G. Dall’Olmo, E. Boss, M. J. Behrenfeld, T. K. Westberry, C. Courties, L. Prieur, M. Pujo-Pay, N. Hardman-Mountford, and T. Moutin, “Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient,” Biogeosci. 8, 3423–3439 (2011). [CrossRef]

59.

M. D. DuRand and R. J. Olson, “Contributions of phytoplankton light scattering and cell concentration changes to diel variations in beam attenuation in the equatorial pacific from flow cytometric measurements of pico-, ultra-and nanoplankton,” Deep-Sea Res. Part II 43, 891–906 (1996). [CrossRef]

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OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(010.4458) Atmospheric and oceanic optics : Oceanic scattering
(010.1350) Atmospheric and oceanic optics : Backscattering

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: June 20, 2012
Revised Manuscript: August 31, 2012
Manuscript Accepted: August 31, 2012
Published: September 5, 2012

Virtual Issues
Vol. 7, Iss. 11 Virtual Journal for Biomedical Optics

Citation
G. Dall’Olmo, E. Boss, M.J. Behrenfeld, and T.K. Westberry, "Particulate optical scattering coefficients along an Atlantic Meridional Transect," Opt. Express 20, 21532-21551 (2012)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-20-19-21532


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References

  1. IOCCG, Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications., Report Number 5 (International Ocean Colour Coordination Group, 2006).
  2. J. K. B. Bishop, “Autonomous observations of the ocean biological carbon pump,” Oceanography22, 182–193 (2009). [CrossRef]
  3. E. Boss, D. Swift, L. Taylor, P. Brickley, R. Zaneveld, S. Riser, M. J. Perry, and P. G. Strutton, “Observations of pigment and particle distributions in the western north atlantic from an autonomous float and ocean color satellite,” Limnol. Oceanogr.53, 2112–2122 (2008). [CrossRef]
  4. N. Briggs, M. J. Perry, I. Cetinic, C. Lee, E. D’Asaro, A. M. Gray, and E. Rehm, “High-resolution observations of aggregate flux during a sub-polar north atlantic spring bloom,” Deep Sea Res. Part I58, 1031–1039 (2011). [CrossRef]
  5. IOCCG, Bio-optical sensors on Argo floats, Report Number 11 (International Ocean Colour Coordination Group, 2011).
  6. H. C. van de Hulst, Light Scattering by Small Particles (Wiley, New York, 1957).
  7. H. Loisel and A. Morel, “Light scattering and chlorophyll concentration in case 1 waters: A reexamination,” Limnol. Oceanogr.43, 847–858 (1998). [CrossRef]
  8. T. K. Westberry, G. Dall’Olmo, E. Boss, M. J. Behrenfeld, and T. Moutin, “Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments,” Opt. Express18, 15419–15425 (2010). [CrossRef]
  9. A. Morel and S. Maritorena, “Bio-optical properties of oceanic waters: a reappraisal,” J. Geophys. Res.-Oceans106, 7163–7180 (2001). [CrossRef]
  10. Y. Huot, A. Morel, M. S. Twardowski, D. Stramski, and R. A. Reynolds, “Particle optical backscattering along a chlorophyll gradient in the upper layer of the eastern south pacific ocean,” Biogeosci.5, 495–507 (2008). [CrossRef]
  11. M. J. Behrenfeld and E. Boss, “The beam attenuation to chlorophyll ratio: an optical index of phytoplankton physiology in the surface ocean?” Deep-Sea Res. Part I50, 1537–1549 (2003). [CrossRef]
  12. G. Dall’Olmo, T. K. Westberry, M. J. Behrenfeld, E. Boss, and W. H. Slade, “Significant contribution of large particles to optical backscattering in the open ocean,” Biogeosci.6, 947–967 (2009). [CrossRef]
  13. D. Antoine, D. A. Siegel, T. Kostadinov, S. Maritorena, N. B. Nelson, B. Gentili, V. Vellucci, and N. Guillocheau, “Variability in optical particle backscattering in contrasting bio-optical oceanic regimes,” Limnol. Oceanogr.56, 955–973 (2011). [CrossRef]
  14. H. Pak, D. A. Kiefer, and J. C. Kitchen, “Meridional variations in the concentration of chlorophyll and microparticles in the north Pacific ocean,” Deep Sea Res. Part A35, 1151–1171 (1988). [CrossRef]
  15. D. Stramski and D. Kiefer, “Light scattering by microorganisms in the open ocean,” Progr. Oceanogr.28, 343–383 (1991). [CrossRef]
  16. A. Morel and Y. H. Ahn, “Optics of heterotrophic nanoflagellates and ciliates - a tentative assessment of their scattering role in oceanic waters compared to those of bacterial and algal cells,” J. Mar. Res.49, 177–202 (1991). [CrossRef]
  17. Y. Ahn, A. Bricaud, and A. Morel, “Light backscattering efficiency and related properties of some phytoplankters,” Deep-Sea Res. Part A38, 1835–1855 (1992). [CrossRef]
  18. D. Stramski, A. Bricaud, and A. Morel, “Modeling the inherent optical properties of the ocean based on the detailed composition of the planktonic community,” Appl. Opt.40, 2929–2945 (2001). [CrossRef]
  19. M. S. Quinby-Hunt, A. J. Hunt, K. Lofftus, and D. Shapiro, “Polarized-light scattering studies of marine chlorella,” Limnol. Oceanogr.34, 1587–1600 (1989). [CrossRef]
  20. J. C. Kitchen and J. R. V. Zaneveld, “A three-layered sphere model of the optical properties of phytoplankton,” Limnol. Oceanogr.37, 1680–1690 (1992). [CrossRef]
  21. M. J. Behrenfeld, E. Boss, D. A. Siegel, and D. M. Shea, “Carbon-based ocean productivity and phytoplankton physiology from space,” Global Biogeochem. Cy.19, 1–14, doi:(2005). [CrossRef]
  22. O. Ulloa, S. Sathyendranath, and T. Platt, “Effect of the particle-size distribution on the backscattering ratio in seawater,” Appl. Opt.33, 7070–7077 (1994). [CrossRef]
  23. M. S. Twardowski, E. Boss, J. B. Macdonald, W. S. Pegau, A. H. Barnard, and J. R. V. Zaneveld, “A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in case I and case II waters,” J. Geophys. Res.-Oceans106, 14129–14142 (2001). [CrossRef]
  24. A. L. Whitmire, E. Boss, T. J. Cowles, and W. S. Pegau, “Spectral variability of the particulate backscattering ratio,” Opt. Express15, 7019–7031 (2007). [CrossRef]
  25. M. S. Twardowski, H. Claustre, S. A. Freeman, D. Stramski, and Y. Huot, “Optical backscattering properties of the “clearest” natural waters,” Biogeosci.4, 1041–1058 (2007). [CrossRef]
  26. D. Stramski, “Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern south Pacific and eastern Atlantic oceans (vol 5 pg 171, 2008),” Biogeosci.5, 595–595 (2008). [CrossRef]
  27. F. Nencioli, G. Chang, M. Twardowski, and T. D. Dickey, “Optical characterization of an eddy-induced diatom bloom west of the island of Hawaii,” Biogeosci.7, 151–162 (2010). [CrossRef]
  28. A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water),” J. Geophys. Res.-Oceans93, 10749–10768 (1988). [CrossRef]
  29. A. Morel, Advisory Group for Aerospace Research and Development (NATO, 1973), chap. Diffusion de la lumiere par les eaux de mer. Resultat experimentaux et approach theorique, 3.1.1–76.
  30. E. Boss, M. S. Twardowski, and S. Herring, “Shape of the particulate beam attenuation spectrum and its inversion to obtain the shape of the particulate size distribution,” Appl. Opt.40, 4885–4893 (2001). [CrossRef]
  31. E. Boss, W. H. Slade, M. Behrenfeld, and G. Dall’Olmo, “Acceptance angle effects on the beam attenuation in the ocean,” Opt. Express17, 1535–1550 (2009). [CrossRef]
  32. H. Loisel, J. M. Nicolas, A. Sciandra, D. Stramski, and A. Poteau, “Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean,” J. Geophys. Res.-Oceans111, C09024 (2006). [CrossRef]
  33. T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Retrieval of the particle size distribution from satellite ocean color observations,” J. Geophys. Res.-Oceans114, C09015 (2009). [CrossRef]
  34. T. S. Kostadinov, D. A. Siegel, and S. Maritorena, “Global variability of phytoplankton functional types from space: assessment via the particle size distribution,” Biogeosci.7, 3239–3257 (2010). [CrossRef]
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