The use of equivalent size distributions of natural phytoplankton assemblages for optical modeling

doi:10.1364/OE.15.001995

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The use of equivalent size distributions of natural phytoplankton assemblages for optical modeling

S. Bernard, F. A. Shillington, and T. A. Probyn »View Author Affiliations

Optics Express, Vol. 15, Issue 5, pp. 1995-2007 (2007)
http://dx.doi.org/10.1364/OE.15.001995

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▸ Article Outline
– Abstract
1. Introduction
2. Methods
3. Results and discussion
4 Application and conclusions
– References and links

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Abstract

The effective cell size is expected to be one of the principal causes of variability in the inherent optical properties (IOPs) of a phytoplankton population. However, establishing simple size descriptors is complicated by the typically complex particle size distributions of natural phytoplankton assemblages. This study compares the use of measured and equivalent particle size distributions on the modeled IOPs of a wide range of natural phytoplankton assemblages. It demonstrates that several equivalent size distributions, using simple parameterizations of complex size distributions based on the effective radius or diameter, are capable of modeling phytoplankton IOPs with sufficient accuracy for further use in marine bio-optical models. The results offered here are expected to be of use in bio-optical studies of phytoplankton dynamics e.g. harmful algal bloom oriented inverse reflectance models.

1. Introduction

Phytoplankton size is of considerable importance to phycologists, exerting a strong influence upon metabolic rates and physiological behaviour [1

Z.V. Finkel and A.J. Irwin, “Modelling size-dependent photosynthesis: light absorption and the allometric rule,”. J. Theor. Biol. 204, 361–369 (2000) [CrossRef] [PubMed]

], algal optical properties [2

A. Morel and A. Bricaud, “Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton,” Deep-Sea Res. 28, 1375–1393 (1981). [CrossRef]

], algal biomass and carbon concentrations [3

D.J.S. Montagnes, J.A. Berges, P.J. Harrison, and F.J.R. Taylor, “Estimating carbon, nitrogen, protein, and chlorophyll a from cell volume in marine phytoplankton,” Limnol. Oceanogr. 39,1044–1060 (1994). [CrossRef]

], and physical behaviour [4

J. Rodriguez, J. Tintore, J.T. Allen, J.M. Blanco, D. Gomis, A. Reul, J. Ruiz, V. Rodriguez, F. Echevarria, and F. Jimenez-Gomez, “Mesoscale vertical motion and the size structure of phytoplankton in the ocean,” Nature 410, 360–363 (2001). [CrossRef] [PubMed]

]. There is thus a need to provide simple descriptors of algal size, and formulations for algal size distributions, that can account for both morphological variations and the polydispersity typically displayed by phytoplankton populations. Such schemes, by reducing algal size descriptors to one or more proxy parameters, can allow a greater understanding of cultured and natural algal populations from optical, physiological and ecological perspectives. In addition, size distribution formulae for marine particle populations allow the rapid and semi-continuous assessment of marine particle size variability through the application of inversion techniques to marine optical measurements [5

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]

A variety of distribution formulations have been employed to represent both phytoplankton and total marine particle distributions, typically from fitting such functions to measured size distribution data. Monospecific phytoplankton cultures display expectedly narrow size distributions, and can be represented by log-normal [6

A. Bricaud and A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25, 571–580 (1986). [CrossRef] [PubMed]

] and gamma [7

D. Risovic, “Two component model of sea particle size distribution,” Deep-Sea Res. 40, 1459–1473 (1993). [CrossRef]

] distributions. However, natural phytoplankton populations are rarely monospecific, and the use of log-normal or gamma distributions has been restricted to application with total marine particle distributions, either decomposition techniques to describe measured distributions [8

M. Jonasz and G. Fournier, “Approximation of the size distribution of marine particles by a sum of log-normal functions,” Limnol. Oceanogr. 41, 744–754 (1996). [CrossRef]

], or distribution functions suitable for oligotrophic waters [7

D. Risovic, “Two component model of sea particle size distribution,” Deep-Sea Res. 40, 1459–1473 (1993). [CrossRef]

]. The most commonly used distribution type for marine particulate, with specific regard to modeling optical properties, is the power law or Junge distribution [5

E.C. Junge, Air chemistry and radioactivity (Academic Press 1963), pp.382.

]. Particle size distributions in phytoplankton-dominated waters typically display complex shapes, with variable intermediate maxima associated with the presence of dominant phytoplankton groups or species [8

M. Jonasz and G. Fournier, “Approximation of the size distribution of marine particles by a sum of log-normal functions,” Limnol. Oceanogr. 41, 744–754 (1996). [CrossRef]

,10

R.W. Sheldon, A. Prakash, and W.H. Sutcliffe, Jr., “The size distribution of particles in the ocean,” Limnol. Oceanogr. 17, 327–340 (1972). [CrossRef]

]. Such complex size distributions deviate markedly from the smooth exponential representation of a Junge distribution [11

S. Bernard, T.A. Probyn, and R.G. Barlow, “Measured and modelled optical properties of particulate matter in the southern Benguela,” S. Afr. J. Sci. 97, 410–420 (2001).

]. Thus, whilst a Junge distribution offers a simple scheme conforming to general trophic and dynamic paradigms [10

R.W. Sheldon, A. Prakash, and W.H. Sutcliffe, Jr., “The size distribution of particles in the ocean,” Limnol. Oceanogr. 17, 327–340 (1972). [CrossRef]

], and allows robust application to optical inversion, there is some doubt as to whether it is the most suitable function to describe algal optical properties, particularly in productive waters [8

M. Jonasz and G. Fournier, “Approximation of the size distribution of marine particles by a sum of log-normal functions,” Limnol. Oceanogr. 41, 744–754 (1996). [CrossRef]

An alternative approach, from an optical perspective, to finding best-fit functions to measured size distribution data is to consider simply parameterised equivalent size distributions: formulations that, whilst dissimilar in distribution shape, can reproduce the optical properties of polydispersed particle populations. The approach has been successfully employed by atmospheric physicists to describe the optical properties of aerosol and cloud size distributions for the purposes of radiative transfer inversions [12–14

Y.X. Hu and K. Stamnes, “An accurate Parameterization of Cloud Radiative Properties Suitable for Climate Modeling,” J. Climate. 6, 728–742 (1993). [CrossRef]

]. Central to the concept of equivalent distributions is that dissimilar size distributions with identical moments display the same optical characteristics [12

Y.X. Hu and K. Stamnes, “An accurate Parameterization of Cloud Radiative Properties Suitable for Climate Modeling,” J. Climate. 6, 728–742 (1993). [CrossRef]

,14

R. McGraw, S. Nemesure, and S. E. Schwartz, “Properties and evolution of aerosols with size distributions having identical moments,” J. Aerosol. Sci. 29, 761–772 (1998). [CrossRef]

,15

J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]

], where the kth radial (or diametric) moment of a size distribution is given by:

{< r}^{k} > = \int_{0}^{\infty} r^{k} F (r) d (r)

(1)

where r is the particle radius (m), and F(r) d(r) is the number of particles per unit volume in the size range r ± 1/2d(r). The best single parameter describing the optical properties of a size distribution is the effective radius r_eff or diameter D_eff (r_eff = 0.5 D_eff ) – the ratio of the third to second moment (<r ³>/<r ²>), or the mean volume to surface area ratio of the distribution [15

J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]

]. Another parameter of importance is the effective variance V_eff [(<r ⁴><r ²>/<r ³>²)-1)], which describes the width of the distribution [15

J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]

This study seeks to establish whether a moment-based approach can be employed to define single parameter equivalent size distributions for the simulation of algal optical properties through a wide range of algal assemblage types. A functional size distribution form that is based upon optical equivalence rather than the replication of sometimes complex observed phytoplankton size distributions would have application in bio-optical and ocean colour modeling and inversion techniques. Such techniques are of particular relevance to the bio-optical monitoring and detection of harmful algal blooms.

2. Methods

Bio-optical measurements were employed in conjunction with Mie models to assess the ability of several common size distribution formulations to simulate the optical properties of algal populations calculated with measured size distributions. The following steps were taken:

1. Calculation of algal spectral refractive index data from absorption and size distribution measurements, in conjunction with an anomalous diffraction approximation model [6
A. Bricaud and A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25, 571–580 (1986). [CrossRef] [PubMed]
] and a refractive index dispersion model [11
S. Bernard, T.A. Probyn, and R.G. Barlow, “Measured and modelled optical properties of particulate matter in the southern Benguela,” S. Afr. J. Sci. 97, 410–420 (2001).
]. Principal measurements consisted of particulate absorption, particle size distributions and intra-cellular pigments, and were made on 34 surface samples in a variety of waters in the southern Benguela (Table 1).
2. Derived refractive index data were used to model a suite of algal inherent optical properties (IOPs), using both measured size distribution data and several equivalent size distribution formulations. Equivalent size distributions were scaled to have the same total projected area and effective diameters as those measured. IOPs include the absorption, attenuation, scattering, and backscattering coefficients, and the package effect parameter.
3. An assessment of the potential errors associated with use of the equivalent size distribution formulations for the suite of IOPs was performed.

The principal focus of this study is a comparison between two modeling approaches using different size data, and the same refractive index data, as input. It is not the purpose of this study to provide an entire optical modeling technique that could be used to simulate the absolute optical properties of natural phytoplankton assemblages. It should be realised that both the optical models used (simple, spherical, homogeneous geometry) and the input refractive index data (similar real refractive index) are somewhat simplistic, although adequate for the purpose of this preliminary study.

Table 1. General description of sampled assemblages.

Date	Location/Cruise	Chl a [mg m^-3]	a_ϕ (674) [m^-1]	Dominant Assemblage Types
1999	Lamberts Bay/shore based	50-120	0.8-1.8	Dinoflagellates
1999	Saldanha Bay/shore based	9-12	0.3-0.4	Aureococcus anophagefferens
1999	Southern Benguela/Benefit	0.5-14	0.02-0.2	Mixed
2000	Lamberts Bay/SBHABE	19-46	0.4-0.8	Diatoms/Dinoflagellates
2001	False Bay/shore based	19	0.36	Karenia mikimotoi
2001	Lamberts Bay/shore based	2-195	0.04-3.9	Dinoflagellates/Ciliates
2003	Saldanha Bay/shore based	12-14	0.25-0.35	Aureococcus anophagefferens

2.1 Particulate absorption

Particulate absorption data were measured with the quantitative filter pad technique [16

C.S. Yentsch, “Measurement of visible light absorption by particulate matter in the ocean,” Limnol. Oceanogr. 7, 207–217 (1962). [CrossRef]

,17

C.S. Roesler, “Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique,” Limnol. Oceanogr. 43, 1649–1660 (1998). [CrossRef]

] using a Shimadzu UV-2501 spectrophotometer equipped with an ISR-2200 internal integrating sphere. Discrete seawater samples were filtered under less than 10 mm mercury pressure using 25 mm Whatman GF/F filters, which were placed at the entrance port of the sphere, and scanned from 350 nm to 750 nm using an air reference and baseline. In cases where filters could not be read immediately, they were stored frozen in liquid nitrogen. Blank filter pads were prepared by filtering several hundred ml of Milli-Q water through fresh GF/F filters, which were then read in the same manner as the samples. Filtration volumes were adjusted to maintain a large amount of material on the filter while avoiding excessive clogging. Absorption coefficients were calculated using the pathlength amplification factor of Roesler [17

], assuming a null-point correction at 750 nm. Detrital measurements were made using methanol extraction on the filter, followed by re-reading in the spectrophotometer [18

M. Kishino, M. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).

]. Phytoplankton absorption a_ϕ(λ) data were then obtained by subtraction of detrital absorption a_d(λ) from total particulate absorption a_p(λ).Both the filter pad and null-point correction methods suffer from unknown errors associated with the poorly known scattering properties of marine particulates [19

D. Stramski and J. Piskozub, “Estimation of scattering error in spectrophotometric measurements of light absorption by aquatic particles from 3-D radiative transfer equations,” Appl. Opt. , 42, 3634–46 (2003). [CrossRef] [PubMed]

]. However, these errors are unlikely to significantly impact this study, as the principal focus is a comparison between two modeling approaches using representative refractive index data as input.

2.2 Particle size distributions (PSD)

Particle size measurements were made using a 128 channel Coulter Multisizer II in manometer mode, using freshly prepared 0.2 μm filtered seawater as both blank and electrolyte. Choice of aperture size was dictated by size of the dominant algal species, with 140 μm used for all samples other than those dominated by the small pelagophyte Aureococcus anophagefferens. Samples were diluted to keep coincidence levels below 10%, and 40 ml of sample was typically counted. A numerical technique was employed to fractionate measured size distributions into algal and non-algal components. The detrital component of the particle population was assumed to obey a Junge distribution (Eq. 8), with diameters ranging from 0.7 μm to 100 μm, in log-spaced bins. An inverse anomalous diffraction model was then used to fit measured detrital absorption a_d (λ), using an imaginary refractive index of ń =0.001066 exp(-0.007168λ) [20

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]

]. The detrital size distribution resulting from the fit was then subtracted from the total measured size distribution to obtain the algal particle size distribution. Finally, data for optical analyses were re-sampled to linear size bins of 1 μm, with a range of 1 μm to 100 μm, through calculation of the spectral density.

2.3 Pigments

Pigments were analysed using High Performance Liquid Chromatography (HPLC). Seawater was filtered through 25 mm GF/F filters and filters stored frozen in liquid nitrogen until analysis. Filtration volumes were adjusted to maintain approximately constant amounts of material on the filter and ranged from 0.1 l to 2 l. Analysis of pigments followed the reverse-phase HPLC procedure outlined by [21

R.G. Barlow, D. G. Cummings, and S. W. Gibb, “Improved resolution of mono- and divinyl chlorophylls a and b and zeaxanthin and lutein in phytoplankton extracts using reverse phase C-8 HPLC,” Mar. Ecol. Prog. Ser. 161, 303–307 (1997). [CrossRef]

] using a 3 μm Hypersil MOS2 C8 column (100 × 4.6 mm), a Varian ProStar pump, a Thermo Separations AS3000 autosampler, a Thermo Separations UV6000 diode array absorbance detector, and ChromQuest chromatography software.

2.4 Refractive index determinations

The absorption coefficient of a phytoplankton population can be described by the following expression [22

A. Morel and A. Bricaud, “Inherent properties of algal cells including picoplankton: theoretical and experimental results,” Can. Bull. Fish. Aquat. Sci. , 214, 521–559 (1986).

a_{ϕ} (λ) = {π \overset{}{\overset{̅}{Q}}}_{a} (λ) \int_{0}^{\infty} F (r) r^{2} d (r)

(2)

where a_ϕ (λ) is the algal absorption coefficient (m² mg^-1), Q̅_a (λ) is the absorption efficiency factor (where the overbar signifies the mean efficiency factor of a particle population), r is the particle radius, F(r)d(r) is the number of particles per unit volume in the size range r ± 1/2 d(r), and λ denotes wavelength. Using the measured algal absorption coefficients and size distribution data, as described above, the mean absorption efficiency factor Q̅_a (λ) can be calculated for natural algal assemblages [6

A. Bricaud and A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25, 571–580 (1986). [CrossRef] [PubMed]

]. Employing the anomalous diffraction approximation [23

H.C. Van de Hulst,. Light Scattering by Small Particles (Wiley 1957), pp. 470.

Q_{a} (λ) = 1 + \frac{{2 e}^{- ρ'}}{ρ'} + 2 \frac{e^{- ρ'} - 1}{{ρ'}^{2}}

(3)

dimensionless absorption efficiency factors can be expressed in terms of the optical thickness

\overset{´}{ρ}

= 4αń, where α is the Mie size parameter 2rπ/λ, and ń(λ) is the imaginary part of the refractive index. Calculations of additional inherent optical properties, namely attenuation and scattering coefficients, may be made if appropriate values of the real part of the refractive index are also derived. The Kramers-Kronig relations [11

S. Bernard, T.A. Probyn, and R.G. Barlow, “Measured and modelled optical properties of particulate matter in the southern Benguela,” S. Afr. J. Sci. 97, 410–420 (2001).

,24

C.F. Bohren and D.R. Huffman, Absorption and scattering of light by small particles (John Wiley and Sons, 1983), pp. 530.

] were employed to derive spectral variations in the real part of the index, typically denoted as Δn(λ) from the imaginary part of the index. The central value of Δn around which Δn(λ) varies, denoted as 1+ε, was fixed at 1.05 for all samples, chosen as a representative value for phytoplankton [6

A. Bricaud and A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25, 571–580 (1986). [CrossRef] [PubMed]

,25

A. Bricaud, A.L. Bedhomme, and A. Morel, “Optical properties of diverse phytoplanktonic species: experimental results and theoretical interpretation,” J. Plankton Res. 10, 851–873 (1988). [CrossRef]

]. Note that these n(λ) data should be considered as representative theoretical values generated for the sole purpose of comparing the optical properties of equivalent size distributions - accurate determinations of 1+ ε would require additional use of attenuation or scattering data, not available to this study [6

A. Bricaud and A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25, 571–580 (1986). [CrossRef] [PubMed]

All refractive index determinations, and subsequent optical modeling runs, were made with Chl a-specific data to negate the impact of varying biomass concentration upon model performance assessment. Whilst Chl a-specific phytoplankton absorption is a common bio-optical parameter [25

], Chl a -specific size distributions are rarely used. However, test results employing absorption and size data normalised to Chl a concentrations both before and after optical modeling confirmed that such scaling made no impact upon the refractive index and efficiency factor analyses.

2.5 Equivalent size distributions

Equivalent distributions were calculated using the same effective radius as the corresponding measured size distribution and scaled to the total projected surface area <SA> of the measured algal size distribution, as given by:

< SA > = \int_{0.5}^{50} {πr}^{2} F (r) d (r)

(4)

The ASF (Area Scaling Factor) term is introduced here as the total projected surface area scaling parameter, i.e. it is used to manipulate the magnitude of the equivalent distributions by matching the total projected surface area to that of the measured distribution. The following four size distribution functions were assessed, expressed in radial terms for algebraic simplicity:

1. The special -7/2 generalised inverse Gaussian distribution [13
M.D. Alexandrov and A.A. Lacis, “A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function,” Appl. Math. Comput. 116, 153–165 (2000) [CrossRef]
]

$F (r) = ASF \frac{r^{- \frac{7}{2}} r_{eff}^{\frac{5}{2}}}{\sqrt{{2 πv}_{eff}} (1 + {3 v}_{eff} + {3 v}_{eff}^{2})} \exp [\frac{1}{{2 v}_{eff}} (2 - \frac{r_{eff}}{r} - \frac{r}{r_{eff}})]$

(5)
2. The Standard distribution [15
J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]
]

$F (r) = {ASEr}^{[(1 - {3 v}_{eff}) / v_{eff}]} \exp [r / (r_{eff} v_{eff})]$

(6)
3. The 0^th order log normal distribution [8
M. Jonasz and G. Fournier, “Approximation of the size distribution of marine particles by a sum of log-normal functions,” Limnol. Oceanogr. 41, 744–754 (1996). [CrossRef]
,15
J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]
]

$F (r) = ASF \exp [\frac{- {(\ln (r) - \ln (r_{g}))}^{2}}{{2 σ}_{g}^{2}}]$

(7)
where r_g = r_eff/(l + v_eff)^5/2 and σ² _g = ln(l + v_eff).
4. The Junge distribution [9
E.C. Junge, Air chemistry and radioactivity (Academic Press 1963), pp.382.
]

$F (r) = {ASEr}^{- ξ}$

(8)
In addition to the above distributions, a further approximation was assessed:
5. A single size approximation of r = r_eff (or D = D_eff )

The first two distributions were chosen due to their proven ability as moment-matched equivalent distributions in atmospheric physics. In addition, these distributions can be conveniently expressed analytically as a function of their effective radii and variance. The second two distributions were chosen due to their common use in the marine bio-optical field [5

A. Bricaud and A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25, 571–580 (1986). [CrossRef] [PubMed]

]. Note that the Junge distribution is the only formula that is not explicitly expressed in terms of effective radius and variance, and equivalent distributions were calculated by iteratively adjusting the Junge slope ξ until r_eff of the Junge distribution matched that of the measured distribution. The single size approximation was analysed as a simple and computationally economic alternative. Two analyses were carried out with regard to the effective variance r_eff : the first using the variable experimental r_eff values determined for each sample, and the second using a constant r_eff value of 0.63, the mean value for all samples.

2.6 Optical modeling

Analogous expressions to Eq. 2 can be used to express the relationships between the attenuation coefficient c(λ), the scattering coefficient b(λ), the backscattering coefficient b_b (λ) and their relative efficiency factors. Additional detail on such models can be found in [22

A. Morel and A. Bricaud, “Inherent properties of algal cells including picoplankton: theoretical and experimental results,” Can. Bull. Fish. Aquat. Sci. , 214, 521–559 (1986).

]. In addition, the dimensionless package effect parameter (Q_a ^*) can be calculated from the following expression [2

Q_{a}^{*} = \frac{3}{2} \frac{Q_{a}}{a_{cm} 2 r}

(9)

where a_cm is the absorption of cellular material (m^-1) and is given by a_cm = 4πń/λ [ibid.]. For each sample two calculations of the suite of IOPs were made. The first was made using the measured algal size distribution (calculated as described in section 2.2) with corresponding refractive index data (as described in section 2.4) – these are referred to as the “measured” properties. The second was made using the equivalent algal size distribution (calculated as described in section 2.5) using exactly the same refractive index data as for the “measured” calculations – these are referred to as the “equivalent” properties.

The ability of the equivalent size distribution to match the IOPs calculated from the measured size distribution were assessed using the mean and standard deviations (SD) of the RMS errors (in percent) of the entire data set for each wavelength, as given by

RMS error (λ) = {[\sqrt{mean (\frac{a_{equiv} (λ) - a_{meas} (λ)}{a_{meas} (λ)})}]}^{2}

(10)

SD of RMS error (λ) = {[\sqrt{SD (\frac{a_{equiv} (λ) - a_{meas} (λ)}{a_{meas} (λ)})}]}^{2}

(11)

where a_equiv is the modeled absorption of the equivalent size distribution, and a_meas is the modeled absorption of the measured size distribution. The above expressions are for absorption – analogous expressions are employed for other IOPs.

The Aden-Kerker [26

A.L. Aden and M. Kerker, “Scattering of electromagnetic waves from two concentric spheres,” J. Appl. Phys. 22, 1242–1246 (1951). [CrossRef]

] formulations, as provided by the Fortran code of Toon & Ackerman [27

O.B. Toon and T.P. Ackerman, “Algorithms for the calculation of scattering by stratified spheres,” Appl. Opt. 20, 3657–3660 (1981). [CrossRef] [PubMed]

] were employed for all forward optical modeling, in a combined Matlab/Fortran environment. The Aden-Kerker formulations allow the absorbing and scattering properties of a two-layered particle to be calculated using size and refractive index data as input, in a way analogous to Mie theory use for a homogeneous particle. The suitability of the Aden-Kerker formulations for homogenous particle geometry was confirmed by testing sample results against the Fortran code of [24

C.F. Bohren and D.R. Huffman, Absorption and scattering of light by small particles (John Wiley and Sons, 1983), pp. 530.

], which produced equivalent results.

Fig. 1. Optical and size related data for all samples analysed. (a) Chl a-specific phytoplankton absorption, (b) Chl a-specific algal size distributions by volume, (c) imaginary part of the phytoplankton refractive index, (d) real part of the phytoplankton refractive index. Coloured lines are used simply to discriminate between samples.

Table 2. Maximal RMS errors (SD) between IOPs for measured and equivalent distributions.

Distribution	v_eff	Error c_ϕ	Error b_ϕ	Error a_ϕ	Error b_bϕ	Error Q^_a ^*
Inverse Gauss	Measured	3.1 (4.6)	4.6 (5.8)	0.9 (3.1)	9.6 (9.1)	1.4 (4.9)
Inverse Gauss	0.63	2.8 (4.9)	3.9 (5.5)	1.0 (3.8)	7.7 (12.7)	1.1 (4.6)
Standard	Measured	5.5 (4.2)	7.8 (5.2)	1.9 (2.7)	8.1 (17.4)	0.8 (5.7)
Standard	0.63	4.9 (4.1)	6.4 (4.8)	0.9 (3.5)	8.4 (17.1)	1.9 (5.2)
Log-normal	Measured	3.4 (8.4)	3.1 (10.8)	18.3 (11.8)	19.6 (17.9)	8.4 (7.9)
Log-normal	0.63	2.7 (6.2)	2.6 (7.8)	18.3 (1.4)	19.5 (12.0)	8.8 (6.1)
Junge	Variable	36.6 (12.9)	36.7 (12.8)	27.4 (8.0)	14.4 (22.0)	33.3 (28.5)
Distributiona		Error Q_c	Error Q_b	Error Q_a	Error Q_bb	Error Q_a ^*
Inverse Gauss	Measured	3.0 (4.7)	4.3 (5.4)	0.6 (2.7)	8.6 (9.7)	1.4 (4.9)
Single size	0.0	6.7 (25.3)	8.1 (24.6)	14.5 (8.2)	33.3 (17.6)	4.1 (3.0)

^a Single size distributions are assessed with regard to efficiency factors only- analogous errors for the inverse Gaussian distribution are shown for comparison.

3. Results and discussion

All Chl a-specific phytoplankton absorption, size distributions and refractive index data are displayed in Fig. 1. The absorption data (Fig. 1(a)) demonstrate the effects of varying assemblage size and pigmentation [2

,25

]; the effects of which can also seen in the imaginary refractive index data (Fig. 1(c)), the magnitudes of which compare well with algal culture data [25

]. The multimodal phytoplankton volume distributions (Fig. 1(b)) are typical of the productive, phytoplankton-dominated waters of the southern Benguela, and demonstrate the difficulties of simulating the complex shapes of natural phytoplankton distributions using simple distribution functions.

3.1 Optical properties of equivalent size distributions

The two equivalent size distributions previously used for analogous atmospheric work, the inverse Gaussian [13

M.D. Alexandrov and A.A. Lacis, “A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function,” Appl. Math. Comput. 116, 153–165 (2000) [CrossRef]

] and Standard [15

J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]

], perform well in simulating the optical properties of algal assemblages. Maximal RMS errors and their standard deviations are reported in Table 2 and Fig. 2 for all distributions, calculated in percentage terms for all optical properties relative to the derived optical properties of the full, i.e. measured, distributions. Realistic values of the maximum expected errors can be obtained by summing the maximal RMS error and standard deviation – the single values in Table 2 represent the largest spectral errors seen in Fig. 2. The best performing inverse Gaussian distribution therefore appears capable of simulating beam attenuation c and total scattering b to within 10%, absorption a and package effect parameter Q_a ^* to within 6%, and backscattering b_b to within 20%. The Standard distribution, offering a simpler algebraic expression, gives similar performance with the exception of slightly higher maximum backscattering errors of ∼25 %.

Both these distributions appear capable of reproducing salient bio-optical variables with sufficient accuracy for inversion application through a wide range of algal assemblage types. Of particular importance is the ability of the inverse Gaussian and Standard distributions to reproduce spectral absorption coefficients and package effect parameters accurately (< 6 %), as phytoplankton absorption is an often dominant determinant of light attenuation in the sea [28

A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water),” J. Geophys. Res. , 93, 10,749-10,768 (1988). [CrossRef]

], and offers an optical signal with the ability to provide algal assemblage descriptors [29

A.M. Ciotti, M. R. Lewis, and J.J. Cullen, “Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient,” Limnol. Oceanogr. 47, 404–417 (2002). [CrossRef]

The relatively high errors associated with the backscattering coefficient (∼20%) relative to the other coefficients (∼5 % to ∼10 %) are presumed to be a result of the greater sensitivity to small size changes of the scattering phase function relative to integrated variables such as the attenuation or absorption coefficient [15

J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]

,30

M.I. Mishchenko and A.A. Lacis, “Morphology-dependent resonances of nearly spherical particles in random orientation,” Appl. Opt. 42, 5551–5556 (2003). [CrossRef] [PubMed]

]. Both interference phenomena and morphology-dependent resonances, at their most pronounced in the phase function [30

M.I. Mishchenko and A.A. Lacis, “Morphology-dependent resonances of nearly spherical particles in random orientation,” Appl. Opt. 42, 5551–5556 (2003). [CrossRef] [PubMed]

], are likely to result in much larger size-dependent variations in dependent variables such as the backscattering coefficient.

Previous studies considering hypothetical particle polydispersions [22

A. Morel and A. Bricaud, “Inherent properties of algal cells including picoplankton: theoretical and experimental results,” Can. Bull. Fish. Aquat. Sci. , 214, 521–559 (1986).

] have demonstrated that the relationship between optical efficiency and particle size is considerably more complex for backscattering than those for attenuation, scattering or absorption, which tend to limiting values in the size range under consideration.

Forcing both the inverse Gaussian and Standard distributions to a constant V_eff appears to have had little adverse effect on the returned errors, allowing both distributions to be expressed through two parameters for potential inversion applications: the effective diameter and a scaling parameter. The relative lack of sensitivity to V_eff would appear to be due at least in part to the relatively dispersed nature of the majority of the algal assemblages analysed, even in high biomass bloom scenarios. Thus, whilst the assumption of a relatively high constant V_eff of 0.63 appears appropriate for natural algal assemblages in productive coastal systems, application either in truly oligotrophic waters or to very highly size-constrained mono-specific blooms or cultures may require further validation.

The single size approximation would appear to offer poor returns for all optical variables (∼25% to ∼50% errors), with the important exception of the package effect parameter (∼7% error) which offers comparable performance to the inverse Gaussian and Standard distributions (∼6% to ∼7% errors). The acceptable performance of the single-size package effect derivation, in comparison to the poorer performance with regard to the other optical coefficients, is likely to result both from the monotonic nature of the Q_a ^* vs D_eff (or

\overset{´}{ρ}

) relationship [2

], and the high relative impact of the optical thickness

\overset{´}{ρ}

upon direct Q_a ^* calculations (Eq. 9). Whilst Q_a and Q_a ^* are obviously both directly dependent upon

\overset{´}{ρ}

, the average deviation of

\overset{´}{ρ}

(675) is four times higher than Q_a (675) when considering the entire data set – thus the assemblage-averaged effective diameter and imaginary refractive index data play a greater role in the package effect calculations relative to those of the other inherent optical properties. The good performance of the single-size package-effect derivation offers a potentially extremely useful formulation: the ability to robustly express the Chl a-specific phytoplankton absorption of natural assemblages through a single effective diameter parameter via the package effect [2

Fig. 2. Mean spectral RMS errors and standard deviations of the best performing inverse Gaussian equivalent distribution. (a) attenuation coefficient, (b) scattering coefficient, (c) absorption coefficient, (d) backscattering coefficient (e) package effect parameter. High magnitude morphology-dependent resonance effects [30

M.I. Mishchenko and A.A. Lacis, “Morphology-dependent resonances of nearly spherical particles in random orientation,” Appl. Opt. 42, 5551–5556 (2003). [CrossRef] [PubMed]

] can cause extremely high mean errors in phase function simulations, and such errors are therefore not calculated for the phase function. Fig. 2(f) shows a comparison between computed phase functions from the full, inverse Gaussian and Junge distributions at 530 nm for the same example assemblage as Fig.3.

The Junge size distribution, perhaps the most commonly used in marine optics, performed markedly less well, with errors ranging from 35% to 62% (Table 2). Given the peaked nature of the measured phytoplankton size distributions, and the noted limitations of the Junge distribution for phytoplankton-dominated waters [5

,10

R.W. Sheldon, A. Prakash, and W.H. Sutcliffe, Jr., “The size distribution of particles in the ocean,” Limnol. Oceanogr. 17, 327–340 (1972). [CrossRef]

], the poor performance of the Junge distribution is not unexpected. The log-normal distribution, while offering more accurate returns than the Junge distribution, offers a mixed performance in comparison to the inverse Gaussian and Standard distributions. The log-normal returns for the attenuation and scattering coefficients (10% to 14% errors) are comparable to those of the inverse Gaussian and Standard distributions. The log-normal returns for the absorption and backscattering coefficients, and the package effect parameter, are however noticeably poorer (Table 2). Whilst this may be partially due to the particular 0^th order formulation of the log-normal distribution employed here [15

J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]

], it is also likely to be due to the less desirable asymptotic characteristics of the log-normal distribution at higher sizes [8

M. Jonasz and G. Fournier, “Approximation of the size distribution of marine particles by a sum of log-normal functions,” Limnol. Oceanogr. 41, 744–754 (1996). [CrossRef]

,13

Fig. 3. Equivalent size distributions and their IOPs for an example assemblage, composed of mixed dinoflagellate and diatom species. Chlorophyll specific data shown are (a) measured and equivalent phytoplankton size distributions, (b) phytoplankton beam attenuation coefficient c_ϕ ^*, (c) phytoplankton absorption coefficient a_ϕ ^*, (d) package effect parameter Q_a ^*, (e) phytoplankton scattering coefficient b_ϕ ^*, (f) phytoplankton backscattering coefficient b_bϕ ^*. Close matches for the calculated optical properties of the equivalent inverse-Gaussian and Standard distributions can be observed, as can the difference in the shapes of the equivalent size distributions relative to the highly multimodal measured distribution.

Figure 3 details the measured and equivalent algal size distributions, and associated optical properties of a mixed dinoflagellate and diatom bloom in the southern Benguela. The example station is specifically chosen to illustrate the close simulation of optical properties using equivalent size distributions very different in shape to the highly multimodal measured size distribution. The equivalent size distributions in Fig. 3(a) are calculated using the mean effective variance of 0.63, and can therefore be parameterised using a maximum of two variables: the effective diameter D_eff , and the scaling parameter ASF. Such data demonstrate both the utility of equivalent size distributions with regard to optical simulation, and the disadvantages of replicating size distributions by matching measured shape. Simulation of the measured volume size distribution shape in Fig. 3(a) would require a minimum of three discrete distributions, each described by two parameters [8

M. Jonasz and G. Fournier, “Approximation of the size distribution of marine particles by a sum of log-normal functions,” Limnol. Oceanogr. 41, 744–754 (1996). [CrossRef]

]. With regard to the optical properties of the example assemblage, the close replication of all optical properties by the inverse Gaussian and Standard distributions can be observed. The approximate 7% difference between the measured phytoplankton absorption and that reproduced from the Mie modeling (Fig 3(c)) can be attributed to the errors associated with the anomalous diffraction approximation, used to derive the spectral refractive index data.

4 Application and conclusions

Further consideration of the scaling parameter ASF for the inverse Gaussian and Standard distributions reveals a close relationship with effective diameter D_eff for the samples analysed using a mean effective variance of 0.63 (Fig. 4). The ASF parameter is used to manipulate the magnitude of the equivalent distributions by matching the total projected surface area to that of the measured distribution. The ability to express the ASF to D_eff relationship using a power law allows both distributions to be parameterised using a single variable, the effective diameter (Fig 4).

Fig. 4. Scaling parameters ASF for the (a) inverse Gaussian and (b) Standard distributions, calculated as surface area equivalent to the thirty-four measured algal Chl a specific size distributions. The results suggest that ASF can be confidently expressed as a function of D_eff for a wide variety of algal assemblages for the above distributions.

This further simplifies potential inversion of the inverse Gaussian and Standard equivalent distributions by allowing Chl a-specific algal size distributions to be expressed as single variable functions, assuming a mean effective variance of 0.63:

1. Inverse Gaussian Chl a-specific algal size distribution

$F (r) = 2 \times 10^{9} {({2 r}_{eff})}^{- 3.0264} [\frac{r^{- \frac{7}{2}} r_{eff}^{\frac{5}{2}}}{7.9232}] \exp [0.7939 (2 - \frac{r_{eff}}{r} - \frac{r}{r_{eff}})]$

(12)
2. Standard Chl a-specific algal size distribution

$F (r) = 1 \times 10^{9} {({2 r}_{eff})}^{- 2.6443} r^{(- 1.4127)} \exp [r / {0.63 r}_{eff}]$

(13)

It should also be noted that it is possible to use the above expressions as simple weighting formulae without the scaling parameters shown in Fig. 4, for example to calculate optical efficiency factors given refractive index data.

The equivalent distribution approach demonstrates that by shifting paradigm from attempting to simulate or reconstruct the shape of a marine particle distribution to simulating the optical properties of a size distribution, simple parameterizations of common size distribution formulae can be employed for optical purposes. In particular, such an approach circumvents the necessity to consider the detailed structure of complex particle size distributions in biologically dominated waters, even in the case of intense algal blooms.

The current study has several limitations: those of a methodological nature with regard to the derivation of algal size distributions, subsequent refractive index determinations, and the simplifications arising from the use of spherical, homogenous particle geometries in optical calculations. In this regard, the study is preliminary in nature, and it is to be hoped that a more comprehensive evaluation of the equivalent size distribution approach can be carried out, using more sophisticated optical models and independent measurements of algal inherent optical properties. Nevertheless, the study demonstrates the utility of the equivalent size distribution approach, providing the ability to simulate the primary inherent optical properties of a wide range of algal assemblage types. In addition, the distributions described here allow simple size descriptions of the chlorophyllous particle population, as opposed to the more general size distribution formulations used in the past for the total marine particle population.

References and links

1.	Z.V. Finkel and A.J. Irwin, “Modelling size-dependent photosynthesis: light absorption and the allometric rule,”. J. Theor. Biol. 204, 361–369 (2000) [CrossRef] [PubMed]
2.	A. Morel and A. Bricaud, “Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton,” Deep-Sea Res. 28, 1375–1393 (1981). [CrossRef]
3.	D.J.S. Montagnes, J.A. Berges, P.J. Harrison, and F.J.R. Taylor, “Estimating carbon, nitrogen, protein, and chlorophyll a from cell volume in marine phytoplankton,” Limnol. Oceanogr. 39,1044–1060 (1994). [CrossRef]
4.	J. Rodriguez, J. Tintore, J.T. Allen, J.M. Blanco, D. Gomis, A. Reul, J. Ruiz, V. Rodriguez, F. Echevarria, and F. Jimenez-Gomez, “Mesoscale vertical motion and the size structure of phytoplankton in the ocean,” Nature 410, 360–363 (2001). [CrossRef] [PubMed]
5.	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]
6.	A. Bricaud and A. Morel, “Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling,” Appl. Opt. 25, 571–580 (1986). [CrossRef] [PubMed]
7.	D. Risovic, “Two component model of sea particle size distribution,” Deep-Sea Res. 40, 1459–1473 (1993). [CrossRef]
8.	M. Jonasz and G. Fournier, “Approximation of the size distribution of marine particles by a sum of log-normal functions,” Limnol. Oceanogr. 41, 744–754 (1996). [CrossRef]
9.	E.C. Junge, Air chemistry and radioactivity (Academic Press 1963), pp.382.
10.	R.W. Sheldon, A. Prakash, and W.H. Sutcliffe, Jr., “The size distribution of particles in the ocean,” Limnol. Oceanogr. 17, 327–340 (1972). [CrossRef]
11.	S. Bernard, T.A. Probyn, and R.G. Barlow, “Measured and modelled optical properties of particulate matter in the southern Benguela,” S. Afr. J. Sci. 97, 410–420 (2001).
12.	Y.X. Hu and K. Stamnes, “An accurate Parameterization of Cloud Radiative Properties Suitable for Climate Modeling,” J. Climate. 6, 728–742 (1993). [CrossRef]
13.	M.D. Alexandrov and A.A. Lacis, “A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function,” Appl. Math. Comput. 116, 153–165 (2000) [CrossRef]
14.	R. McGraw, S. Nemesure, and S. E. Schwartz, “Properties and evolution of aerosols with size distributions having identical moments,” J. Aerosol. Sci. 29, 761–772 (1998). [CrossRef]
15.	J.E. Hansen and L.D. Travis, “Light scattering in planetary atmospheres,” Space. Sci. Rev. 16, 527–610 (1974). [CrossRef]
16.	C.S. Yentsch, “Measurement of visible light absorption by particulate matter in the ocean,” Limnol. Oceanogr. 7, 207–217 (1962). [CrossRef]
17.	C.S. Roesler, “Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique,” Limnol. Oceanogr. 43, 1649–1660 (1998). [CrossRef]
18.	M. Kishino, M. Takahashi, N. Okami, and S. Ichimura, “Estimation of the spectral absorption coefficients of phytoplankton in the sea,” Bull. Mar. Sci. 37, 634–642 (1985).
19.	D. Stramski and J. Piskozub, “Estimation of scattering error in spectrophotometric measurements of light absorption by aquatic particles from 3-D radiative transfer equations,” Appl. Opt. , 42, 3634–46 (2003). [CrossRef] [PubMed]
20.	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]
21.	R.G. Barlow, D. G. Cummings, and S. W. Gibb, “Improved resolution of mono- and divinyl chlorophylls a and b and zeaxanthin and lutein in phytoplankton extracts using reverse phase C-8 HPLC,” Mar. Ecol. Prog. Ser. 161, 303–307 (1997). [CrossRef]
22.	A. Morel and A. Bricaud, “Inherent properties of algal cells including picoplankton: theoretical and experimental results,” Can. Bull. Fish. Aquat. Sci. , 214, 521–559 (1986).
23.	H.C. Van de Hulst,. Light Scattering by Small Particles (Wiley 1957), pp. 470.
24.	C.F. Bohren and D.R. Huffman, Absorption and scattering of light by small particles (John Wiley and Sons, 1983), pp. 530.
25.	A. Bricaud, A.L. Bedhomme, and A. Morel, “Optical properties of diverse phytoplanktonic species: experimental results and theoretical interpretation,” J. Plankton Res. 10, 851–873 (1988). [CrossRef]
26.	A.L. Aden and M. Kerker, “Scattering of electromagnetic waves from two concentric spheres,” J. Appl. Phys. 22, 1242–1246 (1951). [CrossRef]
27.	O.B. Toon and T.P. Ackerman, “Algorithms for the calculation of scattering by stratified spheres,” Appl. Opt. 20, 3657–3660 (1981). [CrossRef] [PubMed]
28.	A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water),” J. Geophys. Res. , 93, 10,749-10,768 (1988). [CrossRef]
29.	A.M. Ciotti, M. R. Lewis, and J.J. Cullen, “Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient,” Limnol. Oceanogr. 47, 404–417 (2002). [CrossRef]
30.	M.I. Mishchenko and A.A. Lacis, “Morphology-dependent resonances of nearly spherical particles in random orientation,” Appl. Opt. 42, 5551–5556 (2003). [CrossRef] [PubMed]

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(290.4020) Scattering : Mie theory

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: September 13, 2006
Revised Manuscript: November 23, 2006
Manuscript Accepted: November 28, 2006
Published: March 5, 2007

Virtual Issues
Vol. 2, Iss. 4 Virtual Journal for Biomedical Optics

Citation
S. Bernard, F. A. Shillington, and T. A. Probyn, "The use of equivalent size distributions of natural phytoplankton assemblages for optical modeling," Opt. Express 15, 1995-2007 (2007)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-15-5-1995

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References

Z. V. Finkel and A. J. Irwin, "Modelling size-dependent photosynthesis: light absorption and the allometric rule," J. Theor. Biol. 204, 361-369 (2000) [CrossRef] [PubMed]
A. Morel and A. Bricaud, "Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton," Deep-Sea Res. 28, 1375-1393 (1981). [CrossRef]
D. J. S. Montagnes, J. A. Berges, P. J. Harrison and F. J. R. Taylor, "Estimating carbon, nitrogen, protein, and chlorophyll a from cell volume in marine phytoplankton," Limnol. Oceanogr. 39,1044-1060 (1994). [CrossRef]
J. Rodriguez, J. Tintore, J. T. Allen, J. M. Blanco, D. Gomis, A. Reul, J. Ruiz, V. Rodriguez, F. Echevarria and F. Jimenez-Gomez, "Mesoscale vertical motion and the size structure of phytoplankton in the ocean," Nature 410, 360-363 (2001). [CrossRef] [PubMed]
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]
A. Bricaud and A. Morel, "Light attenuation and scattering by phytoplanktonic cells: a theoretical modeling," Appl. Opt. 25, 571-580 (1986). [CrossRef] [PubMed]
D. Risović, "Two component model of sea particle size distribution," Deep-Sea Res. 40, 1459-1473 (1993). [CrossRef]
M. Jonasz and G. Fournier, "Approximation of the size distribution of marine particles by a sum of log-normal functions," Limnol. Oceanogr. 41, 744-754 (1996). [CrossRef]
E. C. Junge, Air chemistry and radioactivity (Academic Press 1963), pp.382.
R. W. Sheldon, A. Prakash and W. H. Sutcliffe, Jr., "The size distribution of particles in the ocean," Limnol. Oceanogr. 17, 327-340 (1972). [CrossRef]
S. Bernard, T. A. Probyn and R. G. Barlow, "Measured and modelled optical properties of particulate matter in the southern Benguela," S. Afr. J. Sci. 97, 410-420 (2001).
Y. X. Hu and K. Stamnes, "An accurate Parameterization of Cloud Radiative Properties Suitable for Climate Modeling," J. Climate. 6, 728-742 (1993). [CrossRef]
M. D. Alexandrov and A. A. Lacis, "A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function," Appl. Math. Comput. 116, 153-165 (2000) [CrossRef]
R. McGraw, S. Nemesure and S. E. Schwartz, "Properties and evolution of aerosols with size distributions having identical moments," J. Aerosol. Sci. 29, 761-772 (1998). [CrossRef]
J. E. Hansen and L. D. Travis, "Light scattering in planetary atmospheres," Space. Sci. Rev. 16, 527-610 (1974). [CrossRef]
C. S. Yentsch, "Measurement of visible light absorption by particulate matter in the ocean," Limnol. Oceanogr. 7, 207-217 (1962). [CrossRef]
C. S. Roesler, "Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique," Limnol. Oceanogr. 43, 1649-1660 (1998). [CrossRef]
M. Kishino, M. Takahashi, N. Okami and S. Ichimura, "Estimation of the spectral absorption coefficients of phytoplankton in the sea," Bull. Mar. Sci. 37, 634-642 (1985).
D. Stramski and J. Piskozub, "Estimation of scattering error in spectrophotometric measurements of light absorption by aquatic particles from 3-D radiative transfer equations," Appl. Opt., 42, 3634-46 (2003). [CrossRef] [PubMed]
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]
R. G. Barlow, D. G. Cummings and S. W. Gibb, "Improved resolution of mono- and divinyl chlorophylls a and b and zeaxanthin and lutein in phytoplankton extracts using reverse phase C-8 HPLC," Mar. Ecol. Prog. Ser. 161, 303-307 (1997). [CrossRef]
A. Morel and A. Bricaud, "Inherent properties of algal cells including picoplankton: theoretical and experimental results," Can. Bull. Fish. Aquat. Sci., 214, 521-559 (1986).
H. C. Van de Hulst, Light Scattering by Small Particles (Wiley 1957), pp. 470.
C. F. Bohren and D. R. Huffman, Absorption and scattering of light by small particles (John Wiley and Sons, 1983), pp. 530.
A. Bricaud, A. L. Bedhomme and A. Morel, "Optical properties of diverse phytoplanktonic species: experimental results and theoretical interpretation," J. Plankton Res. 10, 851-873 (1988). [CrossRef]
A. L. Aden and M. Kerker, "Scattering of electromagnetic waves from two concentric spheres," J. Appl. Phys. 22, 1242-1246 (1951). [CrossRef]
O. B. Toon, and T. P. Ackerman, "Algorithms for the calculation of scattering by stratified spheres," Appl. Opt. 20, 3657-3660 (1981). [CrossRef] [PubMed]
A. Morel, "Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water)," J. Geophys. Res., 93, 10,749-10,768 (1988). [CrossRef]
A. M. Ciotti, M. R. Lewis and J. J. Cullen, "Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient," Limnol. Oceanogr. 47, 404-417 (2002). [CrossRef]
M. I. Mishchenko and A. A. Lacis, "Morphology-dependent resonances of nearly spherical particles in random orientation," Appl. Opt. 42, 5551-5556 (2003). [CrossRef] [PubMed]

Appl. Math. Comput.

M. D. Alexandrov and A. A. Lacis, "A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function," Appl. Math. Comput. 116, 153-165 (2000) [CrossRef]

Appl. Opt.

Bull. Mar. Sci.

M. Kishino, M. Takahashi, N. Okami and S. Ichimura, "Estimation of the spectral absorption coefficients of phytoplankton in the sea," Bull. Mar. Sci. 37, 634-642 (1985).

Can. Bull. Fish. Aquat. Sci.

A. Morel and A. Bricaud, "Inherent properties of algal cells including picoplankton: theoretical and experimental results," Can. Bull. Fish. Aquat. Sci., 214, 521-559 (1986).

Deep-Sea Res.

D. Risović, "Two component model of sea particle size distribution," Deep-Sea Res. 40, 1459-1473 (1993). [CrossRef]
A. Morel and A. Bricaud, "Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton," Deep-Sea Res. 28, 1375-1393 (1981). [CrossRef]

J. Aerosol. Sci.

R. McGraw, S. Nemesure and S. E. Schwartz, "Properties and evolution of aerosols with size distributions having identical moments," J. Aerosol. Sci. 29, 761-772 (1998). [CrossRef]

J. Appl. Phys.

A. L. Aden and M. Kerker, "Scattering of electromagnetic waves from two concentric spheres," J. Appl. Phys. 22, 1242-1246 (1951). [CrossRef]

J. Climate.

Y. X. Hu and K. Stamnes, "An accurate Parameterization of Cloud Radiative Properties Suitable for Climate Modeling," J. Climate. 6, 728-742 (1993). [CrossRef]

J. Geophys. Res.

A. Morel, "Optical modeling of the upper ocean in relation to its biogenous matter content (case 1 water)," J. Geophys. Res., 93, 10,749-10,768 (1988). [CrossRef]

J. Plankton Res.

A. Bricaud, A. L. Bedhomme and A. Morel, "Optical properties of diverse phytoplanktonic species: experimental results and theoretical interpretation," J. Plankton Res. 10, 851-873 (1988). [CrossRef]

J. Theor. Biol.

Z. V. Finkel and A. J. Irwin, "Modelling size-dependent photosynthesis: light absorption and the allometric rule," J. Theor. Biol. 204, 361-369 (2000) [CrossRef] [PubMed]

Limnol. Oceanogr.

C. S. Yentsch, "Measurement of visible light absorption by particulate matter in the ocean," Limnol. Oceanogr. 7, 207-217 (1962). [CrossRef]
C. S. Roesler, "Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique," Limnol. Oceanogr. 43, 1649-1660 (1998). [CrossRef]
D. J. S. Montagnes, J. A. Berges, P. J. Harrison and F. J. R. Taylor, "Estimating carbon, nitrogen, protein, and chlorophyll a from cell volume in marine phytoplankton," Limnol. Oceanogr. 39,1044-1060 (1994). [CrossRef]
R. W. Sheldon, A. Prakash and W. H. Sutcliffe, Jr., "The size distribution of particles in the ocean," Limnol. Oceanogr. 17, 327-340 (1972). [CrossRef]
M. Jonasz and G. Fournier, "Approximation of the size distribution of marine particles by a sum of log-normal functions," Limnol. Oceanogr. 41, 744-754 (1996). [CrossRef]
A. M. Ciotti, M. R. Lewis and J. J. Cullen, "Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient," Limnol. Oceanogr. 47, 404-417 (2002). [CrossRef]

Mar. Ecol. Prog. Ser.

R. G. Barlow, D. G. Cummings and S. W. Gibb, "Improved resolution of mono- and divinyl chlorophylls a and b and zeaxanthin and lutein in phytoplankton extracts using reverse phase C-8 HPLC," Mar. Ecol. Prog. Ser. 161, 303-307 (1997). [CrossRef]

Nature

J. Rodriguez, J. Tintore, J. T. Allen, J. M. Blanco, D. Gomis, A. Reul, J. Ruiz, V. Rodriguez, F. Echevarria and F. Jimenez-Gomez, "Mesoscale vertical motion and the size structure of phytoplankton in the ocean," Nature 410, 360-363 (2001). [CrossRef] [PubMed]

S. Afr. J. Sci.

S. Bernard, T. A. Probyn and R. G. Barlow, "Measured and modelled optical properties of particulate matter in the southern Benguela," S. Afr. J. Sci. 97, 410-420 (2001).

Space. Sci. Rev.

J. E. Hansen and L. D. Travis, "Light scattering in planetary atmospheres," Space. Sci. Rev. 16, 527-610 (1974). [CrossRef]

Other

E. C. Junge, Air chemistry and radioactivity (Academic Press 1963), pp.382.
H. C. Van de Hulst, Light Scattering by Small Particles (Wiley 1957), pp. 470.
C. F. Bohren and D. R. Huffman, Absorption and scattering of light by small particles (John Wiley and Sons, 1983), pp. 530.

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The use of equivalent size distributions of natural phytoplankton assemblages for optical modeling

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Abstract

1. Introduction

2. Methods

2.1 Particulate absorption

2.2 Particle size distributions (PSD)

2.3 Pigments

2.4 Refractive index determinations

2.5 Equivalent size distributions

2.6 Optical modeling

3. Results and discussion

3.1 Optical properties of equivalent size distributions

4 Application and conclusions

References and links

References

Appl. Math. Comput.

Appl. Opt.

Bull. Mar. Sci.

Can. Bull. Fish. Aquat. Sci.

Deep-Sea Res.

J. Aerosol. Sci.

J. Appl. Phys.

J. Climate.

J. Geophys. Res.

J. Plankton Res.

J. Theor. Biol.

Limnol. Oceanogr.

Mar. Ecol. Prog. Ser.

Nature

S. Afr. J. Sci.

Space. Sci. Rev.

Other

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