The relationship between beam attenuation and chlorophyll concentration and reflectance in Antarctic waters

Deep-Sea

Pergamon

Research

0967-0645(95)ooo73-9

II, Vol. 42, No. 4-5. pp. 983-996, 1995 Copyright @ 1995 Elsetier Science Ltd Printed in Great Britain. All rights reserved 09674645195 $9.50+0.00

The relationship between beam attenuation and chlorophyll concentration and reflectance in Antarctic waters S. SAGAN,*

(Received

A. R. WEEKS,t§ I. S. ROBINSON,t and J. AIKEN$

G. F. MOORE+

20 June 1994; in revised form 10 May 1995; accepted 31 May 1995)

Abstract-Optical and biological data collected during the two-month R.R.S Discovery expedition in Antarctic waters in the Austral spring were used to examine the relationships between c (660 nm), chlorophyll a and the reflectance ratios R(443)/R(550), R(490)lR(550) and R(443 nm). Values of (c - c,)(660 nm) varied from 0.20 to 1.2 m-‘, being generally positively correlated with the chlorophyll concentration. The computed values of the pigment specific beam attenuation coefficient c* were greater than those typical in Case I waters, exceeding 0.9 n? mg-’ , reducing the accuracy of the chlorophyll remote sensing algorithms designed for Case I waters. The covariance of phytoplankton and related pigments with beam attenuation was examined by the regression equation c - c, = A + B(Ch1 a + phaeo), and the residuals were used to identify the non-chlorophyll dependent data in the region. When the non-chlorophyll dependent data were removed, the accuracy of the chlorophyll remote sensing model was significantly improved.

INTRODUCTION The spatial variation of optical properties of the water and the underwater light-field depend largely on the constituents of the water. Some of them, like phytoplankton, yellow substance and organic detritus (so-called autochthonous substances), are associated with primary production. They are correlated with the chlorophyll concentration, while other allochthonous, optically active constituents (mineral suspensions, river-borne material etc.) are not. Case I water is characterized by low concentrations of material not correlated with chlorophyll a (Morel and Prieur, 1977). More than 98% of the waters in the World Ocean are Case I, being usually oligotrophic and stratified. The remainder, mostly in the coastal zones of oceans and in enclosed seas, are Case II. The spectral reflectance of the water R(1) is modified by processes of light absorption and scattering by the constituents of the water. In Case I water reflectance is determined mainly by phytoplankton, in which the biomass is closely related to the concentration of pigments (chlorophyll) within phytoplankton cells. This dependence is the key to the remote determination of chlorophyll and ultimately to the productivity of the ocean. The *Institute of Oceanology, Polish Academy of Sciences, Powstancow Warszawy 55,81-967 Sopot, Poland. tDepartment of Oceanography, The University, Southampton Oceanography Centre, Waterfront Campus, European Way, Southampton SO14 3ZH, U.K. SPlymouth Marine Laboratory, Prospect Place, Plymouth PLl 3DH, U.K. OPresent address: Southampton Institute, East Park Terrace, Southampton SO9 4WW, U.K. 983

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task of developing accurate reflectance algorithms for determining chlorophyll concentrations from reflectance is therefore of great importance if ocean colour measurements from satellites are to yield accurate estimates of phytoplankton biomass. The concentration of chlorophyll is usually well correlated with the beam attenuation coefficient (c) for A> 600 nm in Case I waters, with the correlation coefficient r exceeding 0.7-0.8 (Mitchell and Holm-Hansen, 1991). The reason for this usually significant correlation is that beam attenuation is dominated by scattering throughout the visible spectrum. Beam attenuation in the red part of the spectrum depends on the optical properties of the suspension and its concentration in the seawater and the attenuation by the water itself (c,), while the role of dissolved substances is negligible (Jerlov, 1976). The relation between (c - c,) and (Chl a + phaeo) can vary for several reasons. There may be other water constituents, both of organic or non-organic origin, that are not related to chlorophyll concentration, or there may be changes in optical properties of plankton cells. For example variations in the microbial composition of seawater may cause significant changes in the bulk optical properties of seawater even if total chlorophyll concentrations remain constant (Mobley and Stramski, 1994). This is because different microbial components have different optical properties (scattering and absorption crosssections and scattering phase functions). Furthermore, the attenuation cross-section of even a single species of phytoplankton may vary due to size, shape or refractive index. These parameters can vary due to varying levels of light, as a function of growth conditions or even throughout the die1 cycle (Stramski and Morel, 1990; Stramski and Reynolds, 1993). The ratio of chlorophyll to carbon may vary significantly, depending on species composition and changes in light/nutrient regimes (Riemann et al., 1989; Furuya, 1990). For example, the relationship between beam attenuation and chlorophyll may change if the cell size increases but the cellular chlorophyll remains the same. Morel (1988) shows a clear and non-ambiguous relationship between carbon and chlorophyll, and that nonlinear relationships are true for both the carbon/chlorophyll ratio and for inherent water optical properties (coefficients of scattering and absorption) and chlorophyll. Regardless of the cause, the alterations in the relationship between (c - c,) and (Chl a + phaeo), indicating changes in either light absorbtion (u) or scattering (b), may affect the reflectance ratio and chlorophyll remote-sensing algorithms. This study examines the relationship between (c - c,) and (Chl a + phaeo) and determines whether knowledge of the variability in the in situ bio-optical properties of the water can be used to increase the accuracy of the chlorophyll remote-sensing algorithms. METHODS

AND INSTRUMENTATION

This study is based on data collected during the BOFS STERNA Cruise, which was carried out in the Bellingshausen Sea in November and December 1992 on the British vessel R.R.S. Discovery (Turner, 1993) (Fig. 1). The data used for this paper come from the stations marked on the enlarged areas on Fig. 2 (Survey 1 and Survey 2). Beam attenuation ~(660 nm) was measured using a Seatech transmissometer (Bartz and Zaneveld, 1978) by automatic sampling from a pumped supply, from an intake 3 m below the surface. The sampling frequency was every 30 s. The signal was corrected for the index of refraction and compressibility, using in situ values of pressure, temperature and salinity. The value of light attenuation at L= 660 nm for the clear water was subtracted

Beam attenuation,

Fig. 1.

The track

of the R.S.S.

Discovery

985

chlorophyll

during cruise D198, 1992.

11 November

to 17 December

(Morel, 1974), resulting in (c -c,) values that depend only on the suspended material in the water. Continuous chlorophyll fluorescence was measured by an underway fluorometer (Chelsea Instruments) from the pumped supply and was also logged every 30 s (the same as that used for c). Measurements of the chlorophyll a and phaeopigments were made by analysing 131 samples drawn from the same pumped water supply at regular intervals of approximately 13 km throughout the cruise. Analyses of chlorophyll a and phaeopigments were carried out by filtering 100 ml aliquots of seawater onto 25 mm glass fibre filters (Whatman GF/F) and by extraction in 10 ml of 90% acetone for a minimum of 15 h. Analyses were carried out on a Turner Designs 1OAU fluorometer fitted with a 25 mm cuvette system. The fluorometer was calibrated before and during the cruise with chlorophyll a standard (Sigma C5753). Chlorophyll estimated from in vivo fluorescence and “extracted” (Chl a + phaeo) showed a strong linear relationship (Fig. 3), with a high 2 coefficient (0.987). This allowed the recalculation of chlorophyll fluorescence data in terms of the (Chl a + phaeo) by applying the calculated formula, which gives a significantly bigger data set (N = 718). The optical sensors installed on Seasoar (Turner, 1993) provided spectral upwelling and downwelling irradiance (E,, Ed) at seven wavelengths equivalent to the SeaWiFS bands (Aiken and Bellan, 1990). The reflectance ratio R(il) was then calculated at the depth of 3 m. The horizontal resolution of the measurements in the upper water layer was approxi-

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The ship’s track during Survey I(*) and Survey 2 (0). Survey 1 was carried out between 23 and 28 November 1992, Survey 2 was between 2 and 4 December 1992.

mately 3 km. The values of R(443)lR(550), to model the chlorophyll contents.

R(490)lR(550) and R(443 nm) were later used

RESULTS The spatially overlapping Surveys 1 and 2 covered an hydrographic front and an extensive phytoplankton bloom, which was situated between approximately 69.10‘S to 68.30% (Turner and Owens, 1995) (Fig. 4). A strong east-west frontal boundary was observed, with the sum of the concentrations of chlorophyll a and phaeopigments ranging from 0.5 mg m3 north of the bloom, to 9.6 mg m3 within the bloom area. The range of the concentrations of (Chl a + phaeo) during Survey 2 was 1.0 to 6.5 mg rnp3. The ratio (c - c,)/(Chl a + phaeo) was calculated for both areas. The distribution of this ratio, called the pigment specific particulate beam attenuation coefficient (c*), is plotted in Fig. 5. The reciprocal of c* was found to follow closely the chlorophyll patterns (Fig. 4). In the bloom area it has values from 0.1 to 0.4 m2 mg-’ and 0.4 to 0.9 m2 mg-’ outside the area. Values outside the bloom were particularly high in respect to those usually found in the open ocean, where typically they are between 0.05 to 0.5 m2 mgg’ (Mitchell and Kiefer, 1988; Pak et al., 1988). The value of c* = 0.5 has been used as a criterion for differentiating between Case I (c* < 0.5) and Case II (c* > 0.5) waters (Mitchell and

987

Beam attenuation, chlorophyll

r2 =

so.0

0.0

I,

2.0

0.98

I

I

I

,

4.0

(Chla+phaeo) Fig. 3.

1

t

t

,

6.0

II

I,

8.0

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u

I

1C 0

[ mg/m3]

Relationship between chlorophyll measured by in vivo fluorescence and chlorophyll + phaeopigments taken from water samples drawn from 3 m below the sea surface.

Holm-Hansen, 1991). Such high ratios were unexpected, since the investigated area is a part of the open ocean (Fig. 1). Chlorophyll remote sensing algorithms developed for Case I waters show significant differences from those for Case II waters (Doerffer, 1990). Since part of the water in the investigated area had features associated with Case II water, it may have affected the accuracy of the remotely estimated chlorophyll derived with a Case I algorithm approach. This was examined by comparing c - c, with Chl a + phaeo (Fig. 6) and applying both linear and nonlinear approximations. The decision was made to use the linear approximation. However, it is understood that such a model does not reflect exactly how the physical processes work but only allowed a better separation of data with high variability of c - c, associated with smaller changes in the chlorophyll concentration. The covariance of phytoplankton and related pigments with beam attenuation was determined by applying the regression in the form c - cW= A + B(Ch1 a + phaeo)

(1) to the data taken from Survey 1 and Survey 2. This resulted in 2 = 0.63, compared with ? = 0.55 for the nonlinear model (c - c_,= A*(Chl a + phaeo)B). The difference between the linear and non-linear model results are small, and close examination of Fig. 6 suggests that the slope may be steeper at low values of chlorophyll. This was explored further by dividing the data into two sets with chlorophyll greater than

988

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SURVEY

1

SURVEY 2 -67.00

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-68.00

S

-85.10 Fig. 4.

-69.00 -84.14 w

Horizontal distribution of Chl a + phaeo (mg me3) for Survey 1 and Survey 2. Samples (.) were taken from pumped supply 3 m below surface.

5 mg mP3 and less than 5 mg m-s, and carrying out the non-linear regression. The slopes were 0.002 and 0.09, respectively, but the r* values were 0.01 for chlorophyll > 5 mg me3 and 0.57 for chlorophyll < 5 mg mP3, revealing a poor fit of the model to the data at high chlorophyll values. It is therefore not possible to determine whether there was a change in slope, denoting a non-linear relationship between c - c, and pigments. Despite a relatively high correlation coefficient, there is a cluster of data in which the variability of c - ~4660 nm) is independent of the chlorophyll (Fig. 6). The range of this c - 4660 nm) variability is between 0.2 and 0.6 m-’ and is confined to low chlorophyll concentrations, less than 2.0 mg m-3, originating from the area outside the phytoplankton bloom. Application of the linear rather than the nonlinear model gave the smaller correlation of residuals vs c - c,, (T,in = 0.60, Ynonlin= 0.65), providing a better explanation of the variance. This leads to the assumption that there may be other substances not related to chlorophyll present in the water. The plot of residuals of the regression model (1) against c - c, (Fig. 7) shows that some of the data are still dependent on c - c,. When the data within the cluster were analyzed separately, the correlation of the residuals with c - c, was 0.99. The data in the cluster from Fig. 7 were found to fall in the range (Chl i I zaeo)

> o’4

(2)

Beam attenuation,

SURVEY

989

chlorophyll

1

SURVEY 2 -67.00

-67.00

-67.50

-67.50

-68.50 S

-85.10 Fig. 5.

Horizontal

distribution

of c* [(c - c,)/(Chl a + phaeo)] for Survey shaded area corresponds to c* > 0.4.

-84.14

ii6g.oo

1 and Survey 2. The

which falls within the criteria for Case II waters. These data are confined to the hatched area on Survey 1 and Survey 2 (Fig. 5)) which in both cases were outside the bloom. There were no other data satisfying criterion (2). To determine if these optical characteristics have an influence on the accuracy of the remote estimation of chlorophyll biomass, the remote sensing model (3) was applied to the data collected: log,” (Chl a + phaeo) = C + D[R@,)lR(&)].

(3)

Ratios of R(443)lR(550), R(490)lR(550) were used in equation (3). Values of R(443) were also used for R(&)IR(I,). The results of the calculations are presented in Table 1. The second column represents the results for all the data collected (Survey 1 and Survey 2); in the third column the same calculations have been applied to the data set with the cluster fulfilling c* > 0.4 criteria (equation (2), Fig. 7) removed. In all three versions of the algorithm, after removing the cluster of data, the r2 coefficients were significantly greater, and the standard errors were lower. As an example, the plots of R(443) vs chlorophyll (for all the data and the reduced data set) are presented (Fig. 8). Estimation of the chlorophyll concentration using the reflectance R(443) algorithm gave better results than the reflectance ratios, both for the whole and the reduced data set. This may be explained by the fact that irradiance (E,(L), _&(A)) measurement errors can accumulate when R(L) is calculated. Subsequent calculations of R&)/R(&) cause the

990

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1.20 A 1.00 ‘E

et al.

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Relationship between the beam attenuation coefficient c - c,,,(660 nm) (m-l) and Chl phaeo (mg m-‘).

Fig. 6.

g .r( z

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a +

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Fig. 7.

Residuals of the c - c, =

A +

B(Chl D + phaeo) regression. Squares represent c* > 0.4.

Beam attenuation, Table 1.

991

chlorophyll

Chlorophyll estimation by the model: log,,(Chl a + phaeo)= C + D[R(I,)/R&)] algorithms for all the data and the reduced data set (c* < 0.4) c < 0.4

All data Algorithm R(433)lR(550) R(490)lR(550) R(443) 2 is the squared

r2

c

0.37 0.05 0.54

0.649 0.370 0.843

correlation

coefficient;

for a number of

D -0.268 -0.058 -12.461

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c

0.257 0.310 0.201

0.48 0.52 0.75

0.746 0.855 0.913

C, D are model coefficients;

u is the standard

D -0.279 -0.655 -12.296

o 0.196 0.190 0.141

error of estimation.

errors to increase. For example since measurements were carried out close to the surface, the waves (of observed heights l-l.5 m) can affect the lengths of optical light paths for different Ed(&), contributing to errors in calculations of R(&)IR(&J. The results presented here show that the optical data within the high chlorophyll area provided a better measure of the phytoplankton biomass than that outside the area where the ocean colour signal gave an inferior measure of the chlorophyll biomass. The reasons for this may be explained by differences in the optical properties of the suspensions. DISCUSSION The values of c* in the part of the investigated area with low chlorophyll values (4 mg me3) were significantly greater than those usually found in the open ocean. This may be caused by the packaging effect in the large phytoplankton cells typical of high latitude waters, or by adaptation to low light levels, where the proportion of pigment per cell is increased (Mitchell, 1992). Furthermore, Antarctic phytoplankton are known to have lower concentrations of protective carotenoid pigments, and this reduces the pigment specific absorption. The relationship between beam attenuation and pigments can be compared with the relationship between the scattering coefficient and pigments. It is clear from Fig. 6 that this relationship is less typical of scattering versus pigments for Case I waters (Morel, 1987) than for that for Case II waters, where there is greater variability in the relationship (Gordon and Morel, 1982). This is further confirmation that the waters described here are not typical of the open ocean (Case I). Removing the data by application of criterion (2) led to a significant improvement of the remote sensing model results. As a consequence of changes in regression coefficients calculated in (3) when the discussed portion of data was removed, higher pigment predicted concentrations were obtained. There are a number of possible reasons for the departure from the expected optical signal in the areas outside the bloom. For example the die1 cycle variability can have a significant effect on values of c* , but it is believed to have negligible influence in this study since during both surveys the northern end was carried out at night/early morning and the southern end during the day. Furthermore the variability in c* is different to that described by Stramski and Reynolds (1993), who noted higher c* during the daytime hours. In this study values of c* were lower in the north, the darker periods of the die1 cycle, but were also lower in the south, during the middle of the day.

992

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8 I 0.04

’ I 0.06

8 1 0.08

1 0.10

R( A=443

Reflectance

’ 0

2

nm)

b) r2 = 0.75

t ** 0

zli

c

F

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0.00

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-0 .+

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O.dO

II 0.02

$ 0.04

0 II 0.06

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II 0.08

I 0.10

R( A=443

c

0. 2

nm)

Fig. 8. Plot of Chl a + phaeo versus R(443 nm) according to the model obtained from the regression: logIO(Chl a + phaeo) = C +D * R(443 nm) model (see Table 1). (a) All data, 54% of variance

explained;

(b) c* > 0.4 removed,

76% of variance

explained.

Changing growth irradiance may cause a difference in c* according to Stramski and Morel (1990) who show that c* is higher for high light adapted cells. This phenomenon was certainly observed here since the phytoplankton in the bloom area, between 67.3”s and 68.5’S, experienced less light as they circulated through the mixed layer than those outside

Beam attenuation, chlorophyll

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the bloom, due to the high concentration of mixed-layer phytoplankton (Weeks et al., 1994). Therefore the phytoplankton north and south of the bloom may have been adapted to relatively high light levels, since the mean mixed-layer irradiance values were greater than those within the bloom. The variation in the species of the phytoplankton may be expected to influence c” by as much as an order of magnitude. Bricaud et al. (1988) found variations from 0.05 to 0.5 m* mg-t Chl a at 660 nm in a study of 10 phytoplankton species. Morel et al. (1993) demonstrated the optical variability for cyanobacteria and prochlorophytes. Mobley and Stramski (1994) noted significant variations in the scattering cross-sections for different phytoplankton species, and these differences were subsequently observed in simulations of the apparent optical properties of the water. The differences in the composition of the phytoplankton varied to some extent in the region. The percentages of centric diatoms, flagellates and pennate diatoms made up 89%, 4% and 7%, respectively, at 67.5’S, 85.O”W, in the bloom and 58%, 33% and 9% at 68.25”S, 85.O”W, south of the bloom (Robins et al., 1995). In addition some of the centric diatoms were in poor condition at the station south of the bloom. The variation in c* may be explained by the light and nutrients regimes, which can affect the carbon/chlorophyll ratio. This ratio is usually higher in the surface layer, where the chlorophyll concentration in phytoplankton cells is lower than in deeper layers (Furuya, 1990), and therefore may have been higher in the area outside the bloom, where more light was available in the mixed layer (Weeks et al., 1995). Since chlorophyll represents only a minor portion of cellular dry weight, in the area with low chlorophyll concentrations the phytoplankton cells themselves may contribute more to light attenuation by comparatively higher scattering, resulting in lower correlation values between c - c, and low chlorophyll concentrations (0.5-2.0 mg mP3, Fig. 6), and an increase in the ratio c* (2). The possibility of any additional non-chlorophyll related constituents, such as suspended sediments and/or DOC, in the water is small because the study was carried out in the open sea. Since there were no measurements of the concentration of suspended sediments during the experiment, it is not possible to determine whether non-chlorophyll suspended material was present in the water, and this cannot be excluded. Similarly. it is not possible to account for the optical influence of DOC as no measurements were made. The optical properties of the water are determined by the history of the water as well as by the chlorophyll content. Therefore the presence of even small quantities of any remnants of a previous bloom (e.g. organic detritus) may affect optical properties of the area with the low chlorophyll concentration. If this were the case then the result would be similar to that described above; higher c*, and the reflectance signal modified by changes in absorbtion and scattering processes. The superiority of the linear model (1) over non-linear models is surprising considering other work on the subject. Morel (1988) showed that non-linear models for the relationship between the diffuse attenuation coefficient (Kil) are appropriate. This is because at high concentrations of phytoplankton KA is influenced by the relatively high absorption by pigments whereas at low concentrations KA is influenced by the more even absorption by pigments and detrital material. Microscopic observations of the material along with the phytoplankton suggest that outside the bloom there was significant detrital material, and that the phytoplankton were less healthy outside the bloom. These observations provide additional support to the view that there should be a non-linear relationship between c” and phytoplankton chlorophyll. The fact that the improvement of fit of the linear model

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over the non-linear model is small (8%) makes it inappropriate to challenge the use of non-linear models in other applications. It is interesting to note that there is considerable scatter in the data when values of chlorophyll exceed 5 mg me3 which may be a characteristic of the bloom studied. We can only conclude that this ambiguity is caused by a feature of the water constituents, and further examination of this would be purely speculative. The plot of the mean reflectance spectra (Fig. 9) reveals differences between flatter spectral shape and low values within the bloom and steeper spectral slope with higher values away from the bloom, corresponding to shaded areas on Fig. 5. A steeper reflectance spectrum may suggest a relatively greater contribution of light scattered by particles and water. These spectra are comparable to those expected for different pigment concentrations in Case I waters (Morel, 1988). However, the reflectance spectra for the water area out of the bloom ~(600 nm) > 0.4 have higher slopes than those for the equivalent chlorophyll content. Since in both cases there is a low chlorophyll concentration (< 1 mg me3), the observed differences may be attributed to the presence of some other water constituents within the water characterized by ~(606 nm) > 0.4. Regardless of the mechanism of the process, our results demonstrate that the accuracy of reflectance algorithms is reduced by variations in the optical properties of the suspensions of phytoplankton. Removing the part of the data which is not strongly related to chlorophyll (c* > 0.4), but still contributing to the reflectance signal, led to a significant improvement in the results derived from the application of the chlorophyll remote sensing algorithm.

0.00

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t

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1

Wavelength

11

I

I

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t

1

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Fig. 9. Mean reflectance spectra for three regions in the survey area. Dashed line is c* < 0.4, high chlorophyll area; dotted line is c* < 0.4, low chlorophyll area, north-west of Survey 1; solid line is c* > 0.4, low chlorophyll area.

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Acknowledgements-The

work was carried out by means of grants from the Natural Environment Research Council Special Topic for the World Ocean Circulation Experiment and for the NERC British Antarctic Survey Antarctic Special Topic. Dr Sagan was supported by a Royal Society Fellowship. The authors gratefully acknowledge these grants. The authors would like to thank the officers and crew of the R.R.S. Discovery and R.R.S. James Clark Ross and the technical staff from the research vessel services and Southampton University Department of Oceanography for their contribution to the work. The authors thank Darius2 Stramski and Marcel Wernand for careful review of the manuscript. This is BOFS contribution number 247.

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