A NOVEL TECHNIQUE TO ESTIMATE PRIMARY PRODUCTION DIRECTLY FROM EARTH OBSERVATION DATA: AN INHERENT OPTICAL PROPERTY APPROACH

Kathryn Barker (1), Tim Smyth (2), Samantha Lavender (1), Jim Aiken (2)

(1)School of Earth, Ocean and Environmental Sciences, A504 Portland Square, Drake Circus, University of Plymouth, Plymouth, Devon, United Kingdom. PL4 8AA.

Email: kathryn.barker@plymouth.ac.uk; s.lavender@plymouth.ac.uk : (2)Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, United Kingdom. PL1 3DH.

Email:tjsm@pml.ac.uk; ja@pml.ac.uk ABSTRACT

Phytoplankton primary production (PP) plays a major role in the uptake of oceanic carbon and is hence a key process for the carbon cycle. Traditionally PP models have always included some parameterisation of chlorophyll. However, chlorophyll retrieval can be complicated in Case 2 regions, usually coastal areas, where the chlorophyll estimated from the satellite sensor may be impaired by other substances. The resulting overestimation of chlorophyll then perpetuates through to PP models, causing inaccuracies in the estimate. Results of a new Inherent Optical Property (IOP) based spectral model are presented. The model is a triple integral of wavelength, depth and day length with the dependency on chlorophyll concentration estimates removed by replacing the chlorophyll parameter with absorption by phytoplankton. A basin mask, applied to the model output, delineated yearly and monthly PP values for the globe and 24 sub-regions globally, including the Pacific Ocean, Atlantic Ocean, Mediterranean Sea, Southern Ocean, Artic Ocean and Indian Ocean. Comparison to existing PP models utilising chlorophyll provides a quantitative indication of model performance. Results are at an early stage, but global PP patterns correspond to chlorophyll distributions except in regions where chlorophyll concentration is supposed to be overestimated and the IOP PP model suggests lower PP. Global yearly IOP PP is estimated to be around 30 Gt C y-1 which may reflect a more appropriate capability to deal with the presence of interfering in-water constituents such as CDOM. Therefore, results indicate the potential for this approach as a viable alternative to the use of satellite-derived chlorophyll concentrations.

INTRODUCTION The major carbon flux in the ocean is that due

to photosynthesis by phytoplankton. Primary production (PP) utilises CO2 and sunlight through photosynthesis and plays an integral role in the global carbon cycle. Establishing the rate of production is

important, providing a dynamic parameter linking phytoplankton to CO2 drawdown from the atmosphere and hence carbon fluxes in and out of the ocean. Estimates of carbon uptake through PP range from around 30 Gt C y-1 based on in situ measurements and prior to developed satellite productivity models [1-3] to between 45 and 50 Gt C y-1 [4], based on satellite measurements.

The logistic problems of large spatial- and temporal-scale ship-borne measurements are such that determination of PP has become a major target of global earth observation (EO), and modeling is required to further improve these estimates. PP models are of varying degrees of complexity [e.g. 5, 6], involving (or not) some temperature and light parameterisation and have traditionally always included some parameterisation of the primary photosynthetic pigment chlorophyll-a (Chl). Marine primary production increases with the amount of solar radiation available for photosynthesis, PAR, and with the abundance of Chl [7]. Phytoplankton respond to changes in light, nutrients and temperature conditions by adjusting cellular pigment levels to match their new demands for photosynthesis [8]. Current PP estimates must use Chl, a routine product of satellite measurements, as an index of phytoplankton biomass as remote determination of phytoplankton carbon (C) biomass is not possible as yet [9]. Empirical PP models, derived from field measurements, rely on constant relationship between Chl and PP. However, these relationships are not constant and vary as a function of light levels, nutrients, mixing stress etc [e.g. 10] so more complex, semi-analytical, models have also been developed.

The response of phytoplankton to available light is well established, [10, 11] and more complex algorithms utilise a knowledge of physiological response and light absorption properties. Recent modelling efforts have focused on the parameterisation of the in water light field [e.g. 6, 9, 12]. Behrenfeld and Falkowski [12] developed the vertical generalised productivity model (VGPM), which is depth independent but was one of the first models to develop a parameterisation of the light field, and is still widely used. A different approach was taken by Behrenfeld et

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Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

al. [9] who worked on the physiological basis of the carbon to Chl ratio. The spectral PP model of Morel [13] and Morel et al. [14] is a triple integral of time, depth and wavelength and includes light and Chl parameters. Smyth et al. [15] took the Morel [13, 14] model further and coupled it to a look up table (LUT) encompassing a broad range of CDOM and sediment concentrations, and corresponding PP values resulting from various scenarios of these concentrations. In this way the problem that has eluded PP models so far, to accurately predict PP in Case 2 [as defined by 16] waters, was significantly tackled.

It is well recognised that Chl retrieval from satellites can be complicated in coastal areas where Chl algorithms, designed to utilise satellite data, can be impaired by other substances that vary independently of Chl. These substances, namely Coloured Dissolved Organic Material (CDOM) and suspended sediments interact with light at the same wavelengths as Chl. Differentiation between the Chl and CDOM components becomes difficult and consequent overestimation of Chl by retrieval algorithms [e.g. 17] propagates through to PP models causing inaccuracies in the estimate. In coastal and shelf sea regions CDOM and suspended sediment concentrations are often high, the effects these constituents have on the in situ and satellite-observed light being termed the Inherent Optical Properties (IOPs). Kirk [8] and Mobley [18] explain these effects in detail, but this study is concerned primarily with the total absorption (at), total backscatter (bb) and absorption due to phytoplankton (aph) IOPs.

As coastal zone PP accounts for around 30% of the global ocean production [19], improvements in modeling PP in these optically complex waters are necessary. Correct parameterisation of the effects of IOPs on the in-water light field using satellite data offers a unique approach to PP modelling. In particular, the aph parameter has been recognised as having potential for PP modeling [e.g. 20, 21], as at(λ) is directly remotely-sensed and not hindered by any other water-leaving signal. Given that at(λ) and bb(λ) are estimated from model inversion methods [e.g. 22, 23] rather than retrieval algorithms, the use of aph(443) in the calculation of PP is subject to less potential for error. A direct relationship between Chl and aph(443) is given by Bricaud et al. [24, 25] as:

728.0][0654.0)440( Tchlaa ph =

and may be utilised to convert Chl to aph if necessary. Tilstone et al.[26] agreed that this relationship stands even in Case 2 waters such as the Irish Sea. Moreover, a simple relationship of aph = a*.Chl is also applicable,

where a* is the specific chlorophyll absorption, forming the basis of inversion algorithms to derive Chl from IOPs and employed in the present study to remove direct dependence upon Chl algorithms.

METHODS The basis of the IOP-based PP model is the spectral formulation of Morel [13] following the parameterisations of Morel et al. [14]. Dependence on remotely-sensed Chl estimates was removed by replacing a* and Chl with aph; this can be represented using the triple integral of wavelength, depth of the euphotic zone (1% light level) and day length:

dtdZdtZtZEtZahcN

PL D o

phA

λφλλ μ

λ

λ),(),,(),,(12

0 0

1

2∫ ∫ ∫=

Where P is daily realised column production (g C m-2 d-1), oE is the

scalar irradiance (W m-2 nm-1), μφ is net growth rate (mol quanta)-1,

h is Plancks constant (6.626 x 10-34 Js), c is the speed of light (2.998 x 108 ms-1) and NA is Avagadro’s number (6.02 x 1023 mol quanta)-1. EO data was used, through a radiative transfer model [23], to determine spectral absorption, at(λ) and its components, including aph(λ), and backscattering,

bb(λ). Scalar irradiance oE (λ) was calculated to 201 m

depth using the HYDROLIGHT program [27], look-up tables of which were referenced to the PP model code using log-space values of at(λ) and bb(λ). The model was run over a wide range of several parameters: day length, aph, iPAR, at, bb, SST, and assumed a Chl concentration of 0.1 mg m-3, although this has little impact on the model. A large look up table (LUT) of ~4,000,000 PP values at all combinations of the input parameters was produced to alleviate the complications of such a computationally expensive model, and is ultimately capable of deriving PP through interpolation for a wide range of marine conditions.

Global satellite-derived (1998 – 2005, monthly level 3, 18 km Sea-viewing Wide Field of view Sensor, SeaWiFS) PAR, Advanced Very High Resolution Radiometer (AVHRR) SST and the new aph(λ) data was then used to reference the LUT for corresponding PP values via interpolation, pixel by pixel, and global, monthly PP maps were output. A basin mask was developed and applied to the output PP, from which monthly and yearly values for the globe and 24 sub-regions globally were obtained, although the present paper will focus on coastal regions, namely the Baltic and Irish Seas, both predominantly Case 2 [e.g. 28].

RESULTS 2000

Mar

Jun

Sept

Dec

0 6 Gt C y-1

Results indicate the potential for use of aph coupled to a LUT as a viable alternative to the use of satellite-derived Chl. On a global basis seasonal PP patterns have been reproduced (Fig. 1), and they correspond to global Chl distributions, as predicted from MERIS and SeaWiFS. Sensitivity analyses showed PP response to the input parameters to be as expected, increasing with increased temperature, light and aph.

Global monthly PP (Fig. 2) was predicted at no more than 3 Gt C y-1. Averaged over 1998-2005, yearly global PP was predicted at 30 ± 0.25 Gt C y-1. Fig. 3 shows how PP estimates varied from 1998-2004. Some interesting differences in PP distribution have been observed, compared to Chl-based PP maps, and the basin mask has allowed for a more definitive consideration of the model’s performance. In Fig. 4 a comparison of the major basins shows the model has reproduced seasonal cycles of the basins and Fig. 5 compares the IOP PP model output with Chl-based models.

Traditionally global PP maps clearly indicate PP to be high in regions where CDOM and/or suspended sediments concentrations are known to be high. Examples of such regions include the Baltic Sea (Fig 5a) and the Irish Sea (Fig. 5b). Shown also in Fig. 4c is the North Atlantic, less dominated by CDOM and suspended sediments, but still a highly productive region. Fig. 5 shows how different approaches to modelling PP can produce very different estimates, with Chl-based depth-independent models such as the Behrenfeld and Falkowski (1997) VGPM estimating at the higher end of the range, and triple integrals such as Smyth et al. [15] and the IOP PP model lowering the estimate. Moreover, the IOP PP model has lowered variability in the estimate also.

In the Baltic the effect of using aph is especially evident, suggesting aph to deal very differently with CDOM than do Chl retrieval algorithms. Fig. 5 illustrates this effect, with apparently much lower PP predicted in the Baltic compared to the VGPM. The model estimated a yearly PP value for the Baltic Sea of 0.11 ± 0.01 Gt C y-1 for 1999, compared to 0.19 ± 0.02 Gt C y-1 from the Smyth et al. [15] model which also utilises a LUT approach but for ranging concentrations of CDOM and sediments.

DISCUSSION AND CONCLUSIONS The advantage of using aph in estimating PP is evident, and has highlighted a distinct difference in the way PP in Case 2 waters may be measured. Coastal regions account for approximately 10% of the global ocean area [19] and around 30% of global productivity, and as such contribute significantly to the global estimate of PP. While the results of this IOP-

Figure 1. Global PP for selected months of each season of 2000. Global PP patterns and distribution are reproduced by the IOP PP model as expected. 1998 1999 2000 2001 2002 2003 2004

0

10

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80

90

Year

ly P

P (G

t C y

)

Behrenfeld et al., 2005.Behrenfeld et al., 1997Morel 1991Smyth et al., 2005Eppley 1985IOP PP Model

Figure 2. Global yearly PP estimates by the IOP PP model, in addition to Chl-based models. Shown 1998-2004 as Smyth et al., 2005 is not implemented to 2005.

-1

Joint and Groom 2000

.

Figure 3. Global monthly PP, averaged 1998-2005 (or 2003 for Smyth et al., 2005). based approach at first appear low in comparison to Chl-based models, the issue of overestimation of Chl in some coastal regions is one which requires addressing. Smyth et al. [15] improved PP estimates in coastal areas by developing a LUT, but their approach was still based upon satellite-derived Chl. The results of this aph based PP model so far indicate that IOPs evade the overestimation problem, bringing PP estimates down to a yearly estimate in line with in situ PP estimates determined prior to our now heavy reliance on satellite data, of around 30 Gt C y-1 [1-3]. The improved basin mask was essential to beginning the evaluation of the IOP PP model in costal areas. Masks which are limited

0.9

J F M A M J J A S O N D2

3

4

5

6

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8N. PacificS. Pacific

Figure 4. Major basin PP derived from the IOP PP model. Seasonality within the basins is evidenced by the model as expected. to major basins, such as the one used by Smyth et al. [15] and Carr et al., [5], are not sufficient for ascertaining model performance in more localised regions. Moreover, further definition of regions such as the Mediterranean and inland seas and lakes away from the major basins allowed for a more comprehensive assessment of PP in coastal and open ocean areas. Seasonality has been highlighted in the Pacific and Atlantic and, more importantly, coastal areas of known Chl overestimation have been identified specifically. Taking the Case 2 Baltic Sea as an example, the strong presence of CDOM in these waters has evidently been dealt with by aph, not being susceptible to its

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Smyth et al., 2005IOP PP Model

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Figure 5. Basin mask output highlighted regional differences in modeled PP in a) the N. Atlantic, b) Irish and Celtic Sea and c) Baltic Sea, illustrated in d) by image subsets of the Baltic Sea in June 2000, showing the difference between Chl-based PP (VGPM) and aph-based PP.

0 6 Gt C y-1

IOP Baltic VGPM Baltic

reflectance or absorbance properties. Attention in the optics community is now focused on IOPs as the future for PP modeling and this spectral triple integral is much advanced compared to other simplistic approaches that are beginning to appear in the literature [e.g. 20].

The IOP approach requires derivation of IOPs from satellite data via complex inversion methods, which means that at present there is a lack of globally available satellite aph data. So, anyone wishing to use aph in place of satellite Chl would need to model it first, whereas Chl is standard product from satellites such as MERIS, MODIS, SeaWiFS and now the GlobColour website (www.GlobColour.info). Regional aph data is becoming available via modeling efforts or through in situ measurements. However, there is much work to be done on IOPs and currently little to compare this IOP-based PP model to.

While validation is required these initial results are encouraging. A more detailed PP validation is currently underway, dependant on the availability of in situ data, a prevailing problem in this field. Case 1 data are available online, from ongoing time-series experiments such as HOTS and BATS. However, in situ data for Case 2 regions is a shortfall, paradoxically highlighting a problem that modeling in general aims to overcome. Algorithms that utilise knowledge of physiological response and light absorption properties are required for large scale (global) applications but should perform well in localised areas if the wide-ranging influences are captured. A LUT approach appears to be the best way to achieve this, offering a flexibility that other PP models do not have, and accounting for the effects of in-water constituents, rather than their concentrations. Once the LUT is developed, any remotely-sensed aph, SST and PAR can be used, with only interpolation required to acquire a PP value. While at present there is limited global aph data readily available, the potential is there for its acquisition through modeling with several IOP models currently available [29].

The fundamental basis of the photosynthetic process, that it takes places within the chlorophyll pigment molecule, is such that no PP model can entirely remove some consideration of Chl. However, the physiological response of Chl to environmental parameters should be reconsidered. The importance of observing water productivity and investigating the potential influences of climate change on the magnitude or redistribution of oceanic productivity is considered a high priority [30], and the preliminary results of this study indicate that an aph approach has potential. A more detailed validation and variance analysis will form the next stage of this investigation, and will eventually lead to the derivation of error budgets for the model, recognised [e.g. 29] as essential for IOP models and PP models alike.

Acknowledgements This study was supported by the U.K. Natural Environment Research Council, through the Centre for Observation of Air-Sea Interactions and Fluxes (CASIX), as a PhD Case Studentship.

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