Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

ORIGINAL RESEARCH published: 13 April 2017 doi: 10.3389/fmars.2017.00104 Frontiers in Marine Science | www.frontiersin.org 1April 2017 | Volume 4 | Article 104 Edited by: Chris Bowler, École Normale Supérieure, France Reviewed by: Ramaiah Nagappa, National Institute of Oceanography, India Salvatore Marullo, National Agency For New Technologies, Energy and Sustainable Economic Development, Italy *Correspondence: Robert J. W. Brewin [email protected] Specialty section: This article was submitted to Ocean Observation, a section of the journal Frontiers in Marine Science Received: 13 January 2017 Accepted: 27 March 2017 Published: 13 April 2017 Citation: Brewin RJW, Ciavatta S, Sathyendranath S, Jackson T, Tilstone G, Curran K, Airs RL, Cummings D, Brotas V, Organelli E, Dall’Olmo G and Raitsos DE (2017) Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups. Front. Mar. Sci. 4:104. doi: 10.3389/fmars.2017.00104 Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups Robert J. W. Brewin 1, 2*, Stefano Ciavatta1, 2, Shubha Sathyendranath1, 2, Thomas Jackson1, Gavin Tilstone1, Kieran Curran1, Ruth L. Airs1, Denise Cummings 1, Vanda Brotas3, Emanuele Organelli 1, Giorgio Dall’Olmo1, 2 and Dionysios E. Raitsos1, 2 1Plymouth Marine Laboratory, Plymouth, UK, 2National Centre of Earth Observation, Plymouth Marine Laboratory, Plymouth, UK, 3Faculdade de Ciências, Marine and Environmental Sciences Centre, Universidade de Lisboa, Lisboa, Portugal Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (<2µm), nano- (2–20 µm) and micro-phytoplankton (>20 µm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups. Keywords: phytoplankton, size, function, chlorophyll, ocean-color, uncertainty Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll 1. INTRODUCTION The size structure and taxonomic composition of phytoplankton influence many processes in phytoplankton biology, marine biogeochemistry and marine ecology (Chisholm, 1992; Raven, 1998; Le Quéré et al., 2005; Marañón, 2009, 2015; Finkel et al., 2010). Photosynthesis, growth, light absorption, nutrient uptake, carbon export, and the transfer of energy through the marine food chain, are all influenced by phytoplankton community structure (Platt and Denman, 1976, 1977, 1978; Morel and Bricaud, 1981; Prieur and Sathyendranath, 1981; Probyn, 1985; Geider et al., 1986; Legendre and LeFevre, 1991; Maloney and Field, 1991; Chisholm, 1992; Sunda and Huntsman, 1997; Raven, 1998; Laws et al., 2000; Ciotti et al., 2002; Bricaud et al., 2004; Devred et al., 2006; Guidi et al., 2009; Briggs et al., 2011). In the face of considerable challenges (Shimoda and Arhonditsis, 2016), growing emphasis has been placed on the representation of biogeochemistry in ecosystem models by explicitly incorporating different phytoplankton groups as state variables, often partitioned according to their size or taxonomic composition (Aumont et al., 2003; Blackford et al., 2004; Le Quéré et al., 2005; Kishi et al., 2007; Marinov et al., 2010; Ward et al., 2012; Butenschön et al., 2016). With this aspiration comes a demand for observations on phytoplankton groups (e.g., for model validation and data assimilation) that is not being met with current in situ observations that are sparse in time and space. To address the issue of data availability, the past decade has seen many attempts to estimate phytoplankton groups using satellite remote-sensing (IOCCG, 2014), which is capable of viewing the ocean with high temporal and spatial coverage. Current techniques to estimate phytoplankton groups using satellite data can be partitioned into three categories: spectral, abundance and ecological approaches (Nair et al., 2008; Brewin et al., 2011b; IOCCG, 2014). Spectral-based approaches seek to use the optical signatures of the phytoplankton groups directly for their detection from space. Abundance-based approaches invoke relationships between the phytoplankton groups and some index of phytoplankton abundance or biomass (e.g., chlorophyll concentration) that can be retrieved from satellites. Ecological-based approaches use ocean-color together with additional environmental data (e.g., sea surface temperature (SST), irradiance, wind) that can also be retrieved from satellite to identify ecological niches where particular phytoplankton communities may be found. Spectral-based approaches are more direct as they target known optical signatures, whereas abundance-based and ecological-based approaches are indirect, in that they use satellite remote-sensing as a means to extrapolate known relationships between the phytoplankton groups and a property that can by derived accurately from space (e.g., chlorophyll concentration, SST). Though it would appear more sensible to use a direct approach, issues with spectral-based techniques can arise when the signal-to-noise ratio in the oceancolor data is too low to detect the targeted signature (Garver et al., 1994; Wang et al., 2005), when the phytoplankton group being targeted has a similar optical signature to other groups, when the spectral signatures are not known sufficiently well, or when the spectral resolution is not adequate for detecting the target signature. In such cases, an indirect method (e.g., ecological or abundance based) would be more suitable. Future oceancolor missions will help address some of these issues through improved accuracy and spectral resolution. For instance, the recently launched Ocean and Land Color Instrument (OLCI) onboard ESA’s Sentinel-3a satellite offers more spectral wavebands than its predecessor (MERIS), and NASA’s planned Pre-Aerosol Clouds and ocean Ecosystem (PACE) mission will aim to provide hyperspectral ocean-color data, improving the potential for phytoplankton group retrievals. For further details on all of these methods, the reader is referred to the works of Nair et al. (2008), Brewin et al. (2011b), De Moraes Rudorff and Kampel (2012), IOCCG (2014), and Mouw et al. (2017). Recently, efforts have been made to combine abundance and ecologicalbased approaches, for instance, Brewin et al. (2015) and Ward (2015) modified the relationship between the chlorophyll concentration of the phytoplankton groups and total chlorophyll (abundance-based) according to the environmental (ecologicalbased) conditions (e.g., temperature or light availability). Phytoplankton group-specific satellite products are now being used for the validation of (Ward et al., 2012; Hirata et al., 2013; Hashioka et al., 2013; Rousseaux et al., 2013; Vogt et al., 2013; Holt et al., 2014; de Mora et al., 2016; Laufkötter et al., 2016), or assimilation of data into (Xiao and Friedrichs, 2014), ecosystem models. However, there are two challenges that modelers face when undertaking such analyses (Bracher et al., 2017). Firstly, there is often a mismatch between phytoplankton products provided by the remote-sensing community and those required by the ecosystem modelers. These difficulties arise in cases where a phytoplankton group adopted by the ecosystem modeler has similar optical properties to other phytoplankton groups, meaning they may not be detected directly using spectralbased methods, or the phytoplankton group does not co-vary in a predictable manner with variables amenable from remotesensing, limiting abundance-based and ecological-based methods and rendering the use of satellite products difficult. Greater dialog between ecosystem modelers and the remote-sensing community is required to bridge this mismatch where feasible. The second challenge is associating a level of uncertainty to the satellite phytoplankton group products, ideally on a per-pixel basis (per grid cell of the model). This is an essential prerequisite for both ecosystem model validation and data assimilation. If the uncertainties in the satellite products are too high they may not be useful for validation and may have little impact on a data assimilation scheme, since the target for data assimilation is to modify model simulations such that they agree with the observations within their uncertainties (e.g., Gregg et al., 2009; Ford et al., 2012; Ciavatta et al., 2014, 2016). Whereas many approaches have been proposed to derive satellite phytoplankton group products (IOCCG, 2014), few provide estimates of perpixel uncertainty. There are two methods commonly used to estimate uncertainty in ocean-color products: error propagation, or model-based uncertainties, and comparison of satellite estimates with in situ data (validation). Error propagation typically involves propagation of errors from input to output products, knowing the uncertainties in the input and model parameters. Frontiers in Marine Science | www.frontiersin.org 2April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll These techniques have been used for estimating uncertainties in chlorophyll concentration and inherent optical properties (Maritorena et al., 2010; Lee et al., 2011; Werdell et al., 2013a), and for some satellite phytoplankton group products (Kostadinov et al., 2009, 2016; Roy et al., 2013; Brewin et al., 2017). In addition to estimating per-pixel uncertainty, these techniques can be very useful for understanding the sensitivity of model parameters and model inputs on the output products (Roy et al., 2013; Kostadinov et al., 2016; Brewin et al., 2017). In a user consultation of ocean-color products, conducted as part of the ESA Ocean Colour Climate Change Initiative (OCCCI), there seemed to be a preference from ecosystem modelers for estimates of uncertainties based on comparison with in situ data, rather than model-based uncertainties (Sathyendranath, 2011). For most techniques, satellite phytoplankton group products have been validated with in situ data (see Table 3 of Mouw et al., 2017). However, this information is typically provided as a single statistic (e.g., root mean square error), which can be difficult to convert to a per-pixel error, considering uncertainties are likely to vary with the environmental conditions and the magnitude of the product. Furthermore, the distribution of data used in validation datasets may not be an adequate representation of the spatial and temporal variability in the region under study. To overcome these issues, Moore et al. (2001, 2009, 2012) proposed the use of an optical classification of pixels, together with fuzzy-logic statistics, to estimate per-pixel errors in satellite ocean-color products based on comparison with in situ data. In this approach, satellite and in situ match-ups are segregated into dominant optical water types (ranging from oligotrophic to turbid waters), then error statistics are computed for each dominant optical water-type. An ocean-color spectrum (at a given pixel) is then compared with all the optical water type spectra to determine its fuzzy membership. The fuzzy membership is then used to compute the error by weighting the errors in each dominant optical water type according to the fuzzy membership. This approach can, to a certain degree, overcome issues with the distribution of data used in the validation, and account for uncertainties varying with the conditions and the magnitude of the product. It has been adopted in the ESA OC-CCI project and is used to provide per-pixel errors (root mean square error and bias) for all OC-CCI products, including: chlorophyll, diffuse attenuation coefficient, and the inherent optical properties of oceanic waters. However, this approach has not been applied to satellite phytoplankton group products. The Copernicus Marine Environment Monitoring Service (CMEMS) project “Toward Operational Size-class Chlorophyll Assimilation (TOSCA)” seeks to address these issues by: (i) providing remotely-sensed products on phytoplankton groups that map onto those simulated by the European Regional Seas Ecosystem model (ERSEM; Butenschön et al., 2016), which is the ecosystem model adopted in this project; and (ii) provide uncertainty estimates for the remotely-sensed products on a perpixel basis, based on in situ match-ups (the preferred choice for ecosystem modelers; Sathyendranath, 2011). In this paper, we retuned an abundance-based method (Brewin et al., 2010, 2015) to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size, from satellite data in the North Atlantic. The abundance-based method was modified to account for the influence of SST (i.e., combining the method with an ecological-approach), and partition microphytoplankton into diatoms and dinoflagellates, so that the phytoplankton groups provided by the satellite approach match those simulated by ERSEM. Using an optical classification of pixels with fuzzylogic statistics (Moore et al., 2001, 2009, 2012; Jackson and Sathyendranath, 2015), we present a method for deriving perpixel uncertainty for each phytoplankton group based on a validation dataset of satellite and in situ match-ups, which is independent of the data used to parameterize the method. 2. METHODS 2.1. Study Area: The North Atlantic The chosen study site was the North Atlantic (Figure 1), spanning 46◦W to 13◦E and 20◦N to 66◦N, and categorized by the CMEMS Ocean Colour Thematic Assembley Centre (OCTAC) as the Atlantic (ATL) region. This region encompasses a range of bio-optical conditions from clear, deep open-ocean waters to shallower optically-complex shelf seas. We chose this site because of two factors: (i) it is a region that has been extensively sampled over the past few decades, resulting in a relatively large number of in situ observations on phytoplankton groups when compared with other regions of the ocean; and (ii) it has been subject to many studies on marine ecosystem modeling (e.g., Holt et al., 2014). The North Atlantic is also home FIGURE 1 | Locations of High Performance Liquid Chromatography (HPLC) and size-fractionated filtration (SFF) in situ data (<20 m depth) used in this study (CMEMS OCTAC ATL region). Background color show pixel-by-pixel correlation coefficients (r) of monthly Sea Surface Temperature (ESA SST products) and monthly average light in the mixed-layer between 2000 and 2010 [computed using Equation 11 of Brewin et al. (2015) with a monthly climatology of mixed-layer depth (de Boyer Montégut et al., 2004), monthly photosynthetic available radiation products from NASA SeaWiFS (http://oceancolor.gsfc.nasa.gov/), and Kdestimated from Morel et al. (2007) using OC-CCI monthly chlorophyll products]. Frontiers in Marine Science | www.frontiersin.org 3April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll to one of the largest spring phytoplankton blooms on the planet (Ducklow and Harris, 1993) and is known as a major region for the biological drawdown of seawater CO2(Takahashi et al., 2002, 2009) and primary production (Tilstone et al., 2014). 2.2. Statistical Tests To compare the in situ and satellite chlorophyll concentrations, we used the root mean square error (9) and bias (δ), consistent with the statistical tests adopted in the ESA OC-CCI project and used to provide per-pixel errors. The 9and δvalues were computed according to 9="1 N N X i=1XE i−XM i2#1/2 , (1) and δ=1 N N X i=1XE i−XM i, (2) where Xis the variable (chlorophyll concentration) and Nis the number of samples. The superscript Edenotes the estimated variable (e.g., satellite estimate) and Mthe measured variable (e.g., in situ). Note that the unbiased root mean square error (1) can be computed from 9and δaccording to 1=(92−δ2)1/2. In addition we also used the Pearson linear correlation coefficient (r), to see how well estimated variables and measured variables are correlated. All statistical tests were performed in log10 space, considering that the chlorophyll concentration is approximately log-normally distributed (Campbell, 1995). Definitions for all symbols used in the paper are provided in Table 1. 2.3. Data 2.3.1. High Performance Liquid Chromatography (HPLC) Pigment Data A total of 2,791 samples collected in the North Atlantic region and analyzed by High Performance Liquid Chromatography (HPLC) were used in this study (Figure 1), spanning 1995–2014. This dataset comprised of samples from: the Atlantic Meridional Transect (AMT) cruises 1-23 (Gibb et al., 2000; Barlow et al., 2002; Aiken et al., 2009; Brewin et al., 2010; Airs and MartinezVicente, 2014a,b,c; Brewin et al., 2015); the GeP&CO program (Dandonneau et al., 2004); the North Atlantic bloom experiment (Werdell et al., 2003; Westberry et al., 2010); the eastern Atlantic Ocean (Brotas et al., 2013); the North Atlantic, collected by the Bedford Institute of Oceanography (Sathyendranath et al., 2001; Devred et al., 2006); the Western Channel Observatory in the English Channel (Station L4 and E1; Smyth et al., 2010); a series of UK NERC-funded research cruises (D261, D262, D264, D325, JC011, JC037, and JCR656) in the North Atlantic and North Sea (Tilstone et al., 2015); and from the NASA bio-Optical Marine Algorithm Dataset (NOMAD Version 2.0 ALPHA, Werdell and Bailey, 2005), following the removal of any AMT data so as to avoid duplication. Details of HPLC methods used can be found in the aforementioned references. Only samples collected within the top 20 m of the water column (or within the 1st optical depth as in the case of the NASA NOMAD dataset) were used [i.e., within the surface mixedlayer depth (rarely <20 m; de Boyer Montégut et al., 2004)]. To control the quality of the pigment data, we used only HPLC data for which the total chlorophyll concentration was greater than 0.001 mg m−3(Uitz et al., 2006), and the difference between the total chlorophyll concentration and the total accessory pigments was less than 30% of the total pigment concentration (Trees et al., 2000; Aiken et al., 2009; Brewin et al., 2015). 2.3.1.1. Size-fractionated chlorophyll estimates from HPLC The fractions of total chlorophyll for the three phytoplankton size classes (Fp,Fn, and Fm, for pico-, nano-, and microplankton, respectively) were estimated following the methods of Brewin et al. (2015), adapted from Vidussi et al. (2001),Uitz et al. (2006), Brewin et al. (2010), and Devred et al. (2011). Note, whenever we refer to microplankton, nanoplankton and picoplankton, we are referring to phytoplankton. First, the total chlorophyll concentration (C) was estimated from the weighted sum of the seven diagnostic pigments, hereafter denoted Cw, according to Cw= 7 X i=1 WiPi, (3) where, the weights are denoted [W], and the diagnostic pigments [P] = {fucoxanthin; peridinin; 19′-hexanoyloxyfucoxanthin; 19′-butanoyloxyfucoxanthin; alloxanthin; total chlorophyll-b; zeaxanthin}. We computed the weights [W] using multi-linear regression on the 2,791 samples. Retrieved values for the weights compare reasonably to values derived globally (Table 2), and total chlorophyll (C) and total chlorophyll estimated from Equation (3) (Cw) were in good agreement (r= 0.99, 9= 0.10). Having derived Cw, the fractions of chlorophyll in each size class relative to the total chlorophyll concentration were estimated. Following Brewin et al. (2015), the fraction of picoplankton chlorophyll concentration (Fp) was computed according to Fp=   (−12.5C+1)W3P3 Cw+P7 i=6WiPi Cwif C≤0.08 mg m−3 P7 i=6WiPi Cwif C> 0.08 mg m−3. (4) The fraction of nanoplankton chlorophyll concentration (Fn) was estimated by first apportioning part of the fucoxanthin pigment (P1) to the nanoplankton pool, as conducted by Devred et al. (2011), such that P1,n=10{q1log10(P3)+q2log10(P4)}, (5) where P3and P4refer to 19′-hexanoyloxyfucoxanthin and 19′-butanoyloxyfucoxanthin. This is to account for the fact that fucoxanthin is a precursor to 19′-hexanoyloxyfucoxanthin and 19′-butanoyloxyfucoxanthin (Devred et al., 2011). We recomputed these coefficients (q1and q2) using the 2,791 HPLC samples, and arrived at values of q1=0.14 and q2=1.35. For any sample where P1,nwas higher than P1, then P1,nwas set to equal P1. Following Brewin et al. (2015), the fraction of Frontiers in Marine Science | www.frontiersin.org 4April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll TABLE 1 | Symbols and definitions. Symbol Definition Units CTotal chlorophyll concentration mg m−3 CwTotal chlorophyll concentration estimated from the seven diagnostic pigments (Equation 3) mg m−3 CpChlorophyll concentration for picophytoplankton (cells <2µm) mg m−3 Cp,nChlorophyll concentration for combined nano-picophytoplankton (cells <20 µm) mg m−3 CnChlorophyll concentration for nanophytoplankton (cells 2 −20 µm) mg m−3 CmChlorophyll concentration for microphytoplankton (cells >20 µm) mg m−3 Cdiat Chlorophyll concentration for diatoms mg m−3 Cdino Chlorophyll concentration for dinoflagellates mg m−3 Cm p,nAsymptotic maximum value of Cp,n(cells <20 µm) mg m−3 Cm pAsymptotic maximum value of Cp(cells <2 µm) mg m−3 CSST iChlorophyll concentration for group i(where i=p,n,m,diat and dino) estimated using the SST dependent paramaterizations (Equations 10–16) mg m−3 Dp,nFraction of total chlorophyll in combined nano-picophytoplankton (cells <20 µm) as total chlorophyll tends to zero Dimensionless DpFraction of total chlorophyll in picophytoplankton (cells <2µm) as total chlorophyll tends to zero Dimensionless FpFraction of total chlorophyll for picophytoplankton (cells <2µm) Dimensionless Fp,nFraction of total chlorophyll for combined nano-picophytoplankton (cells <20 µm) Dimensionless FnFraction of total chlorophyll for nanophytoplankton (cells 2 −20 µm) Dimensionless FmFraction of total chlorophyll for microphytoplankton (cells >20 µm) Dimensionless Fdiat Fraction of total chlorophyll for diatoms Dimensionless Fdino Fraction of total chlorophyll for dinoflagellates Dimensionless G1Parameter of Equation (12) controlling lower and/or upper bound in Cm p,nmg m−3 G2Parameter of Equation (12) controlling slope of change in Cm p,nwith SST ◦C−1 G3Parameter of Equation (12) controlling the SST mid-point of G2◦C G4Parameter of Equation (12) controlling lower and/or upper bound in Cm p,nmg m−3 H1Parameter of Equation (13) controlling lower and/or upper bound in Cm pmg m−3 H2Parameter of Equation (13) controlling slope of change in Cm pwith SST ◦C−1 H3Parameter of Equation (13) controlling the SST mid-point of H2◦C H4Parameter of Equation (13) controlling lower and/or upper bound in Cm pmg m−3 J1Parameter of Equation (14) controlling lower and/or upper bound in Dp,nDimensionless J2Parameter of Equation (14) controlling slope of change in Dp,nwith SST ◦C−1 J3Parameter of Equation (14) controlling the SST mid-point of J2◦C J4Parameter of Equation (14) controlling lower and/or upper bound in Dp,nDimensionless K1Parameter of Equation (15) controlling lower and/or upper bound in DpDimensionless K2Parameter of Equation (15) controlling slope of change in Dpwith SST ◦C−1 K3Parameter of Equation (15) controlling the SST mid-point of K2◦C K4Parameter of Equation (15) controlling lower and/or upper bound in DpDimensionless PiDiagnostic pigments (where i=1 to 7) for: fucoxanthin (1), peridinin (2), 19′-hexanoyloxyfucoxanthin (3), 19′-butanoyloxyfucoxanthin (4), alloxanthin (5), total chlorophyll-b (6), and zeaxanthin (7) mg m−3 P1,nDiagnostic pigment fucoxanthin in nanophytoplankton mg m−3 q1→2Empirical coefficients used to compute P1,nfrom P3and P4(Equation 5) Dimensionless rPearson correlation coefficient Dimensionless SST Sea surface temperature ◦C TiMembership for each Optical Water Type (OWT) Dimensionless WiWeights in Equation (3) (where i=1 to 7) for: fucoxanthin (1), peridinin (2), 19′-hexanoyloxyfucoxanthin (3), 19′-butanoyloxyfucoxanthin (4), alloxanthin (5), total chlorophyll-b (6), and zeaxanthin (7) Dimensionless αParameter of Equation (16) controlling slope of change in Cdino/Cmwith SST ◦C−1 βParameter of Equation (16) controlling the SST mid-point of α◦C δBias between log10-transformed concentrations from estimated and measured data Dimensionless 1Unbiased root mean square error between log10-transformed concentrations from estimated and measured data Dimensionless 9Root mean square error between log10-transformed concentrations from estimated and measured data Dimensionless Frontiers in Marine Science | www.frontiersin.org 5April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll TABLE 2 | Key taxonomic groups of phytoplankton, their typical size class, their category in the ERSEM model and their diagnostic pigment. Key taxonomic groups Typical size class& ERSEM Group# Pigment [W] This study (N. Atlantic)$ Brewin et al. (2015) (Global)$ Uitz et al. (2006) (Global) Diatoms MicroaDiatoms Fucoxanthine(P1) 1.65 (±0.01) 1.51 (±0.01) 1.41 Dinoflagellates Micro DinoflagellatesdPeridinin (P2) 1.04 (±0.03) 1.35 (±0.02) 1.41 Prymnesiophytes NanobNano 19′-hexanoyloxyfucoxanthin2(P3) 0.78 (±0.01) 0.95 (±0.01) 1.27 Pelagophytes Nano Nano 19′-butanoyloxyfucoxanthin (P4) 1.19 (±0.03) 0.85 (±0.02) 0.35 Cryptophytes Nano Nano Alloxanthin (P5) 3.14 (±0.04) 2.71 (±0.05) 0.60 Chlorophytes, Prochlorophytes PicocPico Total Chlorophyll-b∗(P6) 1.38 (±0.02) 1.27 (±0.01) 1.01 Cyanobacteria, Prochlorophytes Pico Pico Zeaxanthin (P7) 1.02 (±0.01) 0.93 (±0.00) 0.86 The table also shows a comparison of the weights ([W]) computed for Equation (3) using the 2791 HPLC data samples collected in this study, with weights derived from two other studies of the global ocean. ∗Total Chlorophyll-b refers to the sum of Chlorophyll-b and divinyl chlorophyll-b. &Micro refers to cell cells >20 µm, Nano cells 2–20µm and Pico cells <2 µm in size. $Bracketed values refer to the standards deviations for each coefficient. #Phytoplankton state variables in ERSEM model. aDiatoms can be found in the nano size class. bPrymnesiophytes and 19′-hexanoyloxyfucoxanthin pigment can be found in the pico size class. cSome chlorophytes can be found in the nanoplankton size class (Latasa et al., 2004). dAlso named microplankton in ERSEM. eFucoxanthin can be found in the nano size class. nanoplankton chlorophyll concentration (Fn) was then estimated according to Fn=   12.5CW3P3 Cw+P5 i=4WiPi+W1P1,n Cwif C≤0.08 mg m−3 P5 i=3WiPi+W1P1,n Cwif C> 0.08 mg m−3. (6) Finally, following Devred et al. (2011) and Brewin et al. (2015), the fraction of microplankton chlorophyll concentration (Fm) was estimated as Fm=P2 i=1WiPi−W1P1,n Cw . (7) Note that Fmcan also be computed by simply subtracting Fn and Fpfrom one. The fractions of chlorophyll in each size class were then multiplied by the corresponding HPLC-derived total chlorophyll concentration (C) to derive the size-specific chlorophyll concentrations for each sample (Cp,Cp,n,Cn, and Cp, where the subscripts “p” refers to pico-, “n” nanoand “m” microphytoplankton, and the subscript “p,n” refers to combined pico and nanophytoplankton). 2.3.1.2. Partitioning the fraction of microphytoplankton chlorophyll into fractions of diatoms and dinoflagellates The fraction of microphytoplankton chlorophyll concentration (Fm) is estimated from two diagnostic pigments, fucoxanthin in microphytoplankton (P1,m) and peridinin (P2). It is generally assumed that fucoxanthin in microphytoplankton is the primary pigment for diatoms (Stauber and Jeffrey, 1988) and peridinin for dinoflagellates, as the majority of photosynthetic dinoflagellates contain a chloroplast with peridinin as the major carotenoid (see Table 1 and Zapata et al., 2012). Following Hirata et al. (2011), this assumption was used to partition microphytoplankton chlorophyll into the concentrations of the two groups. The fraction of microplankton diatoms to total chlorophyll (Fdiat) and the fraction of microplankton dinoflagellates to total chlorophyll (Fdino) were computed as Fdiat =W1P1−W1P1,n Cw , (8) and Fdino =W2P2 Cw , (9) respectively. The chlorophyll concentrations for diatoms and dinoflagellates (Cdiat and Cdino) were then obtained by multiplying the fractions by the corresponding HPLC-derived total chlorophyll concentration (C). 2.3.2. Size-Fractionated Filtration (SFF) Data A total of 263 size-fractionated fluorometric chlorophyll (SFF) measurements collected previously in the North Atlantic region were also used in this study (Figure 1), spanning 1996–2015. This comprised of samples from: the Atlantic Meridional Transect cruises 2–23 (see Marañón et al., 2001; Serret et al., 2001; Robinson et al., 2002; Brewin et al., 2014a,b; Tilstone et al., 2017, for details); the Western Channel Observatory in the English Channel (Station L4 and E1; see Barnes et al., 2014, for details); and the NERC shelf seas biogeochemistry programme. In all cases, ∼200–300 ml samples were sequentially filtered through 20, 2, and 0.2 µm polycarbonate filters. Following filtration, pigments were extracted by storing the filters in 90% acetone at −20◦C for between 10 and 24 h. Samples were then analyzed using a Turner Design Fluorometer, preand postcalibrated using pure chlorophyll-a in 90% acetone as a standard. The total chlorophyll concentration was taken as the sum of the size fractions for each sample. The concentration of chlorophyll passing through the 2 µm filter was designated Cp(picoplankton Frontiers in Marine Science | www.frontiersin.org 6April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll chlorophyll), chlorophyll retained on the 20 µm filter designated Cm(microplankton chlorophyll) and the chlorophyll retained on the 2 µm filter, having passed through the 20 µm filter, designated Cn(nanoplankton chlorophyll). 2.4. Merging of in situ Datasets Systematic biases in size-fractionated chlorophyll estimated from HPLC pigments and from SFF have been observed in the Atlantic Ocean (Brewin et al., 2014a), with implications for models that estimate size-fractionated chlorophyll as a function of total chlorophyll (Brewin et al., 2014b) and models that estimate sizefractionated primary production (Brewin et al., 2017). Therefore, care needs to be taken when combining these two datasets. Figure 2 shows a comparison of 31 concurrent and co-located data points of total chlorophyll (Figure 2A), picoplankton chlorophyll (Figure 2B), nanoplankton chlorophyll (Figure 2C) and microplankton chlorophyll (Figure 2D), from the HPLC and SFF dataset used here. Despite there being biases in size-fractionated chlorophyll consistent with those observed by Brewin et al. (2014a) (Figure 2), these biases are notably smaller (e.g., for picoplankton chlorophyll δ= −0.07 compared with δ= −0.27 in see their Figure 3 Brewin et al. (2014a), and for nanoplankton δ=0.15 compared with δ=0.22), suggesting for surface waters in the North Atlantic, there is reasonable agreement between the two methods, at least for the datasets used here. Given the good agreement in Figure 2, the two datasets were combined into a single dataset, providing 3,054 measurements of size-fractionated chlorophyll (2,791 HPLC and 263 SFF). Figure 3 shows a schematic diagram of how the datasets were combined and subsequently used for model parameterization and validation. For each sample, SST data were extracted by matching each in situ sample in time (daily temporal match-up) and space (closest latitude and longitude) with daily, 1/4◦ resolution Optimal Interpolation Sea Surface Temperature (OISST) data (Version 2.0; Reynolds et al., 2007) acquired from the NOAA website (http://www.esrl.noaa.gov/psd/data/gridded/ data.noaa.oisst.v2.highres.html). 2.5. Partitioning Into Parameterization and Validation Datasets The merged dataset was matched to daily, level 3 (4 km sinusoidal projected) satellite chlorophyll and optical water type (OWT) data, from version 3.0 of the Ocean Colour Climate Change Initiative (OC-CCI, a merged MERIS, MODIS-Aqua, SeaWiFS and VIIRS product available at http://www.oceancolour.org/), between 1997 and 2015. Each in situ sample was matched with a single satellite pixel in time (daily match-up) and space (closest pixel with a distance <4 km away). Of the 3,054 samples, there FIGURE 2 | Concurrent and co-located size-fractionated chlorophyll estimated from High Performance Liquid Chromatography (HPLC) and size-fractionated filtration (SFF) for surface waters in the North Atlantic region. (A) shows a comparison of total chlorophyll (C), (B) picoplankton chlorophyll (Cp), (C) nanoplankton chlorophyll (Cn), and (D) microplankton chlorophyll (Cm). Black line represents the 1:1 line and dotted lines represent the 1:1 line ±30% log10 chlorophyll. Nrefers to the number of samples used to compute statistics, rrefers to the Pearson linear correlation coefficient, 9the root mean square error (Equation 1) and δthe bias (Equation 2). Frontiers in Marine Science | www.frontiersin.org 7April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll FIGURE 3 | A flow chart of the processing techniques. Data collected in the OCTAC ATL region [both High Performance Liquid Chromatography (HPLC) and size-fractionated filtration (SFF)] were partitioned into two databases [parameterization (Database A) and satellite validation (Database B)], and used to re-tune, adapt and validate the model of Brewin et al. (2010), compute the root mean square error (9) and bias (δ) for each optical water type (OWT), and map phytoplankton group products and associated errors using ocean-color data. were 815 corresponding satellite chlorophyll and optical water type (OWT) data. These 815 measurements were set aside and used for independent validation of the satellite model and for characterizing per-pixel error, leaving 2,239 measurements that were used for model development (parameterization). Figure 3 shows a schematic diagram of how the data were partitioned into the parameterization and validation dataset. The OWT data provided in version 3.0 of the OC-CCI dataset contains the per-pixel membership of 14 different optical classes, ranging from oligotrophic (e.g., OWT 1) to very turbid (OWT 14) waters. Building on the work of Moore et al. (2001, 2009, 2012), this new set of optical classes were constructed for use with OC-CCI remote sensing reflectance (Rrs) spectra (Jackson and Sathyendranath, 2015). These classes were trained using Rrs spectra from satellite data, rather than using a database of in situ observations, as conducted in Moore et al. (2009), and the number of optical water classes were increased to 14, to better cover the range of Rrs spectra observed in the global oceans, particularly the oligotrophic gyres. For further details of the training and production of the 14 OWT the reader is referred to Jackson and Sathyendranath (2015). 2.6. Satellite Model of Phytoplankton Groups 2.6.1. Three-Component Model of Brewin et al. (2010) As a starting point, we used the three-component model of Brewin et al. (2010) to estimate the chlorophyll concentrations in three phytoplankton size classes [pico- (<2µm), nano- (2– 20 µm), and micro-phytoplankton (>20 µm)] as a function of total chlorophyll in the study region (Figure 1). This approach has been successfully tuned to the global ocean (Brewin et al., 2015; Ward, 2015) as well as different oceanic regions, including: the Atlantic Ocean (North and South; Brewin et al., 2010, 2014b; Tilstone et al., 2014); the North East Atlantic (Brotas et al., 2013); the Indian Ocean (Brewin et al., 2012b); the Western Iberian coastline (Brito et al., 2015); the Mediterranean Sea (Sammartino et al., 2015); and the South China Sea (Lin et al., 2014). Estimating size-fractionated chlorophyll from satellite data (using satellite total chlorophyll as input to the three-component model) has been tested extensively with in situ data in different oceanic regions (Brewin et al., 2010, 2012b; Lin et al., 2014; Brewin et al., 2015). The three-component model is based on two exponential functions (Sathyendranath et al., 2001), where the chlorophyll concentration of picoplankton (Cp, cells <2 µm) and combined picoand nanoplankton (Cp,n, cells <20 µm) are obtained from Cp,n=Cm p,n[1 −exp(−Dp,n Cm p,n C)], (10) and Cp=Cm p[1 −exp(−Dp Cm p C)]. (11) The parameters Dp,nand Dpdetermine the fraction of total chlorophyll in the two size classes (<20 µm and <2 µm, respectively) as total chlorophyll tends to zero, and Cm p,nand Cm pare the asymptotic maximum values for the two size classes (<20 µm and <2 µm respectively). The chlorophyll concentration of nano-phytoplankton (Cn) and micro-phytoplankton (Cm) are simply calculated as Cn=Cp,n−Cpand Cm=C−Cp,n. A single set of model parameters was first derived by fitting (Equations 10 and 11) using a standard, nonlinear least-squared fitting procedure (Levenberg-Marquardt, IDL Routine MPFITFUN, Moré, 1978; Markwardt, 2008) with relative weighting (Brewin et al., 2011a). The parameters Dp,nand Dpwere constrained to be less than or equal to one, since size-fractionated chlorophyll cannot exceed total chlorophyll. We used the method of bootstrapping (Efron, 1979; Brewin et al., 2015) to compute a parameter distribution, and from the resulting parameter distribution, median values and 95% confidence intervals were computed (see Table 3). The parameters Dp,nand Dpwere found to be significantly different from the global parameters derived in Brewin et al. (2015) (see Table 3). The model was found to capture the trends in the fractions (Fp,Fn,Fp,n, and Fm) and absolute concentrations Frontiers in Marine Science | www.frontiersin.org 8April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll TABLE 3 | Parameter values for Equations 10 and 11 compared with global parameters derived in Brewin et al. (2015). Study Parameters for equations 10 and 11 Location N# Cm p,n*Cm p*Dp,nDp Brewin et al. (2015)$0.77 (0.72↔0.84) 0.13 (0.12↔0.14) 0.94 (0.93↔0.95) 0.80 (0.78↔0.82) Global 5841 This study$0.82 (0.76↔0.88) 0.13 (0.12↔0.13) 0.87 (0.86↔0.89) 0.73 (0.71↔0.76) N Atlantic 2239 This study$(<15oC) 1.83 (1.47↔2.44) 0.31 (0.24↔0.47) 0.60 (0.58↔0.63) 0.26 (0.23↔0.30) N Atlantic 1017 This study$(≥15oC) 0.86 (0.79↔0.96) 0.13 (0.12↔0.14) 0.93 (0.91↔0.94) 0.74 (0.72↔0.77) N Atlantic 1222 $Model parameters are computed as the median of the bootstrap parameter distribution and bracket parameter values refer to the 2.5 and 97.5% confidence intervals on the distribution. #N = Number of samples used for model parameterization ∗Denotes units in mg m−3. (Cp,Cn,Cp,n, and Cm) of the size classes as a function of total chlorophyll for the North Atlantic parameterization dataset (Figure 4). 2.6.2. Modification of Three-Component Model Using SST Brewin et al. (2015) and Ward (2015) have investigated the influence of light availability and SST respectively on the parameterization of the three-component model. In the North Atlantic, seasonal variations in SST and the average light in the mixed-layer are highly correlated (Figure 1). Therefore, considering: (i) that there is, regionally, a covariation of SST with the average light in the mixed-layer (Figure 1); (ii) that three inputs are required to compute the average light in the mixedlayer (photosynthetically-active radiation, diffuse attenuation and mixed-layer depth), one of which is not amenable from remote-sensing (mixed-layer depth); and (iii) that the maturity (operational use) and accuracy of SST retrievals is very high (Merchant et al., 2014), we chose to investigate the influence of SST on model parameters in the study area, similar to the study of Ward (2015) for a global dataset. Figure 4 illustrates the general inverse correlation between SST and total chlorophyll (r= −0.67 for SST and log10(C)), highlighting that higher fractions of smaller cells (lower fractions of large cells) are typically associated with higher SST. To investigate if SST has any influence on the parameters of the three-component model, we partitioned the parameterization data into lower temperature waters (<15◦C) and higher temperature waters (≥15◦C), and fitted the model separately to the two datasets of a roughly equal number (>1,000, see Table 3). We observed significantly different model parameters for high and low temperature waters (see Table 3 and Figure 4), suggesting a relationship between SST and model parameters. We then sorted the dataset according to SST, and conducted a running fit of the three-component model (Equations 10 and 11) as a function of SST with a bin size of 600 samples [chosen to ensure each fit had reasonable representation of observations over the entire trophic range (low to high chlorophyll)]. We used the method of bootstrapping (100 iterations) and derived median values and 95% confidence intervals on each parameter distribution (Figure 5). Significant relationships between all model parameters (Cm p,n, Cm p,Dp,n, and Dp) and SST were observed (Figure 5). The relationship between SST and model parameters could be represented using a logistic function, such that Cm p,nand Cm pmay be expressed as Cm p,n=1− { G1 1+exp[−G2(SST −G3)] +G4}, (12) and Cm p=1− { H1 1+exp[−H2(SST −H3)] +H4}, (13) where G1and G4control the upper and lower bounds of Cm p,n,G2 represents the slope of change in Cm p,nwith SST, and G3is the SST mid-point of the slope between Cm p,nand SST. For Cm p,Hi, where i=1–4, is analogous to Gifor Cm p,n. The parameter Dp,nand Dp were expressed as Dp,n=J1 1+exp[−J2(SST −J3)] +J4, (14) and Dp=K1 1+exp[−K2(SST −K3)] +K4, (15) where J1and J4control the upper and lower bounds of Dp,n, J2represents the slope of change in Dp,nwith SST, and J3 is the SST mid-point of the slope between Dp,nand SST. For Dp,Kiis analogous to Jifor Dp,n. The parameters for Equations (12)–(15) were fitted using a nonlinear least-squared fitting procedure (Levenberg-Marquardt) with bootstrapping, and parameter values are provided in Table 4. The equations are seen to capture the relationships between parameters and SST accurately (Figure 5 and Table 4). Figure 6 shows simulations of size-fractionated chlorophyll as a function of total chlorophyll for different SST, when incorporating (Equations 12–15) into the three-component model (Equations 10 and 11). In general, the performance for all size classes improved when using the SST-dependent parameterization, when compared with that using a single set of parameters (Figure 7), with a significant improvement in the correlation coefficient for Cp(Z-test, p<0.05). Whereas modeled Cp,n,Cn, and Cpreach static asymptotes at high concentrations when using a single set of parameters (see Figure 7, top-row, horizontal purple dashed lines), the SST-dependent parameterization does not, and captures the variability in the size-fractionated chlorophyll at these higher concentrations. Frontiers in Marine Science | www.frontiersin.org 9April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll FIGURE 10 | Satellite estimates of diatom (Cdiat) and dinoflagellate (Cdino) chlorophyll plotted against independent in situ estimates of Cdiat and Cdino in the validation dataset, using the Brewin et al. (2010) model with a single set of parameters (top-row, Table 3) together with estimates of Cdino/Cm(Equation 16), and using the SST-dependent parameterization (bottom row, Equations 12–15) together with estimates of Cdino/Cm(Equation 16). The superscript SST denotes the modeled microplankton chlorophyll using the SST-dependent parameterization (Equations 12–15) multiplied by estimates of Cdino/Cm(Equation 16). The correlation coefficient (r) and root-mean-square-error (9) are also shown. uncertainty are based on comparisons of co-incident discrete in situ point measurements, representing volumes of sea water of the order of 5 litres or less, with 4 km satellite pixels representing a signal from ∼16 ×1010 litres of water, assuming a 10 m optical depth. Additional uncertainties can occur because of vast differences in the temporal scales associated with the two types of measurements. In the future, such uncertainties may be reduced with the aid of new in situ methods capable of continuously measuring the optical and biogeochemical properties of the water (Dall’Olmo et al., 2012; Boss et al., 2013; Chase et al., 2013; Werdell et al., 2013b; Brewin et al., 2016). By computing uncertainty statistics for each OWT, we can overcome issues with the distribution of data used in the validation. For instance, in our validation dataset, the majority of samples came from three OWTs (10, 11, and 12, see Figure 11), yet in the satellite image (Figure 12B), the majority of the region is dominated by OWTs less than 10. If one were to consider a single value of any statistical metric (as provided in Figures 9,10) as representative of the uncertainty in the entire satellite data, it would not be well representative of the majority of the region. Yet, as the number of samples in each OWT vary, so does our confidence in the error statistics for each OWT. Some OWTs (e.g., 1, 2, and 14) have very few observations (Table 5), and consequently we have low confidence in the uncertainty estimates for these OWTs. TABLE 5 | Root mean square error (9) and bias (δ) for 14 OC-CCI optical water types (OWT) for the four phytoplankton groups, using the two approaches (SST-dependent with superscript SST, and single set of parameters) to estimate phytoplankton group chlorophyll from satellite data. OWT Picoplankton Nanoplankton Diatoms Dinoflagellates CSST pCpCSST nCnCSST diat Cdiat CSST dino Cdino 9 δ N9 δ N9 δ N9 δ N9 δ N9 δ N9 δ N9 δ N 1 0.13 −0.03 13 0.14 −0.06 13 0.37 −0.11 13 0.39 −0.16 13 0.28 −0.04 9 0.34 0.19 9 0.18 −0.11 6 0.19 0.12 6 2 0.28 −0.13 9 0.29 −0.15 9 0.40 0.01 9 0.41 −0.04 9 0.50 −0.30 6 0.41 −0.07 6 0.15 −0.07 4 0.20 0.16 4 3 0.16 0.04 67 0.16 0.02 67 0.28 0.09 58 0.27 0.05 58 0.37 0.05 50 0.45 0.26 50 0.17 0.03 40 0.29 0.24 40 4 0.19 0.06 42 0.19 0.04 42 0.30 0.11 39 0.29 0.06 39 0.41 −0.10 33 0.42 0.08 33 0.28 0.12 22 0.39 0.30 22 5 0.22 0.08 36 0.22 0.07 36 0.26 0.09 33 0.26 0.03 33 0.40 0.03 26 0.43 0.18 26 0.30 0.26 14 0.44 0.42 14 6 0.20 0.08 41 0.23 0.11 41 0.32 0.13 41 0.32 0.06 41 0.54 0.23 39 0.59 0.32 39 0.32 0.05 27 0.36 0.14 27 7 0.49 0.22 41 0.50 0.23 41 0.35 0.11 38 0.34 0.09 38 0.60 0.05 36 0.62 0.11 36 0.47 −0.05 27 0.47 0.01 27 8 0.38 0.17 36 0.38 0.20 36 0.39 −0.07 36 0.37 −0.07 36 0.49 0.15 34 0.51 0.14 34 0.33 0.03 31 0.35 0.02 31 9 0.39 0.20 79 0.36 0.19 79 0.40 0.12 67 0.40 0.14 67 0.56 −0.17 64 0.57 −0.15 64 0.42 0.13 49 0.43 0.14 49 10 0.41 0.15 111 0.38 0.06 111 0.38 0.11 108 0.38 0.14 108 0.52 −0.22 99 0.53 −0.21 99 0.50 0.32 91 0.51 0.33 91 11 0.35 0.13 158 0.33 −0.04 158 0.48 0.20 152 0.48 0.22 152 0.42 −0.19 145 0.42 −0.17 145 0.70 0.45 139 0.72 0.47 139 12 0.42 0.08 147 0.42 −0.10 147 0.50 0.21 139 0.54 0.27 139 0.38 −0.05 132 0.38 −0.05 132 0.67 0.39 129 0.68 0.39 129 13 0.58 0.21 21 0.55 0.08 21 0.63 0.18 21 0.61 0.22 21 0.55 0.03 21 0.54 0.04 21 0.83 0.07 17 0.82 0.10 17 14 0.44 0.41 4 0.15 −0.04 4 0.70 0.68 4 0.62 0.60 4 0.79 0.79 4 0.88 0.87 4 1.44 1.37 4 1.52 1.45 4 Frontiers in Marine Science | www.frontiersin.org 16 April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll FIGURE 11 | The average root-mean-square-error (9) and bias (9) for 14 dominant OC-CCI Optical Water Types (OWT) for total chlorophyll (A,B), diatom chlorophyll (C,D), dinoflagellate chlorophyll (E,F), nanoplankton chlorophyll (G,H), and picoplankton chlorophyll (I,J). N(violet lines and squares) shows the number of observations of each dominant OWT. Plots (C–J) are for the SST-dependent parameterization (Equations 12–15) together with estimates of Cdino/Cm(Equation 16). 4. SUMMARY We re-tuned an abundance-based model (Brewin et al., 2010, 2015) for estimating the chlorophyll concentration of three phytoplankton size classes as a function of total chlorophyll (available from satellite data) in the North Atlantic region using a large dataset of size-fractionated chlorophyll measurements. The model was modified to account for the influence of sea surface temperature (SST, also available from satellite data) on model parameters, and on the partitioning of chlorophyll in Frontiers in Marine Science | www.frontiersin.org 17 April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll FIGURE 12 | Satellite estimates of phytoplankton group chlorophyll and per-pixel errors for an 8 day (relatively clear sky) composite (17th to 24th June 2008) of OC-CCI chlorophyll (a), (dominant) optical water type (b) and SST (NOAA OISST) data (c). Example shown is using the SST-dependent parameterization (Equations 12–15) together with estimates of Cdino/Cm(Equation 16): (d) Diatom chlorophyll (Cdiat); (e) per-pixel root-mean-square-error (9) of Cdiat;(f) per-pixel bias (δ) of Cdiat;(g) dinoflagellate chlorophyll (Cdino); (h) 9of Cdino;(i) δof Cdino;(J) nanoplankton chlorophyll (Cn); (k) 9of Cn;(l) δof Cn;(m) picoplankton chlorophyll (Cp); (n) 9of Cp; and (o) δof Cp. large phytoplankton (microphytoplankton) into diatoms and dinoflagellates, so that the phytoplankton groups provided matched those used in a marine ecosystem model (ERSEM). Results indicate that in the North Atlantic: (i) the relationship between size-fractionated chlorophyll and total chlorophyll changes with the environmental conditions (SST); and (ii) the ratio of dinoflagellate chlorophyll to microplankton chlorophyll increases with SST. Application of the method to satellite estimates of total chlorophyll and SST was validated using an independent dataset Frontiers in Marine Science | www.frontiersin.org 18 April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll of satellite and in situ match-ups. This dataset was used with information on the optical water type, based on fuzzy-logic statistics developed within the ESA OC-CCI project, to derive uncertainties in 14 different optical water types, which were then used to map uncertainties in chlorophyll on a per-pixel basis for each phytoplankton group in a satellite image. These satellite products will be useful for those evaluating the performance of the ERSEM model and assimilating chlorophyll for each phytoplankton group into ERSEM in research and operational applications. Such an approach could be extended to other ecosystem models that simulate phytoplankton functional groups in the oceans. AUTHOR CONTRIBUTIONS RB synthesized the data, re-tuned and further-developed the algorithm, organized, prepared and wrote the first version of the manuscript, and prepared all figures and tables. SC, SS, TJ, EO, GD, and DR contributed to the intellectual development of the algorithms, and GT, KC, RA, DC, and VB collected and processed parts of the datasets used in the paper. All authors contributed to the final version of the manuscript. FUNDING This work has been carried out as part of the Copernicus Marine Environment Monitoring Service (CMEMS) project “Toward Operational Size-class Chlorophyll Assimilation (TOSCA).” CMEMS is implemented by MERCATOR OCEAN in the framework of a delegation agreement with the European Union. This work was also supported by the UK National Centre for Earth Observation (NCEO). Additional support from the Ocean Colour Component of the Climate Change Initiative of the European Space Agency (ESA) is gratefully acknowledged. Data collection by GT was supported by NERC-UK ECOMAR (grant no: NE/C513018/1). We thank ESA for covering publication costs. ACKNOWLEDGMENTS The authors would like to acknowledge all scientists and crew involved in the collection of the in situ data used in this manuscript, without which this work would not have been feasible. We owe a debit of gratitude to all those involved in data collection. AMT data were funded through the UK Natural Environment Research Council, through the UK marine research institutes’ strategic research programme Oceans 2025 awarded to PML and the National Oceanography Centre. The authors would like to thank European Space Agency (ESA) for CCI data used, NOAA for the OISST products, and NASA for MODIS-Aqua and SeaWiFS products used. This is a contribution to MARE - UID/MAR/04292/2013, the Ocean Colour Climate Change Initiative of ESA and contribution number 311 of the AMT programme. REFERENCES Aiken, J., Pradhan, Y., Barlow, R., Lavender, S., Poulton, A., Holligan, P., et al. (2009). Phytoplankton pigments and functional types in the Atlantic Ocean: A decadal assessment, 1995-2005. Deep Sea Res. I 56, 899–917. doi: 10.1016/j.dsr2.2008.09.017 Airs, R., and Martinez-Vicente, V. (2014a). AMT18 (JR20081003) HPLC Pigment Measurements from CTD Bottle Samples. Liverpool: British Oceanographic Data Centre - Natural Environment Research Council. Airs, R., and Martinez-Vicente, V. (2014b). AMT19 (JR20081003) HPLC Pigment Measurements from CTD Bottle Samples. Liverpool: British Oceanographic Data Centre - Natural Environment Research Council. Airs, R., and Martinez-Vicente, V. (2014c). AMT20 (JR20081003) HPLC Pigment Measurements from CTD Bottle Samples. Liverpool: British Oceanographic Data Centre - Natural Environment Research Council. Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P. (2003). An ecosystem model of the global ocean including Fe, Si, P colimitations. Global Biogeochem. Cycles 17:1060. doi: 10.1029/2001GB001745 Barlow, R. G., Aiken, J., Holligan, P. M., Cummings, D. G., Mariotena, S., and Hooker, S. (2002). Phytoplankton pigment and absorption characteristics along meridional transects in the Atlantic Ocean. Deep Sea Res. I 49, 637–660. doi: 10.1016/S0967-0637(01)00081-4 Barnes, M. K., Tilstone, G. H., Smyth, T. J., Suggett, D. J., Astoreca, R., Lancelot, C., et al. (2014). Absorption-based algorithm of primary production for total and size-fractionated phytoplankton in coastal waters. Mar. Ecol. Prog. Ser. 504, 73–89. doi: 10.3354/meps10751 Blackford, J. C., Allen, J. I., and Gilbert, F. J. (2004). Ecosystem dynamics at six contrasting sites: a generic modelling study. J. Mar. Syst. 52, 191–215. doi: 10.1016/j.jmarsys.2004.02.004 Boss, E., Picheral, M. P., Leeuw, T., Chase, A., Karsenti, E., Gorsky, G., et al. (2013). The characteristics of particulate absorption, scattering and attenuation coefficients in the surface ocean; Contribution of the Tara Oceans expedition. Methods Oceanogr. 7, 52–62. doi: 10.1016/j.mio.2013. 11.002 Bracher, A., Bouman, H., Brewin, R. J. W., Bricaud, A., Brotas, V., Ciotti, A. M., et al. (2017). Obtaining phytoplankton diversity from ocean color: a scientific roadmap for future development. Front. Mar. Sci. 4:55. doi: 10.3389/fmars.2017.00055 Brewin, R. J. W., Dall’Olmo, G., Pardo, S., van Dongen-Vogel, V., and Boss, E. S. (2016). Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals. Remote Sens. Environ. 183, 82–97. doi: 10.1016/j.rse.2016.05.005 Brewin, R. J. W., Dall’Olmo, G., Sathyendranath, S., and Hardman-Mountford, N. J. (2012a). Particle backscattering as a function of chlorophyll and phytoplankton size structure in the open-ocean. Opt. Exp. 20, 17632–17652. doi: 10.1364/OE.20.017632 Brewin, R. J. W., Devred, E., Sathyendranath, S., Hardman-Mountford, N. J., and Lavender, S. J. (2011a). Model of phytoplankton absorption based on three size classes. Appl. Opt. 50, 4535–4549. doi: 10.1364/AO.50. 004535 Brewin, R. J. W., Hardman-Mountford, N. J., Lavender, S., Raitsos, D. E., Hirata, T., Uitz, J., et al. (2011b). An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing. Remote Sens. Environ. 115, 325–339. doi: 10.1016/j.rse.2010.09.004 Brewin, R. J. W., Hirata, T., Hardman-Mountford, N. J., Lavender, S., Sathyendranath, S., and Barlow, R. (2012b). The influence of the Indian Ocean Dipole on interannual variations in phytoplankton size structure as revealed by Earth Observation. Deep Sea Res. II 77–80, 117–127. doi: 10.1016/j.dsr2.2012.04.009 Brewin, R. J. W., Sathyendranath, S., Hirata, T., Lavender, S. J., Barciela, R., and Hardman-Mountford, N. J. (2010). A threecomponent model of phytoplankton size class for the Atlantic Ocean. Ecol. Model. 221, 1472–1483. doi: 10.1016/j.ecolmodel.2010. 02.014 Frontiers in Marine Science | www.frontiersin.org 19 April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll Brewin, R. J. W., Sathyendranath, S., Jackson, T., Barlow, R., Brotas, V., Airs, R., et al. (2015). Influence of light in the mixed layer on the parameters of a threecomponent model of phytoplankton size structure. Remote Sens. Environ. 168, 437–450. doi: 10.1016/j.rse.2015.07.004 Brewin, R. J. W., Sathyendranath, S., Lange, P. K., and Tilstone, G. (2014a). Comparison of two methods to derive the size-structure of natural populations of phytoplankton. Deep Sea Res. I 85, 72–79. doi: 10.1016/j.dsr.2013.11.007 Brewin, R. J. W., Sathyendranath, S., Tilstone, G., Lange, P. K., and Platt, T. (2014b). A multicomponent model of phytoplankton size structure. J. Geophys. Res. 119, 3478–3496. doi: 10.1002/2014JC0 09859 Brewin, R. J. W., Tilstone, G., Jackson, T., Cain, T., Miller, P., Lange, P. K., et al. (2017). Modelling size-fractionated primary production in the Atlantic Ocean from remote sensing. Prog. Oceanogr. doi: 10.1016/j.pocean.2017. 02.002 Bricaud, A., Claustre, H., Ras, J., and Oubelkheir, K. (2004). Natural variability of phytoplanktonic absorption in oceanic waters: influence of the size structure of algal populations. J. Geophys. Res. 109:C11010. doi: 10.1029/2004JC002419 Briggs, N., Perry, M. J. P., Cetini´ c, I., Lee, C., D’Asaro, E., Gray, A. M., et al. (2011). High-resolution observations of aggregate flux during a sub-polar North Atlantic spring bloom. Deep Sea Res. I 58, 1031–1039. doi: 10.1016/j.dsr.2011.07.007 Brito, A. C., Sà, C., Brotas, V., Brewin, R. J. W., Silva, T., Vitorino, J., et al. (2015). Effect of phytoplankton size classes on bio-optical properties of phytoplankton in the Western Iberian coast: application of models. Remote Sens. Environ. 156, 537–550. doi: 10.1016/j.rse.2014.10.020 Brotas, V., Brewin, R. J. W., Sá, C., Brito, A. C., Silva, A., Mendes, C. R., et al. (2013). Deriving phytoplankton size classes from satellite data: validation along a trophic gradient in the eastern Atlantic Ocean. Remote Sens. Environ. 134, 66–77. doi: 10.1016/j.rse.2013.02.013 Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y., Blackford, J., et al. (2016). ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels. Geosci. Model Dev. 9, 1293–1339. doi: 10.5194/gmd-9-1293-2016 Campbell, J. W. (1995). The lognormal distribution as a model for bio-optical variability in the sea. J. Geophys. Res. 100, 13237–13254. doi: 10.1029/95JC00458 Chase, A., Boss, E., Zaneveld, J. R. V., Bricaud, A., Claustre, H., Ras, J., et al. (2013). Decomposition of in situ particulate absorption spectra. Methods Oceanogr. 7, 110–124. doi: 10.1016/j.mio.2014.02.002 Chisholm, S. W. (1992). “Phytoplankton size,” in Primary Productivity and Biogeochemical Cycles in the Sea, eds P. G. Falkowski and A. D. Woodhead (New York, NY: Springer), 213–237. Ciavatta, S., Kay, S., Saux-Picart, S., Butenschön, M., and Allen, J. I. (2016). Decadal reanalysis of biogeochemical indicators and fluxes in the North West European shelf-sea ecosystem. J. Geophys. Res. 121, 1824–1845. doi: 10.1002/2015jc011496 Ciavatta, S., Torres, R., Martinez-Vicente, V., Smyth, T., Dall’Olmo, G., Polimene, L., et al. (2014). Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling. Prog. Oceanogr. 127, 74–95. doi: 10.1016/j.pocean.2014.06.002 Ciavatta, S., Torres, R., Saux-Picart, S., and Allen, J. I. (2011). Can ocean color assimilation improve biogeochemical hindcasts in shelf seas? J. Geophys. Res. 116:C12. doi: 10.1029/2011JC007219 Ciotti, A. M., Lewis, M. R., and Cullen, J. J. (2002). 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. doi: 10.4319/lo.2002.47.2.0404 Dall’Olmo, G., Boss, E., Behrenfeld, M., and Westberry, T. K. (2012). Particulate optical scattering coefficients along an Atlantic Meridional Transect. Opt. Exp. 20, 21532–21551. doi: 10.1364/OE.20.021532 Dandonneau, Y., Deschamps, P. Y., Nicolas, J. M., Loisel, H., Blanchot, J., Montel, Y., et al. (2004). Seasonal and interannual variability of ocean colour and composition of phytoplankton communities in the North Atlantic, equatorial Pacific and South Pacific. Deep Sea Res. II 51, 303–318. doi: 10.1016/j.dsr2.2003.07.018 de Boyer Montégut, C., Madec, G., Fisher, A. S., Lazar, A., and Iudicone, D. (2004). Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology. J. Geophys. Res. 109:C12003. doi: 10.1029/2004JC002378 de Mora, L., Butenschön, M., and Allen, J. I. (2016). The assessment of a global marine ecosystem model on the basis of emergent properties and ecosystem function: a case study with ERSEM. Geosci. Model Dev. 9, 59–76. doi: 10.5194/gmd-9-59-2016 De Moraes Rudorff, N., and Kampel, M. (2012). Orbital remote sensing of phytoplankton functional types: a new review. Int. J. Remote Sens. 33, 1967–1990. doi: 10.1080/01431161.2011.601343 Devred, E., Sathyendranath, S., Stuart, V., Maas, H., Ulloa, O., and Platt, T. (2006). A two-component model of phytoplankton absorption in the open ocean: theory and applications. J. Geophys. Res. 111:C03011. doi: 10.1029/2005JC002880 Devred, E., Sathyendranath, S., Stuart, V., and Platt, T. (2011). A three component classification of phytoplankton absorption spectra: applications to ocean-colour data. Remote Sens. Environ. 115, 2255–2266. doi: 10.1016/j.rse.2011.04.025 Ducklow, H. W., and Harris, R. P. (1993). Introduction to the JGOFS North Atlantic bloom experiment. Deep Sea Res. Part II Top. Stud. Oceanogr. 40, 1–8. doi: 10.1016/0967-0645(93)90003-6 Efron, B. (1979). Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26. doi: 10.1214/aos/1176344552 Finkel, Z. V., Beardall, J., Flynn, K., Quigg, A., Rees, T. A. V., and Raven, J. A. (2010). Phytoplankton in a changing world: cell size and elemental stoichiometry. J. Plank. Res. 32, 119–137. doi: 10.1093/plankt/fbp098 Ford, D. A., Edwards, K. P., Lea, D., Barciela, R. M., Martin, M. J., and Demaria, J. (2012). Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model. Ocean Sci. 8, 751–771. doi: 10.5194/os-8-751-2012 Garver, S. A., Siegel, D. A., and Mitchell, B. G. (1994). Variability in near-surface particulate absorption spectra: what can a satellite ocean color imager see? Limnol. Oceanogr. 39, 1349–1367. doi: 10.4319/lo.1994.39.6.1349 Geider, R. J., Platt, T., and Raven, J. A. (1986). Size dependence of growth and photosynthesis in diatoms: a synthesis. Mar. Ecol. Prog. Ser. 30, 93–104. doi: 10.3354/meps030093 Gibb, S. W., Barlow, R. G., Cummings, D. G., Rees, N. W., Trees, C. C., Holligan, P. M., et al. (2000). Surface phytoplankton pigment distributions in the Atlantic Ocean: an assessment of basin scale variability between 50◦N and 50◦S. Prog. Oceanogr. 45, 339–368. doi: 10.1016/S0079-6611(00)00007-0 Gin, K. Y.-H., Lin, X., and Zhang, S. (2000). Dynamics and size structure of phytoplankton in the coastal waters of Singapore. J. Plank. Res. 22, 1465–1484. doi: 10.1093/plankt/22.8.1465 Gregg, W. W., Friedrichs, M. A. M., Robinson, A. R., Rose, K. A., Schlitzer, R., Thompson, K. R., et al. (2009). Skill assessment in ocean biological data assimilation. J. Mar. Syst. 76, 16–33. doi: 10.1016/j.jmarsys.2008.05.006 Guidi, L., Stemmann, L., Jackson, G. A., Ibanez, F., Claustre, H., Legendre, L., et al. (2009). Effects of phytoplankton community on production, size and export of large aggregates: a world-ocean analysis. Limnol. Oceanogr. 54, 1951–1963. doi: 10.4319/lo.2009.54.6.1951 Hashioka, T., Vogt, M., Yamanaka, Y., Le Quéré, C., Aita, M. N., Alvain, S., et al. (2013). Phytoplankton competition during the spring bloom in four plankton functional type models. Biogeosciences 10, 6833–6850. doi: 10.5194/bg-10-6833-2013 Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow, R., Suzuki, K., et al. (2011). Synoptic relationships between surface chlorophylla and diagnostic pigments specific to phytoplankton functional types. Biogeosciences 8, 311–327. doi: 10.5194/bg-8-311-2011 Hirata, T., Saux Picart, S., Hashioka, T., Aita-Noguchi, M., Sumata, H., Shigemitsu, M., et al. (2013). A comparison between phytoplankton community structures derived from a global 3D ecosystem model and satellite observation. J. Mar. Syst. 109–101, 129–137. doi: 10.1016/j.jmarsys.2012. 01.009 Holt, J., Allen, J. I., Anderson, T. R., Brewin, R. J. W., Butenschön, M., Harle, J., et al. (2014). Challenges in integrative approaches to modelling the marine ecosystems of the North Atlantic: physics to fish and coasts to ocean. Prog. Oceanogr. 129(Part B), 285–313. doi: 10.1016/j.pocean.2014.04.024 IOCCG (2000). Remote Sensing of Ocean Colour in Coastal, and Other Optically Complex Waters. Technical report, ed S. Sathyendranath, Reports of the Frontiers in Marine Science | www.frontiersin.org 20 April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll International Ocean-Colour Coordinating Group, No. 3, IOCCG, Dartmouth, NS. IOCCG (2014). Phytoplankton Functional Types from Space. Technical Report, ed S. Sathyendranath, Reports of the International Ocean-Colour Coordinating Group, No. 15, IOCCG, Dartmouth, NS. Jackson, T., and Sathyendranath, S. (2015). Ocean Colour Climate Change Initiative (OC-CCI) Phase 2Comprehensive Error Characterization Report 2.0. Kempton, J., Wolny, J., Tengs, T., Rizzo, P., Morris, R., Tunnell, J., et al. (2002). Kryptoperidinium foliaceum blooms in South Carolina: a multi-analytical approach to identification. Harmful Algae 1, 383–392. doi: 10.1016/S1568-9883(02)00051-3 Kishi, M. J., Kashiwai, M., Ware, D. M., Megrey, B. A., Eslinger, D. L., Werner, F. E., et al. (2007). NEMURO–a lower trophic level model for the North Pacific marine ecosystem. Ecol. Model. 202, 12–25. doi: 10.1016/j.ecolmodel.2006.08.021 Kostadinov, T. S., Milutinovi´ c, S., Marinov, I., and Cabré, A. (2016). Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution. Ocean Sci. 12, 561–575. doi: 10.5194/os-12-561-2016 Kostadinov, T. S., Siegel, D. A., and Maritorena, S. (2009). Retrieval of the particle size distribution from satellite ocean color observations. J. Geophys. Res. 114:C09015. doi: 10.1029/2009JC005303 Latasa, M., Scharek, R., Gall, F. L., and Guillou, L. (2004). Pigment suites and taxonomic groups in prasinophyceae. J. Phycol. 40, 1149–1155. doi: 10.1111/j.1529-8817.2004.03136.x Laufkötter, C., Vogt, M., Gruber, N., Aumont, O., Bopp, L., Doney, S., et al. (2016). Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem. Biogeosciences 13, 4023–4047. doi: 10.5194/bg-13-4023-2016 Laws, E. A., Falkowski, P. G., Smith, W. O. Jr., Ducklow, H., and McCarth, J. J. (2000). Temperature effects on export production in the open ocean. Global Biogeochem. Cycles 14, 1231–1246. doi: 10.1029/1999GB001229 Le Quéré, C., Harrison, S. P., Prentice, C. I., Buitenhuis, E. T., Aumont, O., Bopp, L., et al. (2005). Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models. Global Change Biol. 11, 2016–2040. doi: 10.1111/j.1365-2486.2005.1004.x Lee, Z., Arnone, R., Hu, C., Werdell, P. J., and Lubac, B. (2011). Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm. Appl. Optics 49, 369–381. doi: 10.1364/AO.49.000369 Legendre, L., and LeFevre, J. (1991). “From individual plankton cells to pelagic marine ecosystems and to global biogeochemical cycles,” in Particle Analysis in Oceanography, ed S. Demers (Berlin: Springer), 261–300. Lin, J., Cao, W., Wang, G., and Hu, S. (2014). Satellite-observed variability of phytoplankton size classes associated with a cold eddy in the South China Sea. Mar. Pollut. Bull. 83, 190–197. doi: 10.1016/j.marpolbul.2014.03.052 Maloney, C. L., and Field, J. G. (1991). The size-based dynamics of plankton food webs. I. A simulation model of carbon and nitrogen flows. J. Plank. Res. 13, 1003–1038. doi: 10.1093/plankt/13.5.1003 Marañón, E. (2009). “Phytoplankton size structure,” in Encyclopedia of Ocean Sciences, eds J. H. Steele, K. Turekian, and S. A. Thorpe (Oxford: Academic Press). doi: 10.1016/b978-012374473-9.00661-5 Marañón, E. (2015). Cell size as a key determinant of phytoplankton metabolism and community structure. Ann. Rev. Mar. Sci. 7, 241–264. doi: 10.1146/annurev-marine-010814-015955 Marañón, E., Cermeño, P., Latasa, M., and Tadonléké, R. D. (2012). Temperature, resources, and phytoplankton size structure in the ocean. Limnol. Oceanogr. 57, 1266–1278. doi: 10.4319/lo.2012.57.5.1266 Marañón, E., Holligan, P. M., Barciela, R., González, N., Mouriño, B., Pazó, M. J., et al. (2001). Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments. Mar. Ecol. Prog. Ser. 216, 43–56. doi: 10.3354/meps216043 Marinov, I., Doney, S. C., and Lima, I. D. (2010). Response of ocean phytoplankton community structure to climate change over the 21st century: partitioning the effects of nutrients, temperature and light. Biogeosciences 7, 3941–3959. doi: 10.5194/bg-7-3941-2010 Maritorena, S., Fanton d’Andon, O. H., Mangin, A., and Siegel, D. A. (2010). Merged satellite ocean color data products using a bio-optical model: characteristics, benefits and issues. Remote Sens. Environ. 114, 1791–1804. doi: 10.1016/j.rse.2010.04.002 Markwardt, C. B. (2008). “Non-linear least squares fitting in IDL with MPFIT,” in Proceedings of the Astronomical Data Analysis Software and Systems XVIII, ASP Conference Series, Quebec, Canada, vol. 411, eds D. Bohlender, P. Dowler, and D. Duran (San Francisco, CA: Astronomical Society of the Pacific). McQuatters-Gollop, A., Raitsos, D. E., Edwards, M., and Attrill, M. J. (2007). Spatial patterns of diatom and dinoflagellate seasonal cycles in the NE Atlantic Ocean. Mar. Ecol. Prog. Ser. 339, 301–306. doi: 10.3354/meps339301 Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E., Corlett, G. K., et al. (2014). Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change initiative (SST CCI). Geosci. Data J. 1, 179–191. doi: 10.1002/gdj3.20 Moore, T., Campbell, J. W., and Feng, H. (2001). A fuzzy logic classification scheme for selecting and blending satellite ocean color algorithms. IEEE Trans. Geosci. Remote Sens. 39, 1764–1776. doi: 10.1109/36.942555 Moore, T. S., Campbell, J. W., and Dowell, M. D. (2009). A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sens. Environ. 113, 2424–2430. doi: 10.1016/j.rse.2009.07.016 Moore, T. S., Dowell, M. D., and Franz, B. A. (2012). Detection of coccolithophore blooms in ocean color satellite imagery: a generalized approach for use with multiple sensors. Remote Sens. Environ. 117, 249–263. doi: 10.1016/j.rse.2011.10.001 Moré, J. (1978). The Levenberg-Marquardt Algorithm: Implementation and Theory. In: Numerical Analysis. Berlin: Springer-Verlag. Morel, A., and Bricaud, A. (1981). Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton. Deep Sea Res. 28, 1375–1393. doi: 10.1016/0198-0149(81)90039-X Morel, A., Huot, Y., Gentili, B., Werdell, P. J., Hooker, S. B., and Franz, B. A. (2007). Examining the consistency of products derived from various ocean color sensors in open ocean (case 1) waters in the perspective of a multi-sensor approach. Remote Sens. Environ. 111, 69–88. doi: 10.1016/j.rse.2007.03.012 Mouw, C. B., Hardman-Mountford, N. J., Alvain, S., Bracher, A., Brewin, R. J. W., Bricaud, A., et al. (2017). A consumer’s guide to satellite remote sensing of multiple phytoplankton groups in the global ocean. Front. Mar. Sci. 4:41. doi: 10.3389/fmars.2017.00041 Nair, A., Sathyendranath, S., Platt, T., Morales, J., Stuart, V., Forget, M.-H., et al. (2008). Remote sensing of phytoplankton functional types. Remote Sens. Environ. 112, 3366–3375. doi: 10.1016/j.rse.2008.01.021 Pan, X., Wong, G. T., Ho, T. Y., Shiah, F. K., and Liu, H. (2013). Remote sensing of picophytoplankton distribution in the northern South China Sea. Remote Sens. Environ. 128, 162–175. doi: 10.1016/j.rse.2012.10.014 Platt, T., and Denman, K. L. (1976). The relationship between photosynthesis and light for natural assemblages of coastal marine phytoplankton. J. Phycol. 12, 421–430. doi: 10.1111/j.1529-8817.1976.tb02866.x Platt, T., and Denman, K. L. (1977). Organisation in the pelagic ecosystem. Helgoa˜ Ender Wissenschaftliche Meeresuntersuchungen 30, 575–581. doi: 10.1007/BF02207862 Platt, T., and Denman, K. L. (1978). The structure of pelagic marine ecosystems. Rapp. P.-v. Réun. Cons. perm. int. Explor. Mer. 173, 60–65. Prieur, L., and Sathyendranath, S. (1981). An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments, dissolved organic matter and other particulate materials. Limnol. Oceanogr. 26, 617–689. doi: 10.4319/lo.1981.26. 4.0671 Probyn, T. A. (1985). Nitrogen uptake by size-fractionated phytoplankton populations in the southern Benguela upwelling system. Mar. Ecol. Prog. Ser. 22, 249–258. doi: 10.3354/meps022249 Raimbault, P., Rodier, M., and Taupier-Letage, I. (1988). Size fraction of phytoplankton in the Ligurian Sea and the Algerian Basin (Mediterranean Sea): size distribution versus total concentration. Mar. Microb. Food Webs 3, 1–7. Raitsos, D. E., Lavender, S. J., Maravelias, C. D., Haralambous, J., Richardson, A. J., and Reid, P. C. (2008). Identifying four phytoplankton functional types from space: an ecological approach. Limnol. Oceanogr. 53, 605–613. doi: 10.4319/lo.2008.53.2.0605 Raven, J. A. (1998). Small is beautiful: the picophytoplankton. Funct. Ecol. 12, 503–513. doi: 10.1046/j.1365-2435.1998.00233.x Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G. (2007). Daily high-resolution-blended analysis for sea surface temperature. J. Climate 20, 5473–5496. doi: 10.1175/2007JCLI1824.1 Frontiers in Marine Science | www.frontiersin.org 21 April 2017 | Volume 4 | Article 104 Brewin et al. Uncertainty in Ocean-Color Phytoplankton Group Chlorophyll Riegman, R., Kuipers, B. R., Noordeloos, A. A. M., and Witte, H. J. (1993). Size-differential control of phytoplankton and the structure of plankton communities. Netherlands J. Sea Res. 31, 225–265. doi: 10.1016/0077-7579(93)90026-O Robinson, C., Serret, P., Tilstone, G., Teira, E., Zubkov, M. V., Rees, A. P., et al. (2002). Plankton respiration in the Eastern Atlantic Ocean. Deep Sea Res. I 49, 787–813. doi: 10.1016/S0967-0637(01)00083-8 Rousseaux, C. S., Hirata, T., and Gregg, W. W. (2013). Satellite views of global phytoplankton community distributions using an empirical algorithm and a numerical model. Biogeosci. Discuss. 10, 1083–1109. doi: 10.5194/bgd-10-1083-2013 Roy, S., Sathyendranath, S., Bouman, H., and Platt, T. (2013). The global distribution of phytoplankton size spectrum and size classes from their lightabsorption spectra derived from satellite data. Remote Sens. Environ. 139, 185–197. doi: 10.1016/j.rse.2013.08.004 Sammartino, M., Di Cicco, A., Marullo, S., and Santoleri, R. (2015). Spatiotemporal variability of micro-, nanoand pico-phytoplankton in the Mediterranean Sea from satellite ocean colour data of SeaWiFS. Ocean Sci. 11, 759–778. doi: 10.5194/os-11-759-2015 Sathyendranath, S. (2011). Ocean Colour Climate Change Initiative (OC-CCI) - Phase One User Requirements Document. Sathyendranath, S., Stuart, V., Cota, G., Maas, H., and Platt, T. (2001). Remote sensing of phytoplankton pigments: a comparison of empirical and theoretical approaches. Int. J. Remote Sens. 22, 249–273. doi: 10.1080/014311601449925 Savidge, G., Boyd, P., Pomroy, A., Harbour, D., and Joint, I. (1995). Phytoplankton production and biomass estimates in the north-east Atlantic Ocean. Deep Sea Res. Part I 42, 599–617. doi: 10.1016/0967-0637(95)00016-Y Serret, P., Robinson, C., Fernández, E., Teira, E., and Tilstone, G. (2001). Latitudinal variation of the balance between plankton photosynthesis and respiration in the eastern Atlantic Ocean. Limnol. Oceanogr. 46, 1642–1652. doi: 10.4319/lo.2001.46.7.1642 Shang, S., Wu, J., Huang, B., Lin, G., Lee, Z., Liu, J., et al. (2014). A new approach to discriminate dinoflagellate from diatom blooms from space in the East China Sea. J. Geophys. Res. 119, 4653–4668. doi: 10.1002/2014JC009876 Shimoda, Y., and Arhonditsis, G. B. (2016). Phytoplankton functional type modelling: running before we can walk? A critical evaluation of the current state of knowledge. Ecol. Model. 320, 29–43. doi: 10.1016/j.ecolmodel.2015.08.029 Sieracki, M. E., Verity, P. G., and Stoecker, D. K. (1993). Plankton community response to sequential silicate and nitrate depletion during the 1989 North Atlantic spring bloom. Deep Sea Res. Part II Top. Stud. Oceanogr. 40, 213–225. doi: 10.1016/0967-0645(93)90014-E Smyth, T. J., Fishwick, J., Al-Moosawi, L., Cummings, D. G., Harris, R., Kitidis, V., et al. (2010). A broad spatio-temporal view of the Western English Channel observatory. J. Plank. Res. 32, 585–601. doi: 10.1093/plankt/fbp128 Stauber, J. L., and Jeffrey, S. W. (1988). Photosynthetic pigments in fifty-one species of marine diatoms. J. Phycol. 24, 158–172. doi: 10.1111/j.1529-8817.1988.tb04230.x Sunda, W. G., and Huntsman, S. A. (1997). Interrelated influence of iron, light and cell size on marine phytoplankton growth. Nature 390, 389–392. doi: 10.1038/37093 Takahashi, T., Sutherland, S. C., Sweeney, C., Poisson, A., Metzl, N., Tilbrook, B., et al. (2002). Global sea–air CO2flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep Sea Res. Part II Top. Stud. Oceanogr. 49, 1601–1622. doi: 10.1016/S0967-0645(02)00003-6 Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A., Chipman, D. W., et al. (2009). Climatological mean and decadal change in surface ocean pCO2, and net sea–air CO2flux over the global oceans. Deep Sea Res. Part II Top. Stud. Oceanogr. 56, 554–577. doi: 10.1016/j.dsr2.2008. 12.009 Tilstone, G. H., Lange, P. K., Misra, A., Brewin, R. J. W., and Cain, T. (2017). Micro-phytoplankton photosynthesis, primary production and potential export production in the Atlantic Ocean. Prog. Oceanogr. doi: 10.1016/j.pocean.2017.01.006 Tilstone, G., Miller, P., Brewin, R. J. W., and Priede, I. G. (2014). Enhancement of primary production in the North Atlantic outside of the spring bloom, identified by remote sensing of ocean colour and temperature. Remote Sens. Environ. 146, 77–86. doi: 10.1016/j.rse.2013.04.021 Tilstone, G. H., Taylor, B. H., Blondeau-Patissier, D., Powell, T., Groom, S. B., Rees, A. P., et al. (2015). Comparison of new and primary production models using SeaWiFS data in contrasting hydrographic zones of the northern North Atlantic. Remote Sens. Environ. 156, 473–489. doi: 10.1016/j.rse.2014.10.013 Trees, C. C., Clark, D. K., Bidigare, R. R., Ondrusek, M. E., and Mueller, J. L. (2000). Accessory pigments versus chlorophyll a concentrations within the euphotic zone: a ubiquitous relationship. Limnol. Oceanogr. 45, 1130–1143. doi: 10.4319/lo.2000.45.5.1130 Uitz, J., Claustre, H., Morel, A., and Hooker, S. B. (2006). Vertical distribution of phytoplankton communities in open ocean: an assessment based on surface chlorophyll. J. Geophys. Res. 111:C08005. doi: 10.1029/2005JC003207 Vidussi, F., Claustre, H., Manca, B. B., Luchetta, A., and Marty, J. C. (2001). Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter. J. Geophys. Res. 106, 19939–19956. doi: 10.1029/1999JC000308 Vogt, M., Hashioka, T., Payne, M., Buitenhuis, E. T., Le Quéré, C., Alvain, S., et al. (2013). The distribution, dominance patterns and ecological niches of plankton functional groups in Dynamic Green Ocean Models and satellite estimates. Biogeosci. Discuss. 10, 17193–17247. doi: 10.5194/bgd-10-17193-2013 Wang, P., Boss, E., and Roesler, C. (2005). Uncertainties of inherent optical properties obtained from semianalytical inversions of ocean color. Appl. Opt. 44, 4074–4085. doi: 10.1364/AO.44.004074 Ward, B. A. (2015). Temperature-correlated changes in phytoplankton community structure are restricted to polar waters. PLoS ONE 10:e0135581. doi: 10.1371/journal.pone.0135581 Ward, B. A., Dutkiewicz, S., Jahn, O., and Follows, M. J. (2012). A size-structured food-web model for the global ocean. Limnol. Oceanogr. 57, 1877–1891. doi: 10.4319/lo.2012.57.6.1877 Werdell, P. J., and Bailey, S. W. (2005). An improved in-situ bio-optical data set for ocean colour algorithm development and satellite data production validation. Remote Sens. Environ. 98, 122–140. doi: 10.1016/j.rse.2005.07.001 Werdell, P. J., Bailey, S. W., Fargion, G. S., Pietras, C., Knobelspiesse, K. D., Feldman, G. C., et al. (2003). Unique data repository facilitates ocean color satellite validation. EOS Trans. Am. Geophys. Union 84, 377–387. doi: 10.1029/2003EO380001 Werdell, P. J., Franz, B. A., Bailey, S. W., Feldman, G. C., Boss, E., Brando, V. E., et al. (2013a). Generalized ocean color inversion model for retrieving marine inherent optical properties. Appl. Opt. 52, 2019–2037. doi: 10.1364/AO.52.002019 Werdell, P. J., Proctor, C. W., Boss, E., Leeuw, T., and Ouhssain, M. (2013b). Underway sampling of marine inherent optical properties on the Tara Oceans expedition as a novel resource for ocean color satellite data product validation. Methods Oceanogr. 7, 40–51. doi: 10.1016/j.mio.2013.09.001 Westberry, T. K., Dall’Olmo, G., Behrenfeld, M., and Moutin, T. (2010). Coherence of particulate beam attenuation and backscattering coefficients in diverse open ocean environments. Opt. Exp. 18, 15419–15425. doi: 10.1364/OE.18.015419 Widdicombe, C., Eloire, D., Harbour, D., Harris, R., and Somerfield, P. (2010). Long-term phytoplankton community dynamics in the Western English Channel. J. Plankt. Res. 32, 643–655. doi: 10.1093/plankt/fbp127 Xiao, Y., and Friedrichs, M. A. M. (2014). The assimilation of satellite-derived data into a one-dimensional lower trophic level marine ecosystem model. J. Geophys. Res. 119, 2691–2712. doi: 10.1093/plankt/fbp127 Zapata, M., Fraga, S., Rodríguez, F., and Garrido, J. L. (2012). Pigmentbased chloroplast types in dinoflagellates. Mar. Ecol. Prog. Ser. 465, 33–52. doi: 10.3354/meps09879 Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Copyright © 2017 Brewin, Ciavatta, Sathyendranath, Jackson, Tilstone, Curran, Airs, Cummings, Brotas, Organelli, Dall’Olmo and Raitsos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Marine Science | www.frontiersin.org 22 April 2017 | Volume 4 | Article 104