A remote sensing algorithm for planktonic ...

Remote Sensing of Environment 171 (2015) 171–184

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A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns Martí Galí a,⁎, Emmanuel Devred a, Maurice Levasseur a, Sarah-Jeanne Royer b, Marcel Babin a a Takuvik Joint International Laboratory (Université Laval — CNRS France), Biology Department, Université Laval, Pavillon Alexandre-Vachon, Local 2078, Avenue de la Médecine, G1V 0A6 Québec, QC, Canada b Department of Marine Biology and Oceanography, Institut de Ciències del Mar (CSIC), Passeig Marítim de la Barceloneta 37-49, 08003 Barcelona, Catalonia, Spain

a r t i c l e

i n f o

Article history: Received 14 May 2015 Received in revised form 9 October 2015 Accepted 21 October 2015 Available online xxxx Keywords: Dimethylsulfoniopropionate (DMSP) Sulfur cycle Phytoplankton Chlorophyll Stratification Particulate inorganic carbon (PIC) Algorithm

a b s t r a c t Dimethylsulfoniopropionate (DMSP) is a ubiquitous phytoplankton metabolite and the main precursor of the climate-active gas dimethylsulfide (DMS) in the oceans' surface. Here we use total DMSP (DMSPt) and ancillary measurements from a global database to develop a remote sensing algorithm for DMSPt in the upper mixed layer (UML). Over 55% of total DMSPt variability (log10 scale) is explained by in situ chlorophyll a (Chl) after dividing the database into two subsets, according to “stratified” and “mixed” water column criteria, based on the ratio between euphotic layer depth (Zeu) and mixed layer depth (MLD). Up to 70% of the variability is explained when adding sea surface temperature (SST) and log10(Zeu/MLD) as predictors for the stratified and mixed subsets, respectively. Independent validation on satellite Chl match-ups indicates that the algorithm predicts DMSPt across three orders of magnitude with a root-mean-squared error spanning from 0.20 to 0.26 (log10 space) and mean absolute error typically around 45% (linear space). An additional submodel based on remotely sensed particulate inorganic carbon (PIC) is used to predict DMSPt in coccolithophore blooms if satellite Chl is not available. We use the algorithm to produce a monthly global DMSPt climatology, and estimate that DMSP synthesis amounts to 5– 9% of oceanic phytoplankton gross carbon production. Our algorithm provides a new remote sensing tool for resolving temporal and spatial variations in DMSPt concentration, and represents a step forward toward improved diagnosis of contemporary DMS emission based on satellite Earth observation. © 2015 Elsevier Inc. All rights reserved.

1. Introduction Dimethylsulfoniopropionate (DMSP) is a tertiary sulfonium compound that constitutes the main intracellular sulfur pool of many phytoplankton species and usually 1–10% of their cellular carbon (Stefels, Steinke, Turner, Malin, & Belviso, 2007). DMSP is ubiquitous in the sunlit upper layer of the oceans where it plays a major role in trophic interactions, from microbial plankton (Kiene, Linn, & Bruton, 2000; Simó, 2001; Seymour, Simó, Ahmed, & Stocker, 2010) up to the highest food web levels (DeBose, Lema & Nevitt, 2008; Savoca & Nevitt, 2014). Some DMSP degradation pathways give rise to the volatile compound dimethylsulfide (DMS), whose oceanic emission amounts to ~28 Tg S y−1 (Lana et al., 2011) and represents the main natural source of sulfur to the atmosphere (Simó, 2001). DMS is a key player in aerosol formation and growth (Andreae & Rosenfeld, 2008; Saltzman, 2009) and cloud microphysics (Lohmann, 2009; Lana, Simó, Vallina, & Dachs, 2012; Rap et al., 2013). It is therefore a climatic agent, especially in pristine oceanic atmospheres (Chang et al., 2011; Leaitch et al., 2013; McCoy et al., 2015), with potential to mediate ocean–atmosphere climate feedbacks (Charlson, Lovelock, Andreae, & Warren, 1987). DMSP is also the ⁎ Corresponding author. E-mail address: [email protected] (M. Galí).

http://dx.doi.org/10.1016/j.rse.2015.10.012 0034-4257/© 2015 Elsevier Inc. All rights reserved.

precursor of dimethylsulfoxide (DMSO), a major pool of dimethylated sulfur that can act as a secondary DMS source (Spiese, Kieber, Nomura, & Kiene, 2009; Asher, Dacey, Mills, Arrigo, & Tortell, 2011). Sensible and accurate parameterization of DMSP in sulfur cycling models is the first step toward enhanced modeling of oceanic DMS emission (Le Clainche et al., 2010) and its response to global change (Bopp et al., 2004; Gabric, Qu, Matrai, & Hirst, 2005; Cameron-Smith, Elliott, Maltrud, Erickson, & Wingenter, 2011). The ability to predict DMSP concentration from environmental variables is essential to disentangle phytoplankton community dynamics (biogeography, succession) from other biotic and abiotic factors that regulate the fate of DMSP in seawater and ultimately DMS emission (Stefels et al., 2007; Galí & Simó, 2015). However, DMSP prediction from phytoplankton biomass proxies is not straightforward (Kettle et al., 1999) due to the strong variability across taxonomic groups (Stefels et al., 2007) and its interplay with environmental factors (Archer, Cummings, Llewellyn, & Fishwick, 2009; Bell, Poulton, & Malin, 2010; Galí & Simó, 2015). In prognostic ecosystem models DMSP is parameterized either by modulating DMSP:carbon or DMSP:nitrogen quotas as a function of environmental factors, which implicitly accounts for phytoplankton community succession (Lefèvre, Vézina, Levasseur, & Dacey, 2002; Vallina et al., 2008; Vogt, Vallina, Buitenhuis, Bopp, & Le Quéré, 2010), or by assigning different DMSP quotas to a number of phytoplankton functional types (Archer et al.,

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2. Data sources

(UML). Individual measurements were averaged if taken on the same day and station (within a 0.1 km radius); no further temporal or spatial binning was applied. A characterization of the merged datasets is shown in Fig. 1 and a list of the additional studies is given in Supplementary material (SM) Table S1. The DMSP concentrations found in the database were measured after alkaline hydrolysis (Dacey & Blough, 1987). This treatment cleaves DMSP to DMS that can be subsequently analyzed by gas chromatography. Intercomparison studies carried out at sea with natural samples (Bell et al., 2012) or on land with certified materials (Swan et al., 2014) suggest a DMS measurement uncertainty of 25% among laboratories. Further unquantified uncertainties may be introduced by sample handling and storing protocols from different laboratories. In some studies, total DMSP (DMSPt) was operationally partitioned into particulate and dissolved DMSP (DMSPp and DMSPd, respectively). Although DMSPp should be a priori more tightly linked to phytoplankton biomass and to its satellite proxies (such as Chl), we considered that DMSPt was less affected by measurement artifacts (Kiene & Slezak, 2006) and in consequence more amenable to prediction. Thus, we calculated DMSPt as DMSPp + DMSPd when needed. This view is supported by our preliminary analysis of the database (SM Section 1 and Fig. S1). DMSPt versus Chl measurement pairs were subjected to further quality control to ensure the internal consistency of the database and facilitate algorithm development. Some of the criteria (“flags”) were applied on a sample basis, whereas in other cases we flagged all samples from a given database contribution (identified with a contribution number in the GSS database). In order of decreasing importance, quality control criteria included (1) identification of DMSPt “outliers” (e.g. estuarine data); (2) identification of coccolithophore bloom data; (3) identification of samples with potential analytical problems (e.g., Del Valle et al., 2010); and (4) identification of Chl data measured by fluorometry versus HPLC, and comparison to satellite match-up Chl data to ensure their mutual consistency. See the SM for further information on flagging criteria, and on the impact of corrections applied and “outlier” removal on DMSPt parameterizations. Chl concentration was converted to phytoplankton carbon biomass (CPHY) using the model of Sathyendranath et al. (2009). The parameterizations for Turner fluorometer Chl (log10CPHY = 1.81 + 0.63 log10ChlTurner) or HPLC Chl (log10CPHY = 1.90 + 0.65 log10ChlHPLC) were used as appropriate. Assuming that most DMSPt is phytoplankton-bound (Kiene & Slezak, 2006) and taking into account that there are 5 C atoms per DMSP molecule (CDMSPt = 5·DMSPt), phytoplankton community DMSP:carbon quotas (mol mol−1 units) can be estimated as the CDMSPt:CPHY ratio. We used the latter as an additional (and often redundant) quality control criterion in the following way: expected CDMSPt:CPHY ratios were calculated based on Chl-biomass partition among phytoplankton size classes (Uitz, Claustre, Morel, & Hooker, 2006) and literature-derived carbon quotas (Stefels et al., 2007; Vogt et al., 2010). Then, samples with a CDMSPt:CPHY smaller than 10% of the expected value were removed, as illustrated in Fig. 2a (see SM for further details).

2.1. DMSP database and quality control

2.2. Satellite match-up data

Total DMSP (DMSPt) measurements were obtained from the Global Surface Seawater (GSS) DMS database (http://saga.pmel.noaa.gov/dms/ ). This database contains about 48,000 DMS measurements collected by different researchers between 1972 and 2009, and was recently used to produce an updated DMS climatology (Lana et al., 2011). It also contains DMSPt (n ≈ 4600), Chl, sea-surface temperature (SST), salinity, and other ancillary variables. We supplemented the GSS database with over 400 published and unpublished DMSP data obtained in the Barents Sea, the NW Atlantic, the NE Pacific, the Canadian Arctic, the NW Mediterranean and the subtropical and equatorial Atlantic, Pacific and Indian Oceans. Only samples from the top 10 m of the water column were used, and assumed representative of the upper mixed layer

Remotely-sensed Chl, particulate organic carbon (POC), particulate inorganic carbon (PIC) and photosynthetically available radiation (PAR) were obtained from the NASA Ocean Color website (http:// oceancolor.gsfc.nasa.gov/). Level 3-binned (L3BIN) data from the SeaWiFS and MODIS-Aqua sensors (daily and 8-day composites) were matched to simultaneous in situ DMSPt measurements using SeaDAS 6.4 and used for algorithm validation. The match-ups were done using individual pixels (9.2 km and 4.6 km resolution for SeaWiFS and MODIS-Aqua, respectively) and the average of 3 × 3 and 5 × 5 pixel boxes centered on the in situ measurement location. SeaWiFS images spanned the 1998–2007 period, whereas MODIS-Aqua spanned 2003–2012, jointly covering half of the DMSPt measurement record

2002; Six & Maier-Reimer, 2006; Steiner & Denman, 2008; Vogt et al., 2010; Gypens, Borges, Speeckaert, & Lancelot, 2014). Prognostic models have shown mixed success in predicting oceanic DMS, which is partly due to their variable skill in predicting its precursor DMSP (Le Clainche et al., 2010). An alternative approach to marine sulfur cycle modeling is the development of empirical algorithms, which often take advantage of satellite Earth observation to produce estimates of DMS emission with broad spatial and temporal coverage. Empirical DMS algorithms, such as those of Anderson et al. (2001) or Simó and Dachs (2002), usually bypass DMSP. Only the empirical algorithm of Belviso, Moulin, Bopp, and Stefels (2004b), to the best of our knowledge, predicts DMSP in a first step (using a phytoplankton functional types approach) to subsequently convert it to DMS. Although these empirical algorithms can provide a useful shortcut to DMS prediction and even produce reasonable DMS climatologies (see Belviso et al., 2004a), they have limited capacity to represent DMS variability in response to ecosystem changes (Halloran, Bell, & Totterdell, 2010). Surprisingly, global-scale DMSP parameterizations have never been validated, to our knowledge, using the largest public dataset available, the Global Sea Surface (GSS) DMS and DMSP database (Lana et al., 2011). Similarly, the empirical model of Belviso et al. (2004b) was only validated using relatively small datasets. This uncertainty hampers our understanding of the coupled ocean–atmosphere sulfur cycle, and stresses the need for a DMSP climatology that can be cross-validated with existing DMSP parameterizations. Due to the uneven spatialtemporal coverage of DMSP data in the GSS database, objective interpolation techniques can hardly be used to produce a global DMSP climatology, as done for DMS (Kettle et al., 1999; Lana et al., 2011). This gap can be filled using remote sensing algorithms. The latter can also advance our understanding of the marine sulfur cycle and aerosol dynamics in regions characterized by strong temporal variability over short periods or between different years. Here we take advantage of a recent major update of the GSS database, further enlarged and quality-controlled (Section 2), to develop (Section 3) and validate (Section 4) a diagnostic algorithm for total DMSP (DMSPt) concentration based on remotely sensed Chl concentration and the vertical mixing regime. Additionally, a submodel based on particulate inorganic carbon (PIC) is developed to complement DMSPt diagnosis in coccolithophore blooms, where satellite Chl may not be reliable (Balch, Gordon, Bowler, Drapeau, & Booth, 2005). To exemplify the utility of the algorithm, we implement it to derive global fields of DMSPt and DMSPt:carbon ratios using climatological and nonclimatological satellite data (Section 5). Finally, we discuss the observed patterns in the framework of physiological and ecological DMSPt drivers, and the suitability of the algorithm to resolve interannual DMSPt variability (Section 6), which will allow the production of regional or global DMSPt time series from remote sensing data in future studies.


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Fig. 1. Characterization of the in situ DMSP database (DMSPt: total DMSP). A) number of measurements per year between 1985 and 2012. B) Distribution of measurements per month in the Northern (blue) and Southern (red) hemispheres. C) Geographical density of measurements on a 2° × 2° grid. D) Latitudinal distribution in 10° bands. All biomes and seasons are documented in the database except the polar winter, but temperate latitudes and late spring through summer months are overrepresented. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

(Fig. 1d). Chl was computed using the standard OC4 (SeaWiFS) and OC3M (MODIS-Aqua) ocean color algorithms (O'Reilly et al., 1998), and the 2010.0 and 2013.1 reprocessing were used for SeaWiFS and MODIS-Aqua, respectively. Daily gridded SST from the AVHRR Pathfinder Version 5.2 (https://climatedataguide.ucar.edu/climate-data/ sst-data-avhrr-pathfinder-v52-noaa-nodc) was also matched to the in situ database (4.6 km resolution), as well as 8-day-averaged SST corresponding to the SeaWiFS and MODIS-Aqua 8-day periods. 2.3. Climatological data and bathymetry The database DMSPt measurements were matched to monthly climatological data including mixed layer depth (MLD), nitrate and phosphate concentrations, satellite Chl, PAR and SST, to better understand the DMSP drivers and assist in algorithm development. Chl, PAR and SST climatologies were obtained from the same websites mentioned in the previous section. MLD was obtained from the monthly isopycnal/mixed-layer ocean climatology (MIMOC; http://www.pmel. noaa.gov/mimoc/) with 0.5° resolution (Schmidtko et al., 2013), which uses both ship-based and ARGO profiles and a complex MLD algorithm (Holte & Talley, 2009). Monthly nitrate and phosphate 1° climatologies were obtained from the World Ocean Atlas 2009 (WOA09; http:// coastwatch.pfeg.noaa.gov/erddap/griddap/nodcWoa09sea1n.html). Bottom depth was obtained from the 1 km resolution General Bathymetric Chart of the Oceans 2008 (GEBCO08) when not available in the database. 3. Algorithm development using in situ data 3.1. DMSPt versus Chl relationship: stratified and mixed regimes The linear correlation between log10DMSPt and log10Chl was weak in the initial database (R2 = 0.22, n = 2281), and increased notably

after the quality control procedure (R2 = 0.46, n = 2036) (Fig. 2a). Careful examination of the log 10 DMSPt–log10Chl scatter plot revealed the existence of two subgroups in the data related to light penetration in the upper mixed layer (Fig. 2b). The latter was assessed using the quotient between euphotic layer depth (Zeu; 1% penetration of PAR) and mixed layer depth (MLD). Z eu was computed from Chl as: log10 Zeu = 1.524–0.436 log10Chl − 0.0145 (log10Chl)2 + 0.0186 (log10Chl)3 (Eq. (10) from Morel et al., 2007). Samples originating from the upper 10 m of a stratified water column (Zeu/MLD N 1) generally displayed a higher amount of DMSPt at a given Chl concentration than samples from mixed waters (Zeu/ MLD b 1). Hereafter, these two subsets will be referred to as “stratified” and “mixed” regimes for consistency with previous works (Morel & Berthon, 1989). In the stratified subset, Chl concentrations spanned between 0.012 and 3.8 μg L − 1 and DMSPt between 1.5 and 600 nmol L− 1 , whereas in the mixed subset Chl spanned between 0.2 and 58 μg L− 1 and DMSPt between 6.0 and 1126 nmol L− 1. All the data points with Chl b 0.2 μg L− 1 were classified as stratified, and all the data points above 4 μg L− 1 as mixed. However, between these two Chl concentrations both regimes cannot be well separated, which could be partly explained by uncertainty in the determination of Zeu from Chl and the use of climatological MLD (the transition zone with Zeu/MLD ≈ 1 generally corresponded to Zeu − MLD ≤ 10 m). This prompted us to refine the stratified-mixed classification before fitting predictive equations to each subset. First, data points belonging to well-identified or putative coccolithophore blooms that had been diagnosed as mixed were assigned to the stratified regime. Fig. 2c shows that these points clustered together and displayed continuity with the stratified data cloud. This is in accordance with the ecology of coccolithophore blooms, which are unambiguously associated to shallow and well sunlit mixed layers (Iglesias-Rodriguez et al., 2002; Raitsos et al., 2006; Signorini et al., 2006). Second,

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Fig. 2. Classification into “stratified” and “mixed” subsets. A) Raw and quality-controlled DMSPt–Chl data. The red and blue dashed lines depict the threshold for stratified and mixed waters, respectively, used to exclude points below 10% of expected DMSPt (see SM). B) Quality-controlled oceanic data (excluding bottom depth b 200 m and salinity b30), with their Zeu/ MLD quotient highlighted in color. C) Ensemble of the stratified subset with quadratic and logistic fits. The stratified subset includes points with diagnosed Zeu/MLD N 1, a well-documented coccolithophore bloom (Malin et al., 1993, M93, white) and putative coccolithophore blooms (gray). D) Mixed subset (Zeu/MLD b 1) with linear and quadratic fits. Diagonal lines in (C) and (D) represent constant DMSPt:Chl ratios (value indicated on some of them).

samples taken at bottom depths shallower than 200 m were removed if Chl b 6 μg L− 1, to avoid misclassification over continental shelves, were yellow substances and non-algal particles of continental origin (Babin et al., 2003) can affect Zeu prediction from Chl. We estimated that samples with Chl N 6 μg L− 1 could be safely classified as mixed, in agreement with Uitz et al. (2006). In the stratified subset, the relationship between log10DMSPt and log10Chl was best fitted with a quadratic or logistic equation (R2 = 0.59–0.60, RMSE = 0.32) (Fig. 2c). Both equations implied stronger DMSPt–Chl proportionality at Chl N 0.1 μg L− 1, with most points clustering around a DMSPt:Chl ratio of ≈ 100 nmol μg− 1, and a flatter shape at Chl b 0.1 μg L− 1. A similar pattern has been previously described by Aumont, Belviso, and Monfray (2002). The use of a quadratic function was also supported by the shape of the log10DMSPt–log10Chl scatterplot after binning the data into biogeochemical provinces and seasons (Longhurst, Sathyendranath, Platt, & Caverhill, 1995; Longhurst, 2010), as shown in Fig. 3 and Fig. S2. Thus, the quadratic model was selected because it maximized the explained variance with fewer coefficients. The residuals of the quadratic fit were compared against a number of in situ or climatology-derived variables to identify additional DMSPt predictors. Among them, only SST explained a sizable fraction of the residuals (Fig. S3), such that we decided to add this variable to our

diagnostic model in quadratic form. The resulting equation for stratified waters is Log10 DMSPt ¼ 1:70 þ 1:14 log10 Chl þ 0:44 log10 Chl 2

2

þ0:063 SST–0:0024 SST2

ð1Þ

R ¼ 0:72; RMSE ¼ 0:27; n ¼ 1220

with all coefficients significant at p ≪ 10−6. Fig. 2d shows that most mixed subset data points fell between DMSPt:Chl ratios of 10–100 nmol μg−1, and that DMSPt:Chl ratios decreased with increasing Chl. In this subset, the log10DMSPt–log10Chl relationship was best fitted with either a quadratic or a linear function (Fig. 2d). Analysis of the residuals showed that the fit improved slightly when light exposure proxies such as MLD, MLD-averaged PAR (Fig. 3b) or log10(Zeu/MLD) (Fig. 2b) were included in the model, so that we finally chose the simplest formulation including log10(Zeu/MLD) Log10 DMSPt ¼ 1:74 þ 0:81 log10 Chl þ 0:600 log10 ðZeu =MLDÞ R2 ¼ 0:52; RMSE ¼ 0:29; n ¼ 355 with all coefficients significant at p ≪ 10−6.

ð2Þ


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Fig. 3. DMSPt–Chl scatterplot with data binned into Longhurst provinces and seasonal means (DJF, MAM, JJA, SON), separated into stratified (left) and mixed (right) conditions. The value of four different z variables (rows) is shown in color to illustrate how deviations in the DMSPt–Chl relationship do or do not co-occur with anomalies in the z variable. Only SST explains a significant amount of variability in the DMSPt–Chl relationship residuals in stratified waters (Fig. S2). This figure is complemented with Fig. S1, where each province–season point is identified.

It is worth noting that the choice of different MLD products and Zeu algorithms resulted in a different stratified versus mixed partition. Preliminary sensitivity tests included different MLD and Zeu estimates: the MLD climatology of De Boyer Montégut, Madec, Fischer, Lazar, and

Iudicone (2004); the monthly MLD time series between 1980 and 2012 generated by the General Ocean Data Assimilation System (GODAS; http://www.esrl.noaa.gov/psd/data/gridded/); and the Zeu from Lee et al. (2007). Since MLD has a larger dynamic range than Zeu,

Fig. 4. Flow chart for A) the development and B) the validation and implementation of the remote sensing DMSPt algorithm.

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the algorithm is more sensitive to the MLD product choice. The MIMOC MLD and the Zeu from Morel et al. (2007)) provided the best stratified versus mixed partition and therefore the best model fits. 3.2. Discrimination and parameterization of coccolithophore blooms Light-scattering coccoliths shed by coccolithophores modify the water reflectance spectrum (Balch et al., 2005). This invalidates Chl retrieved from remotely sensed blue/green ratios at high coccolith concentrations, precluding its use for DMSPt prediction. Yet, moderate to high concentrations of coccolith calcite, or particulate inorganic carbon (PIC), can be reasonably predicted from satellite data (Gordon et al., 2001; Balch et al., 2005). Since coccolithophore blooms may represent important regional and seasonal contributions to DMSP and DMS production (Holligan et al., 1993; Malin, Turner, Liss, Holligan, & Harbour, 1993; Matrai & Keller, 1993), we developed a DMSPt submodel based on PIC. We used a pragmatic approach to establish a PIC threshold for the algorithm (Fig. 4), which serves two purposes: (1) pixels where PIC is above the threshold are forced into the stratified water column model, overriding the Zeu/MLD criterion, based on the assumption that coccolithophore blooms occur in stratified waters; (2) if Chl is absent in a given pixel, PIC is used to infer DMSPt. The analysis of several PIC and Chl L3BIN 8-day composites for periods and regions prone to coccolithophore blooms (Raitsos et al., 2006; Signorini et al., 2006; Holligan, Charalampopoulou, & Hutson, 2010) allowed us to establish the optimal threshold at 1.5 mmol PIC m−3 (Fig. S4a). This value was chosen because it maximizes the amount of pixels usable for DMSPt calculation while ensuring that no pixels are erroneously forced into the stratified subset (further details in SM). As a final remark, the exclusion of pixels with bottom depth b200 m helped avoid spurious high PIC pixels caused by sediment or diatom frustule resuspension on continental shelves (Broerse et al., 2003). DMSPt was parameterized as a function of PIC using in situ measurements and satellite retrievals. In situ PIC–DMSPt measurement pairs (n = 25) were obtained from Holligan et al. (1993). Additional PIC data (N 1.5 mmol m−3) were obtained from SeaWiFS and MODIS-Aqua match-ups, and averaged together when measurements from the two sensors were available. Satellite and in situ PIC overlapped in the middle range (3–4 mmol PIC m−3), with satellite PIC covering the lower range and in situ PIC covering the upper end, so that we pooled both data sets together and fitted the following quadratic equation (Fig. S4b): 2

log10 DMSPt ¼ −1:052−3:185 log10 PIC−0:783ð log10 PICÞ ; R2 ¼ 0:29; RMSE ¼ 0:26; pb10−4 ; n ¼ 62:

ð3Þ

Although this relationship cannot be validated, the predicted DMSPt ranges (50–154 nmol L−1, with the maximum at 9.2 mmol PIC m−3) are consistent with the DMSPt–Chl scatterplot in coccolithophore blooms (Fig. 2c). Moreover, the DMSPt–PIC relationship resembles the Chl–PIC pattern observed by Holligan et al. (1993): Chl, DMSPt and PIC would all increase during the initial stage of the bloom whereas, during bloom decline, PIC would continue to accumulate but Chl and DMSPt would decrease. 3.3. Comparison with previous algorithms Here we compare the algorithm described in the previous sections (graphically summarized in Fig. 4) to the algorithm proposed by Belviso et al. (2004b). Briefly, these authors proposed a DMSPt parameterization based on the calculation of a community structure index “Fp” that partitions total Chl into microphytoplankton (ChlM) and nano + picophytoplankton (ChlN + P) fractions (as a function of Chl itself). Then, micro-DMSP and nano-DMSP are calculated as a function of ChlM and ChlN + P, respectively, and added together to obtain DMSPt. For the ensemble of the quality-controlled in situ dataset (n = 1575) our algorithm predicts log10DMSPt with R2 = 0.70 and RMSE = 0.27,

whereas the Belviso04 algorithm has R2 = 0.51 and RMSE = 0.73 (Fig. 5). Although the Belviso04 algorithm shows reasonable correlation to observations, it has a strong positive bias that results in large RMSE. This algorithm assigns a constant value to nanophytoplankton DMSPp (21 nmol L−1) at low nano-Chl (ChlN + P b 0.3 μg L−1), so that it cannot resolve DMSP concentrations in vast regions of the ocean. From this comparison we conclude that our algorithm significantly advances remote sensing DMSPt diagnosis. 4. Algorithm validation using satellite match-up data The algorithm developed using in situ data (Fig. 4a) was validated using ChlSAT as input (Fig. 4b). Log10 transformation was applied to avoid skewing the statistics toward the largest values, given the approximate lognormal distributions of Chl and DMSPt. Linear space statistics, though not used in model fitting, were calculated to give a sense of the model behavior in physical units. The validation statistics are defined in Table 1 [R2, RMSE, mean relative bias (MRB), and mean absolute percentage error (MAPE)]. Prior to validation using satellite data, the robustness of the model was tested by randomly splitting the in situ dataset: 80% of data were used for model calibration and the remaining 20% for validation, with the partition affecting proportionally the stratified and mixed subsets. The 95% confidence intervals (standard error) of the model coefficients obtained when fitting the full dataset (Eqs. (1) and (2)) overlapped in all cases with those obtained when fitting the 80% calibration subset. In turn, the goodness-of-fit statistics obtained when applying the latter models to the remaining 20% subset were equivalent to those obtained for the entire dataset. Afterwards, a thorough validation was performed whereby DMSPt measured in situ was compared to the DMSPt predicted from remotely sensed Chl (daily and 8-day SeaWiFS and MODIS-Aqua) and SST (AVHRR). Obviously, the agreement between ChlIN SITU and ChlSAT was better for daily data than for 8-day data (see SM). However, daily composites provided a small number of positive match-ups and therefore limited the statistical power of the comparison, which justifies the validation on 8-day data. The validation statistics for DMSPt were calculated only when concurrent ChlIN SITU was available. Although this procedure further restricted the validation sample size, it allowed us to evaluate the error in predicted DMSPt arising from uncertainty in ChlSAT (due to intrinsic ChlSAT error plus spatio-temporal mismatch). The validation statistics were successively computed using ChlSAT within a ± 30%, ± 50%, ± 100% and ± 200% interval with respect to ChlIN SITU. AVHRR match-ups showed a very good agreement with in situ SST (R2 = 0.99 and RMSE = 0.8 °C). Since SST plays only a secondary role in the algorithm, missing SST match-up data were filled with climatological SST (if the latter was within ±2 °C from in situ SST) to increase the number of match-ups. Fig. 6 shows validation results corresponding to ChlSAT match-ups within ± 50% of ChlIN SITU. R2 values ranged between 0.45 and 0.72, RMSE between 0.20 and 0.26, and MRB between − 1.6% and 11.6% (log10 space statistics). Note that RMSE and MRB provide a better assessment of model-data agreement than R2, because the latter depends strongly on the range spanned by validation data. This range is somewhat coincidental, because it reflects the simultaneous availability of in situ and satellite data (limited by cloudiness). SeaWiFS match-ups were more evenly distributed than MODIS match-ups, which resulted in better validation statistics for SeaWiFS-predicted DMSPt. Daily ChlSAT match-ups gave better DMSPt prediction than 8-day match-ups, as one would expect given the smaller error in daily match-ups. For the validation results showed in Fig. 6, the corresponding MRB in linear space 22% and 23% for SeaWiFS and −2% and 32% for MODIS (daily and 8-day respectively). These figures suggest that the algorithm slightly overestimates DMSPt, which possibly results from fitting the model in log space (i.e., normally distributed residuals become lognormal in linear space, giving more weight to positive residuals). The respective MAPE


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Fig. 5. Comparison between modeled and observed DMSPt concentration using A) the bulk Chl-based model with additional predictors; and B) the model of Belviso et al. (2004b). In A) the stratified (n = 1220) and mixed (n = 355) subsets are shown in red and blue, respectively; total n = 1575 in all plots. The solid line shows perfect agreement (1:1), the dashed lines a twofold over- or underestimation (corresponding to a RMSE = 0.30 in log10 space), and the dotted lines a fourfold over- or underestimation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

values were generally around 45% except for MODIS 8-day matchups. Although these errors may seem large, it has to be noted that the algorithm resolves DMSPt concentrations across more than two orders of magnitude. The insets in Fig. 6 show the change in log10 space RMSE as a function of the error in ChlSAT with respect to ChlIN SITU. Mean RMSE among sensors and periods was 0.22, 0.23, 0.24 and 0.25 for ± 30%, ± 50%, ± 100% and ± 200% error in ChlSAT, respectively. Linear-space MAPE was on average 45%, 48%, 50% and 57% for the same satellite ChlSAT error intervals. The insets also show the progressive increase in RMSE when 3 × 3 or 5 × 5 pixel box averages were used instead of single pixel match-ups. The impact of spatial binning on RMSE was bigger for daily match-ups, since 8-day match-up data already carried the error due to temporal averaging. These tests highlight the consistency between the error in the ChlSAT input and the uncertainty in predicted DMSPt. 5. Algorithm implementation in the global ocean In this section we describe the processing chain used to produce global maps of DMSPt and the CDMSPt:CPHY ratio using satellite data. Then, we compare the frequency distribution of these variables in the in situ database to those obtained with the algorithm (Fig. 7), and describe their spatial and temporal patterns (Figs. 8 and 9). 5.1. Implementation on climatological and non-climatological satellite data The algorithm described in Fig. 4b was implemented using monthly Chl and PIC climatologies from SeaWiFS (1998–2010) and MODIS-Aqua Table 1 Definition of the statistics used to evaluate the algorithm. Pi and Oi stand for predicted and observed values, respectively, for a given variable (DMSPt in our case). Statistics were applied to linear-space or log-transformed variables as detailed in the text. Statistic R2 (R-squared) RMSE (Root-mean-squared error) MRB (%) (Mean relative bias) MAPE (%) (Mean absolute percentage error)

Definition 1–

N

∑i¼1 ðP i %Oi Þ2

2

N

∑i¼1 ðP i %Ōi Þ N

1 ∑i¼1 ðP i % Oi Þ2 ' ½N%m

1 2

N

i Þ 100 N1 ∑i¼1 ðPi O%O i

N

i Þ 100 N1 ∑i¼1 absðPi O%O i

(2003–2012). In both cases, the monthly AVHRR SST climatology and the MIMOC MLD climatology were used. The DMSPt maps derived from 9.28 km (SeaWiFS) or 4.64 km (MODIS) images were then spatially binned to the MLD resolution of 0.5°. The algorithm was then applied using 8-day gridded data for the year 2007 (SeaWiFS and MODIS-Aqua Chl and PIC). Eight-day SST composites were calculated from daily AVHRR data, and gaps were replaced by the corresponding 8-day SST climatology. The monthly MLD climatology was linearly interpolated to the 8-day periods matching the satellite data. The resulting DMSPt maps were then temporally averaged to a monthly period, which increased the proportion of ocean pixels with DMSPt data from 37% (range 25–42%) to 64% (55–71%). Monthly images were then spatially binned to a 0.5° resolution, increasing data coverage to 70% of pixels (59–77%). Finally, the gaps in 0.5° monthly maps were filled with climatological DMSPt. Since DMSPt maps were completed with climatological data only at the end of the temporal-spatial binning process, this procedure affected only 4% of the pixels on average, giving a final proportion of valid pixels averaging 74% (62–81%). The proportion between stratified:mixed pixels was 3.5:1 when using monthly climatological data, similar to that found in the in situ database, and 5:1 with images for the year 2007 (the proportions were extremely similar with SeaWiFS and MODIS). This emphasizes that the performance of the algorithm at global scale depends heavily on the performance of the stratified submodel, which yields the best results among the three submodels (Section 3.1). In pixels where PIC N 1.5 mmol m−3 (the PIC threshold, Fig. 4) ChlSAT is generally available, so that the PIC-based submodel (eq. 3) was only used in 0.21% (0.09– 0.50%) of SeaWiFS and 0.13% (0.05–0.28%) of MODIS-Aqua valid pixels. Thus, the PIC submodel will make a relevant contribution to DMSPt data only in regions known for hosting persistent coccolithophore blooms [the Iceland Basin, the Barents Sea, the Bering Sea, the Patagonian shelves and the Black Sea (Iida, Mizobata, & Saitoh, 2012; Moore, Dowell, & Franz, 2012)] and during certain times of the year (late spring through early fall), allowing a crude regional DMSPt estimation in pixels that would not be considered otherwise. The main role of the PIC threshold is that of diagnosing pixels as stratified even when Chl is present, overriding the Zeu/MLD criterion and thus forcing the utilization of Eq. (1) (Fig. 4). Still, this happened in only 1.3% of pixels on average. The difference between global DMSPt climatologies derived from SeaWiFS and MODIS-Aqua is generally negligible: 74% of pixels differ by less than ± 5% and the global mean difference is 1 ± 9%. When it comes to particular years (e.g. 2007) SeaWiFS-MODIS differences are

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Fig. 6. Validation of the algorithm using satellite Chl match-ups (SeaWiFS and MODIS-Aqua L3bin composites) and SST from AVHRR. A) SeaWiFS daily composites; B) SeaWiFS 8-day composites; C) MODIS daily composites; D) MODIS 8-day composites. Only points with ChlSAT match-ups with an error b50% with respect to ChlIN SITU are shown. Red dots: stratified subset; blue dots: mixed subset. The solid line shows perfect agreement (1:1), and the dashed lines a twofold over- or underestimation as in Fig. 5. The insets show the change in predicted DMSPt RMSE as the relative ChlSAT versus ChlIN SITU error increases from 30% to 50%, 100% and 200% (the 50% value corresponds to that of the main panel scatterplot). Black: single pixel matchups; dark gray: 3 × 3 pixels match-ups; light gray: 5 × 5 pixels match-ups. All statistics have been calculated in log10 space. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

slightly larger and some regional differences (generally between 10– 30%) are apparent in the maps, which can be attributed to slight differences between Chl products (Morel et al., 2007).

expected ranges (Sathyendranath et al., 2009 and references therein), suggesting that global DMSPt and POC derived independently from remote sensing data are mutually consistent.

5.2. Frequency distributions 5.3. Spatial and temporal DMSPt patterns DMSPt productive waters are relatively overrepresented in the in situ database compared to global ocean statistics. When comparing the distributions of modeled DMSPt with in situ database DMSPt, this is reflected in (1) the smaller data density in modeled DMSPt at concentrations above ~30 nmol L−1 (Fig. 7a), and (2) the smaller DMSPt:Chl data density at DMSPt:Chl b 30 nmol μg−1 (Fig. 7b), a value often indicative of productive waters with high diatom abundance (Fig. 2 and S5). Yet, the overall good correspondence between the in situ and modeled DMSPt:Chl histograms and their median values (99 and 91 nmol μg−1) suggests that the algorithm is able to reproduce with little bias most of the DMSPt variability occurring in the surface pelagic ocean. The distributions of modeled CDMSPt:CPHY (Fig. 7c) and CDMSPt:POCSAT ratios (Fig. 7d) are also in good correspondence, though more leptokurtic (less spread) than those found in the database. The modeled CDMSPt:CPHY and CDMSPt:POCSAT ratios both display a bimodal distribution that is not visible in the database, suggesting that the partition between stratified and mixed categories creates a somewhat artificial discontinuity. Despite several uncertainties involved in the calculation of these two variables, the comparison of their median values (3.5– 4.6% for CDMSPt:CPHY and 1.6–2% for CDMSPt:POCSAT) implies that CPHY accounts for 40–45% of POC. This proportion is within the high end of the

Examples of global maps for both DMSPt and the CDMSPt:CPHY ratio in two representative months are displayed in Fig. 8, and the seasonal means across ocean biomes are summarized in Table 2. At first sight, modeled DMSPt maps follow a spatial distribution that resembles that of Chl. However, examination of the CDMSPt:CPHY ratio maps emphasizes the well-known spatial mismatch between phytoplankton biomass and DMSPt content, with highest DMSPt quotas in oligotrophic conditions. Subtropical and equatorial regions have low DMSPt (5–25 nmol L−1) throughout the year, with more marked seasonality in subtropical gyres compared to the equatorial belt. DMSPt above 25 nmol L−1 is generally found during the productive season at latitudes above 35°. Concentrations above 50 nmol L− 1 are mostly restricted to blooming waters in the North Atlantic and eastwards of the Patagonian shelves and to a lesser extent in the North Pacific and around New Zealand, extending to Polar Seas in summer. Concentrations higher than 100 nmol L−1 are frequently reached in eastern boundary upwelling systems and in coastal waters. Stronger variability is found in “coastal” biogeochemical provinces (Table 2) even though regions with bottom depth shallower than 200 m have been excluded, thereby masking probable DMSPt hotspots.


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Fig. 7. Histograms comparing the global ocean distribution of A) observed/modeled DMSPt; B) DMSPt:Chl ratio, based either on in situ variables or modeled DMSPt and ChlSAT match-ups; C) molar ratio between DMSPt–carbon (CDMSPt) and Chl-derived phytoplankton carbon (CPHY), based either on in situ variables or on modeled DMSPt and ChlSAT match-ups; D) ratio between observed CDMSPt and satellite-retrieved POC match-ups, and between modeled DMSPt and satellite POC climatology. Satellite data statistics are calculated on a pixel basis.

Fig. 8. Example maps for the months of January (A, B) and July (C, D) representing (A, C) modeled DMSPt concentration (note the log10 color scale) and (B, D) the quotient between DMSPt–carbon (CDMSPt) and phytoplankton carbon (CPHY), an estimation of the community DMSPt quota. The maps have been created using 8-day 4.6 km images of Chl and PIC (MODIS-Aqua) and SST (AVHRR) for the year 2007, and then averaged within monthly 0.5°×0.5° bins. Continental shelves (bottom depth b 200) or regions with missing data are masked in light gray.

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Fig. 9. Comparison between modeled and observed seasonality in five Longhurst oceanographic provinces where DMSPt is relatively well documented: Arctic (ARCT), North Atlantic drift (NADR), North Atlantic subtropical-East (NASE), North Atlantic subtropical-West (NASW) and Sub-Antarctic (SANT). The colored area represents the interquartile range for all monthly 1° × 1° pixels in a given province of SeaWiFS-retrieved Chl (green) and modeled DMSPt concentration (blue). Colored dots represent their monthly mean, and crosses joined by a white line their monthly median (see also Fig. S2). Empty circles represent the monthly mean of available in situ observations (displayed if n ≥ 3); black crosses with error bars represent their median and interquartile range (IQR; displayed if n ≥ 6). The bottom panels display the mixing status of the province: N75% of pixels identified as “mixed” (black), N75% of pixels identified as “stratified” (yellow) or else (gray). The red line displays the proportion of pixels with valid ChlSAT–MLD data with respect to the total number of pixels in the province. Note the different y-axis scales in the right- and left-side panels. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

To conduct a finer analysis of the seasonal DMSPt cycle we chose five biogeochemical provinces, representative of different biomes, where in situ DMSPt was well documented (Fig. 9; see also Fig. 1). The monthly mean, standard deviation, median and interquartile range were calculated in each province for in situ Chl and DMSPt (when enough data were available) and for climatological ChlSAT and DMSPt modeled

from remote sensing data (using all available pixels). We observed an overall good agreement between the model and the data, and, as expected, positive/negative deviations of ChlIN SITU with respect to the ChlSAT climatology were generally matched by deviations of the same sign in measured versus modeled DMSPt. When excluding data points with relative deviations greater than 30% between in situ and satellite

Table 2 Means (medians) of DMSPt concentration, DMSPt standing stock and phytoplankton community CDMSPt:CPHY (“DMSP carbon quota”) in the upper mixed layer (UML) across ocean biomes and seasons. Polar, Westerlies and Coastal biomes have been defined according to Longhurst (2010). Instead of the ‘Trades’ biome, biogeochemical provinces representative of the core of oligotrophic gyres have been grouped together in the so-called ‘Gyres’ biome, and the remainder in the ‘Equatorial’ biome. These modified grouping accounts better for low-latitude DMSPt production regimes. Winter: DJF (N hemisphere), JJA (S hemisphere); spring: MAM, SON; summer: JJA, DJF; fall: SON, MAM. Variable

Biome

Winter

Spring

Summer

Fall

DMSPt UML concentration (nmol L−1 or μmol m−3)

Polar Westerlies Gyres Equatorial Coastalb Polar Westerlies Gyres Equatorial Coastalb Polar Westerlies Gyres Equatorial Coastalb

10.8a (9.3) 14.1 (12.7) 13.9 (13.2) 10.5 (9.7) 26.5 (20.1) 874a (862) 921 (919) 641 (649) 235 (227) 736 (806) 2.5a (2.5) 3.0 (2.7) 4.8 (4.6) 2.7 (2.6) 3.6 (3.1)

21.2 (14.2) 24.0 (22.7) 16.7 (15.3) 9.1 (8.2) 34.2 (23.8) 823 (844) 952 (960) 499 (443) 187 (166) 677 (556) 2.8 (2.7) 4.3 (3.9) 6.3 (5.7) 2.4 (2.3) 4.0 (3.9)

35.3 (23.2) 32.9 (29.7) 12.3 (10.8) 11.8 (9.3) 38.9 (23.9) 685 (630) 745 (792) 232 (213) 285 (238) 588 (445) 3.9 (3.4) 6.1 (6.4) 5.8 (5.0) 2.6 (2.5) 4.4 (3.9)

25.7 (15.3) 25.0 (22.6) 11.9 (10.4) 10.9 (8.9) 33.5 (21.8) 724 (695) 854 (869) 359 (308) 247 (191) 644 (479) 3.7 (3.4) 5.0 (4.8) 5.3 (4.7) 2.7 (2.6) 4.0 (3.6)

DMSPt UML standing stock (μmol m−2)

CDMSPt:CPHY (molar carbon units, %)

a b

Note that winter statistics in the polar biome are less representative because only 10–30% of satellite pixels have data. Only pixels with bottom depth N 200 m are included.


Chl, we obtained the following statistics for modeled versus observed DMSPt monthly medians in linear space: R2 = 0.73, RMSE = 20 nmol L−1 , MRB = −7% and MAPE = 28% (n = 12). Monthly means were generally equal or higher than medians (in both measured and modeled data) owing to the approximately log-normal distribution of DMSPt (Fig. 7a) and the influence of some extreme in situ values. DMSPt variations through mixed-stratified transitions were relatively smooth despite the two-regime algorithm, because mixed and stratified pixels were averaged together in varying proportions within each province and month (Fig. 9). Overall, these comparisons confirm the previous validation results and the good performance of the algorithm.

6. Discussion 6.1. DMSPt drivers versus DMSPt diagnostics Plankton dynamics in the pelagic ocean depend strongly on the vertical mixing regime (Sverdrup, 1953; Margalef, 1978; Longhurst, 1995; Behrenfeld & Boss, 2014). The distinction between “stratified” and “mixed” water columns according to the value of the Zeu/MLD ratio, although simplistic, reflects first-order ecological differences in terms of light exposure and nutritional status of the phytoplankton community (Fig. 3), and their translation in terms of biomass and taxonomic composition (Chisholm, 1992; Uitz et al., 2006). When applied to the DMSPt–Chl relationship (Figs. 2 and 3) this diagnostic reflects primarily the strong taxonomic dependence of DMSP synthesis, and its signal through mixing-stratification transitions in time and space. Schematically, along an axis of decreasing trophic status, diatom blooms with low biomass-specific DMSP content are replaced by haptophyte nanoplankton-dominated communities with elevated biomass-specific DMSP, and the latter by picoeukaryote- and picocyanobacteriadominated communities with still elevated DMSP per unit biomass (see Section 6.2 and Fig. S5). After fitting the DMSPt–Chl relationship in stratified and mixed regimes, only a small fraction of the residual variance could be explained by additional variables: SST in the stratified regime (discussed below), and log10(Zeu/MLD) or other light exposure proxies in the mixed regime (but with a small increase in explained variance). This suggests that the environmental factors included in our analysis are essentially redundant. However, careful observation of Fig. 3 reveals that the redundancy is not complete, since some neighbor data points in the DMSPt–Chl space belong to biogeochemical provinces that are distant in terms of micro- and macronutrients, light levels and/or SST. This underscores our limited knowledge of the regulation of intracellular DMSP in phytoplankton and its role in physiological acclimation to environmental stressors (Stefels, 2000; Sunda, Kieber, Kiene, & Huntsman, 2002; Franklin et al., 2012). Indeed, other bottom–up and top–down controlling factors that we did not analyze here, such as iron limitation (Stefels & van Leeuwe, 1998; Belviso et al., 2008) or microzooplankton grazing (Archer, Safi, Hall, Cummings, & Harvey, 2011a), respectively, might account for some of the unexplained variability. The bell-shape relationship between the DMSPt residuals and SST in stratified waters (Fig. S2a) seems to reflect regional differences in the DMSPt–Chl relationship, rather than direct physiological effects of temperature. At the high end of Chl concentration (Chl N 0.5 mg m−3) SST discriminates between temperate/subpolar coccolithophore blooms and polar bloom/post-bloom situations with significant haptophyte biomass. The former display positive DMSPt–Chl residuals and SST ranging 10–15 °C (e.g. Malin et al., 1993), whereas the latter display near-zero or negative residuals and SST b 5 °C (e.g. the marginal ice zone Phaeocystis bloom described by Galí & Simó, 2010). Negative DMSPt residuals are also found at sea surface temperature N25 °C, corresponding to lowlatitude low-Chl data points associated to strong thermal stratification (Fig. 3). Overall, these observations suggest a link between strongerthan-average surface stratification, driven by either salinity or

181

temperature, and lower-than-expected DMSP content, a pattern that requires further attention. 6.2. Phytoplankton DMSP content, community structure and biogeography The global DMS(P) database contains no information on the taxonomic composition of phytoplankton assemblages, and only some of the published studies report cell counts or chemotaxonomic pigments. Yet, the relatively large number of samples and their wide environmental range allow for some inferences regarding the links between DMSPt and marine phytoplankton ecology. Community DMSP quotas are highest in stratified oligotrophic conditions, where CDMSPt:CPHY ratios above 10% are frequent. If we assume that non-DMSP-producing picocyanobacteria generally dominate phytoplankton biomass at Chl b 0.2 μg L−1 (Uitz et al., 2006; Fig. S5), this implies that DMSP-producing pico- and nanoeukaryotes may allocate more than 20% of cellular carbon into DMSP. This is a striking figure for a single intracellular compound. The empirical C:Chl conversion method used here (Sathyendranath et al., 2009) provides by definition upper limits for CPHY, so that CDMSPt:CPHY ratios shown in Figs. 7 and 8 are conservative estimates. The presence of DMSP in detritus and in microzooplankton grazers (Belviso et al., 1993; Levasseur et al., 2006) could partly explain the elevated CDMSPt:CPHY ratios. Still, some observed DMSPt concentrations would require DMSP quotas in the order of 10% in DMSP-producing phytoplankton. These calculations call for a reassessment of DMSP content of natural populations (e.g. Archer et al., 2011b), especially the poorly known picoeukaryotes (Massana, 2011), and for further investigation of DMSP turnover through heterotrophic and detrital compartments. Haptophytes deserve special attention among DMSP producers. Since they represent on average ~ 70% of nanophytoplankton biomass (Uitz et al., 2006; Liu et al., 2009), they appear as the main contributors to oceanic DMSP synthesis, generally accounting for over 30% of the DMSPt pool (Fig. S5). The classical view holds that high-latitude haptophyte blooms, sometimes accompanied by significant dinoflagellate biomass, are the major open-ocean DMS(P) production events; for instance, Emiliania huxleyi in the Northeast and Northwest Atlantic (Holligan et al., 1993; Malin et al., 1993; Matrai & Keller, 1993), Chrysochromulina sp. in the Northwest Atlantic (Scarratt et al., 2002; Lizotte et al., 2012), and Phaeocystis sp. in polar seas (DiTullio, Jones, & Geesey, 2003; Galí & Simó, 2010). However, many studies have lately highlighted the importance of non-calcifying haptophytes at low latitudes in terms of biomass and activity (Massana, 2011). Our analysis suggests that high-latitude haptophyte blooms, though regionally important, contribute less to global DMSP production and subsequent DMS emission than non-blooming haptophytes from lower latitudes. The combination of our algorithm with satellite-based coccolithophore detection (Moore et al., 2012) may allow a more detailed evaluation of this issue. Remote sensing algorithms for the discrimination of phytoplankton size classes (PSCs) and phytoplankton functional types (PFTs) have seen a major development during the last decade (Brewin et al., 2011) and might help improve DMSPt diagnosis. In this line of thought, we attempted to predict DMSPt using the chemotaxonomic PSC approach of Uitz et al. (2006). Yet, we obtained worse results (not shown) than with the bulk Chl-based algorithm, probably because the Uitz et al. model was not developed to discriminate phytoplankton groups according to their DMSP content. Uncertainty propagation through the several steps that separate satellite ocean color from DMSPt, including the estimation of group-specific C-biomass and DMSPt:C quotas, may also hamper DMSPt estimation at the PFT level. 6.3. Spatial-temporal resolution Validation results indicate that our algorithm is efficient in terms of explained DMSPt variability (Figs. 5 and 6). The separate

182


parameterization of stratified and mixed regimes creates a somewhat artificial discontinuity (Fig. 8c) and may increase prediction error as Zeu/MLD approaches 1, which can be compensated through spatial and temporal averaging (see 5.3). In other words, more robust spatial and temporal estimates (Figs. 8 and 9) are achieved at the price of coarser resolution. The testing of the algorithm at finer spatial scales is limited by the dearth of high-resolution DMSP surveys (e.g. Scarratt et al., 2002; Belviso, Sciandra, & Copin-Montégut, 2003; Kiene et al., 2007) and the lack of concurrent physical measurements of water column stratification. Lagrangian surveys covering stratified-mixed transitions in the temporal domain would also help to refine the algorithm and better establish its optimum resolution. These tasks will be greatly facilitated as high-frequency automated DMSP measurement systems become available, following those already developed for DMS (Tortell, 2005; Saltzman, De Bruyn, Lawler, Marandino, & Mccormick, 2009; Kameyama et al., 2009). One assumption implicit in our approach is that spatial and temporal variability are modulated by the same underlying processes (Section 6.1), allowing space-for-time substitutions (Li, Carmack, McLaughlin, Nelson, & Williams, 2013). The reasonable agreement between modeled and measured DMSPt seasonality in contrasting biogeochemical provinces (Fig. 9) supports this assumption. The capacity of the algorithm to reproduce DMSPt seasonal cycles and the CDMSPt versus CPHY decoupling (Fig. 8) is a crucial feature regarding its use to improve DMS emission estimates, given the strong seasonality of atmospheric DMS effects (Lana et al., 2012). To illustrate the magnitude of satellite-diagnosed temporal DMSPt variability, we calculated monthly DMSPt anomalies in 2007 with respect to the climatology (Fig. S6). Although the global mean anomaly is small (1.6 ± 15.5%), some region-wide anomalies greater than 30% occurred when using either SeaWiFS and MODIS-Aqua; for example, in the Equatorial Pacific in fall, and in the subpolar North Atlantic or the temperate NW Pacific in summer. These regional anomalies likely reflect the natural variability of the marine ecosystem and, if they induce proportional variations in DMS emissions, they would imply substantial interannual variation in DMS-derived aerosol and cloud forcing over vast regions (Woodhouse, Mann, Carslaw, & Boucher, 2013; Mahajan et al., 2015; McCoy et al., 2015). Future attempts to resolve multi-year time series in regions undergoing concomitant changes in stratification and Chl concentrations (Sarmiento et al., 2004) will require further testing the sensitivity of the algorithm to different MLD and Zeu inputs, and may benefit from the joint use of satellite and ARGO float data. 6.4. Global estimates Gross DMSPt synthesis rates can be calculated as the product of DMSPt concentration and the gross DMSPt turnover rate constant (k). The latter displayed a median of 0.67 d−1, with most values ranging 0.50–1.10 d− 1 in a recent global meta-analysis (Galí & Simó, 2015). Since k was uncorrelated to Chl, nutrient concentration or mixed-layer irradiance, we used a constant value of 0.67 d−1 to make order-ofmagnitude estimations. After vertical integration of the DMSPt·k product over the UML, and accounting for latitudinal changes in pixel area, we find that around 3.8 Pg C y−1 were invested in DMSP synthesis by upper ocean phytoplankton in 2007 (3.4 Pg C y−1 using climatological data). These figures provide a lower limit because they do not include continental shelves or phytoplankton from below the mixed layer. Although slower turnover of organic carbon and DMSP may be expected below the mixed layer, this deeper horizon can account from less than 20% of the standing stock in mixed conditions (e.g. Archer et al., 2011a) to more than 80% in stratified and oligotrophic conditions (Galí, unpublished) (see Table 2). Therefore, oceanic DMSPt synthesis may amount to twice the UML production (i.e. ~ 7 Pg C y− 1). Vertically-integrated net primary production (NPP) for the same domain (excluding continental shelves) gives a range of 38–48 Pg C y−1

using three different remote sensing primary production models for year 2007 satellite Chl images (http://www.science.oregonstate.edu/ ocean.productivity/custom.php). In turn, gross carbon production may be a factor of 2–3 times greater than the NPP deduced from severalhours-long 14C assimilation measurements used to calibrate satellite models (Halsey & Jones, 2015). Thus, comparison of global gross DMSPt production to gross carbon production (sensu Halsey & Jones, 2015) suggests that 5–9% of global ocean carbon fixation may be invested into DMSP. Though highly uncertain, these calculations highlight the importance of DMSP as an integral component of marine carbon flow.

7. Conclusions and future prospects We presented a simple algorithm that diagnoses planktonic DMSPt concentration using satellite-observed Chl and the quotient between euphotic layer depth and mixed layer depth. The algorithm builds upon macroecological relationships linking the vertical mixing regime with phytoplankton biomass and taxonomy and light penetration, whose impact on marine sulfur cycling had already been highlighted (Simó & Pedrós-Alió, 1999; Simó & Dachs, 2002). An additional submodel based on satellite-observed PIC allows DMSPt diagnosis in coccolithophore blooms. The algorithm predicts DMSPt with remarkable skill in contrasting regions and across three orders of magnitude. We are aware that several DMSPt datasets have not yet been submitted to the global database. Inclusion of these datasets, accompanied by comprehensive ancillary data (e.g. phytoplankton biomass and community composition), is crucial to further improve and validate DMSPt parameterizations. The DMSPt climatology derived from our algorithm is a valuable product for testing and cross-validating other marine sulfur biogeochemistry models. Our algorithm is the first piece of a model aimed at diagnosing DMS emission at unprecedented spatial and temporal resolution. Such a model can largely benefit from remotely sensed geophysical data: biological DMSPt-to-DMS conversion yields and bacterial DMS consumption show robust relationships to irradiance and trophic status (Galí & Simó, 2015); DMS photolysis varies according to irradiance, its absorption by colored dissolved organic matter, and SST (Toole, Kieber, Kiene, Siegel, & Nelson, 2003); and DMS sea-air transfer coefficients depend on wind speed and SST (Land, Shutler, Bell, & Yang, 2014). In turn, emitted DMS variability may leave its signature in satellite-observed aerosol and cloud properties (Lana et al., 2012; McCoy et al., 2015). New remote sensing tools like the one presented here will enhance the study of marine plankton-climate interactions.

Acknowledgments We thank the NASA Ocean Biology Distributed Active Archive Center (OB.DAAC) for access to MODIS and SeaWiFS datasets; T.S Bates (NOAA/ PMEL) for the maintenance of the GSS DMS(P) database; the DMS-GO project for the recent database update (a joint initiative of the SOLAS Integration Project and the EU projects COST Action 735 and EUROCEANS); Eric Rehm and Maxime Benoît-Gagné for IT support; the Takuvik team for helpful discussions; and Rafel Simó for sharing DMSPt data from the Malaspina (CSD2008-00077) and SUMMER cruises (CTM2008-03309/MAR), funded by successive MICINN and MINECO ministries (Spain). M. G. acknowledges a Beatriu de Pinós postdoctoral fellowship funded by the Generalitat de Catalunya.

Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.rse.2015.10.012.


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