Establishing a global climatology of marine ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, C08026, doi:10.1029/2012JC007958, 2012

Establishing a global climatology of marine phytoplankton phenological characteristics M. R. P. Sapiano,1 C. W. Brown,2 S. Schollaert Uz,1 and M. Vargas3 Received 2 February 2012; revised 9 July 2012; accepted 10 July 2012; published 21 August 2012.

[1] The timing or phenology of the annual cycle of phytoplankton biomass can be monitored to better understand the underpinnings of the marine ecosystem and assess its response to environmental change. Ten-year, global maps of the mean date of bloom onset, peak concentration and termination of bloom duration were constructed by extracting these phenological metrics from Generalized Linear Models (GLM) fit to time series of 1 1 daily estimates of SeaWiFS chlorophyll concentrations dating from September 1997 to December 2007 as well as to MODIS chlorophyll concentrations from July 2002 to July 2010. The fitted models quantitatively define the annual cycle of phytoplankton throughout the global ocean and from which a baseline of phenological characteristics was extracted. The analysis revealed regionally consistent patterns in the shape and timing of the annual cycle of chlorophyll concentration that are broadly consistent with other published studies. The results showed that a single bloom predominates over the global ocean with secondary, autumn blooms being limited in both location and spatial extent. Bloom duration tended to be zonally consistent, but meridionally complex and did not become progressively shorter with increasing latitude as is sometimes depicted. Both the shape of the annual cycle and the phenological climatologies can be used in future studies to detect significant departures over time. Citation: Sapiano, M. R. P., C. W. Brown, S. Schollaert Uz, and M. Vargas (2012), Establishing a global climatology of marine phytoplankton phenological characteristics, J. Geophys. Res., 117, C08026, doi:10.1029/2012JC007958.

1. Introduction [2] Climate change affects the pattern and variability of numerous environmental conditions, such as temperature, wind, ocean circulation, and precipitation. Among other repercussions, such changes can be expected to lead to alterations in the distribution pattern, in both time and space, of marine phytoplankton biomass and productivity. Consequently, documenting these temporal and spatial patterns in phytoplankton biomass and their change provides a means to detect and quantitatively evaluate the response of the marine ecosystem to environmental change and its ability to uptake atmospheric CO2 [Hughes, 2000; Platt and Sathyendranath, 2008]. Multiple investigations have examined the magnitude of marine phytoplankton biomass and its variability in the world’s ocean over the past decade [Behrenfeld et al., 2001, 2006; Boyce et al., 2010; Gregg et al., 2003; Henson et al., 1 Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA. 2 Center for Satellite Applications and Research, National Oceanic and Atmospheric Administration, College Park, Maryland, USA. 3 Center for Satellite Applications and Research, National Oceanic and Atmospheric Administration, Silver Spring, Maryland, USA.

Corresponding author: M. R. P. Sapiano, Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Ct., Suite 4001, College Park, MD 20740-3823, USA. ([email protected]) ©2012. American Geophysical Union. All Rights Reserved. 0148-0227/12/2012JC007958

2010; Martinez et al., 2009; Polovina et al., 2008; Sarmiento et al., 2004; Vantrepotte and Melin, 2011], but only one study has attempted to document the timing or phenology of their annual cycle and its variability on the global scale [Racault et al., 2012]. These phenological markers include, but are not limited to the timing of bloom onset, peak concentration and termination. [3] The annual cycle of phytoplankton exhibits differences in the timing (and magnitude) of growth and decline within different regions of the ocean because processes affecting their growth and demise, such as incident solar irradiance, water column stratification, nutrient supply, and grazing pressure, vary with latitude and oceanographic conditions. At subpolar and higher latitudes, for example, the annual phytoplankton biomass cycle is characterized by a single spring bloom. In accordance to the critical depth hypothesis [Sverdrup, 1953], phytoplankton concentrations increase significantly in the spring, with increases in irradiance and water column stability in the nutrient replete waters, due to exposure to sufficient levels of light, allowing net growth to exceed net loss. Alternatively, it has been proposed that this seasonal increase in phytoplankton abundance may result from reduced grazers due to increased mixing in the winter [Behrenfeld, 2010; Boss and Behrenfeld, 2010]. As stratification intensifies and surface nutrient concentrations are depleted through phytoplankton uptake and eventual sedimentation from the mixed layer, phytoplankton biomass starts to decline and eventually reaches its background level

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SAPIANO ET AL.: PHYTOPLANKTON PHENOLOGY CHARACTERISTICS

due to resource limitation [Platt et al., 2005], herbivore grazing [Harrison et al., 1993; Pommier et al., 2009; Takahashi et al., 2008] and infection by virus [Bidle and Falkowski, 2004]. At temperate latitudes, phytoplankton biomass follows a similar cycle, though peaking at a lower concentration than in higher latitude water. However, the cycle may exhibit a secondary, yet reduced peak in biomass during the late summer and early autumn due to injection of nutrients into the mixed-layer by wind and convective induced vertical mixing before light becomes limiting. In the subtropics, clearly defined blooms are rarely observed as a result of relatively small seasonal variation in the availability of nutrients. Nonetheless, late autumn and winter convective overturning destratifies the water column, enhancing nutrient fluxes to surface waters and stimulating modest increases in phytoplankton in the subtropics [Follows and Dutkiewicz, 2002]. Additional factors, such as the wind-induced upwelling, tidal mixing and fresh-water input, can substantially affect the availability of light and nutrients and consequently modify the typical annual cycle in certain regions, e.g., the coastal ocean [Ji et al., 2010; Legendre, 1990; Lomas et al., 2009; Longhurst, 1995; Nelson et al., 2004; Winder and Cloern, 2010]. [4] Changes in the phenological characteristics of the phytoplankton annual cycle can have important consequences on fisheries and the oceanic carbon cycle. Changes in the timing of primary producer abundance, such as the timing of the spring bloom peak, affect trophic interactions by altering the temporal overlap between predator and prey and can significantly impact fisheries and other related living marine resources [Edwards and Richardson, 2004; Hays et al., 2005; Koeller et al., 2009; Platt and Sathyendranath, 2008; Stenseth and Mysterud, 2002]. The survival of larval haddock (Melanogrammus aeglefinus) on the Nova Scotia Shelf, for example, is associated with an early spring bloom, permitting the greatest overlap between larvae and their food [Platt et al., 2003]. Comparable studies have also documented an association between earlier timing of the spring bloom and the hatching and size of young shrimp (Pandalus borealis) in the North Atlantic [Fuentes-Yaco et al., 2007; Koeller et al., 2009]. Changes in the phenology of phytoplankton also affect the oceanic carbon cycle. As the sedimentation of diatom blooms is considered to be an important component of the biological pump, a change in their timing and duration in relation to their zooplankton grazers may affect the resulting flux of particulate carbon to depth [Hays et al., 2005]. Establishing a baseline of the phenological metrics is therefore necessary to detect future changes in the phenological markers of phytoplankton and assess their potential effect on the marine ecosystem. [5] The time series required to detect changes in the phenology of marine phytoplankton have traditionally been constructed by programs that regularly collect in situ samples in certain regions, such as the Continuous Plankton Recorder (CPR) Survey in the North Atlantic [e.g., Edwards and Richardson, 2004]. More recently, phenological studies of oceanic phytoplankton have utilized measurements from satellite ocean color radiometry (OCR) sensors, such as NASA’s Sea-viewing Wide Field-of-View Sensor (SeaWiFS) [McClain et al., 2004] and the Moderate Resolution Imaging Spectroradiometer (MODIS) [Esaias et al., 1998]. Though not depth resolved nor as taxonomically specific as

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in situ observations, satellite OCR measurements offer a spatially explicit time series of chlorophyll–a concentration, a proxy for phytoplankton biomass, at daily to weekly intervals and synoptic scales for extended periods of time that are amenable to investigating the phenology of phytoplankton biomass. The retrieval of chlorophyll concentration, based essentially on the ratio of blue to green light exiting the water, is good in the open ocean where phytoplankton and their derivative products co-vary with one another and play a dominant role in determining the optical properties of the surface layer [O’Reilly et al., 1998]. [6] Investigators have objectively extracted phenological markers from the oceanic chlorophyll time series either directly (after some form of spatial or temporal filtering) [Henson et al., 2006; Racault et al., 2012; Siegel et al., 2002] or from models fit to it, where the models have ranged from simple least squares [Ueyama and Monger, 2005] to more advanced fitting techniques, such as a Gaussian model [Platt et al., 2009; Song et al., 2010; Yamada and Ishizak, 2006] and Generalized Linear Models (GLM) [Vargas et al., 2009], by applying a threshold criteria based on relative or absolute chlorophyll values. Vargas et al. [2009] extracted several phenological markers from GLMs fit to pentad (five-day) estimates of SeaWiFS chlorophyll concentrations dating from 1998 to 2006. The GLM approach offers a more accurate technique than linear regression in fitting models to chlorophyll concentration time series [Vargas et al., 2009]. [7] The majority of satellite studies documenting the phenology of marine phytoplankton are geographically limited. Most investigations have focused on the North Atlantic [Follows and Dutkiewicz, 2002; Henson et al., 2006, 2009a; Platt et al., 2009, 2010; Siegel et al., 2002; Song et al., 2010; Ueyama and Monger, 2005; Vargas et al., 2009; Zhai et al., 2011], with only a few describing the phenological characteristics of phytoplankton in the North Pacific [Henson and Thomas, 2007; Sasaoka et al., 2011; Yamada and Ishizak, 2006], the Arctic [Kahru et al., 2011], and the Southern Ocean [Thomalla et al., 2011]. Only recently has a global perspective of marine phytoplankton phenology been provided [Racault et al., 2012]. The approach of Racault et al. [2012] involved extracting the phenological characteristics from a 3-week running mean time series of weekly global SeaWiFS chlorophyll concentration, and thus excluded potentially important autumnal blooms from the analysis. [8] In this manuscript, we apply the robust GLM approach developed in Vargas et al. [2009] to fit the time series of SeaWiFS chlorophyll concentration throughout the world’s oceans. Our approach extends the current literature by applying a more flexible model that quantitatively describes the annual cycle of phytoplankton and other aspects not permissible using other techniques from which we generate climatologies of several common phenological characteristics in order to establish a baseline for phytoplankton phenology on a global scale. In addition, the technique is applied to chlorophyll concentrations derived from MODIS/Aqua OCR data to assess the robustness of the SeaWiFS result.

2. Data and Methodology [9] Ten-year, global maps of the mean date of bloom onset, peak concentration and termination, and of bloom duration, were constructed by extracting these phenological markers

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Figure 1. Global maps of the percentage of missing SeaWiFS data by season for the period September 1997 to December 2007. from Generalized Linear Models (GLM) [McCullagh and Nelder, 1989] with Gamma distributions fit to time series of 1 1 daily estimates of satellite-derived chlorophyll concentrations using the technique described in Vargas et al. [2009]. The ideal model for this analysis should be globally applicable to avoid fitting the wrong model at any particular location, and the highly flexible Gamma distribution is well suited for this purpose because it can resolve a range of distributions by varying the shape parameter. [10] Global fields of one degree, daily mean chlorophyll concentration were created from SeaWiFS and MODIS/Aqua daily L3 binned data (Version R2009.1; http://oceancolor. gsfc.nasa.gov/WIKI/OCReproc20091.html), dating from 4 September 1997 to 31 December 2007, and 4 July 2002 to 13 July 2010, respectively. This version of the algorithms improved on the previous iteration in several ways including a reduction in the discrepancy between SeaWiFS and MODISAqua missions of open ocean, global mean chlorophyll concentrations from 12% to less than 2% over their common mission times (2002–2009) and removal of erroneous calibration issues that had large impacts on the climate record. The daily L3 binned data were first mapped onto a common 1/12 grid that was further averaged to one-degree resolution for use in the statistical model using the geometric mean. The SeaWiFS chlorophyll product in the deep ocean has been assessed to possess a root-mean- square error (RMSE) of 0.406, while globally the RMSE is higher (0.638) [Bailey and Werdell, 2006], reflecting the increased uncertainty of ocean color

radiometry products in optically complex coastal waters. As a consequence of the reduced accuracy of chlorophyll retrievals in coastal regions, and the multitude of regional factors that can modify the typical annual cycle of bloom dynamics in these waters, we restrict our analysis to the open ocean in this study. Furthermore, we excluded regions from our analysis that did not possess data for a month or more, limiting our results to latitudes extending from 55 S to 60 N (Figure 1). We assume that the missing data within the remaining region are caused by cloud cover that is evenly spread in time and does not systematically affect any single month, thus allowing the model to generate an accurate representation of the annual cycle. [11] The GLM model was fitted to the daily time series at each 1 grid box. An extended land/coast screen was used to remove land grid-boxes and the two grid-boxes around the coast in order to fit only to open ocean grid-boxes. The model was fit to all nonzero observations in grid-boxes that met the above missing data criteria. The model selection strategy followed the approach of Vargas et al. [2009] with forward selection using the measure of reduction of deviance tested using a chi-square test. For details, see McCullagh and Nelder [1989]. Table 1 lists the eight possible models that could be fitted at each pixel or grid box. Eight different parameters could be included in the model, although the sine and cosine terms must be considered together since they are jointly required in order to resolve the sinusoidal model. Models 1 and 2 imply no statistically significant annual cycle

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Table 1. Options for Models to be Fit Using GLM With Relevant Parameters Listed Model Number

Intercept

1 2 3 4 5 6 7 8

b0 b0 b0 b0 b0 b0 b0 b0

Time (t) b1 b1 b1 b1 b1

sin(2pt), cos(2pt)

b 2, b 2, b 2, b 2, b 2, b 2,

b3 b3 b3 b3 b3 b3

sin(4pt), cos(4pt)

b 4, b 5 b 4, b 5 b 4, b 5

sin(2pt) t, cos(2pt) t

b 6, b7 b 6, b7

and represent either a simple mean or a trend in time. The other models make use of a sinusoidal annual cycle where models 5, 6 and 8 include a second harmonic and models 7 and 8 include linear time trends in the sinusoidal model. [12] Dates of bloom initiation, peak, and termination were extracted from the fitted functions. Bloom initiation and termination were defined as the date when fitted chlorophyll concentration attained and declined to 5% above the local annual median value, respectively, as used previously [e.g., Henson et al., 2009b; Racault et al., 2012; Siegel et al., 2002;

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Thomalla et al., 2011]. Bloom duration was computed by subtracting the dates of bloom initiation from termination. The calculation of initiation and termination is complicated for grid boxes where a double peak in the annual cycle was fit. If the minimum of the fit chlorophyll concentration located between the two maxima was greater than the criterion above (5% above the local annual median), that grid box was treated as a single bloom with the initiation calculated before the first maximum and the termination calculated after the second maximum. In the case where the minimum between the two maximums was less than 5% above the threshold, the initiation and termination were based on the peak with the larger maximum only.

3. Results [13] The geometric mean and standard deviation of SeaWiFS chlorophyll concentration for December–February (DJF) and June–August (JJA) from the period September 1997 to December 2007 illustrate the commonly accepted distribution pattern of relatively low variability in the oligitrophic subtropical gyres and of seasonally eutrophic subpolar-polar gyres, with higher chlorophyll concentrations in the hemispheres during their respective summers and at coastal and equatorial upwelling “hot spots” (Figure 2). The

Figure 2. (a and b) Geometric mean and (c and d) standard deviation of SeaWiFS chlorophyll concentration for December–February (DJF) and June–August (JJA), respectively. Data cover the period from 1997 to 2007. The white circles and squares in 2a indicate location of grid boxes depicted in Figures 3 and 10, respectively. 4 of 16

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observed nonzero, often skewed distribution of ocean chlorophyll concentration is in many areas consistent with the exponential distribution, where the mean and standard deviation are the same. For observed data, it is probably unreasonable to expect perfect exponential behavior, but similar patterns in the observed mean and standard deviation (Figure 2) would suggest that such areas are likely to follow an exponential-type distribution. By comparing the corresponding pair of mean and standard deviation for each season (Figures 2a and 2c and 2b and 2d), it can be seen that there are similarities near the poles, but are quite different across large parts of the tropics. This suggests that the exponential distribution would be unsuitable generally for modeling chlorophyll in the oligitrophic waters of the tropic and subtropic latitudes (excluding the major upwelling areas). These findings regarding relative values of the mean and standard deviation highlight the unsuitability of the exponential or Gaussian distribution and support the use of the more flexible Gamma distribution for a global study such as this.

Figure 3. Examples of fitted model with SeaWiFS chlorophyll concentrations at the six sites indicated in Figure 2a.

3.1. Fitted Model for SeaWiFS Chlorophyll Concentration [14] In order to illustrate the possible shapes that can be represented by the sinusoidal GLM model, Figure 3 shows the fitted model overlaid on the original SeaWiFS chlorophyll estimates at six locations. The six locations, indicated as white, solid circles on Figure 2, were chosen to highlight areas with different behaviors in terms of the parameters in the model including the shape parameter. Chlorophyll concentration from the location in the tropical East Pacific, immediately off the west coast of South America (5 S, 100 W), has a relatively flat pattern with no annual cycle and no time trend (Figure 3a). This pattern is consistent with the near constant presence of phytoplankton albeit with some occasional periods of increase/decrease as seen at the end of 1998/start of 1999 during the recovery from El Niño. Model 1 was fitted to this grid box, which is the model with the intercept only. [15] Grid boxes in the Southern Ocean (60 S, 130 E) and Tropical Pacific (3 N, 165 E) each appear to have a single maximum in chlorophyll concentration (Figures 3b and 3c). In addition, the fitted model of the tropical Pacific site displays a (negative) time trend (Figure 3c). Despite the similar annual cycle at these two locations, the characteristics of the distributions are quite different. The Southern Ocean site (Figure 3b) has a higher chlorophyll peak value than the tropical box (Figure 3c), but blooms are seasonal at this high latitude location with apparently no data during austral winter (Figure 2b). Furthermore, the Southern Ocean box has significant missing data (Figure 1c) that may hamper accurate estimation of the annual cycle and is in an area that was screened in the full analysis. Figure 3d is an example from a location in the North Atlantic transition zone (40 N, 40 W), exhibiting a minor peak in chlorophyll concentration in late autumn/early winter and a major one in spring. This example demonstrates how the model faithfully resolves the double peak pattern. Note that, as expected, the magnitude of the spring bloom is higher than that of the fall bloom, but that the lowest fitted chlorophyll concentration was present during the intervening summer and not the following winter (Figure 3d). Grid boxes in the Tasman Sea off New Zealand

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Figure 4. Maps of the fitted parameters of the gamma GLM for SeaWiFS chlorophyll in each grid box for the period from September 1997 to 31 December 2007. The parameters correspond to those listed in Table 1: (a) intercept, (b) trend in peak chlorophyll concentration (mg / m3 / year), (c and d) single harmonics terms, (e and f) second harmonic terms and (g and h) trend in annual cycle. (Figure 3e) and in the northeastern Atlantic (Figure 3f), respectively, illustrate increasing and decreasing time trends in the amplitude of chlorophyll, as well as either a shift in the timing of the annual cycle or (more likely) a change in its amplitude. [16] Inspection of the model parameters (Figure 4) offers additional insight and detail in the characteristics of the locations of the fitted models. Their interpretation, however, can be challenging because the parameters need to be considered simultaneously. For example, the intercept (Figure 4a) would be expected to resemble the mean; it is largely negative,

indicating mean values less than 1 mg m 3 chlorophyll in most places. The large negative areas in the intercept correspond to areas with positive time trends (Figure 4b), so in these areas the mean value cannot be understood by considering the intercept alone. [17] The GLM also yields an estimate of the shape parameters of the gamma distribution that provides information on statistical distribution at each grid box (Figure 5). If the shape parameter is exactly unity, the gamma reduces to the exponential distribution. The shape parameter was less than one over most of the globe (Figure 5), which denotes a

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Figure 5. Map of the estimated shape parameter from the fitted Gamma GLM for SeaWiFS chlorophyll concentration at each grid box. probability distribution function resembling an exponential distribution, where the bulk of the distribution is centered on low values. Lowest values were located in the eastern tropical Pacific. The shape parameter was greater than one in coherent grouping in only a few places, e.g., the eastern side of the Kamchatka Peninsula, along the Aleutians, on the Patagonian shelf, and off northeastern South America and of Saharan and equatorial Africa, where high mean and standard deviations in chlorophyll exist (Figure 2). These values are consistent with a more bell-shaped distribution (but with significant skew) as might be expected in locations with consistently higher mean chlorophyll concentration. [18] In order to highlight locations where the model fit is in closer agreement with the data, the pseudo R2 measure [Cox and Snell, 1989; Nagelkerke, 1991] was computed (Figure 6). This measure is analogous to the R2 for ordinary least squares linear regression in that it represents the percentage of variability in the data explained by the model. Pseudo R2 was highest in the zonal bands centered on approximately 30 S and 30 N where values ranged from 15% to 40%. Higher values are also observed in the western Indian Ocean. Because pseudo R2 is high when the data and

the model are most similar, areas where only the intercept was fitted, i.e., models 1 and 2, possess values approaching zero and are indicated by gray. For the same reason, locations where a double peak was fitted tend to possess higher pseudo R2 values than areas with a single peak, which reflects the fact that the double peak model is more flexible in representing different shapes. [19] A map of the model number (Table 1) fit to the daily SeaWiFS chlorophyll concentration illustrates the locations that exhibit similar behavior in the annual cycle (Figure 7a). The map reveals broad, coherent groupings of model number and possesses a relatively low level of spatial noise. That a particular model behavior is common to a large area adds confidence for the method and hints at the coherency of the signal as the model at each grid box is spatially independent of its neighbors. The selected model number map (Figure 7a) was used to generate maps revealing the type of annual cycle present (Figure 7b) and the presence of a time term or trend in the model (Figure 7c). Figure 7b indicates the type of annual cycle fitted at each grid box, where categories are “flat” (models 1 and 2), single peak (models 3, 4 and 7), and “double” peaks (models 5, 6 and 8). The models with parameters

Figure 6. Map of pseudo R2 from the General Linear Model fit to SeaWiFS data for the period September 1997 to December 2007. 7 of 16

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Figure 7. (a) Map of the eight models constructed to represent the annual cycle of SeaWiFS chlorophyll concentration for the period September 1997 to December 2007. Each color represents a different statistical model listed in Table 1. (b) Type of annual cycle. (c) Type of a statistically significant time term in the model. that are capable of resolving a spring and autumn bloom do not always represent a double peak, because the secondary peak is often too small to be visually recognized as a separate and distinguishable secondary peak. A test was applied to remove cases from Figure 7b where the trough between the two maximums was less than 5% above the median at that grid box. [20] The models fit by the GLM and the resulting extracted shape is generally consistent with known patterns of phytoplankton bloom dynamics. For example, the equatorial Pacific and the subtropical gyre of the North Pacific possessed a statistically “flat” annual cycle, consistent with the lack of seasonality in these regions (Figures 3a and 7a). This result follows those found in the California Current System

[Henson and Thomas, 2007], where no seasonal pattern in chlorophyll concentration was found offshore in the oligotrophic waters of the North Pacific Gyre and is consistent with the traditional view of the low amplitude trade wind biomes of Longhurst [1995, 2007]. Regions lacking an annual cycle were most common in the Pacific Ocean and found principally at tropical and subtropical latitudes (Figure 7b). Regions exhibiting a single vernal or spring bloom predominate in the global ocean and were observed at all latitudes, though principally in the subtropical and higher latitudes (Figure 7b). Regions also possessing secondary autumn blooms over the course of the annual cycle were found in regions of coastal upwelling, and in the subtropical

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convergence and other frontal zones (Figure 7b) but were limited in number and spatial extent. [21] The maps fitted model (Figure 7) can be used along with the parameter estimates from the model (Figures 4 and 5) to reveal known and unknown patterns in the data record. For instance, the region in the eastern South Pacific between roughly 20 S to 35 S is known to be extremely oligotrophic [Morel et al., 2007] (see also Figure 1). The area slightly equatorward of this region (roughly 5 S to 20 S) has a very low shape parameter value (Figure 5) and an intercept (Figure 4a) of the same magnitude as that of the higher concentration area around the equator. One possible explanation for this contrast is that this transition area has a longduration bloom typical of the equatorial regions (perhaps caused by persistent upwelling), but with a very low amplitude that is in line with the oligotrophic conditions of the central South Pacific Gyre to the south. There is also an interesting contrast between this behavior in the equatorial Pacific (north of the hyperoligotrophic area) and that in the equatorial Atlantic where the latter possesses a well-defined double peak in chlorophyll concentration whereas the former is predominantly flat with some small areas of double peak identified almost exactly on the equator. [22] The double peak behavior seen in the western North Atlantic and North Pacific Oceans around 40 N to 60 N generally confirms previous studies that concluded, with limited availability of in situ observations, that this bimodal pattern is present at subpolar latitudes and on temperate shelves [Longhurst, 1995]. Figure 7b shows that the double bloom actually extends considerably farther North, up to 60 N in some areas, although the single peak behavior is also common. In addition, the pattern in the North Pacific has a more complex structure than that in the Atlantic, with a transition from single peak to double peak to flat and back to single peak occurring between 35 N and 50 N. There is also a band around 40 S where a double peak behavior is common, although the pattern has a complex zonal structure with areas of no annual cycle occurring. As with the equatorial Pacific region, the areas with flat annual cycle occur in regions with a higher intercept value in the model (Figure 4a) and lower shape parameter (Figure 5), thus indicating the possibility of a very long duration bloom that the model identifies as a flat annual cycle. [23] Both positive and negative trends in the amplitude of chlorophyll concentration were detected (models 2, 4, 6, 7, and 8) in all ocean basins. It should be noted that care is required in interpreting these time trends in these models because the spatial dependence was not accounted for in the modeling framework the period of record is relatively short. Short-term (10 years) model time trends indicate significant positive increases in the amplitude of chlorophyll concentration in the eastern Pacific off the coasts of South and North America (0.02–0.05 mg m 3 yr 1), in the western Pacific in the Tasman Sea (0.02–0.07 mg m 3 yr 1), in the Indian Ocean off Somalia (0.02–0.04 mg m 3 yr 1) and off Australia, and in the Atlantic off the Patagonian Coast and the northern portion of the South Atlantic gyre (0.02–0.04 mg m 3 yr 1) (Figure 4b). Regions exhibiting significant decreases in chlorophyll over the period, some quite extensive, were observed in subtropical areas across the North Pacific and central South Pacific (0.02– 0.08 mg m 3 yr 1), the central eastern Indian Ocean and the southern Bay of Bengal (0.02–0.05 mg m 3 yr 1), and the

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North Atlantic off Saharan Africa and in southwestern portion of the South Atlantic gyre. A large decrease was also found in the temperate, eastern North Atlantic (Figure 4b). [24] Phasing of the sinusoid primary annual chlorophyll cycles in the temperate regions of the Northern and Southern Hemisphere are essentially 180 out of phase (Figures 4c and 4d). The secondary chlorophyll peaks, whose phasing is illustrated in Figures 4e and 4f, were primarily found in the equatorial, and temperate to polar Atlantic Ocean, the western Indian Ocean, and regions of subtropical convergence in the Pacific. Phasing of the secondary peaks was similar for like latitudes, e.g., 40 N and 40 S, in the two hemispheres. [25] A few locations of limited spatial extent exhibited small, statistically significant trends in the timing of bloom dynamics over the 10-year period examined (Figures 4g and 4h). These locations coincide with a subset of the regions that also displayed increasing or decreasing trends in chlorophyll amplitude (Figure 4b). Shifts in timing of the annual phytoplankton biomass cycle coincided with increases in chlorophyll amplitude in waters off Patagonia in the South Atlantic Ocean and in the Tasman Sea (Figures 4g and 4h). Timing of peak chlorophyll in the Tasman Sea, for example, occurs later each year in the period (Figure 3e). A shift in timing and decreases in peak chlorophyll were detected in the tropical western North Pacific in the vicinity of the warm pool and two regions in the North Atlantic, i.e., the eastern central North Atlantic (see Figure 3f) and off Saharan Africa (Figures 4g and 4h). [26] The estimation of chlorophyll from satellite-measurements can be problematic in certain regions and this is somewhat reflected in the results. One example is off Saharan Africa where the presence of absorbing aerosols in the atmosphere [Moulin et al., 2001] can confound the retrieved signal from the surface. Figure 7c shows a large area with a negative trend in the mid-Atlantic that may be at least partly associated with this issue, although the shape parameter (Figure 5) shows a very different distribution was present near the coast with values larger than unity probably being associated with the atmospheric aerosol effect. The shape parameter is also relatively high off northeast South America and this might also indicate issues, this time due to the presence of suspended sediments and colored dissolved organic matter in the outflow of the Amazon and Orinoco Rivers [Hu et al., 2004]. 3.2. Phenological Characteristics [27] Figure 8 shows the month of bloom onset, peak and termination for the first 10 years of SeaWiFS. These metrics cannot be extracted in areas where no annual cycle was found (Figure 7a), which are colored gray in Figure 8. Maps of mean month of bloom onset, peak magnitude and termination displayed generally coherent and zonal patterns, with a progressive poleward delay (Figure 4). The general zonal pattern was disrupted, most noticeably for bloom onset and peak, in the tropical Atlantic off northeastern South America and in the eastern basin of the South Atlantic off Africa, and to a lesser extent in the South Pacific off Peru and northern Chile, where the patterns were aligned diagonally to the zonal structure and the timing was later relative to waters of the same latitude. The pattern of these markers in the Arabian Sea was also anomalous, particularly peak amplitude, compared to those of the Indian Ocean at the same latitude.

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Figure 8. Mean bloom (a) onset, (b) peak and (c) termination extracted from the fitted models for SeaWiFS. Bloom onset and termination are defined as the date when the modeled annual cycle is 5% above the annual median value. Regions where an annual cycle was not detected are colored gray. [28] Blooms at corresponding latitudes in the Northern and Southern Hemisphere were out of phase by approximately six months. For example, bloom initiation in subtropical latitudes occurred in boreal autumn to winter in the north (October–December) and in austral autumn to winter (April– June) in the south (Figure 8a). Blooms peaked (Figure 8b) generally two to four months after bloom onset and ended (Figure 8c) two to four months later. There are several regions where the timing of bloom onset, peak, or termination vary by more than one month with waters at similar latitudes. Examples of this include the southern Arabian Sea in the Indian Ocean where Figure 7c shows the presence of time trends that might distort the bloom statistics, and also

the central subtropical North Pacific, and the tropical North Atlantic off northeastern South America where missing data due to cloud is relatively high and may lower the model accuracy. Timing of bloom onset and peak in the central portion of the subtropical gyres also generally preceded that located at their edges (Figure 8). [29] Bloom onset in the North Atlantic Ocean agrees with the pattern and periods (October to January in the subtropical gyre to May to June in the subpolar gyre) reported by Henson et al. [2009a], Platt and Sathyendranath [2008], Platt et al. [2009, 2010], Siegel et al. [2002], and Ueyama and Monger [2005]. In the North Pacific Ocean, the agreement

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Figure 9. Mean bloom duration (date of bloom termination – onset) in days for SeaWiFS. An annual cycle was not detected in white regions. Regions where an annual cycle was not detected are colored white. is reasonable in areas where a seasonal cycle was detected, with initiation occurring typically between April and August [Henson and Thomas, 2007; Sasaoka et al., 2011]. Slight discrepancies between studies likely reflect the differences in methodologies in fitting the chlorophyll time series, the criteria applied in defining the phenological markers, and the spatial resolution of the chlorophyll estimates. For example, bloom initiation in the western North Atlantic was defined as the time when the 20% of the amplitude of the shiftedGaussian model fit to chlorophyll was reached [Platt et al., 2009], in contrast to our 5% above the local median GLM amplitude. [30] The global map of bloom duration (Figure 9) generally displayed a zonal, albeit complex, pattern. As for previous phenological markers, bloom duration could not be estimated for areas where no annual cycle was found (Figure 7a); these areas are colored white in Figure 9. Prominent zonal bands of long bloom duration (160– 180 days) extended across all basins of the Pacific, Atlantic and Indian Oceans at approximately 30 S and 30 N, with partial bands and patches of similar duration blooms in the Indian and South Atlantic Oceans, in the tropical North Pacific, off the northeastern coast of South America, and in the subpolar latitudes of the northeastern Atlantic. These regions separated waters possessing shorter blooms (125– 165 days) in the tropics and subtropics. A minimal bloom duration of 120–125 days was found at subpolar latitudes, but also in tropical waters of the Indian and Atlantic Oceans (Figure 9). [31] The bloom duration calculated by the model is generally longer than those previously reported. Several studies using a shifted-Gaussian approach estimated that North Atlantic blooms lasted between 24 and approximately 100 days, depending on latitude and location [Koeller et al., 2009; Platt et al., 2009, 2010]. In another study, Platt and Sathyendranath [2008] estimated durations of up to 12 months for the western North Atlantic, but did not fit a model to the chlorophyll time series. In the North Pacific, our

bloom durations were similarly greater than those computed for the North Pacific by Sasaoka et al. [2011], who estimated that mean duration ranged from 39 to 99 days. In regions exhibiting spring and autumn bloom peaks, these large discrepancies are due to the physical interpretation of this value, which represents the time span between bloom onset of the spring bloom and the termination of the autumn bloom, and cannot be directly compared with other studies that calculate the duration of only the spring bloom. In areas with single peak blooms, the discrepancies could be due to the constraint imposed by the single harmonic of the GLM. [32] The results shown in Figure 9 largely appear to contradict the concept of a gradual decrease of bloom duration from equator to pole. In particular, there is a region of longer duration around approximately 30 in each hemisphere. Intriguingly, this region straddles areas with single and double bloom behaviors in Figure 7b. In order to better understand this behavior, Figure 10 shows time series from several points along a transect at 180 E extending from 25 N to 45 N in increments of 5 . These time series show a progression from what the model fitted as a flat annual cycle, to a double peak, then a longer duration single peak, to a shorter duration single peak and then to a low amplitude single peak. This is consistent with the pattern shown in Racault et al. [2012] that was also very zonal in nature although their model did not explicitly parameterize double blooms. 3.3. Fitted Model for MODIS/Aqua Chlorophyll Concentration [33] The same phenological analysis was performed on time series of chlorophyll concentration derived from MODIS / Aqua to evaluate the differences that might be due to time period, calibration of the instrument, sampling characteristics or other sources. The map of model number (Table 1) fitted to MODIS/Aqua chlorophyll concentration indicates large areas of consistent behavior across the globe in both SeaWiFS (Figure 11a, same as Figure 7a) and MODIS (Figure 11b). Note that different periods were used

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for the two maps, i.e., September 1997–December 2007 for SeaWiFS and July 2002–July 2010 for MODIS-Aqua, which partly explains some of the differences. Different periods were used since we are interested in robust similarities between the two maps that should be common regardless of the time period selected for fitting. Figure 11c shows changes from SeaWiFS to MODIS/Aqua categorized as the addition of a time variable, the removal of a time variable, or a change in the shape. Time changes represent the addition/removal of any time trend parameters including interactions. Shape changes include changes to more detailed annual cycle (i.e., intercept only change to single harmonic) as well as changes to less detailed annual cycle (i.e., from two to one harmonic). Although the low amplitude, “flat” areas in the tropical Pacific are slightly larger in extent for MODIS/Aqua, the maps are generally similar, particularly in terms of the annual cycle fitted to each grid box (Figures 7a and 11a). Consequently, the phenological characteristics extracted from the time series of MODIS/Aqua chlorophyll concentration (not shown) were similar to those derived from SeaWiFS data (Figure 8), except in the western Indian Ocean as explained next. [34] Most of the changes observed between the GLMs fit to the time series of SeaWiFS and MODIS/Aqua chlorophyll were with respect to the trend in chlorophyll amplitude, which do not affect the annual cycle and resulting phenological markers related to date, e.g., date of bloom onset. Short-term time trends were slightly more common in models fitted to SeaWiFS chlorophyll (Figure 11c). Time trends are predominantly removed in MODIS/Aqua (denoted by yellow) rather than added (denoted by blue). The western Indian Ocean is an exception to this difference; the area subject to a time trend is larger in MODIS/Aqua than in SeaWiFS.

4. Discussion

Figure 10. Time series of chlorophyll concentration from SeaWiFS with fitted GLM on a transect at 180 E between 25 N and 45 N.

[35] The results presented in this study represent the first statistically robust description of the annual cycle of phytoplankton chlorophyll concentration in the surface mixed layer of the world’s oceans. The results do not include the cycle of the deep-chlorophyll maximum because its signal is not retrieved by satellites nor strictly represent phytoplankton (carbon) biomass because the chlorophyll:carbon ratio varies significantly depending upon environmental conditions [Geider, 1987; MacIntyre et al., 2002]. The pattern of fitted models is generally in good agreement between SeaWiFS and MODIS/Aqua, a finding that suggests that these patterns are quite robust across sensors and time. The broad, coherent groupings of model numbers further adds credibility to the results (Figure 7a), as does the agreement with previous studies [Henson et al., 2010; Vantrepotte and Melin, 2009; Vantrepotte et al., 2011] on the regions and rates of time trends of maximum annual SeaWiFS chlorophyll concentrations over the short period examined (Figures 7c and 4b). [36] Although the gamma GLM proved flexible in fitting the time series of SeaWiFS chlorophyll concentrations, it has limitations that must be considered when interpreting the results. The sinusoidal models containing only the first order harmonic (single peak cycle; Table 1 models 3–8) are unable to resolve non-symmetric annual cycles so that fitted bloom characteristics must be evenly spaced in time. This can result

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Figure 11. Maps of the eight models listed in Table 1 fitted to the time series of (a) SeaWiFS and (b) MODIS/Aqua chlorophyll concentration and (c) the difference between the two. Note that periods used to calculate these models are not the same. in unrealistic estimates of bloom onset and termination, and consequently bloom duration, which is computed from these two phenological markers. In the case of the models containing the higher order harmonic, this constraint is not imposed because the higher order harmonic allows for the model to resolve a longer annual cycle (e.g., Figure 3d). Also, the GLM approach yields only a mean value of the phenological characteristics and not a measure of their interannual variability. [37] Some discrepancies with previous studies were observed. In the central North Atlantic, less area was assigned to model 1 (flat annual cycle) than when the same GLM analysis was performed on a time series of 5-day mean chlorophyll at 3 resolution [Vargas et al., 2009]. This difference most likely is a result of the larger and longer

averaging used in that study: the degraded resolution of the data lead to a lower information content and, thus, the model with only the intercept was more likely to be chosen. Not surprisingly, the averaging had little effect on the existence of trends: the area of trend off the west coast of Saharan Africa in the Mid-Atlantic was present in both analyses. Also, no discernible annual cycle was observed at the poleward portions of the South and North Pacific gyres. These areas are characterized by seasonal production [Longhurst, 2007] and it is likely that the “no annual” cycle was selected because inadequate chlorophyll measurements were available to adequately resolve an annual cycle in theses areas. [38] The results support recent findings by other studies and offer insight into phytoplankton bloom dynamics on a global scale. For example, the regions and the rates of time

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trends of peak chlorophyll concentration observed in our study (Figures 7c and 4b) confirm findings from other recent investigations [Henson et al., 2010; Vantrepotte and Melin, 2009; Vantrepotte et al., 2011]. As echoed in these studies, the time trends should be interpreted with great care since the SeaWiFS period is relatively short [Henson et al., 2010] and should not be extrapolated to longer periods. This conclusion is supported by the lack of a time trend in chlorophyll amplitude in these areas in GLMs fit to MODIS chlorophyll (Figure 11). The time parameter in the SeaWiFS GLM most likely represents short-term changes in phytoplankton that might be associated with inter-decadal variability, El Niño/Southern Oscillation (ENSO), or might be part of a longer-term trend in some cases. Despite the limitations of the record length, areas with statistically significant short-term trends are of great interest, not least of all because they reflect multiyear climate variability such as that associated with ENSO. [39] The approach permitted the construction of the first global map illustrating regions that climatologically experience single and double blooms during the course of a year that can be used to evaluate and refine ecosystem models, specifically the location, timing and magnitude of phytoplankton biomass. Locations where single and double peaks in phytoplankton blooms found in the North Atlantic generally follow the idealized pattern of a single, spring bloom at higher latitudes and of the occurrence of a secondary peak in autumn at lower ones [Cushing, 1959], yet the observed distribution pattern world-wide of these two types is much more complex than the commonly accepted, meridional pattern. The location of the two bloom patterns infer the interplay between the availability of nutrients and light [Platt et al., 2009]. For example, the presence of autumnal blooms associated with the frontal zones is supported by increased nutrient supply due to mixing at lower latitudes [Lévy et al., 2001; Mahadevan and Archer, 2000] and by increased stratification by frontal instabilities at higher latitudes [Taylor and Ferrari, 2011]. [40] The observed timing of bloom maximum (Figure 8b) also agrees with previous studies, with peak blooming typically occurring between April and June in the temperate North Atlantic [Henson et al., 2006; Kahru et al., 2011; Koeller et al., 2009; Platt and Sathyendranath, 2008; Platt et al., 2010]. The bloom duration calculated by the model is, however, generally longer than those previously reported reflecting some differences in methodology in the onset and termination. Bloom duration (Figure 9) tended to be zonal with prominent bands of long bloom duration around 30 S and 30 N as found in Racault et al. [2012]. In fact, bloom duration was found to be zonally consistent, but the meridional pattern was found to be far more complex than the simpler model of a progressively shorter duration with increasing latitude. [41] In addition to contributing a global perspective to several aspects of phytoplankton bloom dynamics, the climatological results of this study establish a baseline that can be used to both quantify interannual variability in the phenology of phytoplankton biomass during the past decade and to detect time trends in phenological markers of phytoplankton biomass in the coming one. This information, in conjunction with other ecological indicators [Platt and Sathyendranath, 2008], can be used to assess and better understand the response of marine

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phytoplankton and ecosystems to environmental change, both now and in the future [Hughes, 2000]. The climatological mean timing of bloom peak, for example, can be compared to near-time, satellite-derived estimates of phytoplankton biomass in a local region in order to assess the utilization (or lack thereof) of phytoplankton by its prey based on the relative timing of the current spring bloom to both predict and plan for the abundance of a year-class in a commercial fisheries. Similarly, any apparent mismatch in timing between phytoplankton and their grazers, as well as metrics on regional bloom duration, can be used to assess the importance of these factors on the export flux of particulate carbon from the surface layer. [42] Acknowledgments. The authors thank the Ocean Biology Processing Group and the Distributed Active Archive Center at the Goddard Space Flight Center, Greenbelt, MD, for the production and distribution of the data used in this study. Funding for this study was provided by the National Aeronautics and Space Administration (NASA) Ocean Biology and Biogeochemistry Program to PIs M. R. P. Sapiano (NNX10AB21G) and C. W. Brown (through an interagency transfer), the Center for Satellite Applications and Research of the National Oceanic and Atmospheric Administration (NOAA), and NOAA grant number NA09OAR4320893#9. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or U.S. Government position, policy, or decision.

References Bailey, S. W., and P. J. Werdell (2006), A multi-sensor approach for the on-orbit validation of ocean color satellite data products, Remote Sens. Environ., 102, 12–23, doi:10.1016/j.rse.2006.01.015. Behrenfeld, M. J. (2010), Abandoning Sverdrup’s critical depth hypothesis on phytoplankton blooms, Ecology, 91(4), 977–989, doi:10.1890/091207.1. Behrenfeld, M. J., et al. (2001), Biospheric primary production during an ENSO transition, Science, 291, 2594–2597, doi:10.1126/science.1055071. Behrenfeld, M. J., R. T. O’Malley, D. A. Siegel, C. R. McClain, J. L. Sarmiento, G. C. Feldman, A. J. Milligan, P. G. Falkowski, R. M. Letelier, and E. S. Boss (2006), Climate-driven trends in contemporary ocean productivity, Nature, 444, 752–755, doi:10.1038/nature05317. Bidle, K. D., and P. G. Falkowski (2004), Cell death in planktonic, photosynthetic microorganisms, Nat. Rev. Microbiol., 2(8), 643–655, doi:10.1038/ nrmicro956. Boss, E., and M. Behrenfeld (2010), In situ evaluation of the initiation of the North Atlantic phytoplankton bloom, Geophys. Res. Lett., 37, L18603, doi:10.1029/2010GL044174. Boyce, D. G., M. R. Lewis, and B. Worm (2010), Global phytoplankton decline over the past century, Nature, 466(7306), 591–596, doi:10.1038/nature09268. Cox, D. R., and E. J. Snell (1989), The Analysis of Binary Data, 2nd ed., Chapman and Hall, London. Cushing, D. H. (1959), The seasonal variation in oceanic production as a problem in phytoplankton dynamics, J. Cons. Int. Explor. Mer, 24, 455–464. Edwards, M., and A. J. Richardson (2004), Impact of climate change on marine pelagic phenology and trophic mismatch, Nature, 430, 881–884, doi:10.1038/nature02808. Esaias, W. E., et al. (1998), An overview of MODIS capabilities for ocean science observations, IEEE Trans. Geosci. Remote Sens., 36(4), 1250–1265, doi:10.1109/36.701076. Follows, M., and S. Dutkiewicz (2002), Meteorological modulation of the North Atlantic spring bloom, Deep Sea Res., Part II, 49(1–3), 321–344. Fuentes-Yaco, C., P. A. Koeller, S. Sathyendranath, and T. Platt (2007), Shrimp (Pandalus borealis) growth and timing of the spring phytoplankton bloom on the Newfoundland-Labrador Shelf, Fish. Oceanogr., 16(2), 116–129, doi:10.1111/j.1365-2419.2006.00402.x. Geider, R. J. (1987), Light and temperature-dependence of the carbon to chlorophyll a ratio in microalgae and cyanobacteria: Implications for physiology and growth of phytoplankton, New Phytol., 106(1), 1–34, doi:10.1111/j.1469-8137.1987.tb04788.x. Gregg, W. W., M. E. Conkright, P. Ginoux, J. E. O’Reilly, and N. W. Casey (2003), Ocean primary production and climate: Global decadal changes, Geophys. Res. Lett., 30(15), 1809, doi:10.1029/2003GL016889. Harrison, W. G., E. J. H. Head, E. P. W. Horne, B. Irwin, W. K. W. Li, A. R. Longhurst, M. A. Paranjape, and T. Platt (1993), The western North

14 of 16

C08026


Atlantic bloom experiment, Deep Sea Res., Part II, 40(1–2), 279–305, doi:10.1016/0967-0645(93)90018-I. Hays, G. C., A. J. Richardson, and C. Robinson (2005), Climate change and marine plankton, Trends Ecol. Evol., 20(6), 337–344, doi:10.1016/j. tree.2005.03.004. Henson, S. A., and A. C. Thomas (2007), Interannual variability in timing of bloom initiation in the California Current System, J. Geophys. Res., 112, C08007, doi:10.1029/2006JC003960. Henson, S. A., I. Robinson, J. T. Allen, and J. J. Waniek (2006), Effect of meteorological conditions on interannual variability in timing and magnitude of the spring bloom in the Irminger Basin, North Atlantic, Deep Sea Res., Part I, 53(10), 1601–1615, doi:10.1016/j.dsr.2006.07.009. Henson, S. A., J. P. Dunne, and J. L. Sarmiento (2009a), Decadal variability in North Atlantic phytoplankton blooms, J. Geophys. Res., 114, C04013, doi:10.1029/2008JC005139. Henson, S. A., D. Raitsos, J. P. Dunne, and A. McQuatters-Gollop (2009b), Decadal variability in biogeochemical models: Comparison with a 50-year ocean colour dataset, Geophys. Res. Lett., 36, L21601, doi:10.1029/ 2009GL040874. Henson, S. A., J. L. Sarmiento, J. P. Dunne, L. Bopp, I. Lima, S. C. Doney, J. John, and C. Beaulieu (2010), Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity, Biogeosciences, 7(2), 621–640, doi:10.5194/bg-7-621-2010. Hu, C., E. T. Montgomery, R. W. Schmitt, and F. E. Muller-Karger (2004), The dispersal of the Amazon and Orinoco River water in the tropical Atlantic and Caribbean Sea: Observation from space and S-PALACE floats, Deep Sea Res., Part II, 51(10–11), 1151–1171. Hughes, L. (2000), Biological consequences of global warming: Is the signal already apparent?, Trends Ecol. Evol., 15(2), 56–61, doi:10.1016/ S0169-5347(99)01764-4. Ji, R. B., M. Edwards, D. L. Mackas, J. A. Runge, and A. C. Thomas (2010), Marine plankton phenology and life history in a changing climate: Current research and future directions, J. Plankton Res., 32(10), 1355–1368, doi:10.1093/plankt/fbq062. Kahru, M., V. Brotas, M. Manzano-Sarabia, and B. G. Mitchell (2011), Are phytoplankton blooms occurring earlier in the Arctic?, Global Change Biol., 17(4), 1733–1739, doi:10.1111/j.1365-2486.2010.02312.x. Koeller, P., et al. (2009), Basin-scale coherence in phenology of shrimps and phytoplankton in the North Atlantic Ocean, Science, 324(5928), 791–793, doi:10.1126/science.1170987. Legendre, L. (1990), The significance of microalgal blooms for fisheries and for the export of particulate organic carbon in the oceans, J. Plankton Res., 12(4), 681–699, doi:10.1093/plankt/12.4.681. Lévy, M., P. Klein, and A.-M. Treguier (2001), Impact of sub-mesoscale physics on production and subduction of phytoplankton in an oligotrophic regime, J. Mar. Res., 59, 535–565, doi:10.1357/002224001762842181. Lomas, M. W., F. Lipschultz, D. M. Nelson, J. W. Krause, and N. R. Bates (2009), Biogeochemical responses to late-winter storms in the Sargasso Sea, part I—Pulses of primary and new production, Deep Sea Res., Part I, 56(6), 843–860, doi:10.1016/j.dsr.2008.09.002. Longhurst, A. (1995), Seasonal cycles of pelagic production and consumption, Prog. Oceanogr., 36, 77–167, doi:10.1016/0079-6611(95)00015-1. Longhurst, A. R. (2007), Ecological Geography of the Sea, 2nd ed., 542 pp., Academic, Amsterdam. MacIntyre, H. L., T. M. Kana, T. Anning, and R. J. Geider (2002), Photoacclimation of photosynthesis irradiance response curves and photosynthetic pigments in microalgae and cyanobacteria, J. Phycol., 38(1), 17–38, doi:10.1046/j.1529-8817.2002.00094.x. Mahadevan, A., and D. Archer (2000), Modeling the impact of fronts and mesoscale circulation on the nutrient supply and biogeochemistry of the upper ocean, J. Geophys. Res., 105(C1), 1209–1225, doi:10.1029/ 1999JC900216. Martinez, E., D. Antoine, F. D’Ortenzio, and B. Gentili (2009), Climatedriven basin-scale decadal oscillations of oceanic phytoplankton, Science, 326(5957), 1253–1256. McClain, C. R., G. C. Feldman, and S. B. Hooker (2004), An overview of the SeaWiFS project and strategies for producing a climate research quality global ocean bio-optical time series, Deep Sea Res., Part II, 51(1–3), 5–42, doi:10.1016/j.dsr2.2003.11.001. McCullagh, P., and J. A. Nelder (1989), Generalized Linear Models, 2nd ed., Chapman and Hall, London. Morel, A., B. Gentili, H. Claustre, M. Babin, A. Bricaud, J. Ras, and F. Tieche (2007), Optical properties of the “clearest” natural waters, Limnol. Oceanogr., 52(1), 217–229, doi:10.4319/lo.2007.52.1.0217. Moulin, C., H. R. Gordon, R. M. Chomko, V. F. Banzon, and R. H. Evans (2001), Atmospheric correction of ocean color imagery through thick layers of Saharan dust, Geophys. Res. Lett., 28(1), 5–8, doi:10.1029/ 2000GL011803.

C08026

Nagelkerke, N. (1991), A note on a general definition of the coefficient of determination, Biometrika, 78(3), 691–692, doi:10.1093/biomet/ 78.3.691. Nelson, N. B., D. A. Siegel, and J. A. Yoder (2004), The spring bloom in the northwestern Sargasso Sea: Spatial extent and relationship with winter mixing, Deep Sea Res., Part II, 51(10–11), 987–1000. O’Reilly, J. E., S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain (1998), Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res., 103(C11), 24,937– 24,953, doi:10.1029/98JC02160. Platt, T., and S. Sathyendranath (2008), Ecological indicators for the pelagic zone of the ocean from remote sensing, Remote Sens. Environ., 112(8), 3426–3436, doi:10.1016/j.rse.2007.10.016. Platt, T., C. Fuentes-Yaco, and K. T. Frank (2003), Spring algal bloom and larval fish survival, Nature, 423, 398–399, doi:10.1038/423398b. Platt, T., H. Bouman, E. Devred, C. Fuentes-Yaco, and S. Sathyendranath (2005), Physical forcing and phytoplankton distributions, Sci. Mar., 69, 55–73. Platt, T., G. N. White, L. Zhai, S. Sathyendranath, and S. Roy (2009), The phenology of phytoplankton blooms: Ecosystem indicators from remote sensing, Ecol. Modell., 220(21), 3057–3069, doi:10.1016/j. ecolmodel.2008.11.022. Platt, T., S. Sathyendranath, G. N. White, C. Fuentes-Yaco, L. Zhai, E. Devred, and C. Tang (2010), Diagnostic properties of phytoplankton time series from remote sensing, Estuaries Coasts, 33(2), 428–439, doi:10.1007/s12237-009-9161-0. Polovina, J. J., E. A. Howell, and M. Abecassis (2008), Ocean’s least productive waters are expanding, Geophys. Res. Lett., 35, L03618, doi:10.1029/2007GL031745. Pommier, J., M. Gosselin, and C. Michel (2009), Size-fractionated phytoplankton production and biomass during the decline of the northwest Atlantic spring bloom, J. Plankton Res., 31(4), 429–446, doi:10.1093/ plankt/fbn127. Racault, M.-F., C. Le Quéré, E. Buitenhuis, S. Sathyendranath, and T. Platt (2012), Phytoplankton phenology in the global ocean, Ecol. Indic., 14(1), 152–163, doi:10.1016/j.ecolind.2011.07.010. Sarmiento, J. L., et al. (2004), Response of ocean ecosystems to climate warming, Global Biogeochem. Cycles, 18, GB3003, doi:10.1029/ 2003GB002134. Sasaoka, K., S. Chiba, and T. Saino (2011), Climatic forcing and phytoplankton phenology over the subarctic North Pacific from 1998 to 2006, as observed from ocean color data, Geophys. Res. Lett., 38, L15609, doi:10.1029/2011GL048299. Siegel, D. A., S. C. Doney, and J. A. Yoder (2002), The North Atlantic spring phytoplankton bloom and Sverdrup’s critical depth hypothesis, Science, 296, 730–733. Song, H. J., R. B. Ji, C. Stock, and Z. L. Wang (2010), Phenology of phytoplankton blooms in the Nova Scotian Shelf-Gulf of Maine region: Remote sensing and modeling analysis, J. Plankton Res., 32(11), 1485–1499, doi:10.1093/plankt/fbq086. Stenseth, N. C., and A. Mysterud (2002), Climate, changing phenology, and other life history traits: Nonlinearity and match-mismatch to the environment, Proc. Natl. Acad. Sci. U. S. A., 99(21), 13,379–13,381, doi:10.1073/pnas.212519399. Sverdrup, H. U. (1953), On conditions for the vernal blooming of phytoplankton, J. Cons. Int. Explor. Mer, 18, 287–295. Takahashi, K., A. Kuwata, H. Saito, and K. Ide (2008), Grazing impact of the copepod community in the Oyashio region of the western subarctic Pacific Ocean, Prog. Oceanogr., 78(3), 222–240, doi:10.1016/j. pocean.2008.06.002. Taylor, J. R., and R. Ferrari (2011), Ocean fronts trigger high latitude phytoplankton blooms, Geophys. Res. Lett., 38, L23601, doi:10.1029/ 2011GL049312. Thomalla, S. J., N. Fauchereau, S. Swart, and P. M. S. Monteiro (2011), Regional scale characteristics of the seasonal cycle of chlorophyll in the Southern Ocean, Biogeosciences, 8(10), 2849–2866, doi:10.5194/bg-82849-2011. Ueyama, R., and B. C. Monger (2005), Wind-induced modulation of seasonal phytoplankton blooms in the North Atlantic derived from satellite observations, Limnol. Oceanogr., 50(6), 1820–1829, doi:10.4319/ lo.2005.50.6.1820. Vantrepotte, V., and F. Melin (2009), Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration, ICES J. Mar. Sci., 66(7), 1547–1556, doi:10.1093/icesjms/fsp107. Vantrepotte, V., and F. Melin (2011), Inter-annual variations in the SeaWiFS global chlorophyll a concentration (1997–2007), Deep Sea Res., Part I, 58(4), 429–441, doi:10.1016/j.dsr.2011.02.003. Vantrepotte, V., H. Loisel, F. Mélin, D. Desailly, and L. Duforêt-Gaurier (2011), Global particulate matter pool temporal variability over the

15 of 16

C08026


SeaWiFS period (1997–2007), Geophys. Res. Lett., 38, L02605, doi:10.1029/2010GL046167. Vargas, M., C. W. Brown, and M. R. P. Sapiano (2009), Phenology of marine phytoplankton from satellite ocean color measurements, Geophys. Res. Lett., 36, L01608, doi:10.1029/2008GL036006. Winder, M., and J. E. Cloern (2010), The annual cycles of phytoplankton biomass, Philos. Trans. R. Soc. B, 365(1555), 3215–3226, doi:10.1098/ rstb.2010.0125.

C08026

Yamada, K., and J. Ishizak (2006), Estimation of interdecadal change of spring bloom timing, in the case of the Japan Sea, Geophys. Res. Lett., 33, L02608, doi:10.1029/2005GL024792. Zhai, L., T. Platt, C. Tang, S. Sathyendranath, and R. H. Walls (2011), Phytoplankton phenology on the Scotian Shelf, ICES J. Mar. Sci., 68(4), 781–791, doi:10.1093/icesjms/fsq175.

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