A Comparison of Methods to Determine Phytoplankton Bloom Initiation Sarah R

JOURNAL OF GEOPHYSICAL RESEARCH: OCEANS, VOL. 118, 2345–2357, doi:10.1002/jgrc.20167, 2013

A comparison of methods to determine phytoplankton bloom initiation Sarah R. Brody,1 M. Susan Lozier,1 and John P. Dunne2 Received 10 January 2013; revised 14 March 2013; accepted 17 March 2013; published 8 May 2013.

[1] Phytoplankton bloom phenology has important consequences for marine ecosystems and fisheries. Recent studies have used remotely sensed ocean color data to calculate metrics associated with the phenological cycle, such as the phytoplankton bloom initiation date, on regional and global scales. These metrics are often linked to physical or biological forcings. Most studies choose one of several common methods for calculating bloom initiation, leading to questions about whether bloom initiation dates calculated with different methods yield comparable results. Here we compare three methods for finding the date of phytoplankton bloom initiation in the North Atlantic: a biomass-based threshold method, a rate of change method, and a cumulative biomass-based threshold method. We use these methods to examine whether the onset of positive ocean-atmosphere heat fluxes coincides with subpolar bloom initiation. In several coherent locations, we find differences in the patterns of bloom initiation created by each method and differences in the synchrony between bloom initiation and positive heat fluxes, which likely indicate various physical processes at play in the study region. We also assess the effect of missing data on the chosen methods. Citation: Brody, S. R., M. S. Lozier, and J. P. Dunne (2013), A comparison of methods to determine phytoplankton bloom initiation, J. Geophys. Res. Oceans, 118, 2345–2357, doi:10.1002/jgrc.20167.

1. Introduction identify the date of bloom initiation in a manner that can [2] Ocean primary productivity plays a fundamental role be efficiently and accurately applied to spatially extensive in controlling the structure and health of marine ecosystems. ocean color data sets. Phenology studies currently use sev- Phytoplankton bloom phenology is of particular interest eral methods to estimate the timing of a phytoplankton because its coincidence with vulnerable phases of larval fish bloom. Ji et al. [2010] identify three broad categories of [Platt et al., 2003] and zooplankton cycles [Koeller et al., methods. Threshold methods based on chlorophyll biomass 2009] may affect survival rates of those species [Quetin define bloom initiation as the time at which a chlorophyll a et al., 1996; Koeller et al., 2009]. A recent study has time series [Siegel et al., 2002; Fleming and Kaitala, 2006; suggested that climate change may affect the timing of Henson et al., 2006; Henson et al., 2009; Thomalla et al., bloom initiation [Kahru et al., 2011], leading to a pos- 2011; Racault et al., 2012; Cole et al., 2012] or a func- sible decrease in the synchrony between phytoplankton tion or model fit to chlorophyll a data [Platt et al., 2009b; blooms and upper trophic level life cycles [Edwards and Vargas et al., 2009; Wiltshire et al., 2008; Yamada and Richardson, 2004; Mackas et al., 2007]. Such a possibil- Ishizaka., 2006; Zhai et al., 2011; Sasaoka et al., 2011; ity emphasizes the importance of examining the drivers of Sapiano et al., 2012] crosses a set threshold. Threshold phytoplankton bloom initiation and their vulnerability to methods based on cumulative chlorophyll biomass, which interannual climate variability and change [Visser and Both, have mainly been used to investigate zooplankton phenol- 2005]. ogy, identify a bloom as the time at which a cumulative [3] Remotely sensed ocean color data provide a valu- summation of chlorophyll biomass crosses a threshold per- able tool for examining phytoplankton bloom initiation at centile of the total biomass [Greve et al., 2005; Mackas the basin scale [Platt et al., 2009a]. However, before using et al., 2012]. Rate of change methods estimate bloom initi- ocean color-derived chlorophyll a to investigate the con- ation from the point of most rapid increase on a chlorophyll trols on phytoplankton bloom initiation, it is important to time series or function fit to that time series [Sharples et al., 2006; Rolinski et al., 2007; White et al., 2009]. 1Division of Earth and Ocean Sciences, Duke University, Durham, [4] In the marine environment, biomass-based thresh- North Carolina, USA. old methods have been most widely used in conjunction 2Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, with ocean color data to identify bloom initiation and other USA. phenological events in the marine environment at basin Corresponding author: S. R. Brody, Division of Earth and Ocean and global scales. However, to date, we are aware of no Sciences, Nicholas School of the Environment, Duke University, Box marine phenology studies critically comparing bloom initi- 90227, Durham, NC 27708, USA. ([email protected]) ation dates derived from biomass-based threshold methods ©2013. American Geophysical Union. All Rights Reserved. with those derived from cumulative biomass-based thresh- 2169-9275/13/10.1002/jgrc.20167 old methods or rate of change methods. Here we compare 2345 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION patterns of bloom initiation dates observed from these three bloom during the year [D’Ortenzio et al., 2012; Martinez methods, investigate the drivers of discrepancies, and sug- et al., 2006]. In this study, “primary” blooms denote the gest questions in the field of phytoplankton phenology high-magnitude blooms caused by the lifting of the major research for which each method is best suited. We also exam- limiting factor on phytoplankton growth, while “secondary” ine how the method we use to fill gaps in the chlorophyll blooms denote smaller blooms created by transient effects. data affects our conclusions. For instance, at some midlatitude locations, after a light- [5] Additionally, we present an application to the study driven primary bloom in the spring exhausts the surface of bloom initiation dates by examining a possible driver of nutrient supply, a deepening mixed layer in the fall can pro- subpolar phytoplankton blooms, as has been done in many vide a new source of nutrients, leading to a secondary bloom of the previously cited studies [Siegel et al., 2002; Henson [Cushing, 1959; Platt et al., 2009b; Sapiano et al., 2012]. et al., 2006, 2009; Thomalla et al., 2011; Sasaoka et al., [8] For all the methods in our study, we use level 3 2011; Kahru et al., 2011; Racault et al., 2012]. For light- SeaWiFS chlorophyll a concentrations for the years limited (subpolar) regions, the mechanics of phytoplank- 1998–2007 (all full years of SeaWiFS data), obtained from ton phenology are generally thought to be consistent with http://oceancolor.gsfc.nasa.gov/. We downloaded the 9 km, the Sverdrup hypothesis [Sverdrup, 1953]. The Sverdrup 8 day resolution SeaWiFS data, then spatially averaged the hypothesis states that during winter months, deep mixed original data to half-degree resolution. We chose 8 day data layers and low surface irradiance result in phytoplankton to circumvent the large amount of missing data and noise consistently mixed to depths with too little light to sus- inherent in daily chlorophyll data yet still maintain suffi- tain growth. A spring bloom can initiate when the winter cient temporal resolution to make meaningful connections mixed layer shoals above a critical depth, where integrated between the timing of positive ocean-atmosphere heat fluxes photosynthesis is equal to integrated respiration. and bloom initiation. We dealt with missing data in the 8 day, [6] Recent studies have challenged the idea that season- half-degree data set by first filling in the small and ubiq- ally shoaling mixed layers create the necessary conditions uitous data gaps due to cloud cover by averaging the four to promote phytoplankton growth, and have proposed other nearest-neighbor points. We performed this spatial averag- mechanisms that may play a role in subpolar bloom ini- ing twice, so that the farthest distance from which a missing tiation. For example, Taylor and Ferrari [2011] posit that pixel could draw information was 1°. We then filled remain- the cessation of turbulent convection in the upper mixed ing data gaps, associated with high-latitude, winter low sun layer, caused by a reversal in atmosphere-ocean heat fluxes angles, with the annual minimum chlorophyll value for each at the end of winter, may stabilize the upper mixed layer pixel’s yearly time series. and that this stabilization, rather than the seasonal shoal- [9] We examine the relationship between ocean- ing of the entire mixed layer, can initiate a phytoplankton atmosphere heat flux changes and bloom initiation using bloom. They provide support for this theory in the subpolar daily net surface heat fluxes for the years 1998–2007, North Atlantic by noting the synchrony between increases in obtained from NCEP/NCAR reanalysis 2 (http://www.esrl. chlorophyll biomass and the onset of positive atmosphere- noaa.gov/psd/). The heat flux data was plotted on a T62 ocean heat fluxes or net warming of the ocean surface. Gaussian grid, which we averaged to half-degree resolution, However, Mahadevan et al. [2012] have challenged these then temporally averaged to 8 day resolution. We employed conclusions by noting that in their detailed study of the North the same method of filling small data gaps to the regridded Atlantic Bloom Experiment, chlorophyll began increasing heat fluxes as in the chlorophyll data but did not fill in large while heat fluxes were still negative. Here we assess the rela- data gaps with the time series minimum. tionship between heat fluxes and phytoplankton blooms by comparing the time at which heat fluxes become positive 2.2. Method 1: Rate of Change Method (ROC Method) to the date of bloom initiation determined using the three [10] We designed a rate of change method for find- different methods. ing bloom start dates (BSDs). We based this method on HANTS-FFT (Harmonic analysis of time series-fast Fourier 2. Data and Methods transform) method [Roerink et al., 2000], which performed well in a terrestrial phenology study [White et al., 2009]. 2.1. Study Site and Data Our method first performs a discrete FFT on the 10 year [7] We conduct our study of bloom phenology methods in SeaWiFS data set to obtain a set of Fourier coefficients the North Atlantic over the spatial domain 30°N–65°N and at each location. The first 20 Fourier coefficients, corre- 80°W–0°W. Chlorophyll in the North Atlantic has a complex sponding to the 20 largest sinusoidal periods, are used and variable temporal and spatial structure. Convention- to reconstruct the data set (Figure 1). The first frequency ally, productivity in this region is divided into subtropi- has a period spanning the 10 year time series; the twen- cal (nutrient-limited) and subpolar (light-limited) regimes tieth frequency has a 6 month period. To find the yearly [Follows and Dutkiewicz., 2001; Henson et al., 2006]. The blooms, we first identify all of the local maxima in the main bloom in subtropical areas occurs in fall or winter, reconstruction. We select the 10 largest maxima as the pri- when deep mixed layers entrain nutrients into the euphotic mary bloom peaks, then check to see if any of those 10 zone, whereas the main bloom in subpolar areas occurs maxima occur less than 8 months apart. If so, one of the in spring and summer, by the mechanisms explained in 10 largest maxima is a large secondary bloom, rather than section 1. The transition zone between these two regimes a primary bloom, and we substitute the next-largest local can experience blooms due to the lifting of light and/or maxima for the secondary bloom peak. After determin- nutrient limitation [Henson et al., 2009]. Conditions at tran- ing the 10 primary bloom peaks in the reconstruction, we dchl sition zone latitudes may also permit a second, smaller define the bloom start date for the yearly blooms as the dt 2346 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

a. ) 0.3 −3 0.25 0.2 0.15 0.1

chlorophyll (mg m 0.05 0 Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07 chl 10 coef. reconstruction 20 coef. reconstruction 30 coef. reconstruction 10 coef. BSD 20 coef. BSD 30 coef. BSD 20 coef. max 21−25 coeff. BSDs b.

) 0.3 −3 0.25 0.2 0.15 0.1

chlorophyll (mg m 0.05 0 Sept’03 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Figure 1. Chlorophyll time series (36°N, 59°W) (a) for all 9 years and (b) for 2003–2004, showing the chlorophyll data (blue line) and the reconstruction using 10 (red line), 20 (black line), and 30 (green line) Fourier coefficients. Diamonds show BSDs calculated for each reconstruction; filled black circles show the bloom peaks selected using the 20-coefficient reconstruction. At the bottom of Figure 1a, the BSDs calculated using the 20-coefficient reconstruction (black diamonds) are shown on the x axis, and the BSDs calculated using the 21–25 coefficient reconstruction (red, green, blue, magenta, and cyan crosses) are shown above. Dashed lines denote year start dates. maximum prior to the bloom peak (Figure 1a). Because the the reconstruction (i.e., BSDs calculated using 20 coeffi- ROC method determines BSDs based on the yearly chloro- cients and BSDs calculated using 21–25 coefficients are very phyll maxima, results using this method have the advantage similar), but as increasing numbers of coefficients are used, of not being sensitive to the time series start date or the the reconstruction will tend to over-emphasize secondary season in which the primary bloom occurs. blooms. For example, the reconstruction using 30 coeffi- [11] Using the first 20 Fourier coefficients, we produce cients resolves the secondary spring bloom in this time series reconstructions of chlorophyll time series that display both to such a degree that it identifies the BSD as occurring imme- spring and fall blooms, accurately reproduce the timing diately prior to the secondary bloom (Figure 1). Thus, we of the chlorophyll peak, and remove variability at sub- conduct the remainder of our investigation with an ROC seasonal and lower scales (Figure 1). We show an exam- method that uses 20 Fourier coefficients to reconstruct the ple of the problems associated with using too few or too chlorophyll time series. many Fourier coefficients in the reconstruction by plotting a chlorophyll time series from 36°N, 59°W, with the 2.3. Method 2: Threshold Method (TH) reconstructions and associated BSDs using 10, 20, and 30 [12] The threshold bloom initiation method was intro- coefficients (Figure 1). This time series represents a sub- duced for marine phenology studies in Siegel et al. [2002]. tropical regime, in which deepening mixed layers in fall This method finds the yearly or climatological median of a (September–November) entrain nutrients from below the chlorophyll time series, then identifies the bloom start date mixed layer, creating a primary bloom that extends through as the first point at which chlorophyll levels rise a certain the winter [Follows and Dutkiewicz, 2001]. For most years, percentage above the median. The BSDs identified by the the time series also shows a secondary chlorophyll peak threshold method have been found to be relatively insensi- occurring near the end of the primary bloom, possibly due to tive to the percentage used [Siegel et al., 2002]. Here we increased spring irradiance prior to mixed layer depth shoal- used a threshold of 5% above the median to be consis- ing and consequent nutrient depletion. The primary bloom tent with previous threshold-based phenology studies [Siegel still begins in fall for these years, but the peak of the pri- et al., 2002; Henson et al., 2006, 2009; Thomalla et al., mary bloom occurs in spring. Reconstructions of the time 2011; Racault et al., 2012; Sapiano et al., 2012; Cole et al., series that use fewer than 20 coefficients do not resolve the 2012]. The threshold is calculated after filling in the missing secondary peak; therefore, the bloom peak falls in winter data. rather than in spring. While Figure 1 shows the results of the [13] The threshold method is often modified to mini- 10-coefficient reconstruction, the same problems occur for mize the misidentification of secondary blooms as primary any reconstruction using fewer than 20 coefficients. Using blooms by shifting the chlorophyll time series to begin more than 20 coefficients has a less significant impact on close to an estimated bloom season, for example, by starting 2347 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

0.8 standard deviations above the yearly median and replacing chl time series these points with the quality-controlled time series max- 0.7 threshold imum. The time at which this curve rises above a set maximum point percentage of the total biomass is identified as the BSD. We 0.6 points below designed the CS method (illustrated in Figure 3) to be as threshold 0.5 1 insensitive as possible to the time period in which the pri- BSD mary bloom occurs by identifying the minimum point of 0.4 each yearly time series (cross in Figure 3) then shifting the time series to put the minimum at the beginning (dashed 0.3 line). The rationale for this shift is the expectation that the lowest chlorophyll level generally occurs prior to the pri- 2 0.2 mary bloom and will create a cumulative biomass curve (dotted line) that does not significantly increase until the 0.1 primary bloom begins. [16] BSDs identified by the CS method are sensitive to the 0 Sept Oct Nov Dec Jan Feb threshold used to define them, in contrast to the TH method. We determined that a threshold of 15% of the total biomass Figure 2. Half year of chlorophyll data (42°N, 33°W, (cross) best predicted the bloom initiation date for our study 1997) showing the threshold method, using a threshold of area using two techniques. First, we visually inspected mul- 5% above the yearly median chlorophyll. The straight line tiple chlorophyll time series over the entire study area with denotes the threshold, the open circle shows the chloro- BSDs determined using six thresholds (5–30%). From these phyll maximum or start point of the method, closed circles time series, we found that thresholds of 10–15% best pre- show the points below the threshold used to determine the dicted the bloom initiation date in subtropical regions, while BSD, and the diamond denotes the BSD. Arrows show threshold of 15–20% best predicted the bloom initiation date the direction in which the TH algorithm searches for the in subpolar regions. We then plotted, for the six thresholds, below-threshold points (arrow 1) and BSD (arrow 2). the number of occurrences in which chlorophyll levels at each threshold’s BSD exceeded chlorophyll levels prior to the time series in late summer for regions with fall pri- the BSD but were not larger than the yearly chlorophyll mary blooms [Siegel et al., 2002; Henson et al., 2006, 2009; median plus one standard deviation. We found that the 15% Thomalla et al., 2011]. Our method, illustrated in Figure 2, threshold had the largest number of points with increasing calculates the BSD based on the time series maximum, rather chlorophyll levels at the BSD, which confirmed the results than on an imposed time series start date. This calculation of the visual inspection and led to the choice of the 15% is generally consistent with recent phenology studies [e.g., threshold. Racault et al., 2012; Cole et al., 2012]. Our method finds the Dec Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov maximum point of the time series (open circle in Figure 2), 1.2 chl time series then works backward from the maximum (arrow 1) to find minimum point where chlorophyll levels go below the threshold for two con- shifted time series 1 secutive weeks (closed circles), in order to ensure that the cumulative sum ) identified decrease in chlorophyll is robust over the season −3 threshold crossing 0.8 rather than a transient effect of data noise. We subsequently BSD identify the BSD as one point closer to the maximum than the below-threshold points (arrow 2, diamond). 0.6 [14] While some previous studies implementing the threshold method with remotely sensed chlorophyll data 0.4 have first fit a function or model to the data for smoothing chlorophyll (mg m purposes before identifying the BSD [Platt et al., 2009b; Vargas et al., 2009; Wiltshire et al., 2008; Yamada and 0.2 Ishizaka., 2006; Zhai et al., 2011; Sasaoka et al., 2011; Sapiano et al., 2012], we find that using chlorophyll data 0 that has already been temporally and spatially averaged and Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug requiring chlorophyll levels to stay below the threshold for two consecutive weeks provides sufficient filtering of noisy Figure 3. Chlorophyll time series (21° W, 55° N, 2001) data. To confirm this, we calculated TH BSDs using the showing the CS method. The thick line is the chlorophyll Fourier reconstruction of the chlorophyll data described in time series, the cross is the minimum point of the time series, section 2.2 and found very little difference between the TH the dashed line shows the shifted time series, the dotted line BSDs calculated with reconstructed chlorophyll data and shows the cumulative sum of the chlorophyll time series original chlorophyll data. (with the maximum value of the cumulative sum normalized to the maximum value of the time series), the plus shows the 2.4. Method 3: Cumulative Sum Method (CS Method) threshold (15% of the total biomass), and the diamond shows [15] Our third method is based on a cumulative biomass the BSD. The bottom axis represents the original chlorophyll curve of the chlorophyll time series, created after screen- time series; the top axis represents the shifted chlorophyll ing out points in each yearly time series greater than three time series and cumulative sum curve. 2348 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

ROC TH CS ° ° ° ° ° ° ° ° ° 80 W 40 W 0 80 W 40 W 0 80 W 40 W 0 ° ° ° a. 60 N b. 60 N c. 60 N 50° N 50° N 50° N

40° N 40° N 40° N

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

TH minus ROC TH minus CS CS minus ROC 80°W 40°W 0° 80°W 40°W 0° 80°W 40°W 0° ° ° ° 6 5 60 N 6 5 60 N 6 5 60 N d. 4 e. 4 f. 4 50° N 50° N 50° N

3 40° N 3 40° N 3 40° N 2 2 2 1 1 1

−20 −15 −10 −5 0 5 10 15 20

Figure 4. Bloom start dates calculated using (a) ROC, (b) TH, and (c) CS methods, and difference plots of (d) TH minus ROC BSDs, (e) TH minus CS BSDs, and (f) CS minus ROC BSDs. All plots show the average of the 10 year time series, with BSDs calculated using a time series start date of 1 September. The scale of the bottom axis is in 8 day periods. Diamonds denote the locations of the time series in Figures 6, 9, 11, and 12.

3. Results 3.2. Comparison of Heat Flux Changes and BSDs 3.1. Comparison of Bloom Phenology Patterns [18] We expand upon the idea introduced in Taylor and Ferrari [2011] by subtracting the 8 day period on which [17] We begin our comparison by noting the similarities heat fluxes become positive (the “zero-crossing” or ZC) and differences in the patterns of bloom initiation dates from the BSDs calculated using each method (Figure 5). calculated using the three methods (Figures 4a–4c). All The difference plots are shown with a discretized colorbar methods show the same major pattern in BSDs: a sharp tran- and a highlighted zero-contour to facilitate the comparison. sition between subtropical latitudes, where blooms occur in In the subtropics, blooms occur well before the heat flux the fall and winter, and subpolar latitudes, where blooms ZC, as expected, since phytoplankton growth in these areas occur in the spring and summer, indicating that this feature is not light-limited. The largest difference between BSDs of North Atlantic bloom phenology is robust to the method and ZCs in the subtropics occurs at the Gulf Stream, where used to identify blooms. However, in several areas, bloom consistently warm sea surface temperatures delay the zero- phenology patterns differ between the methods, with dif- crossing. Over most of the subpolar region, ROC and CS ferences that can exceed 2 months. We have highlighted BSDs (Figures 5a and 5c) are approximately synchronous these areas by plotting the difference between BSDs calcu- with ZCs, occurring within four 8 day periods of each other. lated using each method (Figures 4d–4f). First, ROC BSDs Both of these methods show patchiness in whether the BSD show a band of early blooms centered at approximately occurs before or after the zero-crossing. Almost all subpo- 36°N–39°N, which TH and CS BSDs do not show. Sec- lar TH BSDs (Figure 5b) occur after the zero-crossing, with ond, CS BSDs shift the transition between subtropical and some areas occurring over four 8 day periods, or 32 days, subpolar regimes northward, especially in the central and after the zero-crossing. We use the same time series as in eastern North Atlantic. Third, in the eastern North Atlantic, Figures 4d–4f (locations plotted in Figures 5a–5c as purple between approximately 55°N and 60°N, the TH method diamonds) to examine the relationship between the heat flux calculates late blooms relative to the rest of the subpolar ZC and increases in chlorophyll. study area, while the ROC and CS methods show blooms in this area occurring at the same time as, or slightly earlier than, the rest of the subpolar North Atlantic. Finally, 3.3. Time Series 1 and 2 all three methods show an area of earlier blooms west of [19] We use time series 1 and 2 (Figure 4), both located Greenland, but this feature is exaggerated in ROC blooms. in the nutrient-limited subtropics, to determine why the In sections 3.3–3.6, we will explore the reason for these ROC method shows early blooms in a band centered at differences using time series located at the diamonds in 36°N–39°N (time series 2), compared with the rest of the Figures 4d–4f. subtropical region (time series 1). Figure 6 shows the time 2349 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

ROC minus ZC difference between the two BSDs (TH minus ROC) is –2.7 ° ° ° 80 W 40 W 0 periods. In contrast, at 36°N (time series 2), TH BSDs often ° a. 60 N 12 occur when the bloom is well underway, with an average ° 50 N difference between the two BSDs of 8.5 periods. 20 ° [ ] Time series 1 and 2 also show that at 36°N, the yearly 40 N 8 median threshold is elevated relative to the time series, leading to later TH blooms than at 32°N. The elevated median appears to be caused by the longer duration of the blooms 4 TH minus ZC at 36°N, as compared with 32°N. Indeed, an overlay of the ° ° ° 80 W 40 W 0 Fourier reconstructions of the two time series (Figure 7) ° 0 shows that at 36°N the bloom both begins earlier and lasts b. 60 N longer than at 32°N. The earlier blooms are likely not iden- ° 50 N tiﬁed by the TH method because the longer bloom duration ° −4 40 N elevates the median threshold relative to the chlorophyll time series. [21] We generalize this observation for the entire band of −8 early ROC BSDs at 36°N–39°N (Figure 4a) by deﬁning a CS minus ZC metric called the relative median position: ° ° ° 80 W 40 W 0 −12 ° c. 60 N ° chl – chl 50 N max min −16 . (1) ° chlmax – chlmedian 40 N

−20 As seen in the schematic (Figure 8a), long-duration blooms (solid curve) tend to have higher relative median positions Figure 5. (a) ROC, (b) TH, and (c) CS BSDs minus than short-duration blooms (dashed curve). Plotting relative the 8 day period during which heat ﬂuxes become pos- median positions for the study area (Figure 8b) shows that itive (zero-crossing or ZC). The zero-contour is shown the band of ROC-calculated early blooms at 36°N–39°N in black. Monthly heat ﬂux data was downloaded from corresponds with high relative median positions. Thus, our http://www.esrl.noaa.gov/psd/data/gridded/data.godas.html conclusion from time series 1 and 2 that high relative median and linearly interpolated to match the 8 day SeaWiFS data. positions create earlier blooms likely holds for the entire band of early blooms. series plotted for the nine full years of data. At both time [22] CS BSDs generally show slightly earlier blooms than series, ROC BSDs occur as chlorophyll levels are beginning either ROC or TH BSDs for the subtropical regions in the to increase. At 32°N (time series 1), TH BSDs occur at the domain but, like TH BSDs, do not show a band of espe- same time as or slightly earlier than ROC BSDs. The average cially early blooms at 36°N–39°N for similar reasons: the

Time Series 1

)

−3 0.3 0.25 0.2 0.15 0.1 0.05

Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07

Time Series 2 ) chlorophyll (mg m (mg ) chlorophyll

−3 0.3 0.25 0.2 0.15 0.1 0.05 chlorophyll (mg m Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07 Figure 6. Time series with BSDs calculated using all three methods at 32°N and 36°N. The values of the CS threshold have been normalized to the values of the chlorophyll time series. Dashed lines indicate September of each year. 2350 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

° ° 0.25 32 N 36 N )

−3 0.2

0.15

0.1 chlorophyll (mg m 0.05

Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07

Figure 7. Fourier reconstructions of both time series in Figure 6.

longer bloom duration, which increases the total cumula- well with the band of early CS BSDs, supporting the tive biomass, raises the cumulative sum threshold compared explanation that where CS BSDs occur much earlier than TH with other subtropical latitudes. The band of early blooms at and ROC BSDs, they identify the start of secondary blooms. 36°N–39°N coincides approximately with the Gulf Stream, which creates increased eddy kinetic energy and mixing in 3.5. Time Series 4 and 5 this area [Richardson, 1983; Fratantoni, 2001]. Therefore, [25] Time series 4 and 5 (Figure 11) are both located in a increased mixing, and the consequent increase in entrained relatively large area within the light-limited, subpolar North nutrients, likely create the conditions necessary to support a Atlantic, from approximately 55°N to 60°N, where TH longer phytoplankton bloom period. BSDs occur later than either CS or ROC BSDs (Figure 4). Examining these time series shows that blooms in this area 3.4. Time Series 3 [23] Time series 3 (Figure 9) represents an area in which the CS method shows fall and winter BSDs, consistent with a. subtropical areas, while TH and ROC BSDs show spring BSDs, consistent with subpolar regions. Figures 4e and 4f maximum show the degree to which the location of the subtropical- subpolar transition differs between CS BSDs versus TH and ROC BSDs. The shape of the chlorophyll data and its Fourier reconstruction in time series 3 indicate that in most maximum years this area experiences a primary bloom in the spring and a secondary bloom in the fall, likely due to the mechanisms described in section 2.1 and consistent with the findings of median Martinez et al. [2011] and D’Ortenzio et al. [2012]. The timing of the heat flux ZC just prior to or coincident with the median primary bloom provides further evidence for a light-limited spring bloom and a nutrient-limited fall bloom in this region. Further, the minimum chlorophyll levels occur between the 80° W 40° W0° blooms during the highly stratified, nutrient-depleted sum- b. mer months. These two observations explain the early CS 60° N BSDs: for the time series used in the CS method, which is shifted to start at the minimum chlorophyll point, the secondary bloom occurs before the primary bloom, and the 50° N chlorophyll increase during the secondary bloom is large enough to raise the cumulative chlorophyll sum above the prescribed threshold. The other two methods, which search 40° N for the BSD in relation to the yearly maximum of the chlorophyll time series, identify the BSD very close to the heat flux ZC. [24] The conclusions drawn from time series 3 can be extended to the entire area of early CS BSDs by plotting 1 1.2 1.4 1.6 1.8 2 the magnitude of the 20th Fourier coefficient, which corresponds to the bi-yearly bloom period, divided by the sum of Figure 8. (a) Schematic diagram illustrating the relative the magnitudes of all the Fourier coefficients, over the study median position metric (vertical solid line divided by vertical domain (Figure 10). Areas on the map where the bi-yearly dashed line). The solid curve is an example of a low relative bloom period is large compared with the other periods can be median position; the dashed curve is an example of a high used to estimate areas where there are prominent spring and relative median position. (b) Relative median positions, fall blooms. The band of spring and fall blooms corresponds averaged over the 9 year time series. 2351 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

Time Series 3

) 0.8 −3 0.7 0.6 0.5 0.4 0.3 0.2

chlorophyll (mg m 0.1

Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07

Figure 9. Time series with BSDs calculated using all three methods shown for 31°W, 43°N. The values of the CS threshold have been normalized to the values of the chlorophyll time series. Dashed lines indicate September of each year. begin with several small increases in chlorophyll prior to the study, the heat flux ZC also occurs at the beginning of the chlorophyll maximum. The difference in average TH ver- phytoplankton bloom, indicating that stabilization due to sus CS and ROC BSDs is driven by large differences in net ocean surface warming may also play a role in driv- BSDs for a few years out of each time series (shaded years ing blooms here. However, BSDs calculated with the ROC in Figure 11) rather than systemic differences in BSDs. For method are earlier than blooms calculated using TH and CS the years that do contain late-occurring TH BSDs, the dif- methods. Figure 12 shows that the chlorophyll time series ference is due to an especially large decrease (two or more in this area is composed of long periods of very low chloro- weeks below the threshold) in chlorophyll levels between phyll punctuated by brief, high-magnitude blooms. The low the small chlorophyll spikes and the chlorophyll maximum, chlorophyll levels throughout the fall, winter, and spring which the TH algorithm identifies as the seasonal minimum months are likely due to a combination of low irradiance and prior to the bloom. This effect is likely accentuated by high the large amount of missing data in this region, which is then relative median positions in this region of the North Atlantic, filled with the yearly time series minimum (see section 3.7). as compared to the western North Atlantic at the same lati- Comparing the Fourier reconstruction of the data to the tudes (Figure 8). By contrast, the Fourier reconstruction used chlorophyll time series shows that the 20 coefficients chosen to identify ROC BSDs smooths the multiple chlorophyll to create the reconstruction cannot replicate the extremely increases into one continuous bloom, and, in most years, the short duration of blooms at this latitude. While the timing low-magnitude chlorophyll increases are still large enough of the chlorophyll maxima in the reconstruction is consistent to raise the cumulative chlorophyll sum used to calculate CS with the timing of the maxima in the time series, the recon- BSDs above the 15% threshold. structed blooms are much longer and thus start earlier than [26] There are several possible explanations for the the blooms in the time series, leading to ROC BSDs that observed low-magnitude increases in chlorophyll prior to occur during the late winter. the main bloom. This pattern could be driven by biology, where each chlorophyll spike represents a successive phy- 3.7. Effect of Filling Missing Data toplankton species that gets quickly grazed down until a larger phytoplankton species establishes a long-duration, [28] While remotely sensed ocean color data provides a high-magnitude bloom [Sommer, 1985; Taylor et al., 1993]. valuable resource for examining phytoplankton bloom phe- Alternatively, this pattern could be driven by physical pro- nology, gaps in the data, especially at high latitudes, can cesses. The mechanism for bloom initiation described in affect the quality of phenology estimates. Cole et al. [2012] Taylor and Ferrari [2011] and in section 1 could allow for more transient chlorophyll increases due to mixed layer stabilization before the onset of seasonal stratification. This explanation finds support in our results: for all years in which TH blooms are delayed relative to ROC blooms, the heat flux ZC occurs at roughly the same time as the small chlorophyll increases identified by the ROC method as the BSD.

3.6. Time Series 6 [27] Time series 6 (Figure 12) is located west of Greenland, also a light-limited area for phytoplankton growth. In this area, all three methods show earlier BSDs than at the same latitude in other locations (Figure 4). This pattern is also seen in the phenology study by Henson et al. [2009] and is attributable to early stratification induced by Figure 10. Magnitude of the 20th Fourier coefficient (cor- westward advection of relatively light freshwater melt from responding to a 6 month or bi-yearly bloom period) divided Greenland [Frajka-Williams et al., 2009]. In the present by the sum of the magnitudes of all coefficients. 2352 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

Time Series 4

)

−3 1.5

0.5 chlorophyll (mg m 0 Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07

Time Series 5 1.2 )

−3 1

0.8 0.6

0.4

0.2 chlorophyll (mg m Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07

Figure 11. Time series with BSDs calculated using all three methods shown for 21°W, 55°N and 38°W, 59°N. The values of the CS threshold have been normalized to the values of the chlorophyll time series. Shading indicates years in which TH BSDs occur later than ROC and CS BSDs. Dashed lines indicate September of each year. provided a comprehensive study of the effect of missing section 2.1. However, the BSDs calculated using a data set data on the TH method of estimating BSDs by comparing with only spatial filling were virtually identical to those cal- BSDs and bloom peak dates derived from a SeaWiFS data culated using a data set where no filling was applied, leading set containing missing data with those derived from a SeaW- to the conclusion that the filling of large data gaps with the iFS data-assimilating biogeochemical model. Similar studies time series minimum has the largest effect on BSDs. When conducted for other methods of determining BSDs would be we created the same difference plots as in Figures 4d–4f a useful addition to phenology research. However, in this using unfilled data (Figure 13), very similar general pat- study, our aims in examining missing data are twofold: one, terns appear, although the magnitudes of those patterns to ensure that our method of filling the missing data does not differ (compare Figure 4d with 13a, Figure 4f with 13b, strongly affect our comparisons between the methods; and and Figure 4e with 4c and 4d). In fact, when comparing two, to briefly comment upon the effect of filling data gaps BSDs using the unfilled data, the correspondence between with the time series minimum on the BSDs we observe for areas with early CS BSDs (Figures 13b and 13d) and areas the TH and CS methods. with prominent secondary blooms strengthens. We therefore [29] To address our first aim, we calculated BSDs using conclude that our findings regarding the reason for the dif- the TH and CS methods applied to a data set with no data ferences in BSDs in sections 3.3 through 3.6 are independent filling. We could not conduct this calculation for the ROC of the amount of missing data in each time series and the method, because performing a Fourier reconstruction on the method of filling missing data. chlorophyll data requires a gap-free data set. We also calcu- [30] We address our second aim by examining the differ- lated BSDs using the TH and CS methods for a data set that ences between TH and CS BSDs using filled and unfilled had been filled using only the spatial averaging described in data. Figure 14a shows the average percentage of missing

Time Series 6 10 )

−3 8

chlorophyll (mg m 0

Sept’98 ’99 ’00 ’01 ’02 ’03 ’04 ’05 ’06 ’07

Figure 12. Time series with BSDs calculated using all three methods shown for 53°W, 62°N. The values of the CS threshold have been normalized to the values of the chlorophyll time series. Dashed lines indicate September of each year. 2353 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION

unfilled TH minus ROC unfilled CS minus ROC rather than the late summer, and thus the bloom will occur 80° W 40° W0° 80° W 40° W0° later (farther from 1 September). This explains why the ° ° a.a. 60 N b. 60 N unfilled CS BSDs have an even higher propensity than the 50° N 50° N filled BSDs to identify secondary, fall blooms as primary 40° N 40° N blooms. North of 50°N, the large amount of missing data that has been given a value in the filled data set, rather than

unfilled TH minus CS TH minus unfilled CS treated as a missing, zero value in the unﬁlled data set, 80° W 40° W0° 80° W 40° W0° causes the ﬁlled cumulative sum curve to start increasing

c. 60° N d. 60° N during the period of missing data, and thus rise above the c. d. threshold earlier than in the unﬁlled case. 50° N 50° N

40° N 40° N 4. Discussion

[32] Our examination of the differences between the −20 −10 0 10 20 ROC, TH, and CS methods for calculating BSDs has revealed areas with interesting chlorophyll seasonal cycles Figure 13. Difference between BSDs calculated with the and raised several questions about the biological and phys- three methods, with either (a and c) the TH BSDs or (b and ical underpinnings of those cycles. We find that the TH d) the CS BSDs calculated using unfilled chlorophyll data. method, as we are implementing it, is likely to identify BSDs The scale of the colorbar axis is 8 day periods. as occurring during or immediately after the largest increases in chlorophyll concentrations, especially when the relative median position of the chlorophyll time series is large (loca- data per pixel for the study area, which, as expected, increases at higher latitudes. Comparing filled and unfilled TH-calculated BSDs (Figure 14b) shows that south of missing data 1 ° ° ° approximately 45°N, filled TH BSDs are slightly later than 80 W 40 W 0 unfilled BSDs, especially off the coast of Cape Cod, where ° the percentage of missing data per pixel is somewhat higher a. 60 N than the surrounding areas. North of 50°N, filled TH BSDs 50° N are generally earlier than unfilled TH BSDs. Time series 0.5 from these areas (not shown) reveal that south of 45°N, data 40° N filling tends to occur in winter, when chlorophyll levels are high. While the gaps at these latitudes are small enough that data filling does not noticeably affect the median thresh- 0 old, the gaps can occur as chlorophyll levels are increasing prior to a bloom. If the gaps are two weeks or longer, filling TH minus TH unfilled the gaps with the minimum chlorophyll value can delay the 80° W 40° W 0° filled BSDs until after the gap, even if the chlorophyll time 20 series has increased above the threshold prior to the gap. 60° N North of 50°N, where the amount of missing data per pixel b. is higher, filled TH BSDs occur earlier than unfilled BSDs 50° N because filling large data gaps with the time series minimum 10 lowers the median threshold for filled data as compared to 40° N unfilled data. The tendency of filled data to produce earlier blooms than unfilled data at subpolar latitudes is consistent with Cole et al. [2012], while the tendency of filled data to produce later blooms than unfilled data at subtropical lati- 0 tudes differs from Cole et al. [2012]. In that study, the filled CS minus CS unfilled data set, which came from a data-assimilating biogeochemi- 80° W 40° W 0° cal model, did not replace gaps in the chlorophyll data with 60° N the yearly minimum chlorophyll value. c. −10

[31] Filled versus unfilled CS-calculated BSDs ° (Figure 14c) show the same general patterns as TH BSDs. 50 N In this case, filled CS BSDs occur later than unfilled BSDs 40° N south of 50°N because filling the data set with the time series minimum can change the position of the first occur- −20 rence of the chlorophyll minimum after 1 September, which has been set as day 1 of the year for all methods. Since the Figure 14. (a) Percentage of 8 day periods with missing chlorophyll time series minimum generally occurs in the data per year, averaged for the 10 year time series. (b and late summer or early fall in these regions and data gaps c) Comparison of TH BSDs minus TH BSDs calculated occur in the winter, using filled data will cause the time using the unfilled data set (b) and CS BSDs minus CS BSDs series used to calculate the CS BSD to start in the winter calculated using the unfilled data set (c). 2354 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION tions 1 and 2, Figure 6) or the start of the bloom consists the relevant feature of the chlorophyll times series. The CS of successive, low-magnitude chlorophyll increases (loca- method is also very sensitive to the date used for the start of tions 4 and 5, Figure 11). Based on these findings, the TH the time series and thus cannot be implemented at the basin method might be especially appropriate for investigating scale using a fixed start date. Our method of shifting each the match or mismatch between phytoplankton and upper time series to start at its minimum chlorophyll level, based trophic levels [e.g., Edwards and Richardson, 2004; Mackas on the assumption that the yearly chlorophyll minimum et al., 2007; Koeller et al., 2009], because the match- precedes the primary bloom, accounts for that sensitivity. mismatch hypothesis is based on the timing of the high However, location 3 (Figure 9) shows an area where the phytoplankton biomass period [Cushing, 1959]. The way in yearly chlorophyll minimum precedes the secondary bloom, which we chose to implement the TH method contributed causing the CS method to misidentify secondary fall blooms somewhat to these conclusions, especially at locations 4 and as primary blooms and thus falsely extend the subtropical 5 and in areas where data gaps filled with the time series region of the North Atlantic. Shifting the time series in this minimum occurred near the beginning of the bloom, by manner, combined with our method of data filling, also cre- potentially increasing the likelihood that the identified BSD ated differences in filled and unfilled CS BSDs. However, would occur during a period of high chlorophyll growth. when examining phytoplankton blooms using basin-scale, It would be possible to modify the method we used by remotely sensed chlorophyll data, the need to efficiently increasing the number of 8 day periods below the thresh- process large data sets necessitates the use of simplifying old required to find a bloom, although this heightens the assumptions such as the one we made. Future use of the CS risk of identifying isolated chlorophyll spikes as the main method in remotely sensed phytoplankton phenology stud- bloom. Starting the search for points above the threshold ies should be accompanied by a careful treatment of the time from the beginning of the time series is a more commonly series start date, and it may be found that the CS method used TH method and might also provide somewhat differ- works better as a way of identifying the first bloom in a ent results with chlorophyll time series similar to those at time series regardless of whether that bloom is a primary or locations 4 and 5. Implementing the threshold method in this secondary bloom. manner increases its sensitivity to the time series start date [35] The discussion of positive heat fluxes as a possible and, depending on the start date chosen, could also be used driver of subpolar phytoplankton blooms in section 3.2 illus- to identify the timing of secondary blooms. Finally, the dis- trates the importance of carefully considering which method crepancies in our comparison of TH BSDs from filled and to use for an investigation into phytoplankton bloom phe- unfilled data with the findings of Cole et al. [2012] indi- nology, since comparing the heat flux zero-crossing to the cate that temporal interpolation of missing data may be a ROC-, TH-, and CS-calculated BSDs (Figure 5) produced more appropriate method of filling data gaps in subtropical different results. Comparing subpolar ROC or CS BSDs to areas, where data gaps often occur as chlorophyll levels are the zero-crossing (Figures 5b and 5c) leads to the conclu- increasing prior to the bloom. sion that blooms begin very near the time that heat fluxes [33] The ROC method identifies blooms as starting when become positive, consistent with the theory and results of chlorophyll is increasing rapidly from the pre-bloom min- Taylor and Ferrari [2011]. Additionally, because in some imum, but biomass levels may still remain low. The ROC areas BSDs can occur before heat fluxes become positive, method, therefore, could be useful in examining the sea- ROC and CS BSDs support the findings of [Mahadevan sonal physical or biological mechanisms that create condi- et al., 2012]. In contrast, comparing subpolar TH BSDs to tions in which a bloom can occur [e.g., Behrenfeld, 2010]. the zero-crossing (Figure 5d) leads to the conclusion that, The Fourier reconstruction method we used to smooth the because BSDs occur well after the zero-crossing, positive chlorophyll data to implement the ROC method negatively heat fluxes alone are not sufficient to initiate a bloom. These impacted the ROC BSDs at location 6 (Figure 12) and may two conclusions can be reconciled by the patterns seen in have had similar impacts at other high-latitude locations. time series 4 and 5 (Figure 11): during years with sev- Alternative methods of reconstructing the chlorophyll data, eral low-magnitude chlorophyll increases prior to the main such as the Gamma Generalized Linear Model method in chlorophyll increase, the TH method identifies BSDs as Vargas et al. [2009] and Sapiano et al. [2012] or the method occurring after the low-magnitude increases, while the ROC of filtering coefficients using a Lanczos filter [Duchon, and CS methods identify BSDs as occurring at the beginning 1979] could be tested in the future to determine whether of the low-magnitude chlorophyll increases. It is possible they produce different ROC BSDs, and which smoothing that the low-magnitude chlorophyll increases identified by technique best reconstructs the chlorophyll time series. the ROC and TH methods could be caused by a decrease in [34] While the CS method, like the TH method, depends turbulent convection driven by heat fluxes becoming posi- on a biomass-based threshold, the sensitivity of the CS BSDs tive, while the high-magnitude chlorophyll increase identi- to the particular threshold creates flexibility in the feature fied by the TH method could be caused by the seasonally of the chlorophyll time series that the CS method identifies shoaling mixed layer, consistent with the Sverdrup hypothe- as a bloom. The 15% of the cumulative biomass thresh- sis. However, while the results of our investigation support old we used produced BSDs more closely aligned with the the theory advanced by Taylor and Ferrari [2011], these first increases in chlorophyll biomass, while a 30% threshold results do not provide definitive evidence for the onset of would produce BSDs associated with the largest increases positive ocean-atmosphere heat fluxes as opposed to sea- in biomass. Thus, the CS method could be used for either sonal mixed layer shoaling as a driver of subpolar bloom of the purposes we identified for the TH and ROC methods. initiation. First, the onset of positive ocean-atmosphere heat However, this feature of the CS method also necessitates fluxes may coincide with seasonal mixed layer shoaling. If a careful choice of the threshold to ensure that it identifies this is the case, it would not be clear whether one factor was

2355 BRODY ET AL.: METHODS TO DETERMINE BLOOM INITIATION primarily responsible for phytoplankton bloom initiation. research and future directions, J. Plankton Res., 32(10), 1355–1368, doi: Second, net warming of the ocean surface is not the only 10.1093/plankt/fbq062. Kahru, M., V. Brotas, M. Manzano-Sarabiaz, and B. Mitchell (2011), Are factor that could stratify the upper mixed layer of the ocean. phytoplankton blooms occurring earlier in the Arctic? Global Change For example, the mechanism for bloom initiation described Biol., 17, doi:10.1111/j.1365-2486.2010.02312.X. in Mahadevan et al. [2012] relies on eddy-induced stratifi- Koeller, P., et al. (2009), Basin-scale coherence in phenology of shrimps cation of the mixed layer. Factors such as wind-generated and phytoplankton in the North Atlantic ocean, Science, 324 (5928), 791–793, doi:10.1126/science.1170987. mixing and freshwater fluxes from seasonal sea ice melt, Mackas, D., S. Batten, and M. 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