Introduction
Surface ocean phytoplankton contribute 50 % of global net primary
productivity (Field et al., 1998), form the base of the oceanic food web and
contribute to ocean sequestration of carbon dioxide (Sarmiento and Gruber,
2006). The North Atlantic north of 40∘ N experiences a strong annual
cycle of productivity that is controlled by the interplay of physical and
biogeochemical processes.
In general terms, marine phytoplankton growth is limited by nutrients in the
subtropics and by light at subpolar latitudes (Fay and McKinley, 2017). In
the subtropics, an enhanced bloom occurs with relief of nutrient stress when
vertical mixing is enhanced. In contrast, subpolar regions should have a
reduced bloom with enhanced mixing because mixing enhances light limitation.
Sverdrup (1953) used observations from a weather ship in the Norwegian Sea
to propose the notion of a “critical depth” for subpolar regions. When the
mixed layer reaches below the critical depth, physical mixing cycles
phytoplankton through dark regions at depth, which increases light limitation
and decreases production. Dutkiewicz et al. (2001) and Follows and Dutkiewicz (2002)
directly characterize productivity drivers with the ratio of the
spring critical depth to the winter mixed layer depth (MLD) in a theoretical model
and compare to observations. Their relationships most accurately represent
satellite and in situ observations in the North Atlantic subtropics and are
less predictive in the subpolar gyre. Also identified is an “intergyre” region
where observed relationships do not fit this conceptual model, presumably
because both nutrient and light limitation are of first-order
importance.
In recent decades, ocean color satellites have allowed for synoptic
assessments of surface ocean productivity and its variability (Yoder and
Kennelly, 2003; McClain et al., 2004; Siegel et al., 2005). The first few
years of data from the satellite Sea-viewing Wide Field-of-view Sensor
(SeaWiFS) indicated that the seasonal cycle of productivity is largely
consistent with the “critical depth” hypothesis (Siegel et al., 2002). More
recently, there has been an active debate about whether ecological processes
may be more important to the subpolar spring bloom than the relief of light
limitation due to mixed layer shoaling, as proposed by Sverdrup. Behrenfeld (2010, 2014) uses satellite records to argue that phytoplankton accumulation
is most significant in winter due to mixing that dilutes their interaction
with grazers and other drivers of loss, and further, that the spring bloom
does not represent a significant change in biomass accumulation rates. These
findings are supported by analysis of an ocean model (Behrenfeld et al., 2013). In situ observations using autonomous platforms, however, continue to
support the conclusion that the springtime shoaling of mixed layers that
relieves light limitation is coincident with a substantial increase in the
rate of phytoplankton biomass accumulation (Mahadevan et al., 2012; Mignot et al., 2018). The physical mechanisms most important for springtime shoaling
remain in discussion (Taylor and Ferrari, 2011; Mahadevan et al., 2012).
Longer records of ocean color reveal large-scale interannual changes in
ocean productivity. Explanations for multi-year changes, and by extension
expected future trends with climate warming (Bopp et al., 2013), tend to be
based on the conceptual model of vertical processes controlling either
nutrient or light limitation. Multiple analyses suggest that increased
large-scale middle- and low-latitude stratification due to ocean warming
limits the vertical supply of nutrients to the surface ocean and thus causes
reductions in productivity (Behrenfeld et al., 2006; Polovina et al., 2008;
Martinez et al., 2009). However, in the North Atlantic and the North Pacific
subtropics, it has been found that local interannual variability in
stratification is uncorrelated with local productivity, findings that do not
support a one-dimensional mixing-productivity framework (Lozier et al., 2011;
Dave and Lozier, 2010, 2013). Instead, it has been shown that large-scale
correlations between chlorophyll and sea surface temperature (SST, a proxy
for stratification) at low and middle latitudes can be explained by advective
processes in the equatorial Pacific (Dave and Lozier, 2015). At subpolar and
polar latitudes, Behrenfeld et al. (2016) find that satellite-based
estimates of imbalances between phytoplankton growth and loss can drive
biomass interannual variability. Yet the fundamental importance of
Behrenfeld's proposed ecological mechanism remains in debate both for
seasonal and interannual timescales (Hunter-Cevera et al., 2016; Mignot et al., 2018).
In sum, there is growing evidence that the modification of light and
nutrient limitation by vertical processes is alone insufficient to explain
observed variability in surface ocean productivity. At the same time, there
is growing evidence that horizontal physical processes could play a role,
particularly in the northern subtropical gyre or intergyre region of the
North Atlantic (Williams and Follows, 1998; Dutkiewicz et al., 2001; Follows
and Dutkiewicz, 2002; Oschlies, 2002; McGillicuddy Jr. et al., 2003; Dave et al., 2015).
Williams and Follows (1998) illustrate that with regard to the mean, horizontal Ekman
fluxes are critical to surface nutrient supply in the North Atlantic from
40 to 60∘ N. However, Williams et al. (2000) find variability of horizontal
fluxes to be an order of magnitude smaller than convective flux variability
and thus conclude that vertical processes dominate anomalies. Considering
deeper processes, Williams et al. (2006) compare the magnitude of Ekman
upwelling to the three-dimensional movement of volume or nutrients from the
permanent thermocline to the full mixed layer, or “induction”.
Climatologically, nutrient supply to the subpolar gyre by induction is many
times larger than the supply by Ekman upwelling. Induction is how the
“nutrient stream” (Pelegrí et al., 1996; Palter et al., 2005;
Williams et al., 2006) is accessed to allow for large-scale supply of
nutrients from outside to inside the subpolar gyre. To our knowledge,
interannual variability in induction has not been discussed in the
literature. Further consideration of both horizontal and vertical processes
is warranted with respect to the understanding of temporal variability in
surface ocean productivity in the North Atlantic.
Changing ocean circulation should influence horizontal and vertical
transports of nutrients in the northern North Atlantic. A slowdown of the
gyre should relax isopycnal slopes and decrease geostrophic advection along
isopycnals. The North Atlantic subpolar gyre has exhibited substantial
change since the 1950s when regular observations began to be available
(Lozier et al., 2008). There is evidence these changes occur in response to
changing buoyancy forcing and wind stress, in turn associated with modes of
climate variability, specifically the North Atlantic Oscillation and East
Atlantic (EA; Häkkinen and Rhines, 2004; Hátún et al., 2005;
Lozier et al., 2008; Foukal and Lozier, 2017) pattern. Via Ekman processes,
reduction in wind stress should directly reduce upwelling in the subpolar
gyre and also the horizontal transport of nutrients (Williams et al., 2000;
Dave et al., 2015). Buoyancy and turbulent fluxes also impact mixed layer
depths and influence bloom timing and strength (Bennington et al., 2009).
Consistent with this expectation, links between physical changes in the
subpolar gyre and in situ observed changes in nutrients and ecosystems at
several subpolar time series sites have been suggested (Johnson et al., 2013;
Hátún et al., 2016, 2017).
Surface ocean biomass: (a) estimated from SeaWiFS using the CbPM
model, (b) 0–100 m modeled biomass, (c) SeaWiFS trend 1998–2007, and
(d) 0–100 m modeled biomass trend 1998–2007. In (c) and (d), significant trends are
marked with a black contour. In (d), the three focus regions are outlined in
red.
In this study, we use a regional model to illustrate how changing light
limitation and changing vertical and horizontal nutrient supply led to the
significant changes in surface ocean biomass that were observed by SeaWiFS
over 1998–2007 in the North Atlantic north of 40∘ N (Fig. 1c). This
is a mechanistic analysis of the drivers of SeaWiFS-observed changes in
biomass that are best quantified as linear trends given the 10-year prime
observational period. The degree to which these drivers are responsible for
internal variability across the full model experiment (1948–2009) is also
explored. Our approach can be contrasted with other possible approaches such
as the use of empirical orthogonal functions (EOFs) to consider dominant
modes of variability (Ullman et al., 2009; Breeden and McKinley, 2016). The
negative of EOF analysis is that it tends to explain at most 30 % of
the large-scale variance and thus does not fully explain observations. This
paper is a case study in which we aim to explain the drivers of the observed
changes as fully as possible using a model that represents well the observed
changes.
Methods
Satellite data
Our analysis focuses on the period 1998–2007. Monthly SeaWiFS data become
inconsistent beginning in 2008. For study of interannual trends, avoiding
the need to fill gaps in the record is desirable. For additional comparison
and extension of the record, biomass estimated from MODIS for 2003–2015 is
also presented, again selecting years for which all months are available.
For both SeaWiFS and MODIS, biomass is estimated using the updated CbPM
algorithm (Westberry et al., 2008). Additionally, we compare trends of
modeled net primary productivity (NPP) to NPP from SeaWiFS estimated with
both CbPM and the VGPM algorithms (Behrenfeld and Falkowski, 1997). All data
were provided by the Ocean Productivity Group at Oregon State University
(http://www.science.oregonstate.edu/ocean.productivity/index.php, SeaWiFS
biomass downloaded 28 November 2016; MODIS biomass downloaded 24 January 2018; NPP
downloaded 29 May 2018).
Regional hindcast model
The Massachusetts Institute of Technology General Circulation Model
configured for the North Atlantic (MITgcm.NA) (Marshall et al., 1997a, b) is used. The model
domain extends from 20∘ S to 81.5∘ N, with a horizontal resolution of
0.5∘×0.5∘ and a vertical resolution of 23 levels that
have a thickness of 10 m at the surface and gradually become coarser to 500 m
thickness intervals for depth levels deeper than 2200 m. NCEP/NCAR
Reanalysis I daily wind, heat, freshwater and radiation fields from
1948 to 2009 force the model (Kalnay et al., 1996). To correct for
uncertainties in air–sea fluxes, SST and SSS (sea surface salinity) are
relaxed to monthly historical SST (Had1SSTv1.0, Rayner et al., 2003) and
climatological SSS (Antonov et al., 2006) observations, on the timescale of 2
and 4 weeks, respectively (Ullman et al., 2009). To characterize
subgrid-scale processes, the Gent–McWilliams (Gent and McWilliams, 1990)
eddy parameterization, the K-profile parameterization boundary layer mixing schemes (Large et al.,
1994), and Fox-Kemper et al. (2008) submesoscale physical parameterization
are used. The phosphorus-based ecosystem is parameterized following
Dutkiewicz et al. (2005), and with modest revisions by Bennington et al. (2009). This ecosystem has one zooplankton class and two phytoplankton
classes (“large” diatoms and “small” phytoplankton). The biogeochemical
model explicitly cycles phosphorus, silica and iron, and complete carbon
chemistry is also included. This model is identical to the one presented in
Breeden and McKinley (2016) and uses the same biogeochemical code as
Bennington et al. (2009), Ullman et al. (2009) and Koch et al. (2009).
The coupled model has previously been shown to capture the timing and
magnitude of the subpolar spring bloom chlorophyll and its variability, as
observed by SeaWiFS (Bennington et al., 2009). Mixed layer depths, carbon
system variables and nutrients are well simulated at Bermuda and in the
northwest subpolar gyre (Ullman et al., 2009; Koch et al., 2009). As is common
in this type of moderate-resolution model, productivity in the subtropics is
too low (Bennington et al., 2009). Physical variability since 1948 is
consistent with observations (Breeden and McKinley, 2016).
As in Breeden and McKinley (2016), the physical model was spun up for a 100-year period, with 1948–1987 repeated twice and then followed again by
1948–1967, for a total physical spin-up of 120 years. The biogeochemical
model was then initialized using World Ocean Atlas phosphate concentrations
and spun up for 10 years using 1948–1957 daily forcing. To avoid
initialization shock, the model was then forced for 5 years with repeating
1948 fields before the 1948–2009 experiment started. Due to Had1SSTv1.0
fields only being available through 2009, this model integration ends in
2009. Future studies using Had1SSTv1.1, which extends beyond 2009, will
require re-initialization and new spin-up integrations.
Phosphate diagnostics
To assess the processes modifying phosphate concentration, we employ
phosphate diagnostics that quantify flux convergences (in mmol m-3 yr-1) for net biological processes, vertical advection and diffusion,
and horizontal advection and diffusion. These terms describe the tendency of
each process at every time step during the model simulation, averaged to
monthly for output (Ullman et al., 2009; Breeden and McKinley, 2016). For
conciseness, the biological uptake term presented here is the sum of
separate diagnostic terms for phosphate utilization by primary producers and
remineralization that returns phosphate to the water column.
For analysis of mean and linear trends for 1998–2007, we use biological,
vertical and horizontal diagnostic terms. Unfortunately, the biological
diagnostics prior to 1998 were lost after simulations were completed. Thus,
for correlations for 1949–2009, we use biomass in place of the biological
diagnostics. This choice is supported by strong correlations (R=-0.87 to
-0.98) between biomass and the biological diagnostics in our three focus
regions (defined below) for 1998–2007. Biomass and biological diagnostics
have an opposite sign because phosphate is removed as biomass accumulates.
Light and nutrient limitation
As detailed in Dutkiewicz et al. (2005), model phytoplankton growth is
limited by light and the most limiting nutrient. Limiting nutrients are
phosphate (PO4) and iron (Fe) for small phytoplankton and PO4, Fe
and silicate (SiOH4) for large phytoplankton. There is no nitrogen
cycle in the model, consistent with other ecological models of comparable
complexity (Galbraith et al., 2010). The parameterization uses
Michaelis–Menton ratios that tend to 0 as the resource becomes severely
limiting to growth, and approach 1 when replete. A lower value indicates a
greater stress, and thus the phytoplankton group with the larger half-
saturation constant will be more limited for the same ambient nutrient or
light concentration.
Specifically, maximum growth rates (μmax,small= 1/1.3 d-1, μmax,large= 1/1.1 d-1) are reduced through
multiplication by limitation terms. Tfunc modifies maximum growth based
on temperature following Eppley (1972).
μ=μmax⋅Tfunc⋅γlight⋅minγPO4,γFe,γSiOH4(largeonly)
With half-saturation constants Io,small= 15 W m-2,
Io,large= 12 W m-2, light limitation is
γlight=II+Io
and for nutrients
γX=XX+Ko,X.
For phosphate, X= PO4, Ko,PO4,small= 0.05 mmol m-3
and Ko,PO4,large= 0.1 mmol m-3. For iron, X= Fe, Ko,Fe,small= 0.01 µmol m-3 and
Ko,Fe,large= 0.05 µmol m-3. For large phytoplankton only, silicate limitation also
applies, with Ko,SiOH4,large= 2 mmol m-3. Because of their
higher half-saturation constant for phosphate, modeled large phytoplankton
are more phosphate stressed than small phytoplankton. In contrast, the
higher light half-saturation makes small phytoplankton experience greater
light stress. Due to high levels of aeolian dust deposition in the North
Atlantic, parameterized here with the imposition of climatological fields
from Mahowald et al. (2003), iron is never limiting in our study area and is
not further discussed.
For this analysis, monthly mean light and nutrient fields are used to
calculate limitation terms for light and nutrients for each phytoplankton
type.
Analysis
Throughout the study, annual averages over the top 100 m are used. This
depth is selected because it is a reasonable approximation for both the
euphotic zone and the Ekman layer, and is a computationally efficient choice
consistent with previous work (Williams et al., 2000,
2014; Long et al., 2013). For analysis of light limitation, however, it is important to
consider that deep mixing will move mixed layer phytoplankton to
substantially below 100 m (Sverdrup, 1953). This effect would be poorly
captured if light limitation terms were averaged only over the surface 100 m. The more appropriate choice, used here, is to use either the depth of the
monthly mixed layer or 100 m, whichever is deeper. Light limitation is
calculated monthly in this way and then annually averaged. For consistency,
we apply the same averaging approach for nutrient limitation. However, since
nutrients are homogenized by deep mixing, results for nutrient limitation
are not substantially different from this calculation using a strict 100 m
average. Limitation terms are not biomass-weighted.
For physical comparisons, mixed layer depth is calculated using
monthly density fields and a criteria of 0.03 kg m-3 increase above the
surface density. The barotropic streamfunction is calculated using a north-to-south integration of the full depth zonal velocity fields (Breeden and
McKinley, 2016). To find the minimum barotropic streamfunction of the
subpolar gyre, the minimum within a region 50–65∘ N, 60–30∘ W is
used. A preliminary comparison of nutrient flux variability to climate
indices uses the winter (DJFM) East Atlantic pattern
(http://www.cpc.ncep.noaa.gov/data/teledoc/ea.shtml, downloaded 15 December 2017)
and the winter North Atlantic Oscillation (Hurrell and NCAR, 2017).
This analysis is based on annual mean fields for both the observations and
the model. A 3-month lag of the biology diagnostics and biomass fields after
physical diagnostics and other physical fields is employed to account for
the maximum physical forcing occurring in the winter prior to the spring
bloom. Thus, annual mean physical fields are averaged from October of the
prior year to September of the year in question. The use of 0, 1, 2 or 4
month lags leads to lower correlations, but does not substantially modify
results. Biological fields are January to December averages.
To compare directly to the 10-year period of prime SeaWiFS observations, our
primary focus is on linear trends over 1998–2007, with significance bounds
set at p<0.05 (95 %). To complement this analysis with a
consideration of interannual variability across the full model experiment
(1948–2009), we also consider correlations of physical and biogeochemical
time series calculated as area-weighted averages over three selected regions
(defined below), and then linearly detrended prior to correlation analysis.
Because of the aforementioned biological lag, the time frame for correlations
becomes 1949–2009.
Results
Model comparison to observations
The simulation captures the magnitude of mean 1998–2007 subpolar biomass
reasonably in comparison to the satellite-based observations (Fig. 1a, b).
The detailed spatial pattern of biomass is impacted by the North Atlantic
Current extension being too diffuse and too directly east–west (i.e., not
turning to the northeast as it should at about 25∘ W), as is common in
models of this resolution (Williams et al., 2014). The maximum of biomass is
displaced to the east. Also, subtropical biomass is too high in the Gulf
Stream extension, but otherwise too low in the remainder of the basin, and
thus the gradient from south to north in the model from 35 to 50∘ N is too
sharp (Bennington et al., 2009).
Despite these imperfections, the model captures well the pattern and
magnitude of statistically significant biomass trends north of 40∘ N
over 1998–2007 (Fig. 1c, d). In both observations and the model, biomass
declines to the east of 30∘ W from 40 to 50∘ N and 35∘ W from
50 to 60∘ N, while it increases to the west. For simplicity, we refer to
this boundary as 30–35∘ W in our discussion. Model trends are slightly
weaker than the observed trends, but the coherent regions of statistically
significant change are of similar size. Declines to the east occur in two
regions in both model and observations, one in the northeast and one in the
southeast. Consistent with the mean biomass structure, simulated biomass
trends are not in exactly the same locations as observed, but are displaced
about 5∘ to the south in the southeast and northwest, and 5∘ to
the south and 5∘ west for the northeast region. Comparison to net
primary productivity (NPP) from SeaWiFS estimated with both the CbPM
algorithm and older VGPM algorithm indicate comparable changes to in
biomass, though trends in the northeast are not significant for NPP (Fig. S1 in the Supplement).
In both observations and models, the magnitudes of these changes are large
in comparison to the mean. In the declining regions where mean biomass is
15–25 mgC m-3 (Figs. 1, 2, S2), trends of -0.5 to -1.5 mgC m-3 yr-1 over 10 years lead to changes of 30 %–50 %. In the increasing
region to the west, changes are of similar magnitude. To focus our analysis,
we select three regions in the model that capture these significant biomass
changes (Fig. 1d). We will use these regions for discussion and for
averaging of biogeochemical and physical terms. In the northeastern
subtropical gyre, or intergyre (Follows and Dutkiewicz, 2002), lies our
southeast (SE) region, just south of the physical separation between the
subpolar and subtropical gyre based on the barotropic streamfunction
(Sect. 3.4). The SE region is bounded between 30–15∘ W and 40–50∘ N. The northeast (NE) region lies in the eastern subpolar gyre to the
southeast of Iceland, 35–20∘ W and 55–60∘ N. The northwest (NW)
region is south of Greenland at 35–20∘ W and 50–60∘ N. Regional
mean changes in biomass from SeaWiFS (in the model) are -19 % (-17 %) in
the SE region and -15 % (-10 %) in the NE. To the west of 30–35∘ W
in the NW region, regional mean changes are +6 % (+9 %).
Annual anomalies of surface ocean biomass for SeaWiFS (1998–2007,
red), MODIS (2003–2015, blue) and our model (1998–2009, 0–100 m, black): (a) SE
region, (b) NE region and (c) NW region. The corresponding monthly
time series is shown in Fig. S2.
In these three regions, annual anomalies of simulated biomass are compared
to estimates from the SeaWiFS (1998–2007) and MODIS (2003–2015) satellites
(Fig. 2). Monthly biomass time series are presented in Fig. S2. In all
regions, simulated biomass anomalies are quantitatively different from the
observations to a similar degree that the observations differ from each
other. In the SE region, the shift from positive biomass anomalies before
2004 to negative anomalies after 2004 is found in SeaWiFS and the model, and
MODIS indicates a return to positive anomalies after 2010 (Fig. 2a). Of the
three regions, this is the one where annual changes in the model are
significantly correlated to those in SeaWiFS (R=0.79, p<0.05),
despite the small sample size (n=10). Linear trends for 1998–2017 in
SeaWiFS and the model are the same, -0.41 mgC m-3 yr-1; however,
the observations are better explained by this trend (R2= 0.76 for
SeaWiFS, R2= 0.35 for model). In the NE region, higher frequency
variability is suggested, with mostly positive anomalies over 1998–2003 and
negative anomalies from 2005 to 2009 (Fig. 2b). In this region, the linear
trend for 1998–2007 in SeaWiFS is -0.26 mgC m-3 yr-1 (R2=0.15) and -0.34 mgC m-3 yr-1 (R2=0.50) in the
model. The spatial displacement between the modeled and observed anomalies
(Fig. 1c, d) is not accounted for with the regions used for Fig. 2b, but
these comparisons do not substantially change if the averaging region for
the observations in the NE is shifted 5∘ north and 5∘ east
(not shown). In the NW region, positive anomalies of comparable magnitude
dominate in 2003–2008, the time frame over which the three records coincide
(Fig. 2c). Negative anomalies are largely found both before and after. In
the last 3 MODIS years, positive anomalies return to the NW region. For
1998–2007, the linear trend in SeaWiFS is +0.22 mgC m-3 yr-1
(R2=0.22) and for the model +0.23 mgC m-3 yr-1
(R2=0.55). Having demonstrated that this model reasonably captures
the patterns and magnitudes of biomass change, we now use the model to
explain the mechanistic drivers in all three regions over the SeaWiFS
period, 1998–2007.
Phosphate: (a) World Ocean Atlas (Garcia et al., 2006), (b) 0–100 m
modeled phosphate, (c) 0–100 m modeled phosphate trend 1998–2007. In (c),
significant trends are marked with a black contour and the three focus
regions are outlined in red.
Nutrient changes
Modeled anomalies are not due to zooplankton top-down pressure on biomass,
as evidenced by zooplankton trends that are positively correlated with
biomass trends (Fig. S3). Thus nutrient and light, the bottom-up drivers in
this model that change in a manner that drives biomass changes consistent
with observations (Fig. 1), are the focus of this analysis. The model
captures the large-scale pattern of the phosphate field well, but mean
values are 10 %–20 % too low across most of the subpolar gyre (Fig. 3a, b).
Changes in the nutrient field could drive these observed and modeled
changes, and as temporally resolved large-scale nutrient datasets are not
available, the model alone allows us to evaluate nutrient trends (Fig. 3c).
Modeled nutrient concentrations decline significantly over 1998–2007 across
most of the region north of 50∘ N. The pattern suggests these changes
are important to the declines of biomass in the SE and NE regions. However,
there is no increase in phosphate in the NW region where biomass was
observed to increase.
Surface ocean small and large phytoplankton biomass: (a) 0–100 m
modeled large phytoplankton biomass, (b) 0–100 m modeled small phytoplankton
biomass, (c) large phytoplankton trend 1998–2007 and
(d) small phytoplankton trend 1998–2007. In (c) and (d), significant trends are marked
with a black contour and the three focus regions are outlined in red.
Terms for limitation: (a) phosphate, large phytoplankton
(Eq. 3);
(b) light, small phytoplankton (Eq. 2); (c) 1998–2007 trend in phosphate
limitation; and (d) 1998–2007 trend in light limitation. All are unitless.
In (c) and (d), significant trends are marked with a black contour and the three
focus regions are outlined in red.
Trends of light and nutrient limitation
To better understand drivers of simulated biomass trends, a next step is to
decompose the biomass trends into those occurring in the small and the large
phytoplankton (Fig. 4). With regard to the mean, in the open waters of the North
Atlantic, simulated large phytoplankton have a greater contribution to the
total biomass in the north and west (Fig. 4a), while small phytoplankton are
dominant to biomass throughout the basin and particularly in the south and
east (Fig. 4b). Trends in simulated small phytoplankton contribute most to
total biomass change (Fig. 1d) in the SE and NW regions, while large
phytoplankton trends are more important in the NE region (Fig. 4c, d).
Phosphate limitation for large phytoplankton has a strong gradient of being more
limiting in the south and east to less limiting in the northwest (Fig. 5a),
while light limitation for small phytoplankton has largely a south-to-north
gradient from less to more limiting (Fig. 5b). Trends over 1998–2007 in the
model limitation terms illustrate that the SE and NE declines in simulated
biomass are spatially coherent with enhanced phosphate limitation (Fig. 5c),
while the NW increase in biomass is spatially coherent with regions
experiencing relief of light limitation (Fig. 5d). As shown in Fig. S2, the mean
and trends for light limitation for large phytoplankton and phosphate
limitation for small phytoplankton have nearly identical patterns.
Correlations of phosphate and light limitation for small
phytoplankton to large and small biomass, 1949–2009. For conciseness, shown
here are only small phytoplankton limitation terms, but since these are
ratios calculated from identical fields, the correlations are very similar
large phytoplankton (Table S1). Bold indicates statistical significance (p<0.05). Detrending is applied prior to correlation analysis.
Phosphate
Light
limitation, small
limitation, small
Southeast
Large biomass
phytoplankton
phytoplankton
Small biomass
0.52
0.68
-0.75
Large biomass
–
0.74
-0.57
Phosphate limitation, small
–
-0.87
Phosphate
Light
limitation, small
limitation, small
Northeast
Large biomass
phytoplankton
phytoplankton
Small biomass
0.36
-0.09
-0.08
Large biomass
–
0.31
-0.17
Phosphate limitation, small
–
-0.87
Phosphate
Light
limitation, small
limitation, small
Northwest
Large biomass
phytoplankton
phytoplankton
Small biomass
-0.34
-0.50
0.61
Large biomass
–
0.83
-0.63
Phosphate limitation, small
–
-0.71
This distinction between the dominant limitations driving simulated biomass
change in the east and west is borne out with detrended interannual
correlations over the full model period, 1949–2009 (Table 1). In the SE
region, phosphate limitation is strongly correlated with both small
(RSE(small,PO4)=0.68) and large phytoplankton (RSE(large,PO4)=0.74), while light limitation is anti-correlated with
biomass, i.e., less biomass occurs with more light, clearly illustrating that
light is not the driving limitation. With respect to limitation terms in the
NE region, the only significant correlation for large phytoplankton is to
nutrient limitation (RNE(large,PO4)=0.31). Thus, the large
phytoplankton that quantitatively dominate the 1998–2007 biomass decline
(Fig. 4d) due to nutrient limitation (Fig. 5c) also vary by a similar
mechanism over the full model experiment.
Barotropic streamfunction: (a) 1998–2000 mean and (b) 2005–2007
mean. The zero streamfunction contours between 55 and 15∘ W for each period
(bold black) and for the 1998–2007 mean (white) are marked. See Fig. S5
for map of 1998–2007 trend.
In the NW region, the 1998–2007 biomass trend is dominated by small
phytoplankton (Fig. 4d) via light limitation (Fig. 5d), and these
relationships also hold for the full model experiment. Small phytoplankton
light limitation is positively correlated with small phytoplankton biomass
(RNW(small,light)=0.61), while small phytoplankton biomass is
reduced when more phosphate is available (RNW(small,PO4)=-0.50,
Table 1). Though large phytoplankton have the opposite sensitivities
(RNW(large,light)=-0.63, RNW(large,PO4)=0.83), they
are a smaller portion (40 %) of the total biomass (Fig. 4a, b) and have a
lesser role in total biomass changes (Fig. 4c).
In the model, silicate is also limiting to large phytoplankton and its
limitation also becomes more intense over 1998–2007 to the north of 50∘ N (Fig. S4f). Yet, variability in silicate limitation is highly
correlated to variability of phosphate limitation in the NW and NE areas for
1949–2009 (RNE(PO4,SiOH4)=0.91, RNW(PO4,SiOH4)=0.83, Table S1 in the Supplement). Due to these high correlations and the fact that
large phytoplankton are only dominant to biomass trends in the NE region
(Fig. 4c), the remaining analysis addresses only phosphate fluxes. For
completeness, 1998–2007 light and silicate limitation trends for large
phytoplankton and phosphate limitation for small phytoplankton are shown in
Fig. S2, and 1949–2009 correlations in the three regions are given in Table S1.
Maximum mixed layer depths for the (a) 1998–2000 mean and (b) 2005–2007 mean. Time series
of monthly mixed layers for the (c) SE region, (d) NE
region and (e) NW region.
Physical changes and their impacts on light and nutrient limitation
Significant physical changes in the subpolar gyre influence the simulated
nutrient and light fields. The model barotropic streamfunction experiences a
positive change from a minimum value of -41 Sv for 1998–2000 to -28 Sv for
2005–2007 (Figs. 6, S5). With this anomaly, the zero line of the
streamfunction shifts several degrees north at 45–40∘ W and more
modestly to the north in the east (bold black contours in Fig. 6a and b). The North Atlantic Current (NAC) flows along this contour, indicating a
northward shift of the NAC.
Phosphate diagnostics: (a) vertical, (b) horizontal,
(c) net physical and (d) biological flux convergence (mmol m-3 yr-1) averaged over 0–100 m. Biological is negative because biomass removes phosphate from
the surface ocean. The three focus regions are outlined in red in each
panel.
Consistent with the weakening of the subpolar gyre, mixed layers shoal
substantially, particularly to the west of 30–35∘ W (Fig. 7a, b). A
dramatic shoaling of maximum mixed layers is found in the NW region, going
from almost 1200 m to less than 400 m (Fig. 7e, Våge et al., 2008). This
shoaling explains the strong decline in light limitation in the NW region.
There is modest shoaling of mixed layers in the NE region (Fig. 7d) and
there is no significant trend in the SE region (Fig. 7c). Shoaling in the NE
could contribute to the reduction in phosphate availability and reduced
biomass. However, the lack of mixed layer depth change in the SE suggests
that less vertical mixing is not the dominant driver of reduced biomass
here.
Phosphate diagnostics 1998–2007 annual time series: 0–100 m flux
convergence (mmol m-3 yr-1) for the (a) SE region, (b) NE region and
(c) NW region.
Phosphate diagnostic analysis
To fully assess the three-dimensional physical drivers of phosphate supply
to the NE and SE regions, we employ the phosphate diagnostics that quantify
flux convergences. With regard to the mean for 1998–2007 across the northern North
Atlantic, vertical advection and diffusion supply phosphate to the euphotic
zone (Fig. 8a), with the supply being much stronger in the region of the deepest
mixed layers (Fig. 7). Horizontal advection and diffusion strongly diverges
the converging vertical flux (Fig. 8b), leading to strong negative fluxes
(divergence) coincident with strongly positive vertical fluxes. The
horizontal flux divergence centered at about 30–35∘ W leads to positive
horizontal fluxes (convergence) to the east and west. As the sum of the
vertical and horizontal components is net positive (Fig. 8c), the mean
three-dimensional advection and diffusion supplies net phosphate to the
subpolar gyre. The pattern of this supply is strongly influenced by both
vertical and horizontal processes. Biological processes remove the
physically supplied phosphate from the surface ocean (Fig. 8d).
Phosphate diagnostics 1998–2007 trends: 0–100 m flux convergence
trend (mmol m-3 yr-2) for (a) vertical, (b) horizontal, (c) net
physical, and (d) biological flux convergences. Positive biological trends are consistent with
negative biomass trends because less phosphate is removed as less biomass is
formed. Significant trends are marked with a black contour and the three
focus regions are outlined in red in each panel.
To east of 30–35∘ W, horizontal and vertical phosphate flux
convergences are comparable in magnitude and both supply nutrients to the
surface (Fig. 8, 9). In the SE region, mean 1998–2007 vertical advection and
diffusion supplies 0.13 mmol m-3 yr-1 while horizontal advection and diffusion supplies
0.07 mmol m-3 yr-1, together supporting biological utilization
of -0.20 mmol m-3 yr-1 (Fig. 9a). In the NE region, the mean
vertical supply is 0.24 mmol m-3 yr-1 while horizontal fluxes
supply 0.08 mmol m-3 yr-1, and thus a mean biological utilization of
-0.32 mmol m-3 yr-1is supported (Fig. 9b). The net convergence
of vertical and horizontal fluxes in these regions can be contrasted with the
NW region where the mean vertical flux converges 0.33 mmol m-3 yr-1, and from this the horizontal flux diverges about 25 %
(-0.08 mmol m-3 yr-1) and biology diverges the remainder (-0.24 mmol m-3 yr-1, Fig. 9c). In all three regions, note that the
variability of both horizontal and vertical flux convergences are of
magnitudes comparable to the biological flux variability (Fig. 9).
For 1998–2007, simulated trends in the supply and removal of phosphate
indicate a large decrease in supply via vertical fluxes to the west of 30–35∘ W (Fig. 10a) and a corresponding strong reduction in the
horizontal divergence of phosphate, a positive anomaly (Figs. 9c, 10b). To the
west of 30–35∘ W, these opposing trends of the vertical and horizontal
flux convergence largely negate each other. However, to the east of
30–35∘ W there are weak and mostly negative tendencies in the vertical terms and
significant negative trends in the horizontal terms. Thus, the net physical
phosphate supply in the eastern subpolar gyre has an overall negative trend,
albeit only large enough to be formally statistically significant in parts
of the SE region (Fig. 10c). The pattern of reduced phosphate supply is
consistent with the pattern of significant reduction in biomass (Fig. 1d)
and significant positive tendencies in the biological diagnostic, indicating
reduced phosphate utilization by phytoplankton (Fig. 10d). In summary, the
model indicates that from 1997 to 2008, reduced vertical convergence of
nutrients to the west of 30–35∘ W led to less horizontal convergence of
nutrients to the east of 30–35∘ W, and thus less phosphate available
for biological production. In this model, this mechanism is sufficient to
explain biomass changes consistent with SeaWiFS-observed declines in biomass
in the eastern subpolar gyre (NE region) and northeastern subtropical gyre
(SE region).
Long-term (1949–2009) correlations between physical diagnostic terms and
biomass support the conclusion that variability in horizontal flux
convergence is important to modeled biomass interannual variability to the
east of 30–35∘ W (Table 2). In both the SE and NE regions, horizontal
flux convergence is significantly correlated to biomass
(RSE(Biomass,Horiz)=0.44, RNE(Biomass,Horiz)=0.48),
suggesting that the 1998–2007 relationships are indicative of interannual
behavior over the long-term, wherein reduced horizontal nutrient convergence
leads to reduced biomass. For the SE region in the long term, vertical
fluxes have also a significant correlation (RSE(Biomass,Vert)=0.63), indicating that longer term interannual change in biomass in this
region is determined by variability in both horizontal and vertical flux
convergences. In the NW region, biomass and horizontal flux convergence is
also positively correlated (RNW(Biomass,Horiz)=0.69), but this
appears to be an indirect relationship. As light limitation is relieved,
biomass increases, and at the same time vertical convergence of phosphate is
reduced (Fig. 10a) and there is a positive anomaly in the horizontal
divergence (Fig. 10b). Consistent with this interpretation, vertical and
horizontal convergence are strongly anti-correlated in the NW region
(RNW(Horiz,Vert)=-0.76).
The impact of the physical drivers discussed earlier in this section on
1949–2009 biomass variability varies by study region. For the SE region,
where biomass is positively driven by both horizontal and vertical nutrient
flux convergence, biomass declines with positive anomalies of the minimum
barotropic streamfunction of the subpolar gyre (RSE(Biomass,PsiMin)=-0.37, Table 2), consistent with 1997–2008 relationships. However, the
minimum barotropic streamfunction is not itself correlated to either
horizontal or vertical flux convergence. Vertical supply is not a
significant driver for 1997 to 2008 changes, but for the longer term,
vertical nutrient fluxes decrease with shallower mixed layers (a negative
anomaly) and warmer temperatures (RSE(MLD,Vert)=0.66,
RSE(SST,Vert)=-0.64).
For both the NE and NW regions, correlations between changing minimum
barotropic streamfunction (Fig. 6), shoaling mixed layers (Fig. 7), and
horizontal and vertical nutrient convergences (Fig. 10) for 1949–2009 are
generally weak and, in fact, opposite in sign to the relationships for the
SeaWiFS period. For 1998–2007, the minimum barotropic streamfunction
experienced a positive anomaly, mixed layers shoaled and horizontal fluxes
declined in the NE region. For 1949–2009, positive anomalies of the minimum
barotropic streamfunction are, instead, weakly associated with increased
horizontal nutrient fluxes (RNE(PsiMin,Horiz)=0.25). At the same
time, shallower mixed layers (a negative anomaly) are associated with
decreased vertical fluxes and increased horizontal fluxes
(RNE(MLD,Vert)=0.52, RNE(MLD,Horiz)=-0.42). In the NW
region, light limitation is clearly the driver of biomass changes on both
timescales (Figs. 1, 4, Table 1), but large negative anomalies of vertical
fluxes and positive anomalies of horizontal fluxes also occur as mixed
layers shoal over 1997–2008 (Figs. 9, 10). However, for 1949–2009 in the NW,
the reverse is found; vertical flux convergence increases and horizontal
nutrient flux convergence decreases coincident with shallower mixed layers
(RNW(MLD,Vert)=-0.33, RNW(MLD,Horiz)=0.46). Long-term
correlations of physical changes to nutrient fluxes in the two subpolar
regions differ from those occurring with 1998–2007 trends, consistent with
the weak long-term correlations that explain no more than 30 % of the
variance. The lack of consistent associations between biomass and physical
variability over both timescales illustrates the complexity of the system
and makes clear that relationships revealed by relatively short-lived
observing systems are not necessarily representative of the long term.
Correlations of biomass to physical drivers and horizontal and
vertical phosphate flux convergence, 1949–2009. The minimum barotropic
streamfunction is found within 50–65∘ N, 60–30∘ W; maximum mixed
layer depth (MLD) and sea surface temperature (SST) are area-weighted
averages for each of the three averaging regions. Bold indicates statistical
significance (p<0.05). Detrending is applied prior to correlation
analysis.
Minimum
barotropic
Southeast
streamfunction
Maximum MLD
SST
Horizontal
Vertical
Biomass
-0.37
0.54
-0.57
0.44
0.63
Minimum barotropic streamfunction
–
-0.42
0.38
-0.18
-0.23
Maximum MLD
–
-0.65
0.10
0.66
SST
–
-0.02
-0.64
Horizontal
–
-0.29
Minimum
barotropic
Northeast
streamfunction
Maximum MLD
SST
Horizontal
Vertical
Biomass
0.18
-0.02
0.08
0.48
-0.25
Minimum barotropic streamfunction
–
-0.60
0.72
0.25
-0.22
Maximum MLD
–
-0.76
-0.42
0.52
SST
–
0.34
-0.28
Horizontal
–
-0.64
Minimum
barotropic
Northwest
streamfunction
Maximum MLD
SST
Horizontal
Vertical
Biomass
0.14
0.28
-0.23
0.69
-0.42
Minimum barotropic streamfunction
–
-0.42
0.67
0.08
-0.10
Maximum MLD
–
-0.42
0.46
-0.33
SST
–
-0.52
0.43
Horizontal
–
-0.76
Discussion
The decline in the strength of the subpolar gyre modeled here (Fig. 6) is
consistent with observations in the North Atlantic since the mid-1990s
(Häkkinen and Rhines, 2004; Hátún et al., 2005; Våge et al.,
2008; Foukal and Lozier, 2017). We show here that these physical changes
have the potential to drive substantial impacts on the light field and on
vertical and horizontal nutrient supply. These changes are sufficient to
explain the modeled biomass trends over 1998–2007 that are, in turn,
consistent with satellite observations (Figs. 1, S1).
Foukal and Lozier (2017) provide an updated analysis with respect to the
relationship of physical changes in the gyre to the East Atlantic pattern and the North Atlantic Oscillation (NAO). While the EA pattern indicates the
position of the westerly winds, NAO indicates their strength (Comas-Bru and McDermott, 2014; Foukal and
Lozier, 2017). A preliminary investigation
using the winter (DJFM) EA index from NOAA CPC indicates that only in the
nutrient-limited SE region are there significant correlations. Biomass is
correlated to the EA (RSE(EA,Biomass)=0.48), a relationship
apparently driven by horizontal flux convergence (RSE(EA,Horiz)=0.43). In the SE region, biomass is not significantly correlated to the
winter (DJFM) NAO (Hurrell and NCAR, 2017), which may be due to significant
opposing impacts of the NAO on horizontal and vertical nutrient flux
convergence (RSE(NAO,Horiz)=0.37, RSE(NAO,Vert)=-0.30). These correlations are all zero-lag. We do not find stronger
correlations when biomass lags the EA or NAO by up to 3 years. That there
are no significant correlations north of 50∘ N, in the NE and NW
regions, between these climate modes and biomass is consistent with the weak
correlations of horizontal and vertical flux convergence to physical fields
(Table 2).
Williams and Follows (1998) illustrate that with regard to the mean, horizontal Ekman
fluxes in the surface are critical to nutrient supply in the North Atlantic
from 40 to 60∘ N, particularly for the northeast subtropical gyre. Yet,
Williams et al. (2000) find Ekman nitrate flux variability to be an order of
magnitude smaller than convective flux variability in this region. We find
that 0–100 m horizontal nutrient convergence contributes 25 %–35 % of the
mean nutrient supply in our two regions to the east of 35∘ W. In
contrast to Williams et al. (2000), we do find horizontal flux convergence
to be important to variability, with the 1949–2009 standard deviation of
horizontal flux convergence in the SE region being 66 % of the standard
deviation of the sum of vertical and horizontal, while the vertical flux
convergence standard deviation is 95 % of the sum. In the NE region,
vertical and horizontal flux convergence are anti-correlated
(RNE(Vert,Horiz)=-0.64, Table 2) such that their variability
partially cancels out. The standard deviation of vertical flux convergence here
is 125 % of the sum, while the standard deviation of horizontal flux
convergence is 108 % of the sum. These very different findings can at
least be partially attributed to the fact that Williams et al. (2000) had
only a climatological nitrate data field to couple to their mixed layer
model and wind-stress-based Ekman divergence calculation. The use of a smooth
climatological nutrient field would not likely allow for the strong
co-variance between vertical and horizontal supply terms that these run-time
diagnostics are able to reveal. As variability of nutrient supply to the
surface ocean is critical to subpolar North Atlantic biomass variability,
datasets that temporally resolve upper ocean nutrient fields would be most
valuable to future studies. Large-scale deployment of autonomous floats with
biogeochemical sensors will be essential to the development of these
critical datasets (Johnson et al., 2009).
In our model, reduced horizontal nutrient supply over the SeaWiFS period
(1998–2007) drove the observed reductions in biomass on the northeastern
flank of the North Atlantic subtropical gyre (our SE region). This mechanism
contrasts to previous analyses that attribute the observed changes to
locally increased stratification and the associated reduced vertical supply
of nutrients (Behrenfeld et al., 2006; Polovina et al., 2008; Martinez et al., 2009) or to subtle shifts in the balance between phytoplankton accumulation
and loss (Behrenfeld, 2014). Contrasting mechanisms in two different
time frames are found in this region. For 1949–2009, simulated biomass and
SST are anti-correlated (RSE(Biomass,SST)=-0.57, Table 2) while
biomass anomalies are positively correlated to both vertical and horizontal
nutrient supply changes (RSE(Biomass,Vert)=0.63,
RSE(Biomass,Horiz)=0.44). However, over the SeaWiFS period,
reduced horizontal flux convergence is more important than changes in
vertical flux convergence to simulated biomass declines (Fig. 10). The fact
that horizontal processes are important on both timescales is consistent
with previous findings that horizontal nutrient fluxes are seasonally
important (Dave et al., 2015) and also that SST as a proxy for stratification
is alone insufficient to describe biomass interannual variability in this
region (Lozier et al., 2011). It is reasonable to expect similar mechanisms
to operate on the edges of the subtropical gyres elsewhere around the globe.
Particularly in these intergyre regions, a three-dimensional perspective on
nutrient supply should be taken when observations are interpreted and when
expected mechanisms of future change are considered (Doney, 2006; Bopp et al., 2013).
In the context of 21st century climate-driven changes in biomass,
Laufkötter et al. (2015) find zooplankton grazing to be important to
biomass in some models under a strong climate change forcing scenario
(RCP8.5). Zooplankton is not the driver of biomass changes in this model
(Fig. S3), with the very different timescales and levels of forcing for
change – 10 years of interannual variability in this study, ∼100 years with strong forcing in Laufkötter et al. (2015) – likely
being a factor in this difference. That zooplankton grazing is not
temperature dependent in this model may also contribute, but any potential
effects would be limited by the annual mean temperature change from
1998–2000 to 2005–2007 being substantially smaller (+0.02, +0.28 and +0.13 ∘C, in SE, NE and NW regions, respectively) than over
the 21st century in the RCP8.5 scenario (1–4 ∘C at 40–60∘ N, Laufkötter et al., 2015).
In this simulation, North Atlantic biomass variability to the north of 40∘ N is quite heterogeneous and dependent on different mechanisms at
distinct locations, with the dominant mechanisms shifting across timescales.
Though a large-scale averaging approach may be appropriate for some
biogeochemical studies (Fay and McKinley, 2013, 2014), relationships between
the surface ocean carbon cycle and productivity may not be well captured by
correlations over large-scale ocean biomes that take the whole of the
subpolar gyre as one region (Fay and McKinley, 2017). An approach using
smaller subregions will likely support a deeper understanding of biological
coupling to the carbon cycle in this region.
These findings suggest myriad directions for further analysis. In order to
address the simplest measure of change, we use annual mean fields for both
the observations and the model. A deeper consideration of how these changes
operate in the context of the significant seasonality of the region would be
very interesting. This analysis does not elucidate how variability in the
physical supply of silicate impacts biomass variability. Particularly
considering long-term correlations between physical drivers and phosphate
supply in NE and NW region that are opposite to those evidenced for
1998–2007, a complementary analysis of variability in silicate supply may be
useful. Similarly, it would be of value to study the relative impacts of
large and small phytoplankton size classes on total biomass variability,
particularly in the northwest region where light and nutrient limitation
drive biomass in opposite directions (Table 1). Assessment of the modulation
of subsurface nutrient fields by subpolar gyre physical changes, and in turn
how these subsurface changes influence surface biomass, would be worthwhile.
Spatial analysis based on empirical orthogonal functions (Breeden and
McKinley, 2016) could illustrate the dominant large-scale modes of biomass
variability and may reveal the degree to which climate modes impact biomass
on longer timescales. With respect to the period of satellite observations,
a numerical simulation that covers both the SeaWiFS and MODIS periods would
allow study of the period since 2007 in which 1998–2007 trends appear to
largely have reversed (Fig. 2). For such a simulation, greater physical
resolution should improve representation of the gyre structure and its
variability. Though the current ecosystem is able to capture the large-scale
patterns of biomass change remarkably well, it would also be valuable to
assess the impact of different levels of ecosystem complexity in future
modeling work.