Model validation
In this subsection, we aim at validating our reference simulation “TRI”
with the data previously presented. In the Pacific, phosphate and nitrate
concentrations show maxima in the upwelling regions, i.e. along the western
American coast, and in the equatorial upwelling (Fig. 1a, c), and minima in
the subtropical gyres. First, phosphate patterns show modelled values and
structures in qualitatively good agreement with observations, despite an
underestimation in the areas of high concentrations as within the Costa Rica
dome and along the Equator. In contrast, the nitrate structure shows larger
biases. We observe concentrations higher than 1 µmol L-1 all
along the Equator in CARS, while the model nitrate concentrations are
lower than this value west of 170∘ W. More generally the model tends
to underestimate nitrate concentrations.
(a) Box plots of the 0–150 m averaged Iron (nmol Fe L-1)
data (blue) and the equivalent for the model (red) co-localized with the
observations in space and time. The coloured box represents the 25–75 %
quartile of the distribution, the whiskers the 10–90 % percentile
distribution. The line inside the coloured box is the median. (b, c) Iron
concentrations (nmol Fe L-1) as observed (b) and as simulated
by the model (c). Iron concentrations have been averaged over the
top 150 m of the ocean. Model values have been sampled at the same location,
the same month, and the same depth as the data.
The regions most favourable for Trichodesmium can be defined by
temperatures within 25–29 ∘C .
The model reproduces relatively well the spatial distribution of this
temperature preferendum. This distribution exhibits a significant seasonal
variability, mainly as a result of the variability of the 25 ∘C
isotherm. The latter moves by ∼ 7∘ latitudinally between summer
and winter in the WTSP, and by ∼ 15∘ in the western tropical
North Pacific (WTNP; Fig. 1a). This displacement is well reproduced in the
TRI simulation (Fig. 1b). By contrast, along the Equator the mean position of
the 25 ∘C isotherm is shifted eastward in the TRI simulation
(120∘ W) compared to the observations (125∘ W; Fig. 1a vs.
b), but its seasonal displacements are well reproduced except in the
southeastern Pacific. Overall, this temporal variability is well reproduced
by the model (Fig. 1b), despite this bias. In contrast, nitrate and phosphate
seasonal variability remains low (not shown).
(a, b, c) Annual mean surface chlorophyll concentrations
(in mg Chl m-3) from (a) GLOBCOLOUR data (b) TRI
simulation and (c) TRI_imp simulation. Panel (d) shows the
annual mean surface chlorophyll concentrations of Trichodesmium in
the TRI simulation.
Another important feature that needs to be properly reproduced by the model
is the iron distribution in the upper ocean. We have sampled the modelled
values at the same time and same location as the data. The median value, as
well as the dispersion of the iron surface concentrations over the tropical
Pacific, are displayed for both the data and the model in Fig. 2a.
The Mann–Whitney test reveals that these two normalized distributions are not
significantly different (p value = 0.26). Figure 2b, c display the observed
iron field and the modelled values, respectively. The best sampled
area is the central Pacific Ocean where simulated iron concentrations are low
(0.1 to 0.3 nmol Fe L-1, Fig. 2c), which is consistent with the
observations (Fig. 2b). The southwest Pacific is characterized by relatively
high surface iron concentrations, between 0.4 and 0.8 nmol Fe L-1,
both in the data and in the model. Large scale patterns are thus well
represented by the model. Nevertheless, the model tends to overestimate iron
levels in the South Pacific Gyre, between 180 and 140∘ W at about
20∘ S.
N2 fixation rates (µmol N m-2 d-1) as
observed (a, c) and as simulated by TRI simulation (b, d).
In panels (a) and (b), N2 fixation rates have been
integrated over the top 150 m of the ocean. In panels (c) and
(d), the vertical integration has been restricted to the top 30 m
of the ocean. Model values have been sampled at the same location, the same
month (climatological month vs. real month), and the same depth as the data.
Figure 3 displays a comparison between surface chlorophyll (Chl) concentrations
from GLOBCOLOUR data (a), from TRI (b), and TRI_imp (c) simulations. High
chlorophyll concentrations are found in the eastern equatorial Pacific
upwelling and along Peru in both the observations and our two simulations,
with mean values higher than 0.3 mg Chl m-3. However, the equatorial
chlorophyll maximum simulated by the model (Fig. 3b, c) is too narrow
compared to the observations, especially in the Northern Hemisphere.
Similarly, the model is unable to simulate the elevated chlorophyll levels
around the Costa Rica dome and the localized enhanced chlorophyll off Papua
New Guinea. In TRI (Fig. 3b), chlorophyll values in the southwest Pacific
region vary between 0.1 and 0.2 mg Chl m-3, with maxima located in
the vicinity of the Fiji and Vanuatu islands. These values are within the
range of the data, even if the data tend to be slightly higher (up to
0.3 mg Chl m-3 near the coasts). The spatial structure is well
represented, with maxima simulated around the islands. In the subtropical
gyres, the simulation predicts chlorophyll concentrations of ∼0.05 mg Chl m-3 which are higher than in the observations
(<0.025 mg Chl m-3). In contrast to TRI_imp (Fig. 3c), chlorophyll
values in the southwest Pacific and in the Northern Hemisphere are too low in
comparison with the ocean colour data (Fig. 3a).
Part of the surface chlorophyll in Fig. 3b is associated with
Trichodesmium. Figure 3d shows the annual mean spatial distribution
of surface Trichodesmium chlorophyll in the TRI simulation. This
distribution displays two zonal tongues in the tropics, one in each
hemisphere. Maximum values are located in the southwest Pacific (around
Vanuatu archipelago, New Caledonia, Fiji, and Papua New Guinea) and around
Hawaii, where they reach 0.06 mg Chl m-3. In the South Pacific, high
chlorophyll biomass extends eastward until 130∘ W. Further east,
concentrations drop to below 0.02 mg Chl m-3. It is important to note
that, in the observations, Trichodesmium have never been observed
eastward of 170∘ W. This bias in the model could be explained by the
overestimated iron concentrations in the South Pacific Gyre. In the Northern Hemisphere, between the coasts of the
Philippines (120∘ E) and Hawaii (140∘ W),
Trichodesmium chlorophyll concentrations are greater than
0.03 mg Chl m-3. In the northeast Pacific, Trichodesmium
chlorophyll is lower, yet significant (<0.03 mg Chl m-3).
Otherwise the equatorial Pacific and southeast Pacific oceans are overall
poor in Trichodesmium.
In Fig. 4, the dinitrogen fixation rates predicted by the model in TRI are
compared to the observations from the MAREDAT expanded database. Evaluation
of the model behaviour remains quite challenging because of the scarcity of
the observations. Some large areas are not properly sampled such as the
northwest tropical Pacific and the eastern Pacific. In addition, some areas
are sampled only in the surface layer (0–30 m), while others have been
sampled deeper. This non-homogeneous sampling may alter the distribution of
the N2 fixation rates and undermine the comparison with model
outputs. To overcome this sampling bias we compared the observations with
N2 fixation rates simulated and integrated over two different layers
(0–30 and 0–150 m). Despite their scarcity, some regional patterns emerge
from the observations. Maximum fixation rates (600 to
1600 µmol N m-2 d-1; Fig. 4a) are observed around the
southwest Pacific islands, in the Solomon Sea, around the Melanesian
archipelagos, and around Hawaii, four well-known “hotspots” of N2
fixation
.
The modelled regional patterns of strong fixation are coherent with the
observations (Fig. 4b), showing values in the same range. In the South
Pacific, the TRI simulation is able to reproduce the strong east–west
increasing gradient of N2 fixation
(; ; Fig. 4c,
d). In the equatorial central Pacific, modelled values of mean fixation are
negligible (<0.5 µmol N m-2 d-1) in contrast to the
observations which suggest low but non-negligible fixation rates (between 1
and 2 µmol N m-2 d-1)
. On the whole modelled
domain, and for both integration layers, dinitrogen fixation rates are
overestimated by 70 % in TRI compared to the data. Some recent studies
have shown that the 15N2 tracer addition
method () used in most studies reported in the
MAREDAT database may underestimate N2 fixation rates due to an
incomplete equilibration of the 15N2 tracer in the incubation
bottles. Thus, this overestimation may be an artifact arising from
methodological issues
. However, some other
studies performed in the South Pacific (;
) compared the two methods, and did not find
any significant differences.
Relative contribution (in percentage) of Trichodesmium to
total primary production.
Trichodesmium biomass (mmol C m-2) in
(a) austral summer and (b) austral winter, integrated over
the top 100 m of the ocean.
Seasonal variability in Trichodesmium biomass
Trichodesmium biomass (Fig. 6) and simulated dinitrogen fixation
rates (Fig. 7) display a seasonal variability that is driven by the seasonal
variability of the environmental conditions (light, temperature, currents,
nutrients). The regional maxima of Trichodesmium biomass (exceeding
3 mmol C m-2; integrated over the top 100 m of the ocean) are found
in both hemispheres during the summer season (Fig. 6a, b) even if locally,
maxima can be attained during other periods of the year than summer. In the
South Pacific, the area of elevated Trichodesmium biomass moves by
3∘ southward from austral winter to austral summer. Along Australia
and in the Coral Sea, Trichodesmium biomass exhibits a large
seasonal variability with a very low winter biomass that contrasts with
elevated values in summer. A similar important variability, which is shifted
by 6 months, is simulated in the Northern Hemisphere in the Micronesia
region and in the Philippine Sea.
Unfortunately, due to the scarcity of N2 fixation data, this seasonal
cycle cannot be properly assessed at the scale of the tropical Pacific Ocean.
This is only feasible at the time series station ALOHA located in the North
Pacific Gyre at 22∘45′N, 158∘ W, where
seasonal data of dinitrogen fixation are available from 2005 to 2012
. They proved that vertically integrated
dinitrogen fixation rates are statistically significantly (one-way ANOVA,
p<0.01) lower from November to March (less than
200 µmol N m-2 d-1) than from April to October (about
263 ± 147 µmol N m-2 d-1) as highlighted in
Fig. 7a (blue dots). In the model (red dots; Fig. 7a), the maximum amplitude
of the seasonal cycle appears to be underestimated relative to the
observations (i.e. respectively ∼170 and ∼250 µmol N m-2 d-1). Dinitrogen fixation peaks
1 month earlier in the model than in the data (August for the model and
September for the data). Indeed, the simulated dinitrogen fixation rates are
minimum between December and May (averaging ∼241 ± 27 µmol N m-2 d-1) and maximum the rest
of the year (averaging ∼347 ± 52 µmol N m-2 d-1). These values are
comparable to the data even if they are slightly higher.
Seasonal cycle of the limitation terms of Trichodesmium
production in (a) the South Pacific and (b) the North
Pacific. The right scale (in brown) represents the total limitation.
In order to assess the seasonal cycle of N2 fixation rates in the
South Pacific (red box Fig. 1c; 160–230∘ E, 25–14∘ S), we
have extracted the available data for each month from our database (blue
dots; Fig. 7b), and the corresponding model values in TRI (red dots;
Fig. 7b). In July no observations are available and in January, April, and
August only one data point is available for the entire region. The predicted
seasonal cycle is broadly consistent with the observations. Minimum
dinitrogen fixation rates (239±205 µmol N m-2 d-1) occur during austral winter and
autumn. Maximum rates are reached in February and March, where they exceed
600 µmol N m-2 d-1 in the observations. The increase
in dinitrogen fixation rates occurs 1 month earlier than in the
observations, in December instead of January, and remains 2 to 3 fold
higher from April to June. It is important to note here that the sampling
spatial and temporal distribution may distort the seasonal cycle. Using the
model, it is possible to evaluate how well the seasonal cycle is captured by
the sampling (red dots compared to green dots; Fig. 7b). The general
structure of the seasonal cycle remains relatively unaltered. However, the
amplitude is significantly impacted since it reaches
1100 µmol N m-2 d-1 if sampled at the observed
stations, whereas it is about twice as low at
600 µmol N m-2 d-1 if all the model data points are
considered. We can conclude that the TRI simulation reproduces well the
seasonal cycle of N2 fixation rates at the Pacific scale, even though
more data are needed to improve the evaluation of the model skills.
To further investigate the mechanisms that drive the seasonal variability in
Trichodesmium in the Pacific, we examined the factors that control
Trichodesmium abundance in the TRI simulation (not shown). This
decomposition shows that the physical terms (advection and mixing) are
negligible compared to biological terms. In addition, the seasonal cycles of
grazing and mortality are in phase with the production terms but their sign
is opposite. In conclusion, this analysis indicates that this seasonal
variability is mainly controlled by the levels of PP, the
other terms of tracer evolution dampen its amplitude but do not change its
shape. Hence we further examine the limitation terms of PP
(Fig. 8) in two representative regions characterized by elevated levels of
N2 fixation rates (red boxes; Fig. 1c). A detailed description of
these limitation terms is given in Appendix A. A limitation term reaching 1
means that growth is not limited, whereas a limitation term equal to 0
means that growth ceases.
Trichodesmium growth sustained by nitrate and ammonia is very
slow in LNLC regions due to
their very low availability and is therefore not considered further. Thus,
our analysis is restricted to dinitrogen fixation. Trichodesmium
growth can be limited by iron and phosphate and is inhibited when reactive
nitrogen (nitrate and ammonia) is available. In the WTSP, the model suggests
that iron is the sole nutrient that modulates Trichodesmium growth
(red curve; Fig. 8a, b). The other limiting factors of Trichodesmium
growth are light (green curve; Fig. 8a, b) and temperature (purple curve;
Fig. 8a, b). The product of these three limiting factors gives the limiting
coefficient of dinitrogen fixation (brown curve; Fig. 8a, b). The limiting
factors vary according to the season and the hemisphere. In the South (North)
Pacific, temperature and light are less limiting during the austral summer
(winter) than during the austral winter (summer). The limiting factor
associated with temperature varies from 0.8 to 1, and the light limiting
factor varies from 0.15 to 0.3. Unlike light and temperature, iron is less
(more) limiting in the South (North) Pacific during winter (summer) than
during the austral summer (winter) with values varying between 0.4 and 0.7.
Finally, Trichodesmium growth is more limited during austral winter
(summer) in the South (North) Pacific. The seasonal variability is forced by
light and temperature, and iron mitigates its amplitude. Indeed, nutrients
and iron inputs brought to the euphotic zone by the seasonally enhanced
vertical mixing are counterbalanced by the related inputs (e.g. temperature)
of these water masses.
(a, b) Minimum, mean and maximum in the South box (Fig. 1c)
of (a) the iron concentrations (in nmol Fe L-1), and
(b) of the chlorophyll concentrations of Trichodesmium (in
mg Chl m-3). (c, d, e, f) Annual mean iron concentrations
(shading; in nmol Fe L-1) and current velocities (vectors; in
m s-1) for (c) the TRI_NoFeSed simulation and
(d) the TRI simulation. Annual mean chlorophyll concentrations of
Trichodesmium (mg Chl m-3) for (e) the TRI_NoFeSed
simulation and (f) the TRI simulation. The concentrations have been
averaged over the top 100 m of the ocean. The current velocities are
identical on the panels (a) and (b).