Echevin et al., 2005

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The seasonal cycle of surface chlorophyll along the Peruvian coasts
Vincent Echevin1, Olivier Aumont1, Jorge Tam2, Julien Ferla3
1:LOCEAN, Université Pierre et Marie Curie, Paris, France.
2: IMARPE, Lima, Peru
3: Ecoles des Ponts et Chaussées, Paris, France.
Abstract:
The seasonal cycle of surface chlorophyll along the Peruvian coasts is studied with SeaWifs
satellite observations and a coupled dynamical/biogeochemical model. The observed mean
spatial structure of surface chlorophyll is shown to be in good agreement with that of the
model. However the seasonal cycles contrast: whereas the model produces a spring bloom
consistent with maximum upwelling and deepest mixed layer in the winter season, the
observed seasonal cycle shows two distinct booms in late spring-early summer and fall.
Several hypotheses are suggested to explain the fall bloom and its non-occurrence in the
model.
Introduction:
The Peruvian coast (4°S-18°S) features the strongest upwelling of the Humbold Current
System (HCS) along the eastern coasts of South America. It also has a strong ENSO-related
interannual variability (Carr, 2003, Carr et al., ) which impacts the important fisheries
activities of Peru and Chile (FAO, 1999). The high biological activity off Peru is due to yearlong coastal upwelling due to the trade winds. Cold, nutrient rich subsurface waters are
advected vertically into the euphotic zone, and an intense biological activity develops along
the coast. The maximum of upwelling occurs in winter with the reinforcement of the trade
winds (Bakun and Nelson, 1991). However the seasonal variation of phytoplankton biomass
appears to be out of phase with upwelling intensity (Chavez, 1995, Thomas et al., 2001,
Ochoa et al., 2004) and the dynamical and/or biogeochemical processes at stake remain
unexplained. Here we examine the seasonal cycle of the surface chlorophyll in Seawifs
satellite observations during the period 1998-2003, and use a three-dimensional coupled
dynamical-biogeochemical model of the upwelling area to study the dynamical forcing of the
seasonal variability of surface biomass. Hypothetical explanations of the observed
discrepancies between the model and the observations are presented, and some conclusions
are suggested to explain the observed seasonal cycle. In the following section, we describe
the data and methods used in this work, then present the results obtained in the third section.
In the last section, a discussion and conclusions are given.
Data and methods
The SeaWiFS satellite data collected during the period 1998-2002 is used. The data is gridded
at a rather low resolution (0.5°) every 8 days, in order to avoid the high concentrations
nearshore, which may not correspond to actual chlorophyll values. A climatological time
series is constructed by averaging the data values each month. When focus is given on the
coastal values, the closest grid point near the coast is extracted from the gridded data set.
Results from a coupled dynamical/biogeochemical model are used as a guideline for the
analysis of surface observations. The coupled model is composed of a dynamical component,
the ORCA model (Lengaigne et al., 2002) configured at 0.5° resolution in the Pacific ocean,
forced by ERS/Quickscat winds, NCEP heat fluxes, and Xie precipitation. The
biogeochemical component is the PISCES model (Aumont et al., 2003), an ecosystem model
which is able to represent phytoplankton growth limitations by several nutriments such as
nitrate, phosphate, silicate, dissolved iron. Dissolved inorganic and organic nutrients
including iron, are also provided by the river run-offs on a annual mean basis. The model is
run over the period 1992-1999, the year 1992 is not considered as it is pertubated by
dynamical adjustment. The mixed layer depths, vertical velocities, nutrients and
phytoplankton biomass of the model are used in the following analysis.
Results
The annual mean surface chlorophyll in the model and observations is represented Figure 1.
Both display similar spatial patterns. The maximal chlorophyll is concentrated near the coast
between 3°S and 18°S, with observed maximum values of ~3-4 mgChl/m3 and 0.5-1
mgChl/m3 in the model. The highest values are encountered near 6°S in the observations
(resp. 7°S in the model) and between 10°S and 15°S, colocated with the main upwelling
centers (Strub et al., 1998). The relatively low values produced by the model may be due to
the weak vertical velocities generated by the ORCA dynamical model. At the model’s
resolution, bottom topography is not well resolved and vertical velocities, although maximum
at the coast, remain noisy on the horizontal and weak (~0.5 m/day) compared to the vertical
velocities of high resolution regional models (Penven et al., 2003) and reported in the
literature (5-30 m/day, Strub et al., 1998).
The cross-shore gradients of chlorophyll display strong differences. If one considers the 0.4
mgChl/m3 (resp. 2) as a virtual boundary separating the most productive coastal areas from
offshore waters, chlorophyll values decrease much more rapidly in the offshore direction in
the model results. Very low values of ~0.15 mgChl/m3 are reached within 400-500 km from
the coast. In SeaWiFS such low values are reached ~1000 km offshore. Such a discrepancy
can be due to the absence of mesoscale and submesocale processes in this fairly low
resolution model, which generally act as a onshore-offshore transfer of nutrients and biomass
(Marchesiello et al., 2004) in the coastal transition zone.
The seasonal cycle is calculated over the period 1993-1999 for the PISCES model and over
1998-2002 from SeaWiFS data. Latitude-time hovmuller diagrams of the coastal chlorophyll
values are shown in Figure 2. PISCES surface chlorophyll displays an expectable seasonal
cycle: chlorophyll is maximum in september-october at the end of winter. Indeed the model
mixed layer depth increases during winter due to the stronger trade winds and the cooling of
the surface water. It is maximum (~70 m) at the end of winter, with depth ranges greater than
climatological values (~40 m, de Boyer Montegut et al., 2004) but with the correct seasonal
phase. With this depth increase, nutrients are entrained into the surface layer and a nearshore
spring bloom occurs in october-november over 4°S-16°S latitude range. Note that, except
over three bands of year-long higher production and contrasted seasonal cycle near 6.5°S,
11°S and 12.5°S, the bloom persists during 1-2 months and ends at the beginning of
November. Minimum chlorophyll values are reached in February-March over most of the
Peru coasts.
On the opposite, there is no spring bloom but a summer bloom in the observations, starting in
October and reaching maximum values in November-december. The observed bloom seems
to decrease in January and increase to a second maximum in February-March, during the
season of weakest winds and coastal upwelling. There is also a propagative pattern in the
observations, which is not reproduced by the model. There appears to be a slow southward
propagation of the bloom maximum in October-December and in February-April between 9°S
and 14°S.
Discussion
These results raise many questions: what are the major dynamical and biogeochemical
processes which control the seasonal cycle of surface chlorophyll ? what accounts for the
differences in seasonal cycle phase between the model and the observations? Why isn’t there
a late austral summer/early fall bloom in PISCES over most of the coast ? What could
generate a southward propagation of the bloom as the one observed on Seawifs?
Light is not the limiting in the upwelling region due to the low latitude, and there is evidence
of mixed layer control of productivity in the observations (Guillen and Calienes, 1980). This
seems indeed to be the case in the model, since surface biomass is maximum in october right
after the maximum mixed layer depth, whereas the upwelling maximum is reached in
August-September (Chavez, 1995). Still, there appears a slight time delay, the maximum of
the spring bloom being reached slightly earlier in the model (October) than in the
observations (November). This delay may be caused by the effect of mesocale activity which
is more intense during the upwelling phase, and which may transport the upwelled nutrients
offshore. After the upwelling, eddies and filaments are less active and nutrients remain close
to the coast. Hence an intense biomass can develop nearshore, as confirmed by biomass
measurements (Rojas de Mendiola, 1981). In the low resolution dynamical model, mesocale
processes are not resolved, the nutrients are not advected offshore, and the bloom can start
earlier in the season.
Let us now investigate in more detail the biogeochemical processes at stake in the model. As
noted previously, three locations along the coast show year-long high surface chlorophyll
concentration (Figure 2). These high values are due to constant (the annual mean value is
introduced) iron input from three main rivers located at these nearshore model grid points.
There, iron concentration is high all year round and phytoplankton biomass can grow faster
and longer than elsewhere along the coast, where production of picoplankton and diatoms is
iron limited. Because of this high biomass, there is a strong remineralisation following the
summer bloom and an fall bloom can be triggered when nitrates are brought to the surface as
the first trade wind blows take place, as for example in April 1996 (Figure 3).
Along the Fe-depleted coast, phytoplankton growth is relatively weak all year round. The
spring bloom is triggered when the mixed layer deepens and reaches the iron nutricline depth,
which, in the model, is deeper than the nitracline and phosphate nutricline (Figure 4).
Therefore, this bloom last only one-two months whereas it is more intense and longer in the
observations.
In fall, the production is iron limited and, because of the greater depth of the iron nutricline
than that of the nitracline, the weak upwelling generated by the first wind blows does not
trigger an intense bloom as in places where iron is not limiting. Therefore it is a combinaison
of different colimitations and different nutricline depths which explains the presence, in the
model results, of a fall bloom at nitrate/phosphate limited locations and the absence of it at
Fe-depleted locations.
A combinaison of the two co-limitations could occur in reality. Indeed, phytoplankton Felimitation has been recently measured in the Peru upwelling (Hutchins et al., 2002), but
previous work suggest that silicate (Dugdale, 1972, Guillen et al., 1977) and nitrate could
regulate productivity (Guillen and Calientes, 1980). Both hypotheses are confirmed by our
model simulations depending on the location along the coast.
Two poleward propagating signals of a high chlorophyll signal at 0.2 m/s 0.1 m/s can be
seen from Figure 2 at the end of summer-beginning of fall. The propagation speed is quite
slow compared to measured coastally trapped waves phase speed of ~2-3 m/s (Brink, 1982),
which rules out this dynamical process. The summer-fall period corresponds to the minimum
of wind and upwelling. During these seasons the equatorward Peru coastal current (PCC) is
weakest (Strub et al., 1998, Penven et al., 2003) and the poleward Peru undercurrent (PUC)
may reach the surface (Huyer et al., 1991) and advect nutrients-rich surface water southward.
The source of nutrients may be as far north as 4°S in January, as shown in Figure 5. Poleward
advection due to the surfacing PUC may contribute to the poleward propagation of the bloom.
Three simplified parameterizations in the biogeochemical model may have an impact on the
seasonal cycle of primary productivity:
First the river run-offs are located at only three model grid points. With the low resolution
(1/2°) of the dynamical model, the coastal current is weak and all the iron is consumed
locally. There is no advection from iron-rich to iron depleted areas along the coast, hence the
local “grid-point” high concentration values. In reality, there are numerous small rivers spread
over the coastline. The coastal currents may advect nutrients along the coast and generate an
alongshore continuous bloom between 4°S and 16°S, as observed in Figure 2.
Second, there is a strong seasonal cycle of the river run-off along the Peruvian coasts, which
was not introduced in the model. The main rivers of this very dry area between 6°S and 20°S
receive water only in fall due to the rain season in the Andes. The input of iron and/or other
nutrients from river run-off in fall may enhance the fall bloom.
Third, there is no parameterization of the sediments input of iron in the model. This source of
Fe could be important in the Peru upwelling, since it is highly soluble in the shallow suboxic
waters found in this region (Bruland et al., 1991, Morales et al., 1999) . Owing to the seasonal
variability of the oxycline depth, there may be an effect on the Fe input from sediments,
which is not taken into accound here.
Parameterization of the seasonal cycle of runoff, sediments Fe-input should further improve
the model results, and indicate which process has the greatest impact on productivity.
Furthermore dynamical modelling at a higher horizontal resolution should produce more
realistic upwelling rates and mesoscale circulation, which may have an impact on both the
timing and amplitude of the phytoplankton blooms.
Conclusions:
The seasonal cycle of surface chlorophyll along the Peruvian coasts was studied with SeaWifs
satellite observations and compared to outputs of a coupled dynamical/biogeochemical model
implemented in the region. The spatial structure of surface chlorophyll is maximum
nearshore, and maximum between 4°S and 18°S, and model and observations are in good
agreement. The seasonal cycle shows a summer bloom and a fall bloom in the observations,
whereas in the model, only a spring bloom occurs at most locations along the coast, where
productivity is iron-limited. At coastal locations where iron is brought by river run-off, the
model phytoplankton growth is no longer iron-limited and a weak nitrate-limited fall bloom
takes place at the beginning of the upwelling season. Elsewhere the fall bloom does not occur
because high iron concentrations are buried at greater depths than the nitracline. Furthermore
the fall bloom may be enhanced by the riverine input which is concentrated in the fall season
due to precipitation in the Andes. Poleward propagation of a coastal chlorophyll signal takes
place between 9°S and 14°S. This signal may be linked to poleward advection of nutrients by
a surfacing coastal undercurrent.
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Figures:
Figure 1: Mean surface chlorophyll in (left) PISCES model in 1993-1999 and (right)
SeaWiFS data in 1998-2002. Units: mgChl/m3
Figure 2: time-latitude diagram of the alongshore surface chlorophyll for a climatological year
(left) PISCES model, (right) SeaWiFS data. Units: mgChl/m3
Figure 3: temporal variation of the surface (5m depth) chlorophyll in PISCES model at a
coastal location at 12°S during the years 1993 (black), 1994 (red), 1995( green), 1996 (dark
blue) and climatological (light blue). Units: mgChl/m3
Figure 4: time-depth evolution of the Fe concentration (shading) and NO3 concentration at
12°S. Units: 104 mol/l(Fe), mol/l (NO3)
Figure 5: Time-latitude diagram of the alongshore surface NO3 concentration in Levitus
climatology.