Numerical Model Approaches to Address Recent ... - CiteSeerX

Journal of Oceanography, Vol. 58, pp. 365 to 378, 2002

Review

Numerical Model Approaches to Address Recent Problems on Pelagic Ecosystems MICHIO KAWAMIYA* Institut für Meereskunde an der Universität Kiel, Düsternbrooker Weg 20, D-24105, Kiel, Germany (Received 25 May 2001; in revised form 25 August 2001; accepted 25 August 2001)

A review of ecosystem modeling is presented, classifying the models on the basis of the problems they address. The problems are chosen mainly from those concerning the North Pacific and encompass: limiting factors in high-nutrient/low-chlorophyll (HNLC) regions; nutrient supply to the subtropical gyre; long-term variation; parameter optimization; oceanic provinces. A future research direction should be to sort out priorities of various biogeochemical and ecological processes with the longterm variation problem being the axis, while keeping the model complexity at a minimum and invoking the parameter optimization technique.

Keywords: ⋅ Ecosystem, ⋅ North Pacific, ⋅ modeling.

ers intrigued by the title of this paper may also benefit from some other excellent reviews of ecosystem modeling by Totterdell (1993), Franks (1995), Denman and Gargett (1995), Doney (1999), and Frost and Kishi (1999). The book on turbulence and mixing by Kantha and Clayson (2000) includes a review of ecosystem modeling as a fascinating field of the application of mixed layer models. As Franks (1995) pointed out, “modelers must be guided by the questions they are asking”, so the structure of an ecosystem model is strongly dependent on the problems being attacked. Therefore some of the problems currently attracting the attention of scientists in related fields have been selected, and the related studies have been classified accordingly. A brief survey of each problem is also given. It must be admitted here that the chosen problems do not cover all of the leading issues in the related fields. The limited space, time, and enthusiasm of the author all impeded a thorough survey. Franks (1995) grouped ecosystem models into three categories: (1) theoretical models used to investigate general, conceptual problems; (2) heuristic models used to explore problems associated with some specific data set; and (3) predictive models used to predict possible phenomena that would occur under conditions that the available data do not cover. They are not exclusive. In particular, most models that fall into the category 2 could also be used as predictive models (category 3). This review deals primarily with models of the categories 2 and 3, mainly because the author is much familiar with them and partly because this focus is more suitable when the main emphasis is on a specific region of the oceans, the

1. Introduction Concerns have been expressed about possible undesirable changes in pelagic ecosystems due to environmental change (Denman et al., 1995). In response, scientists have established international programs such as the Joint Global Ocean Flux Study (JGOFS)(1), Global Ocean Ecosystem Dynamics (GLOBEC)(2) and Land-Ocean Interactions in the Coastal Zone (LOICZ)(3). In addition, some new programs are in the planning phase (Ecological Determinants of Ocean Carbon Cycling, EDOCC(4); Ocean Carbon Transport, Exchanges and Transformations, OCTET(5) ; Surface Ocean-Lower Atmosphere Study, SOLAS(6)). In all these programs, modeling is regarded as an indispensable tool to enrich our understanding of oceanic ecosystems. The purpose of this paper is to review papers on pelagic ecosystem modeling, the main focus being on the North Pacific including its equatorial region. Modeling efforts at locations outside the North Pacific are referred to whenever that is considered to be informative. Read-

(1)

http://ads.smr.uib.no/jgofs/jgofs.htm http://www.pml.ac.uk/globec/ (3) http://www.igbp.kva.se/loicz.html (4) http://picasso.oce.orst.edu/ORSOO/EDOCC/ (5) http://www.msrc.sunysb.edu/octet/ (6) http://www.ifm.uni-kiel.de/ch/solas/main.html (2)

* E-mail address: [email protected] Copyright © The Oceanographic Society of Japan.

365

North Pacific. Models of category 1 include the one by Yamazaki and Kamykowski (1991) who directly solved the Navier-Stokes equations with a Lagrangian biological model to study phytoplankton trajectories, and Flierl and Davis (1993) who used a semi-analytical model for the physics, coupled with a simple three-component biological model to study the effect of the meandering of the western boundary currents. 2. Limiting Factors in HNLC Regions How the high-nutrient/low-chlorophyll (HNLC) condition is maintained is one of the classic enigmas of pelagic ecosystems in the subpolar and the equatorial Pacific Ocean. Despite numerous studies, scientists have not yet reached a definite conclusion, perhaps because many factors are mutually related in complicated ways and it is difficult to extract the essence and express it in a clearcut statement. Numerical models for this topic are categorized into three classes according to the limiting factor on which they focus. The classes are subjective, however, and not exclusive: multiple limiting factors in a single model is of course possible. Even in this case, however, we tried to categorize such a model according to which one of the factors the model demonstrates to be the most fundamental. 2.1 Traditional limiting factors Although iron is already becoming a “traditional” factor regulating phytoplankton growth, after the work by Martin and Fitzwater (1988), here we only treat even more traditional factors, such as light, grazing and macro nutrients represented by nitrate. Iron issues will be discussed in the next section. The work by Evans and Parslow (1985) can be regarded as one of the earliest modeling efforts on the maintenance of HNLC, in which they used a zero-dimensional model in which biological activities occur only in the mixed layer, treated as a box. They showed that the relatively shallow winter mixed layer depth (MLD) is responsible for the lack of any notable spring bloom in the eastern subpolar Pacific (Station P, 50°N, 145°W), since it supports a zooplankton biomass in winter that is large enough to suppress rapid phytoplankton growth in spring. Although they did not explicitly mention it, it is likely that the lack of blooms inhibits the ecosystem from a rapid consumption of nitrate, thereby maintaining its concentration high throughout the year. As an extension of their study, Fasham (1995) reinforced their idea, also using a zero-dimensional, somewhat more complex model. He extensively explored the overall model’s sensitivity to the parameters of his biological model, and found that a spring bloom could result with a certain parameter set within a possible range, even

366

M. Kawamiya

when the model was run under the MLD variation corresponding to Station P. Based on this result he speculated that the effect of iron, which was not considered in his model, may also play an important role in setting the parameter values that he assumed a priori in his study. Zero-dimensional models are always subject to the criticisms that they neglect processes occurring below the mixed layer and that their results may depend on the definition of MLD adopted (Eigenheer et al., 1996). A vertical one-dimensional model, in which the seasonal cycle of the vertical diffusion coefficient is prescribed, was used by Frost (1993). His conclusion is consistent with the above studies, in that grazing is the key to the absence of a bloom at Station P. He also found that ammonium inhibition is an essential process, while Fasham (1995) claims that it is only of secondary importance. Another hypothesis had been proposed, viz., that the upward migration in the life history of Neocalanus spp. takes place in spring (Miller et al., 1984) and this imposes a heavy grazing pressure on phytoplankton, thereby inhibiting a bloom. Though the zero-dimensional model of Frost (1987) supported this notion, an observational study (Dagg, 1993) on the grazing pressure from Neocalanus spp. found that it is not large enough to account for the required grazing loss of phytoplankton. The western subpolar Pacific, on the other hand, can have a spring bloom especially near the boundary between the subpolar and the subtropical gyre (Kasai et al., 1997). A more detailed discussion is given in Section 6 on the difference between the eastern and the western North Pacific. Yoshimori et al. (1995) employed a one-dimensional model to demonstrate that the difference in duration of the spring bloom along a periodically repeated observation line, the A-line (43°N, 145°E–39°N, 147°E), can be explained by differences in stratification. Kishi et al. (2001) demonstrated that, at the offshore-most station of the A-line, the seasonal migration pattern of Neocalanus spp. may regulate the species succession of phytoplankton during the course of the spring bloom. 2.2 Iron limitation The debut of the iron hypothesis (Martin and Fitzwater, 1988; Martin and Gordon, 1988) was sensational since it proposed a way to completely solve the problem of global warming (cf., Chisholm and Morel, 1991). Two field experiments, IronEX I (Martin et al., 1994; Watson et al., 1994; Kolber et al., 1994) and IronEX II (Coale et al., 1996; Behrenfeld et al., 1996; Cooper et al., 1996; Turner et al., 1996), on fertilizing the ocean by adding iron have been conducted in the equatorial Pacific. Cooper et al. (1996) indeed observed a significant reduction of fCO2 in the upper ocean during IronEX II. The naive optimism about the complete solution of the global warming problem through iron limitation has, how-

ever, had doubt cast on it by some modeling assessments of fertilization in the Southern Ocean, where the most efficient absorption of atmospheric CO 2 would be expected through the addition of iron (Peng and Broecker, 1991; Joos et al., 1991; Sarmiento and Orr, 1991; Kurz and Maier-Reimer, 1993). Despite the formulations of their models that resulted in optimal CO2 absorption into the oceans, the reduction of atmospheric CO2 in all these model studies was not as large as expected due to negative feedbacks of the ocean. At most the reductions would slow the expected increase in atmospheric CO2 concentrations by about two decades by the latter half of this century. Recently, another iron fertilization field experiment was conducted in the Southern Ocean (Boyd et al., 2000; Abraham et al., 2000; Watson et al., 2000). Again, the result did not support deliberate fertilization as a means of reducing the rate of expected increase in atmospheric CO 2. Although fCO2 was reduced in the upper ocean, export production, estimated from 234Th, was not enhanced. In spite of skepticism on the role of iron as a solution for global warming, the importance of iron in pelagic ecosystems is now widely accepted. The overall picture is, however, modified from that at the time of the exciting debut of the iron hypothesis. A scenario has been proposed by Price et al. (1991) and Miller et al. (1991), among others, that iron concentration regulates community structure: phytoplankton species that respond to iron addition are mainly those of large size classes, whereas ecosystems with low ambient iron concentration are often dominated by the species of a small size, which are not limited by iron (Fig. 1); rather they are limited by light conditions and the grazing pressure from microzooplanktons having rapid growth rates. Living cells in the HNLC regions are thus not necessarily iron-limited while a dramatic response can be expected when a certain amount of iron is added through sporadic atmospheric deposition (Boyd et al., 1998) or equatorial wave activities (Bidigare and Ondrusek, 1996). The co-regulation by iron and grazing has found support from theoretical work (Pitchford and Brindley, 1999). There are also some simulation-oriented models that take iron into consideration. Denman and Peña (1999) presented a one-dimensional model with four compartments in which iron limitation is incorporated in a simple way, and asserted that its incorporation does help better simulate the ecosystem dynamics at Station P. Applying a biological model without explicit iron incorporation embedded in a full general circulation model (GCM), Chai et al. (1996) allowed to explore possible effects of iron limitation by imitating it by changes in the photosynthetic rate. Loukos et al. (1997) constructed an ecosystem model with iron limitation but with a single size class of phytoplankton, and demonstrated that the inclusion of iron

Grazing

NH4 and Urea

Small Phytoplankton (prasinophytes prymnesiophytes < 5 µm)

Microzooplankton (protozoans 20-200 µm)

Mesozooplankton (> 200 µm)

Fish

Large Phytoplankton (diatoms > 10 µm)

Fe

NO3

Sinking (carbon export)

Fig. 1. Simple food chain at Station P, showing bottom-up control of large phytoplankton by Fe and top-down control of small phytoplankton by microzooplankton grazing. Adapted from Harrison et al. (1999).

is really necessary to reproduce the observed amplitude of seasonal variation in the primary production. Leonard et al. (1999) developed a model with two size classes in phytoplankton, zooplankton and detritus and applied its one-dimensional version to investigate the relation between El Niño and the ecosystem in the equatorial ocean. Furthermore, a physiological model such as the one by Armstrong (1999a) may also help to assess the role that iron plays in maintaining HNLC. Geochemical studies are revealing the global distribution of dissolved iron in the deep layer (Johnson et al., 1997; Archer and Johnson, 2000) and of atmospheric iron supply (Duce and Tindale, 1991; Fung et al., 2000; Gao et al., 2001). Combining these efforts with a GCM and an elaborate ecosystem model, such as the ones developed by Leonard et al. (1999), should enable modelers to perform a direct basin-scale simulation of how the dust input and the equatorial wave activities are influencing the ecosystems. Modeling efforts have not so far been directed at investigating the impact of iron concentration on Si:N uptake ratio (Hutchins and Bruland, 1998; Takeda, 1998) and nitrogen fixation rate (Falkowski et al., 1998). 2.3 Silicate limitation The importance of silicate was pointed out by Tsunogai and Watanabe (1983) in relation to species succession in a phytoplankton bloom. When a sufficient quantity of silicate is available in the surface ocean, diatoms tend to dominate the ecosystem during a bloom until they deplete the silicate necessary for their shell formation. They then give way to phytoplankton species with much smaller cell sizes. The zero-dimensional models by Taylor et al. (1993) and Fasham and Evans (2000) did indeed simulate such a succession in the JGOFS North Atlantic Bloom Experiment (NABE) site (47°N, 20°W).

Numerical Model Approaches to Address Recent Problems on Pelagic Ecosystems

367

Dugdale et al. (1995) recognized that silicate is also significant in controlling the export flux out of the surface ocean. They deployed a zero-dimensional model and showed that the biological pump can be accelerated due to the sinking of diatom cells when there is adequate silicate. This function of silicate was termed the silicate pump. Their model results appear to be consistent with the feature seen in the data obtained on a cruise in the eastern equatorial Pacific, viz., that silicate distribution is more correlated with chlorophyll than nitrate. Dugdale and Wilkerson (1998) analyzed data obtained in the JGOFS EqPac study using the above model and suggested that silicate regulates the efficiency of the biological pump in the western equatorial Pacific too. Their argument makes the critical assumption that the ratio between silicate and nitrate uptake by diatoms is 1:1. This ratio, however, is known to be quite variable (Brzezinski, 1985). Hutchins and Bruland (1998) and Takeda (1998) reported that the ratio varies with ambient iron concentration, indicating that the limitation by silicate is not independent of that by iron. While both the equatorial and the subpolar Pacific are HNLC regions, the concept of the silicate pump seems to be more meaningful in the former, where the ambient Si:N ratio is lower. Wong and Matear (1999), however, found that the northeastern subpolar Pacific could sometimes become silicate-limited, compiling long-term data obtained at Station P. Basin- (or larger-) scale significance of the concept is not known. Though Gnanadesikan (1999) proposed a global three-dimensional simulation, he treated silicate cycling only and there was no coupling with nitrate or carbon. He focused on reproducing the silicate distribution in the deep layer, and found a high sensitivity to the inclusion of the eddy mixing parameterization according to Gent and McWilliams (1990). 2.4 Summary It is beyond the scope of this review, and well beyond the present writer’s ability, to formulate any final conclusion on the problem of HNLC maintenance. What may be said is that many factors can be contributing, and that it would be desirable to have a theoretical framework that would allow an integrated discussion on multiple factors at work. In this sense, the scenario mentioned in Subsection 2.2 (Price et al., 1991; Miller et al., 1991) seems plausible. The grazing pressure and the other traditional factors discussed in Subsection 2.1 are indeed all operating interactively here. The overall importance of the silicate pump has yet to be assessed. 3. Nutrient Supply to the Subtropical Gyre Recently, estimates of oceanic primary production have increased dramatically due to the improvement in methodology (cf., Welschmeyer et al., 1993). Estimates

368

M. Kawamiya

from the global maps of oceanic primary production by Longhurst et al. (1995), Antoine et al. (1996), and Behrenfeld and Falkowski (1997) are generally 2–3 times higher than those by Berger et al. (1987). Correspondingly, export production was estimated to be much higher than was previously supposed. Emerson et al. (1997) reported that the export production observed at Station ALOHA, in the subtropical gyre of the North Pacific (23°N, 150°W), amounts to ~2 molC m2 yr which seems to be too large to be balanced by nutrient inputs from the deeper layer. The vertical eddy diffusivity, which is often considered to be the only input mechanism in a subtropical gyre, was shown to be too small to account for the export (Ledwell et al., 1993). Doney et al. (1996) applied a one-dimensional model for the BATS site (31°N, 64°W), which is in the subtropical gyre in the North Atlantic, and obtained an export production similar to the observed. The three-dimensional model by Kawamiya et al. (2000b) also seems to reproduce the export production at a location corresponding to Station ALOHA. However, both models adopted large vertical diffusion coefficients (1.0 cm2 s–1 in the former, and 0.3 cm2 s–1 in the latter) compared to an observational estimate by Ledwell et al. (1993) (~0.1 cm2s –1). Haigh et al. (2001) employed an isopycnic model which requires less vertical diffusion for numerical stability than the other types of models, and obtained a much smaller export at Station ALOHA. Biogeochemical budgets in subtropical gyres has still not been closed. So far, discussions on this issue have related mainly to the North Atlantic. We therefore cite a number of works on the North Atlantic below. 3.1 Nitrogen fixation Karl et al. (1997) suggested the possibility that a substantial fraction of primary production may be attributed to the cyanobacterium Trichodesmium spp. which can assimilate atmospheric nitrogen (nitrogen fixation). Gruber and Sarmiento (1997) analyzed the data collected mainly during the Geochemical Ocean Sections Study (GEOSECS, 1972–1978) and found that areas with high nitrogen fixation rates tend to concentrate in subtropical gyres. They also emphasized that the role of nitrogen fixation in the oceanic biogeochemical cycle may have been underestimated, in accordance with Karl et al. (1997). Gruber et al. (1998) diagnosed the carbon budget at the BATS site, using a one-dimensional model in conjunction with the data. They concluded that the export production, not advection or gas exchange, should account for the carbon drawdown; and they hypothesized that nitrogen fixation is responsible for supporting the production in the absence of excess inorganic dissolved nitrogen. The modeling of nitrogen fixation was pioneered by Hood et al. (2001), although their model failed to explain simultaneously both the carbon draw down and the sediment trap flux.

3.2 Eddy pumping hypothesis Another way to supply the required nutrient was proposed by McGillicuddy and Robinson (1997). Using a very simple biological model coupled with a regional physical model reflecting the mesoscale environment of the Sargasso Sea, they suggested that nutrient inputs could be dominated by the eddy-induced upwelling. Similar results have been obtained by some other authors (Woods, 1988; Kimura et al., 1997). It has also been suggested by some other regional models that eddy-eddy interactions can cause significant upwelling (Yoshimori and Kishi, 1994; Kishi, 1994; McGillicuddy et al., 1995). The hypothesis by McGillicuddy and Robinson (1997) found support in the studies of McGillicuddy et al. (1998b) and Siegel et al. (1999), in which various data observed near the BATS site are extensively analyzed. Mahadevan and Archer (2000) examined the effect of model resolution with a regional biological-physical coupled model similar to that of McGillicuddy and Robinson (1997). Their results highlighted the importance of resolving features at the scale of, and smaller than, the Rossby radius of deformation. Oschlies and Garçon (1998) studied the eddy effect of enhancing primary productivity with an eddy-permitting, basin-scale, biological-physical coupled model for the North Atlantic. Their horizontal resolution (1/3° meridionally and 2/5° zonally) was in fact inadequate to resolve the whole range of wavenumbers contributing to the oceanic kinetic energy. This problem was partly remedied by assimilating sea level hight anomaly data from satellites. Their conclusion, however, partially contradicts the eddy enhancement hypothesis. The eddy enhancement of primary production is appreciable but not sufficient to explain the discrepancy in primary production between observational estimates and the model result simulated in one of their experiments imitating a coarse resolution model. In addition, the analysis by Garçon et al. (2001) on the results of the above model indicated that the main mechanism through which primary production is stimulated by eddies is horizontal advection, which again is inconsistent with the eddy enhancement hypothesis. Kawamiya and Kishi (2001) also appreciated the importance of horizontal advection in their regional three-dimensional models off Sanriku, Japan. Based on observations, they set up the model initial conditions corresponding to an actual date. Comparing the model’s time evolution with satellite observations, they investigated the cause of a sudden increase of chlorophyll detected by satellite in a warm core ring (WCR). The results showed that an interaction between the WCR and Kuroshio water brought about the increase through an intrusion of a productive water mass from outside the WCR. Spall and Richards (2000) pointed out that horizontal cross-frontal transport of nutrient due to instability flow can be a factor under-

lying nutrient supply into an oligotrophic region near a boundary between an oligotrophic and an eutrophic region. 4. Interannual to Interdecadal Variation 4.1 Impact of ENSO events Of the mechanisms that can generate interannual variations, the El Niño Southern Oscillation (ENSO) is thought to be the most powerful. There are a number of studies on observed ecosystem changes associated with ENSO. Most of them report those in the equatorial Pacific (e.g., Halpern and Feldman, 1994; Leonard and McClain, 1996; Foley et al., 1997; Chavez et al., 1998, 1999; Murakami et al., 2000). Others treat the North Pacific subtropical gyre (Karl et al., 1995; Letelier et al., 1996), the northeast Pacific (Takahashi, 1987; Wong et al., 1999; Wong and Matear, 1999) and the coast of California (Chavez, 1996). Some studies on interannual variation that do not deal with ENSO include the following: Conversi and Hameed (1997, 1998) stated that there is evidence of quasi-biennial oscillation (QBO) in zooplankton biomass data collected at Station P; Kishi et al. (2001) showed that a difference in the strength of winter mixing (which may or may not be related to ENSO) can explain most of the observed interannual variation in the timing and intensity of spring blooms in the Oyashio region. Chlorophyll a concentration decreases significantly during an ENSO event over the entire equatorial Pacific. This is consistent with the observation that a downwelling anomaly prevails during ENSO events and the upward nitrate flux is considerably diminished (Halpern and Feldman, 1994). Stoens et al. (1999) adopted this view, that nitrate concentration is critical for biology, in their three-dimensional modeling study with a simple nitrate cycling equation. Sometimes, however, it seems that sizable variability can be observed while macro nutrients such as nitrate, phosphate and silicate remain above the half-saturation concentrations for uptake (Foley et al., 1997). Recent work emphasizes the importance of iron because the equatorial Pacific is regarded as one of the oceanic provinces where iron is limiting the ecosystem (see Subsection 2.2). In the equatorial Pacific current system, the Equatorial Undercurrent (EUC) is said to be rich in iron because it originates in the subtropical gyre where iron is supposed to be relatively abundant. In addition to upwelling due to steady wind stress forcing, equatorial waves such as Kelvin waves and Tropical Instability Waves (TIWs) are attracting attention as mechanisms to supply iron to the surface ocean (Foley et al., 1997; Chavez et al., 1999). Leonard et al. (1999) carried out a simulation study applying a one-dimensional model with iron limitation, at the JGOFS EqPac site (0°, 140°W) for


369

5 years (1990–1994) including an ENSO event. Their model reproduced the striking decrease in plankton abundance during the ENSO event, and the shift in community structure reported by, e.g., Landry et al. (1997). According to the one-dimensional model by Friedrichs and Hofmann (2001), however, the shift is not playing a major role in creating the observed difference in primary production, nutrient concentration, and phytoplankton abundance between the ENSO and non-ENSO periods. Concerning the subpolar gyre, a long-term simulation for 1951–1980 at Station P was performed by McClain and Arrigo (1996) with a one-dimensional model, but no conspicuous variations in primary production and biomass were obtained by the model despite large variations in the simulated MLD and nitrate concentration. By contrast, Wong et al. (1998), in a simulation study for 1965–1990, drew the conclusion that the observed variations in MLD and solar radiation account for a significant portion of the variation in export production. The cause for this difference between the above models might be that Wong et al. (1998) used a zero-dimensional model, which could be too sensitive to changes in MLD. Karl et al. (1995) explained observed ecosystem changes in the subtropical gyre in terms of nitrogen fixation; when the water column is strongly stratified because of an ENSO event, the activity of nitrogen-fixing cyanobacteria is enhanced and thus a shift of the whole ecosystem occurs from nitrate limitation to phosphate limitation. Chavez (1996) ascribed the lower biological activities along the coast of California during an ENSO event to Kelvin waves; Kelvin waves propagating from the equatorial region deepen the thermocline, thereby reducing the supply of nutrient. 4.2 Interdecadal variations As long-term continuous data accumulate, evidence is emerging of decadal variations in pelagic ecosystems, recently thoroughly reviewed by Francis et al. (1998) and McGowan et al. (1998) for the northeast Pacific, and briefly by Miller and Schneider (2000) for the entire North Pacific. Probably the first reported observations on this topic are those of Venrick et al. (1987), who showed that chlorophyll concentration almost doubled after the mid-70s in the eastern subtropical gyre. They associated the doubling with the regime shift that occurred in 1976–77 (cf., Nitta and Yamada, 1989), and stated that the lower sea surface temperature (SST) after the regime shift is indicative of deeper vertical mixing which increases nutrient inputs. Brodeur and Ware (1992) invoked a similar explanation for the significant increase in zooplankton biomass in the eastern subpolor gyre. On the other hand, an extensive analysis of existing data sets by Sugimoto and Tadokoro (1997, 1998) proved that phytoplankton and

370

M. Kawamiya

zooplankton are decreasing in the western subpolar gyre, demonstrating that the way ecosystems respond to climate change can vary depending on the region, even within the same subpolar gyre. Polovina et al. (1995) showed that winter-spring MLD deepened in the subtropical gyre and shallowed in the subpolar gyre during 1960– 88. They also illustrated that the opposite trends in the two gyres both have tendency to increase plankton biomass, using a zero-dimensional model. Note that their statement that declining MLD in the subpolar gyre will increase biomass contradicts that of Brodeur and Ware (1992). Roemmich and McGowan (1995) found that zooplankton biomass declined by 80% from the 1950s to the 1980s near the coast of California. A possible cause is that the coastal upwelling along the coast was weakened by stronger stratification, which is inferred from the observed warming trend of SST. Some studies, however, suggest that the upwelling has intensified (Bakun, 1990; Schwing and Mendelsohn, 1997). Other studies related to interdecadal variations of pelagic ecosystems include those on fisheries (Beamish and Bouillon, 1993; Mantua et al., 1997; Finney et al., 2000), oceanic carbon absorption (Battle et al., 2000) and atmospheric nutrient deposition (Owens et al., 1992; Galloway et al., 1994). By now, the reader may have realized that the situation is complicated, and full of many pieces of information that are sometimes mutually inconsistent. It is the strongly nonlinear nature of the atmosphere-ocean-biosphere system that causes this complication (Francis et al., 1998). It is hoped that numerical models can serve to clarify the confusion arising from the information explosion. Haigh et al. (2001) embedded a simple biological model in an isopycnic general circulation model, and made the first attempt to assess the impact of the regime shift numerically. Their model showed a general deepening trend of winter-spring MLD in the subtropical gyre, and a shallowing trend in limited areas in the subpolar gyre. Correspondingly, plankton biomass increased in the subtropical gyre and in the areas of MLD shallowing in the subpolar gyre. This result is generally consistent with Polovina et al. (1995). Trends in MLD and biomass in the subpolar gyre simulated by Haigh et al. (2001) show complex spatial patterns. This complexity might be the cause of the apparent disagreement between the west and the east subpolar gyre observed in the trends in biomass (Brodeur and Ware, 1992; Sugimoto and Tadokoro, 1997, 1998). 5. Parameter Optimization One of the problems inherent in ecosystem modeling is that no one has yet devised the ultimate governing equations for ecosystems. Every modeler can, to some extent, create the governing equations as he or she would like them to be. This situation is in stark contrast to that of,

for instance, electromagnetics, where they have Maxwell’s equations for the two compartment system composed of an electronic and a magnetic field. Consequently, parameter values in an ecosystem model often have large uncertainty. The traditional approach to this problem of ambiguous parameters is first to take values from the existing literature and then perform sensitivity experiments within the ranges of uncertainty in parameters (e.g., Fasham et al., 1990); it is often the case that the parameter values in the reference run are tuned so that the model yields a satisfactory agreement with the data, before the sensitivity experiments are performed. What is still unsatisfactory in this approach is that one cannot be sure that there is no other parameter set that would give a better fit to the data. Hurtt and Armstrong (1999) pointed out a logical consequence of this approach; when one tries to improve a model, one never knows whether model misfit is due to poor parameter choice or due to inadequacy in the model’s structure. Mathematical techniques of optimization may greatly improve the capabilities of ecosystem modelers. The process of achieving the best fit of an ecosystem model to data can be mathematically formulated as the following: the difference between model results and data is quantified in a certain way; the difference, often termed the cost function, is regarded as a function of parameters; the parameter set that gives the best fit, namely that which minimizes the cost function, is searched for using an optimization technique. For example, Fasham and Evans (1995) adopted the form for the cost function J, J ( p) = ∑

(

x obs − x pred

), 2

(1)

where p denotes the parameter set, xobs and x pred the observed and predicted values, respectively, and the summation is taken over all variables and all observation times. Even though J(p) cannot be expressed in an analytical form, techniques are available for minimizing it (cf., Press et al., 1992). Those that have been applied to ecosystem models to date include, among others, conjugate gradient methods (Fasham and Evans, 1995; Evans, 1999; Kriest, 2001), least squares methods (Murnane, 1994; Prunet et al., 1996a, b; Murnane et al., 1996) and adjoint methods (Matear and Holloway, 1995; Lawson et al., 1996; Spitz et al., 1998; McGillicuddy et al., 1998a; Gunson et al., 1999; Schartau et al., 2001). A drawback common to the above techniques is that they are only able to detect a local minimum and the minimum they find may depend on the parameter set that one provides to a model as the initial guess. Because of the strong nonlinearity of an ecosystem model, it is likely that a cost function has multiple minima. Although this can be partly remedied by trying many initial guesses (Evans, 1999), it

is obviously more desirable to have techniques that are able to identify the global minimum. Techniques supposed to be capable of it have been developed, often including a stochastic step, and applied to ecosystem models: Matear (1995) and Hurtt and Armstrong (1996, 1999) employed simulated annealing; Vallino (2000) introduced the genetic algorithm; and Athias et al. (2000) presented an application of the technique called TRUST (Terminal Repeller Unconstrained Subenergy Tunneling). The first two techniques include a stochastic step while the third is deterministic. A direct advantage in using optimization techniques is, as suggested earlier, that one can be fairly sure that it is the model structure itself that should be improved when a model does not yield a satisfactory result. From this point of view, Spitz et al. (1998) claimed that the model of Fasham et al. (1990) may not be appropriate for reproducing data from the BATS site, which led to extensions to the model by Spitz et al. (2001). Hurtt and Armstrong (1999) found that implicit incorporation of the size dependence in two parameters (photosynthetic rate and the half saturation constant) may greatly enhance the model’s ability to reproduce the BATS data, even with an extremely simple model structure. A positive side effect of optimization is that one can determine the sorts of data that are really valuable for models. This is accomplished by checking the sensitivity of a cost function to each parameter, or eliminating data of some variable from optimization procedure and examining the magnitude of the effect of the elimination. Following this procedure, Evans (1999) demonstrated that data for bacteria and ammonium may not be especially useful in constraining a model that contains these variables as compartments. Gunson et al. (1999) found that satellite ocean color data alone may provide sufficient information for improving some of the parameter values of their model, as had been indicated by the success of the data assimilation study by Ishizaka (1990). This property of optimization techniques provides a way by which modeling efforts can contribute to the design and planning of observational programs. 6. Oceanic Provinces It is well known that the shape of seasonal variation of biomass in the surface ocean varies in space. Banse and English (1994) and Longhurst (1995) discussed spatial differences in chlorophyll variation using the CZCS data. The latter proposed an areal division that divides the global ocean into 56 provinces, and Longhurst (1998) produced exhaustive descriptions for all the provinces. His division has been utilized in a satellite-based primary production estimation algorithm (Longhurst et al., 1995), for employing regionally suitable parameter values. The diversity of ecosystem dynamics is sometimes ascribed to chemical factors such as the concentration of


371

Fig. 2. Relation among the areal division, vertical velocity, and MLD seasonal variation obtained by Kawamiya et al. (2000b). The red contour lines represent 0 m/day for the annual mean vertical velocity at the depth of 100 m. The blue contour lines represent 100 m for the amplitude of MLD seasonal variation (difference between the maximum and the minimum MLD). The red letter “U” indicates upwelling, while “D” is downwelling. The blue letter “H” indicates the regions where the amplitude of the MLD variation is greater than 100 m.

iron (Martin and Fitzwater, 1988) and silicate (Tsunogai and Watanabe, 1983; Dugdale et al., 1995), or biological ones such as the life history of zooplankton (Frost, 1987) and community structure (Price et al., 1991; Miller et al., 1991). On the other hand, many researchers have associated the diversity with physical environments. Obata et al. (1996), using climatological and CZCS data, demonstrated that on global scales the difference in timing of spring bloom onset is tightly coupled to that in surface warming. Applying a three-dimensional model for the North Pacific, Kawamiya et al. (2000a, b) demonstrated that the varying physical environments has the power to create the observed diversity in seasonal variation of ecosystem dynamics (Fig. 2). Evidence is emerging that the eastern and western subpolar Pacific have different dynamics (Taniguchi, 1999; Harrison et al., 1999). Shiomoto et al. (1998) demonstrated that the average standing stocks for the surface layer in summer are higher for nitrate and chlorophyll in the western subarctic gyre (WSG) than in the Alaskan gyre (AG) although both show similar primary production. The CZCS data indicate a chlorophyll increase in late fall in WSG while AS has a flat annual cycle (Banse and English, 1999). The model by Kawamiya et al. (2000a, b) yielded a conspicuous seasonal variation near the Oyashio region to the south of WSG, but this is closely connected with a deficiency of the model that Kuroshio overshoots northward and should be viewed with great caution. The model did not show any indication that the ecosystem varies in behavior from WSG to AG, even 372

M. Kawamiya

though the above observations suggest so. While WSG and AG literally constitute separated gyres, physical factors that directly affect biology such as MLD and solar radiation do not show a distinct contrast between them. The cause for the difference between WSG and AG should be considered with caution. Indeed, dust deposition rate is much higher in WSG (Duce et al., 1991), resulting in a much higher iron supply rate (Duce and Tindale, 1991; Fung et al., 2000; Gao et al., 2001). The modified iron hypothesis mentioned in Subsection 2.2 (Price et al., 1991; Miller et al., 1991) predicts that species composition should differ between WSG and AG, and it has been reported that it actually does (Shiomoto et al., 1998, and references therein). The two gyres may provide scientists with an excellent natural laboratory to test the modified iron hypothesis. A comparative study between Station P and Station KNOT (44°N, 155°E) will prove valuable (cf., Fujii et al., 2001). 7. Ecosystem Modeling in Future Given the ultimate mandate of the existing and forthcoming research projects mentioned in Section 1 to answer the concerns about possible environmental changes, the long-term goal of the ecosystem modeling community should be to improve the predictive skill of models on biogeochemical cycling of greenhouse gases and ocean biomass. After reading this review of the most pressing problems facing pelagic ecosystem modelers, the reader may have recognized that no single existing model can address all problems simultaneously. With the expanding

horizon of knowledge in related fields, modelers may be strongly tempted to make their models ever more comprehensive so that they are suitable for predicting the future. This is the point, however, where they should stop and think. Most of the observational results luring modelers into the pitfall of the complexity are from laboratory experiments or point-wise observations. Even if the observed phenomena are themselves ubiquitous, the required parameter values are poorly constrained when they are incorporated in a model representing large scales. This is where parameter optimization comes in. With this technique one can obtain parameter values that give the best fit to available data from a variety of oceanic regimes. But unfortunately, this is still not the end of the story. When one has neither a sufficient quantity nor quality of data, parameter optimization is not able to reduce parameter uncertainty sufficiently (Matear, 1995), for those very processes that one has newly incorporated. Modelers should therefore restrict the number of modeled processes so that most of parameter values are well constrained. From this point of view, it seems to be promising to model size structure and species composition through size spectra (Kriest and Evans, 1999; Armstrong, 1999b), instead of introducing many compartments and processes. Another philosophy, namely having all the desirable processes based on not-readily-rejected parameters, is of course possible but then many sensitivity experiments would be required to deduce some conclusion with any level of certainty (e.g., Fujii et al., 2001). This will seriously hinder research efficiency, especially for a large scale study. A sufficient quantity and quality of data will also become a prerequisite for the models’ efficiency, as computer architecture has been and will continue to be. As discussed in Section 5, parameter optimization can help here, to determine what kind of data is critical for improved efficiency. Among the five problems discussed in the previous sections, interannual and interdecadal issues will acquire more popularity with increasing availability of long-term data because of the close relevance with the overreaching mission of predicting the future. The relative importance of other problems, including ones we could not mention here (dynamics of dissolved organic matters, influence of numerics, species composition etc.), should be prioritized in terms of relevance to the problem of longterm ecosystem changes. Acknowledgements I thank M. J. Kishi for inviting me to write this review and for his carefull reading of the draft. Comments from the two anonymous reviewers improved the manuscript. This work has benefited from financial support by the German JGOFS program.

References Abraham, E. R., C. S. Law, P. W. Boyd, S. J. Lavender, M. T. Maldonado and A. R. Bowie (2000): Importance of stirring in the development of an iron-fertilized phytoplankton bloom. Nature, 407, 727–730. Antoine, D., J.-M. André and A. Morel (1996): Oceanic primary production: 2. Estimation at global scale from satellite (Coastal Zone Color Scanner) chlorophyll. Glob. Biogeochem. Cycles, 10, 57–69. Archer, D. E. and K. Johnson (2000): A model of the iron cycle in the ocean. Glob. Biogeochem. Cycles, 14, 269–279. Armstrong, R. A. (1999a): An optimization-based model of ironlight-ammonium colimitation of nitrate uptake and phytoplankton growth. Limnol. Oceanogr., 44, 1436–1446. Armstrong, R. A. (1999b): Stable model structures for representing biogeochemical diversity and size spectra in plankton communities. J. Plankton Res., 21, 445–464. Athias, V., P. Mazzega and C. Jeandel (2000): Selecting a global optimization method to estimate the oceanic particle cycling rate constants. J. Mar. Res., 58, 675–707. Bakun, A. (1990): Global climate change and intensification of coastal ocean upwelling. Science, 247, 198–201. Banse, K. and D. C. English (1994): Seasonality of Coastal Zone Color Scanner phytoplankton pigment in the offshore oceans. J. Geophys. Res., 99, 7323–7345. Banse, K. and D. C. English (1999): Comparing phytoplankton seasonality in the eastern and western surarctic Pacific and the western Bering Sea. Prog. Oceanogr., 43, 235–287. Battle, M., M. L. Bender, P. P. Tans, J. W. C. White, J. T. Ellis, T. Conway and R. J. Francey (2000): Global carbon sinks and their variability inferred from atmospheric O2 and δ13 C. Science, 287, 2467–2470. Beamish, R. J. and D. R. Bouillon (1993): Pacific salmon production trends in relation to climate. Can. J. Fish. Aquat. Sci., 50, 1002–1016. Behrenfeld, M. J. and P. G. Falkowski (1997): Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr., 42, 1–20. Behrenfeld, M. J., A. J. Bale, Z. S. Kolber, J. Aiken and P. G. Falkowski (1996): Confirmation of iron limitation of phytoplankton phytosynthesis in the equatorial Pacific Ocean. Nature, 383, 508–511. Berger, W. H., K. Fischer, C. Lai and G. Wu (1987): Ocean productivity and orgenic carbon flux. Part I: Overview and maps of primary production and export production. SIO Reference, Univ. California, San Diego. Bidigare, R. R. and M. E. Ondrusek (1996): Spatial and temporal variability of phytoplankton pigment distributions in the central equatorial Pacific Ocean. Deep-Sea Res. II, 43, 809– 833. Boyd, P. W., C. S. Wong, J. Merrill, F. Whitney, J. Snow, P. J. Harrison and J. Gower (1998): Atmospheric iron supply and enhanced vertical carbon flux in the NE subarctic Pacific: Is there a connection? Glob. Biogeochem. Cycles, 12, 429– 441. Boyd, P. W., A. J. Watson, C. S. Law, E. R. Abraham, T. Trull, R. Murdoch, D. C. E. Bakker, A. R. Bowie, K. O. Buesseler, H. Chang, M. Charette, P. Croot, K. Downing, R. Frew, M. Gall, M. Hadfield, J. Hall, M. Harvey, G. Jameson, J.


373

LaRoche, M. Liddicoat, R. Ling, M. T. Maldonado, R. M. McKay, S. Nodder, S. Pickmere, R. Pridmore, S. Rintoul, K. Safi, P. Sutton, R. Strzepek, K. Tanneberger, S. Turner, A. Waite and J. Zeldis (2000): A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization. Nature, 407, 695–702. Brodeur, R. D. and D. M. Ware (1992): Long-term variability in zooplankton biomass in the subarctic pacific ocean. Fish. Oceanogr., 1, 32–38. Brzezinski, M. A. (1985): The Si:C:N ratio of marine diatoms: Interspecific variability and the effect of some environmental variables. J. Phycol., 21, 1511–1536. Chai, F., S. T. Lindley and R. T. Barber (1996): Origin and maintenance of a high nitrate condition in the equatorial Pacific. Deep-Sea Res. II, 43, 1031–1064. Chavez, F. P. (1996): Forcing and biological impact of onset of the 1992 El Niño in central California. Geophys. Res. Lett., 23, 265–268. Chavez, F. P., P. G. Strutton and M. J. McPhaden (1998): Biological-physical coupling in the central equatorial Pacific during the onset of the 1997–98 El Niño. Geophys. Res. Lett., 25, 3543–3546. Chavez, F. P., P. G. Strutton, G. E. Friederich, R. A. Feely, G. C. Feldman, D. G. Foley and M. J. McPhaden (1999): Biological and chemical response of the equatorial Pacific Ocean to the 1997–98 El Niño. Science, 286, 2126–2131. Chisholm, S. and F. Morel (1991): What controls phytoplankton production in nutrient-rich areas of the open sea? Limnol. Oceanogr., 36, preface. Coale, K. H., K. S. Johnson, S. E. Fitzwater, R. M. Gordon, S. Tanner, F. P. Chavez, L. Ferioli, C. Sakamoto, P. Rogers, F. Millero, P. Steinberg, P. Nightingale, D. Cooper, W. P. Cochlan, M. R. Landry, J. Constantinou, G. Rollwagen, A. Trasvina and R. Kudela (1996): A massive phytoplankton bloom induced by and ecosystem-scale iron fertilization experiment in the equatorial Pacific Ocean. Nature, 383, 495–501. Conversi, A. and S. Hameed (1997): Evidence for quasi-biennial oscillation in zooplankton biomass in the Subarctic Pacific. J. Geophys. Res., 102, 15,659–15,665. Conversi, A. and S. Hameed (1998): Common signals between physical and atmospheric variables and zooplankton biomass in the Subarctic Pacific. ICES. J. Marine Science, 55, 739–747. Cooper, D. J., A. J. Watson and P. D. Nightingale (1996): Large decrease in ocean-surface CO2 fugacity in response to in situ iron fertilization. Nature, 383, 511–513. Dagg, M. (1993): Grazing by the copepod community does not control phytoplankton production in the subarctic Pacific Ocean. Prog. Oceanogr., 32, 163–184. Denman, K. L. and A. E. Gargett (1995): Biological-physical interactions in the upper ocean: The role of vertical and small scale transport processes. Ann. Rev. Fluid Mech., 27, 225–255. Denman, K. L. and M. A. Peña (1999): A coupled 1-D biological/physical model of the northeast subarctic Pacific Ocean with iron limitation. Deep-Sea Res. II, 46, 2877–2908. Denman, K., E. Hofmann and H. Marchant (1995): Marine biotic responses to environmental change and feedbacks to

374

M. Kawamiya

climate. p. 487–516. In Climate Change 1995, IPCC, ed. by J. H. Houghton et al., Cambridge Univ. Press, Cambridge. Doney, S. C. (1999): Major challenges confronting marine biogeochemical modeling. Glob. Biogeochem. Cycles, 13, 705–714. Doney, S. C., D. M. Glover and R. G. Najjar (1996): A new coupled, one-dimensional biological-physical model for the upper ocean: Applications to the JGOFS Bermuda Atlantic Time-series Study (BATS) site. Deep-Sea Res. II, 43, 591– 624. Duce, R. A. and N. W. Tindale (1991): Atmospheric transport of iron and its deposition in the ocean. Limnol. Oceanogr., 36, 1715–1726. Duce, R. A., P. S. Liss, J. T. Merrill, E. L. Atlas, P. Buat-Menard, B. B. Hicks, J. M. Miller, J. M. Prospero, R. Arimoto, T. M. Church, W. Ellis, J. N. Galloway, L. Hansen, T. D. Jickells, A. H. Knap, K. H. Reinhardt, B. Schneider, A. Soudine, J. J. Tokos, S. Tsunogai, R. Wollast and M. Zhou (1991): The atmospheric input of trace species to the wolrd ocean. Glob. Biogeochem. Cycles, 5, 193–259. Dugdale, R. C. and F. P. Wilkerson (1998): Silicate regulation of new production in the equatorial Pacific upwelling. Nature, 391, 270–273. Dugdale, R. C., F. P. Wilkerson and H. J. Minas (1995): The role of a silicate pump in driving new production. DeepSea Res., 42, 697–719. Eigenheer, A., W. Kühn and G. Radach (1996): On the sensitivity of ecosystem box model simulations on mixed-layer depth estimates. Deep-Sea Res., 43, 1011–1027. Emerson, S., P. Quay, D. Karl, C. Winn, L. Tupas and M. Landry (1997): Experimental determination of the organic carbon flux from open-ocean surface waters. Nature, 389, 951–954. Evans, G. T. (1999): The role of local models and data sets in the Joint Global Ocean Flux Study. Deep-Sea Res., 46, 1369–1389. Evans, G. T. and J. S. Parslow (1985): A model of annual plankton cycles. Biological Oceanography, 3(2), 327–347. Falkowski, P. G., R. T. Barber and V. Smetacek (1998): Biogeochemical controls and feedbacks on ocean primary production. Science, 281, 200–206. Fasham, M. J. R. (1995): Variations in the seasonal cycle of biological production in subarctic oceans: A model sensitivity analysis. Deep-Sea Res., 42, 1111–1149. Fasham, M. J. R. and G. T. Evans (1995): The use of optimization techniques to model marine ecosystem dynamics at the JGOFS station at 47°N 20°W. Phil. Trans. R. Soc. Lond., 348, 203–209. Fasham, M. J. R. and G. T. Evans (2000): Advances in ecosystem modelling within JGOFS. p. 417–446. In The Changing Ocean Carbon Cycle, Chapter 15, ed. by R. B. Hanson, H. W. Ducklow and J. G. Field, Cambridge Univ. Press, Cambridge. Fasham, M. J. R., H. W. Ducklow and S. M. Mckelvie (1990): A nitrogen based model of plankton dynamics in the oceanic mixed layer. J. Mar. Res., 48, 591–639. Finney, B. P., I. Gregory-Eaves, J. Sweetman, M. S. V. Douglas and J. P. Smol (2000): Impacts of climatic change and fishing on Pacific salmon abundance over the past 300 years. Science, 290, 795–799.

Flierl, G. R. and C. S. Davis (1993): Biological effects of gulf stream meandering. J. Mar. Res., 51, 529–560. Foley, D. G., T. D. Dickey, M. J. McPhaden, R. R. Bidigare, M. R. Lewis, R. T. Barber, S. T. Lindley, C. Garside, D. V. Manov and J. D. McNeil (1997): Longwaves and primary productivity variations in the equatorial Pacific at 0°, 140°W. Deep-Sea Res. II, 44, 1801–1826. Francis, R. C., S. R. Hare, A. B. Hollowed and W. S. Wooster (1998): Effects of interdecadal climate variability on the oceanic ecosystems of the NE Pacific. Fish. Oceanogr., 7, 1–21. Franks, P. J. S. (1995): Coupled physical-biological models in oceanography. Rev. Geophys., Supplement, 1177–1187. Friedrichs, M. A. M. and E. E. Hofmann (2001): Physical control of biological processes in the ceontral equatorial Pacific Ocean. Deep-Sea Res., 48, 1023–1069. Frost, B. W. (1987): Grazing control of phytoplankton stock in the open subarctic Pacific Ocean: A model assessing the role of mesozooplankton, particularly the large calanoid copepods neocalanus spp. Mar. Ecol. Prog. Ser., 39, 49– 68. Frost, B. W. (1993): A modelling study of processes regulating plankton standing stock and production in the open subarctic pacific ocean. Prog. Oceanogr., 32, 17–56. Frost, B. W. and M. J. Kishi (1999): Ecosystem dynamics in the eastern and western gyres of the Subarctic Pacific—a review of lower trophic level modelling. Prog. Oceanogr., 43, 317–333. Fujii, M., Y. Nojiri, Y. Yamanaka and M. J. Kishi (2001): A one-dimensional ecosystem model of time-series Station KNOT. Deep-Sea Res. II (submitted). Fung, I. Y., S. K. Meyn, I. Tegen, S. C. Doney, J. G. John and J. K. B. Bishop (2000): Iron supply and demand in the upper ocean. Glob. Biogeochem. Cycles, 14, 281–295. Galloway, J. N., H. Levy, II and P. S. Kasibhatla (1994): Year 2020: Consequences of population growth and development on deposition of oxidized nitrogen. Ambio, 23, 120–123. Gao, Y., Y. J. Kaufman, D. Kolber and P. G. Falkowski (2001): Seasonal distributions of aeolian iron fluxes to the global ocean. Geophys. Res. Lett., 28, 29–32. Garçon, V. C., A. Oschlies, S. C. Doney, D. McGillicuddy and J. Waniek (2001): The role of mesoscale variability on plankton dynamics in the North Atlantic. Deep-Sea Res. II, 48, 2199–2226. Gent, P. R. and J. C. McWilliams (1990): Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr., 20, 150–155. Gnanadesikan, A. (1999): A global model of silicon cycling: Sensitivity to eddy parameterization and dissolution. Glob. Biogeochem. Cycles, 13, 199–220. Gruber, N. and J. L. Sarmiento (1997): Global patterns of marine nitrogen fixation and denitrification. Glob. Biogeochem. Cycles, 11, 235–266. Gruber, N., C. D. Keeling and T. F. Stocker (1998): Carbon-13 constraints on the seasonal inorganic carbon budget at the BATS site in the northwestern Sargasso Sea. Deep-Sea Res., 45, 673–717. Gunson, J., A. Oschlies and V. Garçon (1999): Sensitivity of ecosystem paramters to simulated satellite ocean color data using a coupled physical-biological model of the North At-

lantic. J. Mar. Res., 57, 613–639. Haigh, S. P., K. L. Denman and W. W. Hsieh (2001): Simulation of the planktonic ecosystem response to pre- and post1976 forcing in an isopycnic model of the North Pacific. Can. J. Fish. Aquat. Sci., 58, 703–722. Halpern, D. and G. C. Feldman (1994): Annual and interannual variations of phytoplankton pigment concentration and upwelling along the Pacific equator. J. Geophys. Res., 99, 7347–7354. Harrison, P. J., P. W. Boyd, D. E. Varela, S. Takeda, A. Shiomoto and T. Odate (1999): Comparison of factors controlling phytoplankton productivity in the NE and NW subarctic Pacific gyres. Prog. Oceanogr., 43, 205–234. Hood, R. R., N. R. Bates, D. G. Capone and D. B. Olson (2001): Modeling the effect of nitrogen fixation on carbon and nitrogen fluxes at BATS. Deep-Sea Res. II, 48, 1609–1648. Hurtt, G. C. and R. A. Armstrong (1996): A pelagic ecosystem model calibrated with BATS data. Deep-Sea Res. II, 43, 653–683. Hurtt, G. C. and R. A. Armstrong (1999): A pelagic ecosystem model calibrated with BATS and OWSI data. Deep-Sea Res. II, 46, 27–61. Hutchins, D. A. and K. W. Bruland (1998): Iron-limited diatom growth and Si:N uptake ratios in a coastal upwelling regime. Nature, 393, 561–564. Ishizaka, J. (1990): Coupling of coastal zone color scanner data to a physical-biological model of the southeastern U. S. continental shelf ecosystem 3. nutrient and phytoplankton fluxes and czcs data assimilation. J. Geophys. Res., 95, 20,201–20,212. Johnson, K. S., R. M. Gordon and K. H. Coale (1997): What controls dissolved iron concentrations in the world ocean. Mar. Chem., 57, 137–161. Joos, F., J. L. Sarmiento and U. Siegenthaler (1991): Estimates of the effect of southern ocean iron fertilization on atmospheric CO2 concentrations. Nature, 349, 772–775. Kantha, L. H. and C. A. Clayson (2000): Small Scale Processes in Geophysical Fluid Flows. Academic Press, San Diego. Karl, D. M., R. Letelier, D. Hebel, L. Tupas, J. Dore, J. Christian and C. Winn (1995): Ecosystem changes in the North Pacific subtropical gyre attributed to the 1991–92 El Niño. Nature, 373, 230–234. Karl, D., R. Letelier, L. Tupas, J. Dore, J. Christian and D. Hebel (1997): The role of nitrogen fixation in biogechemical cycling in the subtropical North Pacific Ocean. Nature, 388, 533–538. Kasai, H., H. Saito, A. Yoshimori and S. Taguchi (1997): Variability in timing and magnitude of spring bloom in the Oyashio region, the western subarctic Pacific off Hokkaido, Japan. Fish. Oceanogr., 6, 118–129. Kawamiya, M. and M. J. Kishi (2001): Ecological-physical modelling for Warm-Core Ring 93A of Kuroshio extension focused on a blooming event inside WCR. J. Mar. Syst. (in press). Kawamiya, M., M. J. Kishi and S. Suginohara (2000a): An ecosystem-physical combined model for the North Pacific. Part I: Model description and the characteristics of the spatial distributions of biological variables. J. Mar. Syst., 25, 129– 157.


375

Kawamiya, M., M. J. Kishi and S. Suginohara (2000b): An ecosystem-physical combined model for the North Pacific. Part II: Mechanisms of the seasonal variation of chlorophyll. J. Mar. Syst., 25, 159–178. Kimura, S., A. Kasai, H. Nakata, T. Sugimoto, J. H. Simpson and J. V. S. Cheok (1997): Biological productivity of mesoscale eddies caused by frontal disturbances in the Kuroshio. ICES. J. Marine Science, 54, 179–192. Kishi, M. J. (1994): Prediction of phytoplankton growth in a warm-core ring using three dimensional ecosystem model. J. Oceanogr., 50, 489–498. Kishi, M. J., H. Motono, M. Kashiwai and A. Tsuda (2001): An ecological-physical coupled model with ontogenetic vertical migration of zooplankton in the northwestern Pacific. J. Oceanogr., 57, 499–507. Kolber, Z. S., R. T. Berber, K. H. Coale, S. E. Fitzwater, R. M. Greene, K. S. Johnson, S. Lindley and P. G. Falkowski (1994): Iron limitation of phytoplankton photosynthesis in the equatorial Pacific Ocean. Nature, 371, 145–148. Kriest, I. (2001): Two vertically resolved models for the Arabian Sea and their correspondence with observations. DeepSea Res. II (submitted). Kriest, I. and G. T. Evans (1999): Representing phytoplankton aggregates in biogeochemical models. Deep-Sea Res., 46, 1841–1859. Kurz, K. D. and E. Maier-Reimer (1993): Iron fertilization of the austral ocean—the Hamburg model assessment. Glob. Biogeochem. Cycles, 7(1), 229–244. Landry, M. R., R. T. Barber, R. R. Bidigare, F. Chai, K. H. Coale, H. G. Dam, M. R. Lewis, S. T. Lindley, J. J. McCarthy, M. R. Roman, D. K. Stoecker, P. G. Verity and J. R. White (1997): Iron and grazing constraints on primary production in the central equatorial Pacific: An EqPac synthesis. Limnol. Oceanogr., 42, 405–418. Lawson, L. M., E. E. Hofmann and Y. H. Spitz (1996): Time series sampling and data assimilation in a simple marine ecosystem model. Deep-Sea Res. II, 43, 625–651. Ledwell, J. R., A. J. Wilson and C. S. Low (1993): Evidence for slow mixing across the pycnocline from an open-ocean tracer release experiment. Nature, 364, 701–703. Leonard, C. L. and C. R. McClain (1996): Assesment of interannual variation (1979–1986) in pigment concentrations in the tropical Pacific using the CZCS. Int. J. Remote Sensing, 17, 721–732. Leonard, C. L., C. R. McClain, R. Murtugudde, E. E. Hofmann and L. W. Harding (1999): An iron-based ecosystem model of the central equatorial Pacific. J. Geophys. Res., 104, 1325–1341. Letelier, R. M., J. E. Dore and D. M. Karl (1996): Seasonal and interannual variations in photosynthetic carbon assimilation at Station ALOHA. Deep-Sea Res. II, 43, 467–490. Longhurst, A. (1995): Seasonal cycles of pelagic production and consumption. Prog. Oceanogr., 36, 77–167. Longhurst, A. (1998): Ecological Geography of the Sea. Academic Press, San Diego. Longhurst, A., S. Sathyendranath, T. Platt and C. Caverhill (1995): An estimate of global primary production in the ocean from satellite radiometer data. J. Plankton Res., 17, 1245–1271.

376

M. Kawamiya

Loukos, H., B. Frost, D. E. Harrison and J. W. Murray (1997): An ecosystem model with iron limitation of primary production in the equatorial Pacific at 140°W. Deep-Sea Res. II, 44, 2221–2249. Mahadevan, A. and D. Archer (2000): Modeling the impact of fronts and mesoscle circulation on the nutrient supply and biogeochemistry of the upper ocean. J. Geophys. Res., 105, 1209–1225. Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace and R. C. Francis (1997): A pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1078. Martin, J. H. and S. E. Fitzwater (1988): Iron deficiency limits phytoplankton growth in the north-east Pacific subarctic. Nature, 331, 341–343. Martin, J. H. and R. M. Gordon (1988): Northeast Pacific iron distributions in relation to phytoplankton productivity. Deep-Sea Res., 35, 177–196. Martin, J. H., K. H. Coale, K. S. Johnson, S. E. Fitzwater, R. M. Fordon, S. J. Tanner, C. N. Hunter, V. A. Elrod, J. L. Nowicki, T. L. Coley, R. T. Barber, S. Lindley, A. J. Watson, K. V. Scoy, C. S. Law, M. I. Liddicoat, R. Ling, T. Stanton, J. Stockel, C. Collins, A. A. R. Bidigare, M. Ondrusek, M. Latasa, F. J. Millero, K. Lee, W. Yao, J. Z. Zhang, G. Friederich, C. Sakamoto, F. Chavez, K. Buck, Z. Kolber, R. Greene, P. Falkowski, S. W. Chisholm, F. Hoge, R. Swift, J. Yungel, S. Turner, P. Nightingale, A. Hatton, P. Liss and N. W. Tindale (1994): Testing the iron hypothesis in ecosystems of the equatorial Pacific Ocean. Nature, 371, 123– 129. Matear, R. J. (1995): Parameter optimization and analysis of ecosystem models using simulated annealing: A case study at Sation P. J. Mar. Res., 53, 571–607. Matear, R. J. and G. Holloway (1995): Modeling the inorganic phosphorus cycle of the North Pacific using an adjoint data assimilation model to assess the role of dissolved organic phosphorus. Glob. Biogeochem. Cycles, 9, 101–119. McClain, C. R. and K. Arrigo (1996): Observations and simulations of physical and biological processes at Ocean Weather Station P, 1951–1980. J. Geophys. Res., 101, 3697– 3713. McGillicuddy, D. J. and A. R. Robinson (1997): Eddy-induced nutrient supply and new production in the Sargasso Sea. Deep-Sea Res., 44, 1427–1450. McGillicuddy, D. J., A. R. Robinson and J. J. McCarthy (1995): Coupled physical and biological modeling of the spring bloom in the North Atlantic (II): Three dimensional bloom and post-bloom processes. Deep-Sea Res., 42, 1359–1398. McGillicuddy, D. J., D. R. Lynch, A. M. Moore, W. C. Gentleman, C. S. Davis and C. J. Meise (1998a): An adjoint data assimilation approach to diagnosis of physical and biological controls on pseudocalanus spp. in the Gulf of MaineGeorges Bank region. Fish. Oceanogr., 7, 205–218. McGillicuddy, D. J., A. R. Robinson, D. A. Siegel, H. W. Jannasch, R. Johnson, T. D. Dickey, J. McNeil, A. F. Michaels and A. H. Knap (1998b): Influence of mesoscale eddies on new production in the Sargasso Sea. Nature, 394, 263–266. McGowan, J. A., D. R. Cayan and L. M. Dorman (1998): Cli-

mate-ocean variability and ecosystem response in the northeast Pacific. Science, 281, 210–217. Miller, A. J. and N. Schneider (2000): Interdecadal climate regime dynamics in the North Pacific Ocean: Theories, observations and ecosystem impacts. Prog. Oceanogr., 47, 355–379. Miller, C. B., B. W. Frost, H. P. Batchelder, M. J. Clemons and R. E. Conway (1984): Life histories of large, grazing copepods in a subarctic ocean gyre: Neocalanus plumchrus, Neocalamus cristatus, and eucalanus bungii in the norhteast Pacific. Prog. Oceanogr., 13, 201–243. Miller, C. B., B. W. Frost, P. A. Wheeler, M. R. Landry, N. Welschmeyer and T. M. Powell (1991): Ecological dynamics in the subarctic Pacific, a possibly iron-limited ecosystem. Limnol. Oceanogr., 36, 1600–1615. Murakami, H., J. Ishizaka and H. Kawamura (2000): ADEOS observations of chlorophyll a concentration, sea surface temperature and wind stress change in the equatorial Pacific during the 1997 El Niño onset. J. Geophys. Res., 105, 19,551–19,559. Murnane, R. J. (1994): Determination of the thorium and particulate matter cycling parameters at Station P: A reanalysis and comparison of least squares techniques. J. Geophys. Res., 99, 3393–3405. Murnane, R. J., J. K. Cochran, K. O. Buesseler and M. P. Bacon (1996): Least-squares estimates of thorium, particle, and nutrient cycling rate constants from the JGOFS North Atlantic Bloom Experiment. Deep-Sea Res., 43, 239– 258. Nitta, T. and S. Yamada (1989): Recent warming of tropical sea surface temperature and its relationship to the northern hemisphere circulation. J. Meteor. Soc. Japan, 67, 375–383. Obata, A., J. Ishizaka and M. Endoh (1996): Global verification of critical depth theory for phytoplankton bloom with climatological in situ temperature and satellite ocean color data. J. Geophys. Res., 101, 20,657–20,667. Oschlies, A. and V. Garçon (1998): Eddy-induced enhancement of primary production in a model of the North Atlantic Ocean. Nature, 394, 266–269. Owens, N. J. P., J. N. Galloway and R. A. Duce (1992): Episodic atmospheric nirtogen deposition to oligotrphic oceans. Nature, 357, 397–399. Peng, T.-H. and W. S. Broecker (1991): Factors limiting the reduction of atmospheric CO 2 by iron fertilization. Limnol. Oceanogr., 36(8), 1919–1927. Pitchford, J. W. and J. Brindley (1999): Iron limitation, grazing pressure and oceanic high nutrient-low chlorophyll (HNLC) regions. J. Plankton Res., 21, 525–547. Polovina, J. J., G. T. Mitchum and G. T. Evans (1995): Decadal and basin-scale variation in mixed layer depth and the impact on biological production in the Central and North Pacific, 1960–88. Deep-Sea Res., 42, 1701–1716. Press, W. H., S. A. Teukolsky, W. T. Vetterling and B. P. Flannery (1992): Numerical Recipes in Fortran. 2nd ed., Cambridge University Press, Cambridge, 963 pp. Price, N. M., L. F. Andersen and F. M. M. Morel (1991): The equatorial Pacific Ocean: Grazer controlled phytoplankton populations in an iron-limited system. Deep-Sea Res., 38, 1361–1378.

Prunet, P., J.-F. Minster, V. Echevin and I. Dadou (1996a): Assimilation of surface data in a one-dimensional physicalbiogeochemical model of the surface ocean: 2. adjusting a simple trophic model to chlorophyll, temperature, nitrate, and pCO2 data. Glob. Biogeochem. Cycles, 10, 139–158. Prunet, P., J.-F. Minster, D. Ruiz-Pino and I. Dadou (1996b): Assimilation of surface data in a one-dimensional physical-biogeochemical model of the surface ocean: 1. method and preliminary results. Glob. Biogeochem. Cycles, 10, 111– 138. Roemmich, D. and J. McGowan (1995): Climatic warming and the decline of zooplankton in the California Current. Science, 267, 1324–1325. Sarmiento, J. L. and J. C. Orr (1991): Three-dimentsional simulations of the impact of southern ocean nutrient depletion on atmospheric CO 2 and ocean chemistry. Limnol. Oceanogr., 36(8), 1928–1950. Schartau, M., A. Oschlies and J. Willebrand (2001): Parameter estimates of a zero-dimensional ecosystem model applying the adjoint method. Deep-Sea Res. II, 48, 1769–1800. Schwing, F. B. and R. Mendelsohn (1997): Increased coastal upwelling in the California Current system. J. Geophys. Res., 102, 12785–12786. Shiomoto, A., Y. Ishida, M. Tamaki and Y. Yamanaka (1998): Primary production and chlorophyll a in the northwestern Pacific Ocean in summer. J. Geophys. Res., 103, 24,651– 24,661. Siegel, D. A., D. J. McGillicuddy and E. A. Fields (1999): Mesoscale eddies, satellite altimetry, and new production in the Sargasso Sea. J. Geophys. Res., 104, 13,359–13,379. Spall, S. A. and K. J. Richards (2000): A numerical model of mesoscale frontal instabilities and plankton dynamics. I. Model formulation and initial experiments. Deep-Sea Res., 47, 1261–1301. Spitz, Y. H., J. R. Moisan, M. R. Abott and J. G. Richman (1998): Data asimilation and a pelagic ecosystem model: Parameterization using time series observations. J. Mar. Syst., 16, 51–68. Spitz, Y. H., J. R. Moisan and M. R. Abott (2001): Configuring an ecosystem model using data from the Bermuda Atlantic Time Series (BATS). Deep-Sea Res. II, 48, 1733–1768. Stoens, A., C. Menkès, M.-H. Radenac, Y. Dandonneau, N. Grima, G. Eldin, L. Mémery, C. Navarette, J.-M. André, T. Moutin and P. Raimbault (1999): The coupled physical-new production system in the equatorial Pacific during the 1992– 1995 El Niño. J. Geophys. Res., 104, 3323–3339. Sugimoto, T. and K. Tadokoro (1997): Interannual-interdecadal variations in zooplankton biomass, chlorophyll concentration and physical environment in the subarctic Pacific and Bering Sea. Fish. Oceanogr., 6, 74–93. Sugimoto, T. and K. Tadokoro (1998): Interdecadal variations of plankton biomass and physical environment in the North Pacific. Fish. Oceanogr., 7, 289–299. Takahashi, K. (1987): Response of Subarctic Pacific diatom fluxes to the 1982-83 El Niño disturbance. J. Geophys. Res., 92, 14,387–14,392. Takeda, S. (1998): Influence of iron availability on nutrient consumption ratio of diatoms in oceanic waters. Nature, 393, 774–777.


377

Taniguchi, A. (1999): Differences in the structure of the lower trophic levels of pelagic ecosystems in the eastern and western subarctic Pacific. Prog. Oceanogr., 43, 289–315. Taylor, A. H., D. S. Harbour, R. P. Harris, P. H. Burkill and E. S. Edwards (1993): Seasonal succession in the pelagic ecosystem of the North Atlantic and the utilization of nitrogen. J. Plankton Res., 15, 875–891. Totterdell, I. J. (1993): An annoted bibliography of marine biological models. p. 317–339. In Towards a Model of Ocean Biogeochemical Processes, ed. by G. T. Evans and M. J. R. Fasham, Springer-Verlag, Berlin. Tsunogai, S. and Y. Watanabe (1983): Role of dissolved silicate in the occurence of a phytoplankton bloom. J. Oceanogr. Soc. Japan, 39, 231–239. Turner, S. M., P. D. Nightingale, L. J. Spokes, M. I. Liddicoat and P. S. Liss (1996): Increased dimethyl sulphide concentrations in sea water from in situ iron enrichment. Nature, 383, 513–517. Vallino, J. J. (2000): Improving marine ecosystem models: Use of data assimilation and mesocosm experiments. J. Mar. Res., 58, 117–164. Venrick, E. L., J. A. McGowan, D. R. Cayan and T. L. Hayward (1987): Climate and chlorophyll a: Long-term trends in the central North Pacific Ocean. Science, 238, 70–72. Watson, A. J., C. S. Law, K. A. V. Scoy, F. J. Millero, W. Yao, G. E. F. M. I. Liddicoat, R. H. Wanninkhof, R. R. Barber and K. H. Coale (1994): Minimal effect of iron fertilization on sea-surface carbon dioxide concentrations. Nature, 371, 143–145. Watson, A. J., D. C. E. Bakker, A. J. Ridgwell, P. W. Boyd and C. S. Law (2000): Effect of iron supply on Southern Ocean CO2 uptake and implications for glacial atmospheric CO2. Nature, 407, 730–733.

378

M. Kawamiya

Welschmeyer, N. A., S. Storm, R. Goericke, G. DiTullio, M. Belvin and W. Petersen (1993): Primary production in the Subarctic Pacific Ocean: Project SUPER. Prog. Oceanogr., 32, 101–135. Wong, C. S. and R. J. Matear (1999): Sporadic silicate limitation of phytoplankton productivity in the subarctic NE Pacific. Deep-Sea Res. II, 46, 2539–2555. Wong, C. S., F. A. Whitney, R. J. Matear and K. Iseki (1998): Enhancement of new production in the northeast subarctic Pacific Ocean during negative North Pacific index events. Limnol. Oceanogr., 43, 1418 –1426. Wong, C. S., F. A. Whitney, D. W. Crawford, K. Iseki, R. J. Matear, W. K. Johnson, J. S. Page and D. Timothy (1999): Seasonal and interannual variability in particle fluxes of carbon nitrogen and silicon from time series of sediment traps at Ocean Station P, 1982–1993: Relationship to changes in subarctic primary productivity. Deep-Sea Res. II, 46, 2735–2760. Woods, J. D. (1988): Mesoscale upwelling and primary production. p. 7–38. In Toward a Theory on Biological-Physical Interactions in the World Ocean, ed. by B. J. Rothschild, Kluwer Academic Publishers, Dordrecht. Yamazaki, H. and D. Kamykowski (1991): The vertical trajectories of motile phytoplankton in a wind-mixed water column. Deep-Sea Res., 38, 219–241. Yoshimori, A. and M. J. Kishi (1994): Effects of interaction between two warm-core rings on phytoplnkton distribution. Deep-Sea Res., 41, 1039–1052. Yoshimori, A., J. Ishizaka, T. Kono, H. Kasai, H. Saito, M. J. Kishi and S. Taguchi (1995): Modeling of spring bloom in the western subarctic Pacific (off Japan) with observed vertical density structure. J. Oceanogr., 51, 471–488.