Eddy-resolving simulation of plankton ecosystem dynamics …jetsam.ocean.washington.edu/~frenzel/pub/Gruber_dsrI_2006...We study the dynamics of the planktonic ecosystem in the coastal

Deep-Sea Research I 53 (2006) 1483–1516

Eddy-resolving simulation of plankton ecosystem dynamics inthe California Current System

Nicolas Grubera,1,�, Hartmut Frenzela, Scott C. Doneyd, Patrick Marchesielloe,James C. McWilliamsa, John R. Moisanc, John J. Oramb,2, Gian-Kasper Plattnera,3,

Keith D. Stolzenbachb

aInstitute of Geophysics and Planetary Physics (IGPP) & Department of Atmospheric and Oceanic Sciences,

University of California at Los Angeles, Los Angeles, CA 90095, USAbCivil and Environmental Engineering, University of California at Los Angeles, Los Angeles, CA 90095, USA

cLaboratory for Hydrospheric Processes, Observational Science Branch, NASA/GSFC Wallops Flight Facility, Wallops Island,

VA 23337, USAdDepartment of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA

eIRD Centre de Bretagne, Plouzane, France

Received 25 March 2005; received in revised form 14 June 2006; accepted 14 June 2006

Available online 14 September 2006

Abstract

We study the dynamics of the planktonic ecosystem in the coastal upwelling zone within the California Current System

using a three-dimensional (3-D), eddy-resolving circulation model coupled to an ecosystem/biogeochemistry model. The

physical model is based on the Regional Oceanic Modeling System (ROMS), configured at a resolution of 15 km for a

domain covering the entire US West Coast, with an embedded child grid covering the central California upwelling region

at a resolution of 5 km. The model is forced with monthly mean boundary conditions at the open lateral boundaries as well

as at the surface. The ecological/biogeochemical model is nitrogen based, includes single classes for phytoplankton and

zooplankton, and considers two detrital pools with different sinking speeds. The model also explicitly simulates a variable

chlorophyll-to-carbon ratio. Comparisons of model results with either remote sensing observations (AVHRR, SeaWiFS)

or in-situ measurements from the CalCOFI program indicate that our model is capable of replicating many of the large-

scale, time-averaged features of the coastal upwelling system. An exception is the underestimation of the chlorophyll levels

in the northern part of the domain, perhaps because of the lack of short-term variations in the atmospheric forcing.

Another shortcoming is that the modeled thermocline is too diffuse, and that the upward slope of the isolines toward the

coast is too small. Detailed time-series comparisons with observations from Monterey Bay reveal similar agreements and

discrepancies. We attribute the good agreement between the modeled and observed ecological properties in large part to

the accuracy of the physical fields. In turn, many of the discrepancies can be traced back to our use of monthly mean

forcing. Analysis of the ecosystem structure and dynamics reveal that the magnitude and pattern of phytoplankton

biomass in the nearshore region are determined largely by the balance of growth and zooplankton grazing, while in the

offshore region, growth is balanced by mortality. The latter appears to be inconsistent with in situ observations and is a

ARTICLE IN PRESS

www.elsevier.com/locate/dsr

0967-0637/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.dsr.2006.06.005

�Corresponding author.

E-mail address: [email protected] (N. Gruber).1Now at Environmental Physics, Institute of Biogeochemistry and Pollution Dynamics, ETH Zurich, Zurich, Switzerland.2Now at San Francisco Estuary Institute, Oakland, CA 94621, USA.3Now at Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland.

result of our consideration of only one zooplankton size class (mesozooplankton), neglecting the importance of

microzooplankton grazing in the offshore region. A comparison of the allocation of nitrogen into the different pools of the

ecosystem in the 3-D results with those obtained from a box model configuration of the same ecosystem model reveals that

only a few components of the ecosystem reach a local steady-state, i.e. where biological sources and sinks balance each

other. The balances for the majority of the components are achieved by local biological source and sink terms balancing

the net physical divergence, confirming the importance of the 3-D nature of circulation and mixing in a coastal upwelling

system.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Phytoplankton dynamics; Nutrient cycling; Coastal biogeochemistry; California current; Upwelling

1. Introduction

The continental margins are among the mostproductive and biogeochemically active environ-ments on Earth. It is estimated that nearly half ofthe globally integrated oceanic primary productionand the bulk of sedimentary carbon burial occurs inthis narrow zone (about 300 km wide), which coversonly a few percent of the global ocean area (Walsh,1991; Smith and Hollibaugh, 1993; Muller-Kargeret al., 2005). Relative to plankton dynamics andbiogeochemical cycling in the open ocean (e.g.Fasham et al., 2001), comparatively little is knownabout the overall role of the continental margins inthe global cycling of elements, their naturalvariability, or their potential responses and feed-backs to global climate change (e.g. Doney, 1999;Liu et al., 2000).

Eastern boundary current (EBC) systems, such asthe California, Humboldt, Canary, and BenguelaCurrents, belong to the most productive coastalenvironments (Carr, 2002; Carr and Kearns, 2003),providing the base for food webs that support someof the most economically important fisheries. Thehigh rate of formation of organic matter byphytoplankton and the subsequent export of thismaterial into the ocean interior stimulate a veryrapid turnover of biologically important elements inthese systems (Wollast, 1991, 1998). Among manyconsequences, this provides a mechanism for takingup inorganic carbon from the near surface oceanand transporting it downward or toward the openocean as organic carbon, potentially making thesesystems a sink for atmospheric CO2 (Tsunogaiet al., 1999; Thomas et al., 2004).

The high biological productivity in these EBCsystems is fueled by the supply of nutrients fromupwelling, a result of the prevailing equatorwardblowing winds that push the near surface watersoffshore through Ekman transport, causing nutri-

ent-rich waters from mid-depths to upwell to thesurface. The physical environment of these EBCsystems is also characterized by slowly varyinglongshore currents and often intense meso- andsubmeso-scale variability. The latter leads to a tightcoupling between physical and biological processes,documented most evidently by the strong co-variance of sea surface temperature (SST) andchlorophyll (e.g. Denman and Abbott, 1994; Di-Giacomo and Holt, 2001). These observationsindicate clearly the important role of physicaltransports in initiating, sustaining, and dispersingbiological production and in determining theassociated cycling of elements.

Much effort has been spent on systematicobservations of physical, chemical, and biologicalprocesses in EBCs, (e.g. Coastal Upwelling Experi-ment (CUE), Coastal Ocean Dynamics Experiment(CODE), Coastal Ocean Processes (CoOP) in theCalifornia Current System (CCS)), but the oftenspotty spatial coverage and intermittent samplingmake it difficult to determine the pathways ofelemental flow through the system and to establishelemental budgets. As a consequence, relatively littleis known about the dynamics of elemental cycles inEBC regions and how they shape the magnitude andpattern of biological production.

A relatively recently developed method to addressthis question is the use of three-dimensional (3-D)coupled physical-ecological-biogeochemical models.Fasham et al. (1993) and Sarmiento et al. (1993)pioneered this approach by coupling a nitrogenbased, single phytoplankton functional group,single zooplankton, bacteria, and detritus modeldeveloped by Fasham et al. (1990) to a 3-D oceangeneral circulation model (OGCM) of the NorthAtlantic. They demonstrated that the large-scaledistribution of chlorophyll in the North Atlanticcan be simulated with a reasonable degree ofaccuracy. However, detailed comparisons with

ARTICLE IN PRESSN. Gruber et al. / Deep-Sea Research I 53 (2006) 1483–15161484

observations at time-series sites revealed that themodel’s representation of the underlying processessupplying the nutrients were not as satisfactory.This seminal work spawned a large number ofstudies, including, more recently, the use of eddy-resolving models (Oschlies and Garc-on, 1998;Oschlies, 2001; McGillicuddy et al., 2003) orecosystem models of substantially higher complexity(Chai et al., 2003; Gregg et al., 2003; Moore et al.,2004; Lima and Doney, 2004).

In contrast to the many efforts of applying such3-D coupled physical-ecological-biogeochemicalmodels to open ocean environments, there are fewerstudies in coastal environments and EBC systems(see Moisan et al., 2005 for a review). Moisan et al.(1996) were among the first who used a 3-D coupledmodel in an EBC setting, but their simulationextended over a few days only, and thereforepermitted investigation of only a limited aspect ofthe flow of material through the system. Most otherexisting investigations of biological-physical inter-actions in EBCs and their impact on ecology andbiogeochemistry have used reduced-order physicalmodels, such as 1-D models (e.g. Moloney andField, 1991), 2-D models (e.g. Walsh, 1975; Wro-blewski, 1977; Spitz et al., 2003) or box models witha specified upwelling flux of nutrients (e.g. Olivieriand Chavez, 2000; Ianson and Allen, 2002). Suchreduced-order physical models have been chosen fortheir computational efficiency, and the relative easewith which the results can be analyzed.

Over the last two decades, substantial progresshas been made in developing 3-D physical modelscovering limited domains (e.g. Princeton OceanModel (POM); Blumberg and Mellor, 1987; S-Coordinate Rutgers University Model (SCRUM);Song and Haidvogel, 1994; Regional OceanicModeling System (ROMS); Shchepetkin andMcWilliams, 2005). Furthermore, computationalpower and methods have improved dramatically,permitting the study of the dynamics of the couplingbetween biology, biogeochemistry, and physics in alimited domain at eddy-resolving resolution (e.g.Penven et al., 2001; Slagstad and Wassmann, 2001;Spitz et al., 2005; Kone et al., 2005).

In this study, we address the coupling of oceanphysics and planktonic ecosystems using such acomputational modeling approach in the context ofthe CCS. Our objectives are: (i) to describe themodel components and to evaluate the simulatedresults quantitatively with observations; (ii) to studythe impact of the physical-biological coupling on

ecosystem structure, with a particular emphasis onthe relative abundances of fixed nitrogen in thedifferent pools of the ecosystem. Our modelingapproach is based on the coupling of an NPZD-typeplanktonic ecosystem-biogeochemistry model to aUS West Coast configuration of ROMS (Marche-siello et al., 2003). We thereby make use of theembedding capabilities of ROMS, permitting us tosimulate this coupling at a fully eddy-resolvingresolution of about 5 km along most of the centralCalifornia Coast, while resolving the dynamics atabout 15 km resolution for the entire domain fromBaja California in the south to the US/Canadianborder in the north. A characteristic of oursimulations is that because of the use of animproved formulation of the lateral boundaryconditions, we are able to obtain multi-yearequilibrium solutions for both the physical andecosystem models. We will demonstrate that themodel reproduces the observed spatial and temporalvariability in the planktonic ecosystems to firstorder. We will also show that our ecosystem model,by construction, has limited success to simulatesimultaneously the highly productive coastal eco-systems and the relatively unproductive offshoreecosystems. As our focus is on biological produc-tivity and the cycling of elements in the coastalenvironment, we have selected the parameters of theecological model to represent upwelling ecosystems,accepting the shortcomings of this choice in theoffshore regions.

This paper is organized as follows: we firstdescribe the model and its components, and thensubject the results to a quantitative evaluation withdata based metrics. In the second part, weinvestigate the model simulated ecosystem structureand its dynamics, first focusing on the individualcomponents, and then assessing how fixed nitrogenis allocated to the individual ecosystem pools as afunction of the total fixed nitrogen content of themodel. We will use the solution of a mixed-layer boxmodel configuration as a reference.

2. Model description

The main components of the model are: (i) aphysical model that simulates the 3-D time-variantflow and mixing of ocean waters; (ii) an ecosystem-biogeochemical model that computes the source andsink terms for the reactive biological and chemicalcomponents, and (iii) an optical model thatcomputes the vertical penetration of short-wave

ARTICLE IN PRESSN. Gruber et al. / Deep-Sea Research I 53 (2006) 1483–1516 1485

radiation into the ocean, determining the amount oflight available for phytoplankton to grow. Wediscuss each model component in turn. Furtherdetails are given in the online material section.

2.1. Physical model

The physical model we employ is a United StatesWest Coast (USWC) configuration of ROMS(Marchesiello et al., 2003; Shchepetkin and McWil-liams, 2005). We give here only a brief overview ofthe physical model. The reader interested in moredetails is referred to Shchepetkin and McWilliams(2005) and Marchesiello et al. (2003).

ROMS solves the primitive equations of flow, andis discretized in horizontal curvilinear coordinatesand a generalized terrain-following vertical coordi-nate (s coordinate), which is configured to enhanceresolution near the sea surface. The prognosticvariables are surface elevation, barotropic andbaroclinic horizontal velocity components, potentialtemperature and salinity, and the state variables ofthe ecosystem-biogeochemical model (see below).

Of particular relevance for the ecosystem-biogeo-chemistry simulations is that vertical mixing in theinterior and in the planetary boundary layer iscalculated with the non-local, K-Profile Parameter-ization scheme (KPP: Large et al., 1994), whichperforms well in both measurement comparisonsand large-domain model solutions (Large and Gent,1999; Li et al., 2001). Of further relevance are theopen boundary conditions, which are formulated asa combination of outward radiation and flow-adaptive nudging toward prescribed external con-ditions (see Marchesiello et al., 2001). We alsobenefit from the embedding capabilities of ROMS(Penven et al., 2006), which we use to more finelyresolve the coastal region in the central part of ourdomain.

In the USWC configuration adopted here, theouter domain extends in latitude from the middle ofBaja California (281N) to the Canadian border(481N; approximately coincident with the subtropi-cal/subpolar gyre boundary) (see Fig. 1). The modeltherefore spans a domain that is about 2100 km longand 1300 km wide, and encompasses the CCS andits most energetic regions. The horizontal gridspacing of the outer grid is about 15 km (85� 170grid points). We embedded a reduced domain modelwith a resolution of about 5 km (Fig. 1) to morefinely resolve the most dynamic region of the CCS.The domain of this child grid is the central

California upwelling region and extends from PointConception ð34�350NÞ in the south to approximatelythe California/Oregon border ð41�510NÞ in thenorth. The child grid covers about 15% of theparent grid with an offshore extent of about 500 km,and an alongshore extent of about 800 km (95� 191points). The child grid is embedded in a one-waymanner within the parent grid, i.e. informationabout the state of the model is passed from theparent grid to the child grid at each time step, whileno such transfer occurs in the opposite direction.Our limited experience with two-way embeddingshows relatively little difference between one-wayand two-way embedding, so that we opted for one-way embedding because of its computationalsimplicity. Both grids have 20 vertical levels withvertical refinement near the surface to allow for areasonable representation of the surface boundarylayer and the euphotic zone everywhere inthe domain. On average, about eight levels arewithin the euphotic zone, defined here as the 1%light level.

2.2. Ecological-biogeochemical model

The ecological-biogeochemical model is a nitro-gen based NPZD model. It consists of a system ofseven coupled partial differential equations thatgovern the time and space distribution of thefollowing non-conservative scalars: nitrate (NO�3 ,subsequently denoted as Nn to reflect ‘‘new’’nitrogen, e.g. Dugdale and Goering, 1967), ammo-nium (NHþ4 , denoted as Nr to reflect ‘‘regenerated’’nitrogen), phytoplankton (P), zooplankton (Z),small (DS) and large (DL) detritus, and a dynamicphytoplankton chlorophyll-to-carbon ratio (y) (seeFig. 2). The state variables represent concentrationsof nitrogen within the different pools and have unitsof mmolNm�3, except for y, which has units ofmgChl�a ðmgCÞ�1 (the latter is converted to pernitrogen units in the model assuming a constantcarbon to nitrogen ratio of phytoplankton). Thetracer conservation equation for any of the aboveseven scalars B is given by

qB

qt¼ r � KrB�~u � rhB� ðwþ wsinkÞ

qB

qzþ JðBÞ,

(1)

where K is the eddy kinematic diffusivity tensor, andwhere r and rh are the 3-D and horizontal gradientoperators, respectively. The symbols ~u and w denotethe horizontal and vertical velocities of the


fluid, respectively, and wsink is the vertical sinkingrate of the biogeochemical components. Sinkingaffects all particulate pools, except for zooplankton.Finally, JðBÞ represents the source minus sink termfor each biogeochemical scalar, described in detailbelow.

Structurally, the model builds on a long history ofplanktonic ecosystem models, starting, amongothers, with those of Walsh and Dugdale (1971)and Steele (1974) (see Hood and Christian, 2006 fora historical overview). Our model is an evolutionarydescendent of the ecosystem model of Fasham et al.(1990), but was modified in a number of importantways. First, bacteria were eliminated as an explicitlymodeled state variable, and replaced with implicit

parameterizations of remineralization processes.Second, dissolved organic nitrogen (DON) anddetrital organic nitrogen were replaced with twopools of detritus, a large one that sinks fast, and asmall one that sinks slowly, the latter mimickingDON and fine, slow-sinking particles. The smalldetrital pool coagulates with phytoplankton, there-by forming large, fast sinking detritus. Third,sinking is modeled explicitly, thereby permittingall state variables to be advected laterally even in theaphotic zone. Finally, a variable chlorophyll-to-carbon ratio is considered here. Most of thesechanges were made to simplify the model and toreduce the number of parameters, while maintainingor improving the model’s skill.

ARTICLE IN PRESS

LosAngeles

San Francisco

Portland

Seattle

138°W 132°W 126°W 120°W

24°N

30°N

36°N

42°N

48°N

Monterey Bay

CHILD GRID: 5km Reso- lution

PARENT GRID:15km Resolution

Fig. 1. Map of the model domain. The outer line demarcates the domain of the parent grid, which has a horizontal resolution of about

15 km. The model thus encompasses the entire US West Coast and has an alongshore extent of about 2100 km and an offshore extent of

about 1300 km. The inner line shows the domain of the child grid that has a horizontal resolution of about 5 km. Also shown as points are

the station locations of CalCOFI line 70 that we use for comparing model results with in situ observations.

N. Gruber et al. / Deep-Sea Research I 53 (2006) 1483–1516 1487

The full set of source and sink terms, JðBÞ, foreach of the seven biogeochemical model compo-nents are written as

JðPÞ ¼ mmaxP ðT ; IÞ � gðNn;NrÞ � P

� ggrazZ Z

P

KP þ P� Zmort

P P

� kcoagP � ðPþDSÞ, ð2Þ

JðZÞ ¼ ggrazZ bassimZ Z

P

KP þ P

� ZmetabZ Z � Zmort

Z Z2, ð3Þ

JðNnÞ ¼ � mmaxP ðT ; IÞ � gðNnÞ � Pþ knitr

ðIÞ �Nr,

ð4Þ

JðNrÞ ¼ � mmaxP ðT ; IÞ � gðNrÞ � P� knitr

ðIÞ �Nr

þ ZmetabZ Z þ kremin

DSDS þ kremin

DLDL, ð5Þ

JðDSÞ ¼ ggrazZ � ð1� bassimZ Þ � ð1� Oegest

Z Þ � ZP

KP þ P

þ ZmortP Pþ Zmort

Z � ð1� OmortZ ÞZ2

� kcoagDS � ðPþDSÞ � kreminDS

DS, ð6Þ

JðDLÞ ¼ggrazZ � ð1� bassimZ ÞOegest

Z ZP

KP þ P

þ ZmortZ Omort

Z Z2

þ kcoag� ðPþDSÞ

2� kremin

DLDL,

ð7Þ

JðyÞ ¼ mmaxP ðT ; IÞ � gðNn;NrÞ

�mT

P ðTÞ � gðNn;NrÞ � ymaxffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ðmTP ðTÞÞ

2þ ðaPIyÞ2

q � y

0B@

1CA,

ð8Þ

where symbols with parentheses, such as mmaxP ðT ; IÞ

represent functions of the respective variables, whileall other symbols represent parameters. Subscriptsin the parameters and functions refer to the statevariable this parameter/function is associated with,while superscripts refer to the process. A completelist of the values and explanation of all parametersis given in Table 1. In choosing these parameters, weaimed at representing correctly the diatom-domi-nated, eutrophic coastal ecosystems and put lessemphasis on the oligotrophic offshore environ-ments. A description of the functions and the basisfor our choice of parameters is presented in theonline supplementary material, including a deriva-tion of the source and sink term for the chlorophyll-to-carbon ratio, JðyÞ. We describe next solely thegrowth parameters for phytoplankton, as their rolein regulating the phytoplankton distribution isdiscussed in detail in the results section.

Phytoplankton growth is limited in our model bythe amount of photosynthetically available radia-tion (PAR), I, and the concentrations of nitrate and

ARTICLE IN PRESS

Fig. 2. Flow diagram of the ecological-biogeochemical NPZD-type model. Boxes represent the state variables of the model, expressed in

terms of nitrogen concentration, while the arrows show the processes that transform nitrogen from one state variable to another. Not

shown is the dynamic chlorophyll-to-carbon ratio, y, of phytoplankton.

N. Gruber et al. / Deep-Sea Research I 53 (2006) 1483–15161488

ammonium. The effective growth is further con-strained by temperature, T. Following Fasham et al.(1990), we assume that light and nutrient limitationare independent of each other, permitting us towrite the total phytoplankton growth rate, mP, as

mPðT ; I ;Nn;NrÞ ¼ mmaxP ðT ; IÞ � gðNn;NrÞ, (9)

where mmaxP ðT ; IÞ is the temperature-dependent,

light-limited growth rate under nutrient repleteconditions and gðNn;NrÞ is a non-dimensionalnutrient limitation factor. The temperature-depen-dent, light-limited growth rate is given by

mmaxP ðT ; IÞ ¼

mTP ðTÞ � aP � I � yffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ðmTP ðTÞÞ

2þ ðaP � I � yÞ

2q , (10)

where aP is the initial slope in the growth versuslight relationship (see Table 1), and where I standsfor in situ PAR, given in Wm�2.

This light versus growth relationship is based onSmith (1936) and is identical to that used byFasham et al. (1990), except that it has beenmodified to take into account variations in thechlorophyll-to-carbon ratio, y. This modificationattempts to represent the expected increase inphotosynthesis in response to phytoplankton cellsallocating a higher percentage of their structure tothe photosynthetic apparatus, i.e. having higher y.This should lead to higher growth rates in regionsthat are nearly nutrient-replete, such as within theupwelling zone, or in the proximity of the subsur-face chlorophyll maximum. This modification is

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Table 1

Values, units, and definitions for the parameters of the ecological-biogeochemical model

Parameter Symbol Value Units

Phytoplankton parameters

Half-sat. conc. for nitrate uptake KNn 0.75 mmolm�3

Half-sat. conc. for ammonium uptake KNr 0.50 mmolm�3

Phytoplankton linear mortality rate ZmortP 0.024 day�1

Initial slope of P versus I relationship aP 1.0 mgC ðmgChl�aWm�2 dayÞ�1

Max. chlorophyll-to-carbon ratio ymax 0.0535 mgChl�a ðmgCÞ�1

Zooplankton parameters

Zooplankton grazing rate ggrazZ

0.6 day�1

Zooplankton assimilation efficiency bassimZ0.75 —

Zooplankton grazing half-sat. conc. for P KP 1.0 mmolNm�3

Zooplankton quadratic mortality rate ZmortZ 0.1 day�1 ðmmolm�3Þ�1

Zooplankton basal metabolism rate ZmetabZ

0.1 day�1

Zooplankton mortality alloc. fract. OmortZ

0.33 —

Zooplankton egestion alloc. fract. OegestZ

0.33 —

Remineralization and coagulation parameters

Nitrification rate in the dark knitr;max 0.05 day�1

Nitrification inhibition threshold I I thNr0.0095 Wm�2

Nitrification inhibition half-dose I IhdNr0.036 Wm�2

Particle coagulation rate kcoag 0.005 day�1 ðmmolm�3Þ�1

Remineralization rate of DS kreminDS

0.03 day�1

Remineralization rate of DL kreminDL

0.01 day�1

Remineralization rate of SD kreminSD

0.003 day�1

Sinking parameters

Sinking velocity of P wsinkP

0.5 m day�1

Sinking velocity of DS wsinkDS

1.0 m day�1

Sinking velocity of DL wsinkDL

10.0 m day�1

Optical parameters

Light attenuation coeff. for seawater ksw 0.04 m�1

Chl-a specific light attenuation coeff. kchla 0.024 m�1 ðmgChl�am�3Þ�1


supported by the observation that aP generallyincreases with decreasing cell volume, which in turntends to be associated with faster growing phyto-plankton populations (Geider et al., 1986). We didnot adjust our light versus growth relationship forthe fact that we are using a diurnally varying lightfield, while most previous studies used an integralform (Evans and Parslow, 1985). Sensitivity studiesshowed that the inclusion of a diurnally varyinglight field leads, on average, to lower growth withfixed mT

P ðTÞ and aP, mainly because of the concavenature of the light versus growth relationship (10).

The temperature dependent growth rate, mTP ðTÞ, is

parameterized using the relationship of Eppley(1972)

mTP ðTÞ ¼ ln 2 � 0:851 � ð1:066ÞT , (11)

where T is given in degrees Celsius. A factor of ln 2was added to change the units in the relationshipfrom the original doubling per day to day�1.

The nutrient limitation factor, gðNn;NrÞp1, isparameterized using a Michaelis–Menten equation,taking into account that ammonium is taken uppreferentially over nitrate, and that its presenceinhibits the uptake of nitrate by phytoplankton(Wroblewski, 1977). We use an additive functionweighted toward ammonium

gðNn;NrÞ ¼ gðNnÞ þ gðNrÞ

¼Nn

KNn þNn

KNr

KNr þNrþ

Nr

KNr þNr, ð12Þ

where KNn and KNr are the half-saturation con-stants for phytoplankton uptake of nitrate andammonium, respectively. The ammonium inhibitionterm for nitrate uptake, KNr=ðKNr þNrÞ is based onthe work of Parker (1993), and has been shown togive nearly the same results as the exponential decayrelationship originally used by Wroblewski (1977).

2.3. Optical model

We use a spectrally unresolved model to describethe penetration of PAR, I, into the water column.PAR is assumed to be attenuated by seawater withan attenuation coefficient, ksw, and by the presenceof chlorophyll with a chlorophyll specific attenua-tion coefficient, kchla (see Table 1 for values). Thesubsurface profile of PAR, IðzÞ is calculated byvertically integrating

dI

dz¼ �ðksw þ kchlaChl�aðzÞÞ � IðzÞ (13)

from the surface down to the bottom of the watercolumn. The concentration of chlorophyll a, Chl-a,in units of mgChl�am�3 is computed from thephytoplankton concentration and the chlorophyll-to-carbon ratio, y, assuming a constant C:N ratio of106:16.

Surface PAR is calculated from the total surfacesolar radiation used in the physical model, assumingthat PAR represents 43% of total solar radiation atthe sea surface. In order to resolve the diurnal cyclefor PAR, we constructed a diurnally varying PARfield from the monthly climatology that drives thephysical model. Details are given in the onlinesupplementary material.

2.4. Boundary and initial conditions, spinup, and

convergence

We force our physical model at the surface usingmonthly-mean climatologies of wind stress andfluxes of heat and freshwater derived from theComprehensive Ocean Atmosphere Data Set(COADS) (da Silva et al., 1994). Our choice ofneglecting synoptic and interannual variability inthe surface boundary condition is based on ourfocus on the mean state of the CCS and its seasonalevolution. All non-seasonal variability in themodel is therefore entirely intrinsic, i.e. generatedby the instabilities in the flow. For further details onthe lateral and surface boundary conditions, thereader is referred to the online supplementarymaterial.

The model is initialized with climatologicalobservations of temperature, salinity, and nitratefor the average of the months of December andJanuary (Conkright et al., 2002) and no flow. Theremaining state variables are set to very small, butnon-zero values. Winter values are used because thisis a period of minimum wind forcing and currentenergy, which reduces initial spinup problems.

From the above initial conditions, we run the15þ 5 km configuration for 10 years forward intime. Time-series for the different state variablesshow that this configuration converges after about3–4 years. Due to intense meso- and submesoscalevariability, and the chaotic nature of thesevariations, we find substantial year-to-year varia-tions in our results even after the spinup. In order toremove these interannual variations, we generallyshow and discuss 5-year averages from model years6–10.


3. Model evaluation

3.1. Ocean circulation

Under the influences of climatological-meanseasonal forcing from the atmosphere and subtro-pical-gyre open boundary conditions, a robustequilibrium state is established for the CCS on atime scale of a few years. Marchesiello et al. (2003)demonstrated that this solution has mean along-shore, cross-shore, and boundary upwelling currentssimilar to those estimated from hydrographicclimatologies. To evaluate our physical modelresults further, we compare here our results withthe climatological distribution of SST measured byAVHRR (Figs. 3 and 4a) and the vertical structureof upper thermocline properties as observed byCalCOFI (Fig. 5, data obtained from www.calcofi.

org) and at station H3/M1 in Monterey Bay(Fig. 6). We compare long-term averages in orderto remove the effect of mesoscale eddies.

The annual average ROMS solution representswell the observed SST pattern in the CCS (Fig. 3).In particular, the model successfully captures theoffshore extent of the cold upwelling region alongthe central coast of California. However, absolutevalues of modeled SST exhibit a cold bias of about1 1C relative to AVHRR (Fig. 3c) for most ofthe model domain. Modeled SST tend to bemore consistent with the available CalCOFI data(Fig. 4a). Some of the differences between the twoobservational estimates are likely due to spatial and

temporal representation error in the relativelysparsely sampled CalCOFI data, but we also needto consider that these differences reflect truechanges over time. The CalCOFI climatology spansthe period from 1949 to 2000, while the AVHRRclimatology was put together on the basis of theyears 1997–2002 only. As a result, the long-termwarming that has been observed in the offshoreregion of the CCS (Roemmich and McGowan,1995; Di Lorenzo et al., 2005) will lead to SST fromAVHRR being warmer, on average, than thosefrom CalCOFI. Given the observation of anapproximately 1.6 1C warming from the early1950s to the late 1990s (Roemmich and McGowan,1995; Di Lorenzo et al., 2005), the AVHRRclimatology is expected to be about 1 1C warmerthan a climatology based on the entire record. Sinceour model was forced with heat fluxes from theCOADS climatology, which was derived fromobservations collected between 1950 to 1979, weexpect it to be more consistent with CalCOFI SSTthan with AVHRR based SST. We therefore regardat least part of our cold SST bias relative toAVHRR as representing a real difference. The coldbias in the nearshore regions is, on average, smaller(Fig. 3c), indicating that the model is upwellingwater with approximately the right temperaturecharacteristics.

A comparison of the modeled versus observedvertical distribution of temperature along CalCOFIline 70 indicates that the simulated thermocline(Fig. 5, panels (a) and (b)) is at about the right

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130°W 125°W 120°W

33°N

36°N

39°N

42°N

45°N

130°W 125°W 120°W 130°W 125°W 120°W

(a) (b) (c)

Fig. 3. Comparison of annual mean SST between (a) the ROMS model in its USWC 15þ 5km configuration, (b) remote sensing

observations based on AVHRR, and (c) difference between AVHRR and ROMS (AVHRR-ROMS). The modeled annual mean SST is the

average of model years 6–10 of the 15þ 5km configuration, while the observed annual mean is based on the climatology for the years

1997–2002.


depth, but is underestimating the onshore slope,particularly in the nearshore region. Sensitivityexperiments and theoretical considerations haveshown that the large-scale onshore slope is to asignificant degree determined by the magnitude andsign of the curl of the wind stress. In contrast, theslope of the isotherms in the very nearshore region isprimarily determined by the alongshore wind stressalong the coast (X. Capet, personal communica-tion). In analogy to the open ocean, positive windstress curl leads to a heaving of the isotherms.Comparison of the COADS winds with other windproducts revealed that due to its coarse resolutionCOADS very likely underestimates the positivewind stress curl, explaining the underestimationof the offshore–onshore slope in the isotherms(X. Capet, personal communication). The modeledsalinity distribution (Fig. 5, panels (c) and (d)) is ingood agreement with observations, except that italso exhibits too small a slope towards the coast.Since the contribution of salinity to density varia-tions within the CCS is small (outside the ColumbiaRiver plume), salinity can almost be regarded as apassive tracer. Consequently, the underestimation

of the onshore slope in salinity can be interpreted assimply reflecting the same issues with the wind stressand its curl as expressed in the temperaturedeficiency.

An additional evaluation of our physical modelresults is the comparison with the climatologicalmean annual cycle observed at the M1/H3 mooringsite in Monterey Bay (Fig. 6) (Pennington andChavez, 2000). The model successfully reproducesthe key characteristics of the seasonal evolution ofthe upper thermocline at this nearshore site, with astrong shoaling of the isotherms in spring/earlysummer and a deepening of them in late summer,fall and winter. However, the modeled thermoclineis behaving too sluggishly relative to observations,with the amplitude of the shoaling of the isothermsbeing about 30% too small (e.g. for the 10 1Cisotherm, the shoaling amounts to about 70m in themodel, whereas the observations indicate a shoalingof more than 100m). We suspect that many of thesedifferences arise because of the absence of synopticvariability in our physical forcing.

We conclude that, in agreement with the sys-tematic evaluations of our US West Coast physical

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Fig. 4. Comparison of surface properties as a function of offshore distance along CalCOFI line 70. (a) Comparison of sea surface

temperature (SST) between ROMS, AVHRR, and CalCOFI. (b) Comparison of near-surface chlorophyll between ROMS, SeaWiFS, and

CalCOFI. CalCOFI line 70 starts at about 361N, 1221W at the California coast just south of Monterey Bay and extends out for nearly

700 km. See Fig. 1 for exact locations of stations.


solutions by Marchesiello et al. (2003), our modelsetup captures much of the spatial and temporalvariability in ocean physics at the scale of theupwelling region (several hundred kilometers) andover a climatological annual cycle. As will bediscussed below, this capability is key to obtainingrealistic ecosystem solutions given the tight couplingbetween biogeochemical, ecological, and physicalprocesses.

3.2. Biogeochemical and ecosystem properties

The comparison of the modeled annual meanchlorophyll with that inferred by SeaWiFS (Fig. 7)demonstrates the success and shortcoming of our

solutions even more clearly than SST. South of theregion around Cape Mendocino, i.e. south of about40.51N, the model successfully reproduces both theaverage chlorophyll concentration in the nearshore50 km, as well as the negative gradient along anonshore–offshore transect. Similar conclusions arefound by comparing modeled near surface chlor-ophyll with those measured in situ by the CalCOFIprogram in this region (Fig. 4b). However, adetailed inspection also reveals that the modelsimulates much higher chlorophyll in the verynearshore region, i.e. within the first 25 km,compared to both CalCOFI and SeaWiFS. In thecase of SeaWiFS, one would need to be careful witha quantitative comparison, since the retrieval

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Temperature (°C)

Salinity

Nitrate (mmol m-3)

Chlorophyll (mg m-3)

(a) (b)

(c) (d)

(f)(e)

(g) (h)

Fig. 5. Vertical sections along CalCOFI line 70 (see Fig. 1 for location) showing model results (left column), and observed quantities (right

column) for the annual mean. (a) and (b) Modeled and observed temperature ð�CÞ; (c) and (d) modeled and observed salinity; (e) and (f)

modeled and observed nitrate concentration ðmmolm�3Þ; (g) and (h) modeled and observed chlorophyll-a ðmgChl�am�3Þ.


algorithm has not been optimized for quantitativelydetermining chlorophyll in coastal waters (Tooleand Siegel, 2001).

In contrast to the relative success in the southernpart of our domain, chlorophyll begins to beunderestimated by our model north of CapeMendocino, i.e. 40.51N, and remains much lowerthan observations north of Cape Blanco (431N) allalong the Oregon coast. The largest absoluteunderestimation is found in the nearshore region,where annual mean chlorophyll simulated by themodel is well below 1mgChl�am�3, whereasobserved annual mean chlorophyll is above thisvalue along the entire coast, reaching values as highas 6mgChl�am�3 in a few locations. This negativebias extends far into the offshore regions, where

modeled chlorophyll fields are persistently lowerthan the observed ones by about a factor of three.We suspect that a substantial part of this bias is aresult of our use of COADS wind products that lackthe synoptic variability that is typical for theOregon/Washington coasts. Coastal upwelling inthese regions is much more intermittent, driven byshort bursts of strong upwelling, and interleaved byrelaxation periods (Huyer, 1983). Simulations bySpitz et al. (2005) clearly demonstrate the impor-tance of these upwelling events, which are not wellrepresented in our forcing. A monthly mean windproduct will therefore underestimate the upwelling.Furthermore, small-scale wind patterns aroundcomplex topography (Dong and McWilliams,2006), which are not represented in the COADS

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Fig. 6. Comparison of the modeled and observed climatological annual cycle of temperature (a and b), nitrate (c and d), and chlorophyll

(e and f) at the M1/H3 mooring site in Monterey Bay. Observations are from Pennington and Chavez (2000).


climatology, may further lead to biases. Futuresensitivity studies with differing wind products (cf.Capet et al., 2004) will hopefully resolve this bias.

Fig. 8 shows that the good agreement betweenmodeled and observed chlorophyll for the southernportion of our domain extends to all seasons. Thelargest difference is found in fall, when the offshoreextent of the high chlorophyll region is too small.The seasonal comparison also reveals that most ofthe large bias in the northern portion of ourdomain, i.e. north of Cape Mendocino, is drivenby a virtual absence of elevated chlorophyll in falland winter, the seasons of greater storminess. Thenegative bias does not disappear in spring andsummer, but is notably smaller.

A more quantitative comparison between themodeled and observed chlorophyll distributions isdepicted in the Taylor diagrams shown in Fig. 9(Taylor, 2001). A Taylor diagram combines infor-

mation about the correlation between the modeledand observed pattern (plotted as the angle betweenthe abscissa and the line drawn from the origin tothe point) with the standard deviation of themodeled field relative to that of the observed field(distance from origin to the point along the anglegiven by the correlation). On this plot, the observedpattern lies on the abscissa at distance 1 from theorigin, since it correlates perfectly with itself and hasa normalized standard deviation of 1. The centeredpattern root mean square (RMS) error is then givenby the distance between the point defined by themodeled pattern and the observed pattern. Theshorter this distance, the greater the agreementbetween the pattern.

Fig. 9a shows for the annual mean chlorophyllpattern over the entire domain (point denoted by‘‘DOMAIN’’) a correlation of about 0.6 betweenthe model simulated field and the observations

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Fig. 7. Comparison of (a) modeled and (b) observed annual mean chlorophyll concentration in near surface waters. The model fields are

five-year averages of the surface fields. The observations are based on SeaWiFS, averaged over the period from 1997 to 2002 (see text for

details).


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Fig. 8. As Fig. 7, except for individual seasons. Spring: April–June; Summer: July–September; Fall: October–December; Winter:

January–March.


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Fig. 8. (Continued)


inferred from SeaWiFS. The model underestimatesthe observed variance somewhat by having a standarddeviation around the annual mean of the entiredomain that is about 20% smaller than the observedstandard deviation. The large difference in the successof our model in simulating the pattern north andsouth of Cape Mendocino becomes evident bycomputing the correlations and standard deviationsseparately for these two regions. The southern part ofour domain has a correlation exceeding 0.8 with astandard deviation that is nearly identical to the

observed one, whereas the northern part has acorrelation of only about 0.3, with a standarddeviation that is only half of the observed one. As aresult, the RMS between the model and the observa-tions is more than 50% larger in the northerncompared to the southern part of the domain. Thenorth–south difference is somewhat less pronouncedin the nearshore region ‘‘Nearshore’’ (defined as a100km wide strip following the coastline).

The model simulates the observed seasonal cycleof chlorophyll less successfully than the annual

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(a)

(c)

(b)

(d)

Fig. 9. Taylor diagrams of model simulated chlorophyll (a–b) and sea surface temperature (SST) (c–d) in comparison to observed

estimates derived from SeaWiFS (chlorophyll) and AVHRR (SST). Annual mean comparisons are plotted in (a) and (c), while (b) and (d)

show the seasonal components, computed by subtracting at each grid point the annual mean from the monthly means. Each panel shows

separately the results for the entire model domain (DOMAIN), for the region north and south of Cape Mendocino (40.51N) (North and

South), for the 100 km wide nearshore region (Nearshore), and for this nearshore region divided into the region north and south of Cape

Mendocino (Nearshore North and Nearshore South). The root mean square (RMS) misfit between the model and the observational

estimates is given by the distance between the model point and the observation point indicated by the filled circle on the abscissa.


mean pattern (Fig. 9b). The correlation of theseasonal anomalies, i.e. of the monthly means minusthe annual means, for the entire domain is only 0.4,and none of the subregions has a correlation above0.6. The best agreement is found for the nearshoreregion in the northern part of our domain, but itsRMS is only marginally better than the worst RMSfound for any of the sub-regions in the annual meancase. This distinct difference between the annualmean and the seasonal component is not reflected inthe Taylor diagrams for SST (Fig. 9c–d), whichshow generally much better agreement and nomajor distinction between the two temporal com-ponents. The correlation for both annual mean SSTand its seasonal component amounts to nearly 0.99and the model variance is very close to the observedone. This is a remarkable success, but it needs to beadded that by plotting correlations and standarddeviations only, the model bias for SST identifiedabove (Fig. 3c) is not considered here.

In order to evaluate our subsurface modeledfields, we turn to the observations from theCalCOFI program and the M1/H3 mooring inMonterey Bay. The comparison of simulated andmeasured depth distributions of nitrate and chloro-phyll along CalCOFI line 70 presents a supportingpicture, although several mismatches can be identi-fied (Fig. 5). The mean nutricline in the model isvery close to the observed one, but reminiscent ofthe comparison with temperature and salinity, themodeled isolines fail to show the shoreward shoal-ing seen in the observations, particularly in thenearshore region.

The vertical distribution of the modeled chlor-ophyll compares relatively well with the availableCalCOFI data. The model successfully captures thenear-surface chlorophyll maximum in the nearshorezone, followed by a progressive deepening of thechlorophyll maximum as the transect moves off-shore, for reasons discussed in Section 4.1 below.The model tends to overestimate the chlorophyllconcentration of this deep chlorophyll maximumand also tends to exhibit too deep a penetration ofsizeable chlorophyll concentrations. This overesti-mation could be driven by a too large Chl-a:C ratio,or by an overestimation of the phytoplanktonbiomass. We unfortunately lack in situ observationsof either of these two quantities, so we cannotdistinguish between these two possibilities, but thesimulation of a too deep nitracline (Fig. 5) suggeststhat the latter cause may be the main source of thediscrepancy. We suspect that our chosen value for

the initial slope of the phytoplankton response tolight, i.e. aP, may be too large.

Fig. 6 shows that the model successfully capturesthe upward lifting of the nutricline at the MontereyBay site during the summer upwelling season,leading to the injection of new nutrients into thenear surface ocean. The model also simulates therelaxation during winter, when near surface nitratedrops to very low values and the entire upper 50mbecome nitrate deficient. As was the case fortemperature, the observed magnitude of this seaso-nal contrast is not fully reproduced by the model. Itunderestimates both the upward lifting of the highnitrate waters in summer and the downwardrelaxation of the low nitrate waters in winter. Inaddition, the thermocline nitrate concentration for agiven temperature tends to be lower in the model incomparison to the observations, potentially aggra-vating the nutrient injection bias by the too smallupward lifting of the thermocline in the summerseason. Despite these shortcomings, model simu-lated chlorophyll values in Monterey Bay compareremarkably well with observations, with the maindifference being a later onset of the seasonalchlorophyll maximum in summer. The observationsindicate a maximum in spring and early summer,whereas the seasonal maximum in the model occursin late summer.

In summary, the evaluation of our simulatedfields with in situ and remotely sensed biologicaland biogeochemical properties suggest that themodel captures the large-scale pattern and theirseasonal evolution remarkably well. Some mis-matches can be traced back to deficiencies in thephysical model, e.g. induced by the lack of synopticwind forcing and potential biases in the spatialresolution of the winds, while other deficiencies areclearly related to the ecological/biogeochemicalmodel. The largest structural problem is, perhaps,our use of a single phytoplankton functional groupmodel, which prevents the ecological model fromswitching between eutrophic and oligotrophic con-ditions. Since our focus here is on the eutrophicupwelling system, we accept this shortcoming.

4. Upper ocean ecosystem structure and dynamics

We next discuss the different biomass pools in theupper ocean with an emphasis on the processes thatcontrol their spatio-temporal pattern. We focus onthe central California upwelling system, where ourmodel evaluations provided us with confidence that


the model is able to capture the most importantprocesses governing upper ocean ecosystem dy-namics.

We limit our discussion to the distribution of thevarious ecosystem variables within the euphoticzone (defined here as the 1% light level), whosedepth varies between about 50m in the nearshoreand more than 100m in the offshore waters. Thisdistribution is entirely governed by chlorophyll,since this is the only property that absorbs light

besides water in our model. These euphotic zonedepths are everywhere deeper than the model’smixed layer depths, which are between 40 to 50mdeep in winter, and shoal to 20m and less insummer. These mixed layer depths are also sub-stantially shallower than the critical depth (Sverdr-up, 1953), which is of the order of 100m and more(Platt et al., 1991; Siegel et al., 2002). The criticaldepth is defined as the depth at which the verticallyintegrated rates of photosynthesis and community

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Fig. 10. Maps of the euphotic mean standing stocks for: (a) phytoplankton; (b) zooplankton; (c) small detritus, and (d) large detritus. All

properties are five-year annual averages in units of mmolNm�3.


respiration are equal. This suggests that althoughour model phytoplankton within the surface mixedlayer will experience light limitation, it has alwaysenough light to sustain positive net growth.

4.1. Phytoplankton

The annual mean distribution of phytoplanktonbiomass averaged over the euphotic zone (Fig. 10a)shows a slower transition than surface oceanchlorophyll from the high levels nearshore to thelower values offshore. This slower transition isprimarily due to phytoplankton biomass transition-ing from a surface maximum in the nutrient richnearshore region to a deep phytoplankton biomassmaximum in the offshore region (see Fig. 11c). Thevariable Chl-a:C ratio considered in our model haslittle influence on this result, since surface phyto-

plankton has a relatively constant ratio in ourmodel (see below).

The phytoplankton biomass transition from anearshore surface to an offshore subsurface max-imum is mainly a result of the interaction of lightand nutrient availability. Ample light and nutrientsin the surface waters of the nearshore regions permitphytoplankton to grow near maximum growth ratesthere. As evidenced in Fig. 12, however, surfacenitrate concentrations get rapidly drawn down asthe upwelled waters are transported offshore by themean Ekman drift and the abundant meso- andsubmesoscale circulation features. While phyto-plankton uptake draws surface nitrate down tolevels below 0:05mmolm�3 (more than 15 timeslower than the half-saturation concentration fornitrate uptake) in the offshore waters, surfaceammonium concentrations remain somewhat

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Fig. 11. Offshore vertical sections of annual mean ecosystem and biogeochemical properties across the central California upwelling

system: (a) nitrate; (b) ammonium; (c) phytoplankton; (d) zooplankton; (e) small detritus, and (f) large detritus. All properties are in units

of mmolNm�3. The section starts at about 36.51N, 1221W and extends to 33.51N, 1301W. The thick line indicates the annual mean depth

of the 1% light level, used here as the definition for the depth of the euphotic zone.


elevated between 0.05 and 0:10mmolm�3. This iscaused by an active ammonium cycle consisting ofrapid phytoplankton uptake and generation byzooplankton excretion and the breakdown of(small) detritus. However, this ammonium concen-tration is too low to sustain high phytoplanktongrowth rates in the near surface waters, since it is5–10 times smaller than our half-saturation con-centration for ammonium uptake. In contrast,elevated nitrate and ammonium concentrations atdepth in the offshore regions permit phytoplanktonto sustain an appreciable amount of biomass despitethe reduced light level (the deep phytoplanktonbiomass maximum is located only slightly above the1% light level). Although this essentially localgrowth argument explains most of the depthtransition of the phytoplankton biomass, some ofthe phytoplankton is transported there by down-welling and subduction processes as well.

The interaction of light and nutrients and theirinfluence on local growth, as well as the influence oflateral transport are particularly evident when theseasonal evolution of phytoplankton biomass alongthe same offshore section is investigated (seeFig. 13). In winter, phytoplankton biomass iscomparatively low and vertically nearly homoge-neously distributed. This is driven primarily by the

presence of deeper mixed layers, particularly off-shore, which mix the phytoplankton biomassvertically. In the offshore region, this dilution effectis not compensated by increased growth from themixing-induced enhanced nutrient input, because ofthe lower light levels available at this time of theyear. This light limiting effect is particularly strongin the lower parts of the euphotic zone. The lightlevels are, however, not low enough to remove thelight inhibition of nitrification, so that substantiallevels of ammonium are built up at the base of theeuphotic zone (Fig. 11b).

As the upwelling season starts in spring, nearsurface phytoplankton biomass in the nearshoreregion rapidly increases, forming a distinct nearsurface maximum. Some of this elevated phyto-plankton biomass is transported offshore anddownward forming distinct blobs of elevatedphytoplankton biomass at depth. Small detritalmaterial is transported alongside phytoplankton aswell, forming a source of ammonium that can thenfuel additional local growth. In summer, thenearshore and offshore systems appear to becomeuncoupled, with the nearshore region havingcontinued high growth supporting high phytoplank-ton biomass in near surface waters, while a strongphytoplankton biomass maximum develops near

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Fig. 12. Maps of the annual mean surface concentration of: (a) nitrate, and (b) ammonium in mmolNm�3.


the base of the euphotic zone in the offshore region.The offshore subsurface biomass maximum ismaintained through summer and into early fall bylateral advection and local growth, fueled by highnutrients and a relaxation of the local lightlimitation as the surface chlorophyll concentrationsdrop. In fall, when coastal upwelling in the centralCalifornia region starts to decrease, nearshorephytoplankton levels drop sharply, but the max-imum remains near the surface. In the meantime,the offshore region continues to show elevatedphytoplankton biomass at depth, which erode onlyslowly into winter.

The relative roles of light and nutrients incontrolling the growth rate of phytoplankton canbe diagnosed in more detail by splitting thephytoplankton growth rate, mPðT ; I ;Nn;NrÞ, intoits individual driving factors. We obtain thisseparation by extending the growth rate Eq. (9) bythe light-saturated growth rate mT

P ðTÞ, i.e.

mPðT ; I ;Nn;NrÞ ¼mmax

P ðT ; IÞ

mTP ðTÞ

� gðNn;NrÞ

� �� mT

P ðTÞ

ð14Þ

¼ gðIÞ � gðNn;NrÞ � mTP ðTÞ, ð15Þ

where gðIÞ is the ratio of the light-limited growthrates, mmax

P ðT ; IÞ, to the light-unlimited growth rate,

mTP ðTÞ. If either of these two factors, gðIÞ or

gðNn;NrÞ, is equal to 1, then phytoplankton growthis completely unlimited by this factor. If either ofthese factors is equal to 0, then phytoplanktongrowth is zero. In between, whichever factor issmaller has a stronger limiting influence on growth.Since both gðIÞ and gðNn;NrÞ have very similarconcave shapes, i.e. negative second derivatives, asmaller value implies a larger sensitivity in g to afractional change in either light or nutrients.Therefore, this condition satisfies the Monod-typecondition for a proximate limiting factor (seeMonod, 1949).

Fig. 14 shows annual mean vertical sections ofgðIÞ, gðNn;NrÞ, and the logarithm of their ratio,logðgðNn;NrÞ=gðIÞÞ, along the same offshore trans-ect as shown before for phytoplankton (Fig. 11c).The light and nutrient limitation terms show theexpected nearly orthogonal pattern. The light limit-ing term is maximal in near surface waters and thendecreases rapidly with depth, while the nutrientlimiting term is maximal at depth and decreasestoward the surface, except in the nearshore region.The latter pattern reflects directly the combinedconcentrations of nitrate and ammonium shown inFig. 11a and b. In contrast, by exhibiting asubstantial offshore decrease from a maximum inthe nearshore region, the light limiting term shows

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Fig. 13. Seasonal evolution of phytoplankton biomass in mmolNm�3 along an offshore vertical section across the central California

upwelling system: (a) January–March; (b) April–June; (c) July–September, and (d) October–December. The thick line indicates the annual

mean depth of the 1% light level.


more variations than expected based on thedistribution of PAR. The substantially lower valuesof gðIÞ in the offshore region are driven by lowvalues of the Chl-a:C ratio, which reduces theefficiency by which phytoplankton is capable ofabsorbing the incoming PAR, reducing its growthrate.

The section of the logarithm of the ratio,logðgðNn;NrÞ=gðIÞÞ, (Fig. 14c) demonstrates that,except for a relatively thin layer of about 20m depthnear the surface, phytoplankton growth in theannual mean is primarily limited by light. For mostof the euphotic zone, light limitation exceedsnutrient limitation by a factor of 2. In the nearshoreregion, this light limitation extends to the surface,while in the offshore region, nutrients are theproximate factor limiting phytoplankton growth.Part of the reason for the surprisingly largeimportance of light in controlling phytoplanktongrowth are the high concentrations of ammoniumsimulated in the euphotic zone of our model, whichleads to a substantial increase in the nutrientlimiting factor, gðNn;NrÞ. We suspect that withoutthe intense ammonium recycling exhibited by ourmodel, the dominance of nutrient limitation wouldextend much deeper into the thermocline, particu-

larly in the offshore region. A second caveat toconsider is our neglect of a possible iron limitation,such as reported by Hutchins et al. (1998) andHutchins and Bruland (1998) for the CCS. If iron isindeed an important factor limiting growth in theCCS, nutrient limitation may be more dominantthan simulated by our model.

4.2. Chlorophyll-to-carbon ratio

In many coupled physical-ecosystem models, thechlorophyll-to-carbon ratio is assumed to beconstant, with typical values of around20mgChl�a ðmgCÞ�1 (this corresponds to a car-bon-to-chlorophyll ratio of 50mgC ðmgChl�aÞ�1Þ(e.g. Sarmiento et al., 1993; Fasham et al., 1993).We therefore discuss next how our model simulatedchlorophyll-to-carbon ratio varies and how much ofa difference it makes in comparison to assuming itto be constant.

Fig. 15 reveals that the primary factor determin-ing the variations of y in our model is theavailability of light, as there is a strong increase iny from near surface values of around 10 to30mgChl�a ðmgCÞ�1 to values approaching ymax

of 53mgChl�a ðmgCÞ�1 at the bottom of the

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(a)

(c)

(b)

(d)

γ(I ) γ(N )

log(γ(I ) γ(N / )) µ (TPT )

Fig. 14. Offshore vertical sections of the factors that control phytoplankton growth across the central California upwelling system: (a) the

light limiting factor, gðIÞ; (b) the nutrient limiting factor, gðNn;NrÞ; (c) the logarithm of the ratio of the nutrient and light limiting factors,

i.e. logðgðNn;NrÞ=gðIÞÞ, and (d) the temperature dependent maximum growth rate, mTP ðTÞ in units of day�1. In (c) negative values indicate

that nutrient availability is the proximate factor limiting phytoplankton growth, while positive values indicate that light is the proximate

factor limiting phytoplankton growth. All panels show five-year annual mean values.


euphotic zone. Another factor appears to benutrient availability as nearshore surface waterstend to have higher y values than offshore waters.

These variations can be well understood byanalyzing Eq. (9) in the online supplementarymaterial, which was derived from the model ofGeider et al. (1997) and forms the basis for ourmodel of y. This equation describes the fraction offreshly photosynthetically fixed carbon, a, that isused for chlorophyll biosynthesis. Thus, when a ishigh, the chlorophyll-to-carbon ratio will increase,and vice versa. After inserting our parameterizationfor mmax

P ðT ; IÞ from (10), we obtain,

a ¼mT

P ðTÞ � gðNn;NrÞ � ymax� bffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

ðmTP ðTÞÞ

2þ ðaP � I � yÞ

2q , (16)

where we use the symbol b for denoting the productof the conversion factors rP

C:N and 12mgCðmmolCÞ�1.

In the case of high irradiance, typical for nearsurface waters, the second term within the squareroot is much larger than the first term, so that (16)converges to a solution that is inversely propor-tional to the amount of irradiance and proportionalto the nutrient concentration, expressed by thefactor gðNn;NrÞ:

ahigh light ¼mT

P ðTÞ � gðNn;NrÞ � ymax� b

aP � I � y. (17)

This explains the generally low y values in nearsurface waters, and also the increase toward themore productive nearshore region. While we lack

observations to assess our modeled distribution ofy, there exist time-series observations from nearsurface waters in the highly productive MontereyBay (F. Chavez, personal communication). Com-parison of our modeled surface Chl-a:C ratios forMonterey Bay surface waters show an excellentagreement with these observations. Both model andobservations have a winter maximum of y withvalues around 35mgChl�a ðmgCÞ�1, decreasing to asummer minimum of around 25mgChl�a ðmgCÞ�1.

In the case of low irradiance, as encountered atthe bottom of the euphotic zone, the first termwithin the square root is much larger than thesecond term, so that (16) converges to a solutionthat is determined by the nutrient status and ymax:

alow light ¼ gðNn;NrÞ � ymax� b. (18)

Since nutrients often tend to be well above theirhalf-saturation constants at depth, the factorgðNn;NrÞ is near 1 there (see Fig. 14b), explainingwhy y converges to ymax at the bottom of theeuphotic zone.

The large horizontal and vertical variations in ytend to cancel out in the vertical average over theeuphotic zone (Fig. 15c), so that the euphoticmean y varies only between about 20 and30mgChl�a ðmgCÞ�1. Most of the euphotic meanvariations can be traced to either higher growthrates (nearshore) or lower average light levels(higher latitudes). This means that the non-linear-ities in (16) are relatively small, so that its verticalintegral is primarily determined by the amount oflight arriving at the sea surface.

ARTICLE IN PRESS

Fig. 15. Maps of the chlorophyll-to-carbon ratio, y in units of mgChl�a ðmgCÞ�1: (a) surface y; (b) y at the bottom of the euphotic zone,

and (c) euphotic zone mean y. The euphotic zone mean y has been calculated from the euphotic mean Chl-a and phytoplankton biomass.


Given the relatively small variations of theeuphotic mean y, does our consideration of avariable chlorophyll-to-carbon ratio matter (seealso discussion by Doney et al., 1996; Armstrong,2006)? The benefit is relatively small when thesimulation of euphotic mean phytoplankton bio-mass and the associated chlorophyll is considered,but has a substantial influence on their verticaldistributions. A particularly large impact of using avariable ratio exists for the interpretation of satellitechlorophyll. Had we used a canonical value of25mgChl�a ðmgCÞ�1 for converting our modelsimulated phytoplankton biomass into chlorophyll,we would have obtained higher chlorophyll nearlyeverywhere, except for the very nearshore region,where we would have obtained lower chlorophyll.This would have given us rather different skill scoresin the Taylor diagram (Fig. 9). The variablechlorophyll-to-carbon ratio is also of great impor-tance when model simulated chlorophyll is com-pared to in situ observations, particularly atdepth. In this case, using a canonical y of25mgChl�a ðmgCÞ�1 would lead to much lowerchlorophyll values, from which one would concludea very substantial negative bias in the model.

4.3. Zooplankton

The annual mean zooplankton distribution in theeuphotic zone exhibits an offshore gradient that ismore pronounced than that of phytoplankton(compare panels a and b in Fig. 10). Furthermore,zooplankton in the nearshore region tends to bemore abundant in the southern part of the domain,while phytoplankton biomass is meridionally moreevenly distributed. In the offshore regions, zoo-plankton disappears in regions where phytoplank-ton still maintains a substantial biomass. The reasonfor this result is that although phytoplanktonbiomass is well above zero, it is below the minimumlevel required to sustain zooplankton in our model.In steady-state, this minimum phytoplankton level,Pmin can be computed from the zooplanktonconservation equation by setting JðZÞ in (3) tozero, solving the resulting equation for Z, and thensetting that equation to zero. The resulting equation

Pmin ¼KP

ggrazZ � bassimZ =Zmetab

Z � 1, (19)

shows that with the parameter values from Table 1,the minimum phytoplankton biomass to sustainzooplankton, Pmin, is 0:29mmolNm�3. Compar-

ison of this steady-state minimum value with thedistribution of phytoplankton biomass in Fig. 10ashows that offshore phytoplankton biomass isindeed too low to sustain a zooplankton population.

This is fundamentally inconsistent with observa-tions. The phytoplankton threshold for zooplank-ton growth is an artifact of our model structure andparameter choices. In reality, the offshore transitionfrom large to small phytoplankton has a corre-sponding transition of the dominant grazers frommesozooplankton to microzooplankton. Since wechose our zooplankton parameters to mimic meso-zooplankton, our model is structurally inept tomimic this transition. As a result, zooplanktondisappears from the offshore system, makingcoagulation and phytoplankton mortality ratherthan grazing the dominant loss terms for phyto-plankton there, which is opposite to what is knownabout phytoplankton loss in oligotrophic systems(e.g. Roman et al., 2002).

In the vertical, zooplankton in our model nearlyalways shows a maximum near the surface, asevidenced in its annual mean distribution (Fig. 11d).This surface maximum is a result of the nearshoreregion being the only region that can sustain azooplankton population. Offshore values of phyto-plankton biomass, even at the depth of the phyto-plankton biomass maximum, are nearly alwaysbelow the above threshold of 0:29mmolNm�3,except for brief periods in summer, so thatzooplankton has great difficulties growing there.

The abundance and distribution of the simulatedzooplankton population is highly sensitive to ourchoice of the zooplankton parameters, which arenot well constrained by the literature. Unfortu-nately, there exist very few zooplankton biomassobservations, to which we can quantitatively com-pare our model results. This is because mostzooplankton observations are reported as zooplank-ton biomass displacement volumes. Roemmich andMcGowan (1995) report for the upper ocean andfor the stations inshore of 100 km along CalCOFIline 80 (extending southwestward from Point Con-ception) mean values of about 250ml zooplanktonvolume per 1000m3 seawater strained for the period1951–1957, decreasing to about 60ml ð1000m3Þ

�1

for the period 1987–1993. Using the recommendedconversion factor of 96mgC ðml zooplanktonÞ�1 ofCushing et al. (1958) and assuming a fixed C:N ratioof 6.6, these volumes correspond to a zooplanktonbiomass ranging from 0:07mmolNm�3 (1987–1993)to 0:3mmolNm�3 (1951–1957). The long-term


climatology of zooplankton biomass for the near-shore CCS (O’Brien et al., 2002) suggests somewhathigher values with biomass levels between 0.1 and1mmolNm�3, in agreement with detailed observa-tions made in Monterey Bay (B. Marinovic, pers.comm.). Our simulated euphotic mean zooplanktonbiomass levels of between 0.1 and 0:6mmolNm�3

(Fig. 10b) compare therefore well in magnitude tothese in situ observations. More detailed compar-isons with a more careful consideration of thedisplacement volume to biomass conversion factorare needed, however, to evaluate our zooplanktonsimulations more quantitatively.

4.4. Detrital pools

The two modeled detrital pools exhibit strikinglydifferent annual mean distributions within theeuphotic zone (Fig. 10). Small detritus has adistribution very similar to phytoplankton biomassboth in terms of pattern and magnitude, while largedetritus is essentially concentrated very nearshore,with concentrations that are an order of magnitudesmaller than that of small detritus. Most of theonshore–offshore contrast is due to their differentsinking characteristics. Given our choices for sink-ing speeds for the two detrital pools and theirremineralization rates (see Table 1), the reminer-alization length scales for the two pools varydramatically between 30m for small detritus and1000m for large detritus. As a result, small detritusbarely sinks, making it susceptible to offshoretransport, while large detritus, once formed, dis-appears very rapidly from the euphotic zone. Thisdifference is illustrated in the vertical sections (cf.Fig. 11, compare panels e and f), which show atongue of high concentrations of large detritusextending from the euphotic zone into the oceaninterior, while the small detritus has no appreciableconcentrations below 200m.

A second, albeit less important factor causing thelarge onshore–offshore difference in the distributionof small and large detritus are their differingformation mechanisms. In the nearshore region,phytoplankton and small detritus concentrationsare high, making coagulation an important sink forthese two components. This is because of the squaredependence of coagulation on the phytoplanktonand small detritus concentrations. In addition,zooplankton is abundant, further increasing theproduction of large detritus by its mortality as wellas by sloppy feeding and the production of fecal

pellets. In contrast, phytoplankton, small detritus,and zooplankton concentrations are small in theoffshore region, making coagulation less efficientand all the zooplankton formation mechanisms forlarge detritus virtually absent.

The similarity in pattern and magnitude of smalldetrital material to phytoplankton will be discussedin detail in the next section. In summary, thesimilarity in the offshore region can be explained byconsidering the steady-state condition of formationequalling loss. In the nearshore region, steady-stateconditions would require much higher small detritalmaterial concentrations, but those are not attainedbecause the residence time of waters in these regionsis too short for small detritus to come to equili-brium.

Given that the parameters governing phytoplank-ton mortality, particle remineralization, and coagu-lation are not well constrained in the literature, ourparameter choices need to be viewed as somewhatarbitrary. We can evaluate the combined impact ofthese parameters by comparing the simulateddetrital fields with observations. Unfortunately, weare not aware of any systematic assessment ofparticulate organic matter (POM) in the CCS,except for a few observations that indicate thatliving biomass is about half of total POM (Eppley etal., 1983; Eppley, 1986). Gardner et al. (2006)recently estimated the distribution of POM in theocean using a combination of in situ measurementsand satellite observations. They showed that in theCCS, about 1% of POC in mgm�3 exists in theform of Chl-a in mgChl�am�3. Dividing thisnumber with our average surface ocean chloro-phyll-to-carbon ratio, y, of about 20 mgChl�aðmgCÞ�1, we estimate a phytoplankton biomass tototal organic matter ratio of about 0.5, in excellentagreement with our results. This agreement needs tobe viewed cautiously, however, as Gardner et al.(2006) developed their algorithm for the estimationof POM for open ocean environments, and there-fore this algorithm may not work well in the moreturbid coastal waters.

5. Nitrogen allocation

A powerful tool to understand ecosystem struc-ture is the analysis of how nitrogen is allocated tothe different nitrogen pools as a function of the totalamount of fixed nitrogen in the system (see e.g.DeAngelis, 1992; Sarmiento and Gruber, 2006). It is


instructive, however, to first investigate the relativeallocation in the spatial context.

Fig. 16 shows the annual mean concentrations ofthe six fixed nitrogen bearing ecosystem statevariables in the near surface ocean as a functionof longitude (offshore distance) for a section acrossthe central California upwelling system. The totalamount of surface nitrogen in the system decreasesfrom a maximum at the coast relatively mono-

tonically with increasing offshore distance, reflect-ing the supply of new nitrogen into the surfaceocean by upwelling nearshore and the subsequentloss of nitrogen by sinking organic nitrogen as thewater ages along its mean offshore transport path-way. This overall trend is the sum of similar trendsexhibited by the individual nitrogen pools except forthe very nearshore region, where phytoplankton,zooplankton, and detritus are lower than further

ARTICLE IN PRESS

125.0°W 124.5°W 124.0°W 123.5°W 123.0°W 122.5°W

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0

0.5

1

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2

Z

P

DL

DS

NO3

NO3

NH4P

(a) (b)

Fig. 16. Annual mean surface concentrations of nitrogen bearing ecosystem variables as a function of longitude (offshore distance) for the

central California upwelling region. Variables have been averaged meridionally over 50 km centered at 36.41N at the coast line. Panel (a)

shows the variables over the entire range of the ordinate, while panel (b) focuses on the lower range.

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NO3

NH4

P

Z

DS

DL

NH4

2 64 8 102 6

(a) (b)

Fig. 17. Plots of nitrogen allocation in the near surface ocean ecosystem. Plotted are the annual mean concentrations shown in Fig. 16 as a

function of the total nitrogen content of the ecosystem. Panel (a) shows a cumulative plot, while panel (b) depicts the concentrations after

normalization to the phytoplankton concentration.


offshore. This is a result of upwelling waterscontaining low concentrations of these state vari-ables, requiring some time and some aging of theupwelled waters for these pools to grow in. In thefirst 100 km, the total nitrogen content of the systemis dominated by nitrate, while further offshore,phytoplankton and small detritus become thedominant nitrogen pools.

When plotted as a function of the total nitrogencontent, these two distinct regimes emerge moreclearly (Fig. 17). Above a total nitrogen content ofabout 3mmolNm�3, nitrate dominates, and mostother nitrogen pools decrease in relationship tophytoplankton. Below this threshold, phytoplank-ton and small detritus are the dominant pools ofnitrogen in the system, and most pools increase theirsize relative to phytoplankton as more nitrogenbecomes available. Is this behavior an intrinsicproperty of the ecosystem model, or is thisdifference between low and high total nitrogencontent driven by the physical dynamics of theupwelling system? We address this question bycontrasting our ecosystem model results in the 3-Dconfiguration with those obtained in a box modelconfiguration that does not consider lateral trans-

port. The latter configuration is thus stripped of allthe complexities involved in the 3-D nature oftransport and mixing and therefore permits us toclearly delineate the ecosystem model intrinsic partof our solutions. Furthermore, the box modelconfiguration is amenable to analytical steady-statesolutions so that we can also determine whetherdifferences between the two physical configurationsare caused by the absence of local steady-states, i.e.balances between biological sources and sinks.

We therefore implemented our ecological/biogeo-chemical model into a box model configuration,which consists of a well mixed surface box of h ¼

20m depth, and an underlying thermocline box, forwhich all concentrations are prescribed to be zero,except for nitrate, which is set to N th

n ¼ 20mmolNm�3. The level of nitrate input into the surfacebox is determined by �w=h � ðN th

n �NnÞ, where w isthe vertical upwelling velocity. This exchange onlyaffects nitrate, i.e. we do not consider the potentialfor the washing out of phytoplankton, etc., aswould occur in a chemostat with inflow andoutflow. In order to achieve solutions with a widelydiffering amount of total nitrogen in the surfacebox, we varied w in our box model simulations from

ARTICLE IN PRESS

0 2 4 6 8 1210


00

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ogen

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n (m

mol

N m

-3)

0.5

1

1.5

2(a) (b)

(3d)(3d)

Fig. 18. Comparison of nitrogen allocation in the ecosystem between the 3-D simulations and a box model solution as a function of total

ecosystem nitrogen content: (a) results for phytoplankton (P) and zooplankton (Z); (b) results for nitrate (NO3), ammonium (NH4), and

the two detrital pools (DL and DS). The 3-D solutions are the annual mean results for the central California upwelling system shown in

Fig. 16. The box model solutions were computed by implementing the ecosystem in a box model configuration of the upper-ocean mixed

layer, and then by running it to steady-state for various levels of nitrate input (see text for details).


0:001 to 15mday�1. As is the case with the 3-Dmodel, phytoplankton and the two detrital pools aresubject to sinking. All ecosystem parameters are thesame as in 3-D. We created light saturated condi-tions by adopting a PAR of 500Wm�2 in order tofocus on the role of nutrients. This box model isthen run forward in time for different levels ofnitrate input into the surface box until a steady-stateis reached.

A comparison of the box model results with thoseobtained in the 3-D model reveals that phytoplank-ton and zooplankton behave similarly in bothconfigurations when plotted as a function of thetotal nitrogen content, while the detrital pools,nitrate, and ammonium show strongly differingrelationships (Fig. 18). The largest difference isexhibited by nitrate, which is drawn down to verylow levels in the box model regardless of how muchnitrogen is in the system. Is this difference due to thedifferent physical setting or is this an expression ofthe ecosystem being far from steady-state in the 3-Dconfiguration? We address this question for eachstate variable in turn.

5.1. Phytoplankton

The close correspondence between the 3-D andthe box model results could lead to the conclusionthat phytoplankton has achieved a local steady-state in the 3-D configuration, i.e. that its growthand biological losses are locally balanced. Thisappears reasonable, given the fact that at the lightand nutrient saturated conditions typical for thenearshore region, growth rates are of the order of1–2 day�1 (see Fig. 14d). This gives an e-foldingresponse time of 0.5–1 day, likely fast enough torespond to the frequent physical perturbationsimposed by the abundant meso- and submesoscalephenomena.

A closer inspection reveals a more complex story,however. Given the fact that the nutrients nitrateand ammonium hover around 0:1mmolm�3 in thebox model solutions, but attain very high concen-trations in the 3-D simulations, the phytoplanktongrowth rates associated with a particular level ofphytoplankton biomass are much larger in the 3-Dcompared to the box model configuration. Thiscreates a puzzle since steady-state requires that thehigher growth rates in 3-D are compensated byhigher loss rates, yet the loss by mortality and byzooplankton grazing is about the same in bothconfigurations, because both have about the same

zooplankton biomass for a given phytoplanktonbiomass (Fig. 18a). This requires that additionalloss mechanisms remove phytoplankton biomass in3-D, which do not exist in the box model. The onlymechanism of note is the lateral transport ofphytoplankton. Therefore, at the same phytoplank-ton biomass level, the balance in the box model isbetween growth and grazing, both occurring atrelatively low rates, whereas the balance in 3-D isbetween growth and lateral loss, both occurring athigh rates. The similar scaling between the 3-D andbox model configurations when phytoplanktonbiomass is plotted as a function of total nitrogencontent is therefore coincidental.

5.2. Zooplankton

The relatively small differences in the zooplank-ton allocation between the 3-D and box modelresults (Fig. 18) is depicted in more detail in Fig.19a, which shows abundances normalized to and asa function of phytoplankton nitrogen. The 3-Dresults for the zooplankton to phytoplankton ratio(Z=P) are within a factor of two of the box modelsolutions (Fig. 19). This indicates that, to first order,zooplankton biomass is controlled by the samebalance in both configurations. In the absence ofadvective loss terms, the analytical steady-state ofZ=P is given by

ðZ=PÞsteady�state ¼ggrazZ bassimZ � Zmetab

Z ðKP=Pþ 1Þ

ZmortZ ðKP þ PÞ

(20)

i.e. this ratio is a function of only zooplanktonparameters and the phytoplankton abundance.Since the 3-D and box model results agree relativelywell, one can conclude that the lateral loss termsaffect zooplankton and phytoplankton in a verysimilar manner, i.e. have only a secondary effect onZ=P. In fact, the observation that the 3-D solutionsexhibit generally lower Z=P values at high phyto-plankton concentrations and higher Z=P values atlow phytoplankton concentrations can largely beinterpreted by the impact of the lateral flow presentin the 3-D solutions. In the phytoplankton richnearshore zone, zooplankton appears to grow tooslowly to establish tight grazing control on phyto-plankton, while lateral transport of the zooplanktoninto the low phytoplankton offshore zone may leadto an abundance of zooplankton in this region thatis above that expected from the local steady-state.


5.3. Detritus

Fig. 19b shows in more detail the previouslynoted nearly equal concentration of small detritusand phytoplankton in the 3-D model. This nearlyuniform ratio of 1 is not an intrinsic property of ourecosystem, since the box model results show a ratiothat starts around 1, but increases with increasingphytoplankton biomass. Therefore, either lateraltransport processes or the absence of local steady-state must explain this behavior. We can immedi-ately exclude net divergences by lateral transport asan explanation for the difference between 3-D andthe box model, since both constituents have nearlyequal net transport divergences, so that this wouldnot affect the small detritus to phytoplankton ratio.This essentially leaves us with the explanation thatsmall detritus does not achieve local steady-state inthe 3-D model, i.e. that the production andremineralization of small detritus are not balancedlocally. In order to investigate this further, let usfirst look at the processes that determine the steady-state ratio.

In the offshore region, where the zooplanktonpopulation is low, the primary mechanism for theproduction of small detritus is phytoplanktonmortality. Under these conditions, the steady-state

of the ratio of small detritus to phytoplankton canbe approximated by (see online supplementarymaterial for derivation)

ðDS=PÞsteady�state, low Z�

kreminDS

8kcoag . (21)

With our parameter choices the right-hand side is0.75, as observed in both the 3-D and box modelsolutions for low phytoplankton concentrations(Fig. 19b).

In regions with substantial zooplankton abun-dance, sloppy feeding and zooplankton mortalitybecome important sources for small detritus. Forthese conditions, the small detritus to phytoplank-ton ratio is given by

ðDS=PÞsteady�state, high Z�

ffiffiffiffiffiffiffiffiffiffie2

kcoag

r�

kreminDS

2kcoagP, (22)

where e2 is a function that includes severalparameters and is slightly dependent on thephytoplankton concentration P (see online supple-mentary material for definition and the deriva-tion of (22)). For a typical P of 1mmolNm�3,e2 is 0:19 day�1 ðmmolm�3Þ�2, resulting in aðDS=PÞsteady�state, high Z value of 3.2, close to thenumerical value computed by the box model.

ARTICLE IN PRESS

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0

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3

4(a) (b)

Fig. 19. Comparison of the nitrogen allocation relative to phytoplankton abundance between the 3-D simulations and a box model: (a)

comparison for zooplankton (Z), nitrate ðNO�3 Þ, and ammonium ðNHþ4 Þ; (b) comparison for small ðDSÞ and large detritus ðDLÞ. Note

different scaling of the ordinate in (b).


The timescale to achieve this local steady-state forsmall detritus is determined by the coagulation andremineralization rate constants. These rate con-stants are an order of magnitude smaller than thosefor phytoplankton so that the small detritus tophytoplankton ratio is sensitive to the residencetime of near surface waters in a particular region. Inthe nearshore regions, the time waters spend in thewell lit surface region is likely too short to come intolocal equilibrium with regard to the processesgenerating small detritus, resulting in the 3-D modelconsistently having smaller ratios than the boxmodel at high phytoplankton (or total nitrogen)concentrations. In the offshore region, residencetimes of waters are longer and apparently sufficientto reach a local steady-state, as evidenced by theconvergence of the 3-D and box model results. Inconclusion, it appears that the nearly constant 1:1ratio of small detritus and phytoplankton in the 3-Dconfiguration of the model is not an intrinsicproperty of the ecosystem, but the result of chance.

For large detritus, the difference between the 3-Dand box models is even larger, but the samearguments listed above for small detritus are validhere. In fact, they are amplified since the timescalefor the formation of large detritus is even longerthan that for small detritus.

5.4. Nitrate and ammonium

The most dramatic differences between the 3-Dresults and the box model solutions are found forthe two limiting nutrients, nitrate and ammonium(Fig. 18b). The difference is particularly large fornitrate, which remains very high at high totalnitrogen content in the 3-D configuration, but isdrawn down to very low levels in the box modelconfiguration regardless of the total nitrogen con-tent.

The latter behavior can be easily explained by thelocal steady-state condition for nitrate in the boxmodel, i.e. phytoplankton uptake must be equal tothe supply by nitrification and vertical transport/mixing. This condition is only met when nitrate iswell below the half-saturation constant for phyto-plankton uptake, as phytoplankton otherwisewould remove much more nitrate than is beingsupplied. Evidently, in the 3-D configuration,phytoplankton is much less successful in taking upthe supplied nitrate, particularly in the nearshoreregion, leaving a substantial fraction of the totalnitrogen in this pool.

This absence of nearly complete nitrate removalin the nearshore upwelling region of the 3-D modelcan be explained by considering the nitrate balancein this region. In this region, nitrate far exceeds thehalf saturation constant for nitrate uptake, KNn , i.e.NnbKNn , so that the nitrate balance and hencesteady-state nitrate concentration can be approxi-mated by (see online supplementary material fordetails):

Nsteady�state, 3�Dn � Nup

n �h

wmmax

P ðT ; IÞ � P, (23)

where Nupn is the nitrate concentration of the

upwelling waters, and h is the depth of the surfacelayer. The term h=w has units of time and can beinterpreted as the residence time of surface watersrelative to upwelling. This term is small in thenearshore upwelling region, but increases withoffshore distance, as w decreases. We thus expecta monotonic decrease of nitrate as a function ofoffshore distance with a slope that is steeper at highP concentrations, i.e. when total nitrogen is high,becoming less steep as P decreases. These predic-tions compare very favorably with the 3-D resultsshown in Fig. 18b.

We can thus understand the fundamentallydifferent behavior of nitrate in the 3-D modelrelative to the box model as primarily reflectingdifferences in the residence time of waters relative toupwelling. In the box model, this residence time isessentially infinitely long, while surface waters arerelatively rapidly moved offshore in the 3-D model,resulting in short residence times in a fixed locationalong an offshore trajectory. This precludes nitratefrom being drawn down to very low levels as is thecase in the box model. The same explanation appliesto the difference of ammonium between the 3-D andbox model solutions.

5.5. Allocation summary

The nitrogen allocation to the different ecosystempools in our dynamic 3-D simulations differssubstantially from that predicted by the box model.The primary reason for this difference is that thegoverning balance in the 3-D model is one ofupwelling in the nearshore areas, followed by lateraltransport of the upwelled waters offshore, whereasthe box model only considers local vertical supply ofnutrients and vertical sinking of the producedorganic matter. As a result of this lateral transport,a distinct onshore–offshore gradient exists for the


residence time of waters in the surface ocean. Theresidence times in the nearshore regions are shortrelative to the characteristic equilibration time ofthe ecosystem, so that most of the ecosystemvariables are unable to reach a local steady-state,i.e. being balanced by biological sources and sinks.This effect is strongest for the slowest componentsof the ecosystem, i.e. the detrital pools, as evidencedby their concentrations being up to an order ofmagnitude smaller in the 3-D solutions than thosepredicted by the box model. The effect is alsosubstantial for nitrate, primarily because of its highconcentration in the upwelled waters. An importantexception is zooplankton, which appears to reach anabundance relative to that of phytoplankton that isgenerally close to that predicted by local steady-state. This is because both pool sizes start from lowlevels and grow at similar rates.

6. Summary and conclusion

We have coupled an NPZD-type ecosystem/biogeochemistry model to an eddy-resolving 3-Dphysical model of the California Current System(CCS) in order to study biological-physical interac-tions in this upwelling dominated region and howthese interactions shape the planktonic ecosystem.Evaluations of the model results with in situ andremote observations of chlorophyll reveal consider-able success of this coupled model in capturing theannual mean distribution as well as the meanseasonal cycle, particularly south of Cape Mendo-cino (about 40.51N). The most important deficiencyin this region is the tendency of the model tooverestimate the observed chlorophyll in the eu-trophic nearshore region, and to underestimate it inthe oligotrophic offshore region. We attribute thisdeficiency primarily to our use of a single phyto-plankton functional group model, which is structu-rally strongly limited in its ability to simultaneouslycapture these two very different nutrient regimes.

Larger discrepancies exist in the northern part ofour domain, where our model systematically under-estimates the observed chlorophyll. We attributethis deficiency primarily to our use of monthly meansurface forcing, which leads to an underestimationof upwelling in areas, where it tends to occur moreepisodically, such as is the case north of CapeMendocino. This problem is less important furthersouth since upwelling favorable winds persist thereover longer periods. Comparisons of the other statevariables of the ecosystem model with the often

much more limited observations reveal similaragreements and discrepancies.

We conclude that the use of a relatively simpleNPZD-type ecosystem model coupled to a high-resolution physical model is adequate for capturingthe most important features of the observedecosystem variations in the CCS. We attribute asubstantial fraction of this success to the use of ourphysical model, which, by resolving the mesoscale,explicitly captures most of the important physicalprocesses in the CCS. The comparison of the modelsimulated chlorophyll fields also profited markedlyby our consideration of a variable chlorophyll-to-carbon ratio as a state variable. A further improve-ment of our model is the detailed modeling of thedetrital pools. The consideration of two size classesof particles turned out to be of critical importance ingoverning the fate of organic matter produced in thenearshore zone. While most of the large particles areexported vertically, the small detritus particles tendto be exported horizontally. As discussed byPlattner et al. (2005), this leads to a strongdecoupling of new and vertical export productionin the nearshore region.

Model deficiencies that need to be addressed arethe inability of the ecosystem model to simulta-neously represent eutrophic and oligotrophic con-ditions. At the phytoplankton level, this requires atleast the addition of a nano-/picophytoplanktongroup that is adapted to the oligotrophic conditionsin the offshore environment. We also need to addgrazing control of this nano-/picophytoplankton bymicrozooplankton, since our currently modeledmesozooplankton cannot survive at low phyto-plankton concentrations. This may be done byadding an additional zooplankton functional groupto the model, by implementing prey switchingmechanisms, or by modeling this grazing implicitly.We are in the process of adding such extensions toour ecological model by coupling the multiplephytoplankton functional group model of Mooreet al. (2004) to our physical model. Initial analysesof these simulations indicate the expected im-provement in this model’s ability to capture theonshore–offshore transition of chlorophyll that ismore gradual in the observations in comparison toour current results. We anticipate also an improve-ment in our solutions from a switch in our surfaceforcing to include synoptic variability. We are alsoplanning to replace our lateral boundary conditionsto those provided by a Pacific-wide model, permit-ting us to consider also interannual to decadal


variations, since at least part of the variations in theCCS on these timescales are forced by the lateralboundaries.

The spatial and temporal evolution of phyto-plankton growth, and in particular the depthtransition of a phytoplankton maximum near thesurface in the nearshore region to a deep phyto-plankton maximum offshore is controlled in ourmodel by the interaction of light and nutrients. Asexpected, light limitation dominates at depth, whilenutrient limitation dominates in near surfacewaters. An exception is the nearshore region, wherelight limits growth more strongly than nutrientsthroughout the water column. Iron limitation,which we have not considered in this study here,could change this conclusion, however.

Our detailed study of how nitrogen is allocated tothe various pools in the ecosystem has revealedsubstantial deviations from those predicted insteady-state by a box model of the upper oceanmixed layer, with nitrate and the two particulatedetritus pools differing most strongly. The key reasonfor these differences is that the residence time ofwaters along their mean trajectory from the time ofupwelling until they arrive far offshore is too shortrelative to the time the ecosystem needs in order tocome to equilibrium. As a result, much of the totalnitrogen is still in the initial nitrate pool, while thecomponents that are produced last by the ecosystem,i.e. the two detritus components, are much lower thanexpected from the local steady-state.

A number of important questions remain unan-swered. In particular, we have not yet addressed therates of primary production and the relativecontributions of new and regenerated production,respectively. We also have not studied in detail thefate of this freshly produced organic matter. Howmuch is exported vertically and how much isexported laterally to the offshore region? Anotherimportant question is the role of mesoscaledynamics. Is it just a source of variability, or doeddies and other meso- and submesoscale phenom-ena change the solutions in a fundamental manner?What is the impact of the dynamic biological-physical interactions described here on the cyclingof carbon and oxygen? These questions will beaddressed in upcoming publications.

Acknowledgments

We are indebted to Alexander Shchepetkin,Xavier Capet, and Pierrick Penven for their help

and support with ROMS. We thank FranciscoChavez and Baldo Marinovic for sharing their dataand for stimulating discussions. The detailed andconstructive comments of three anonymous re-viewers are greatly appreciated. Most of this workhas been made possible by two grants from NASA.Additional support is acknowledged from NSF’sITR program. We are grateful to the NationalCenter for Supercomputing Applications for pro-viding us computer time.

Appendix A. Supplementary data

Supplementary data associated with this articlecan be found in the online version, at 10.1016/j.tsr.2006.06.009

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