Progress in Oceanography 103 (2012) 16–41

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Progress in Oceanography

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Model and field observations of effects of circulation on the timing and magnitudeof nitrate utilization and production on the northern Gulf of Alaska shelf

Kenneth O. Coyle a,⇑, Wei Cheng c, Sarah L. Hinckley b, Evelyn J. Lessard d, Terry Whitledge a,Albert J. Hermann c, Kate Hedstrom e

a Institute of Marine Science, University of Alaska, Fairbanks, AK 99775-7220, USAb Alaska Fisheries Science Center, 7600 Sand Point Way NE, Seattle, WA 98115, USAc Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Box 357941, Seattle, WA 98195, USAd Dept. Oceanography, University of Washington, Seattle, WA 98195, USAe Arctic Region Supercomputing Center, University of Alaska, Fairbanks, AK 99775, USA

a r t i c l e i n f o

Article history:Received 6 June 2011Received in revised form 1 February 2012Accepted 4 March 2012Available online 20 March 2012

0079-6611/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.pocean.2012.03.002

⇑ Corresponding author.E-mail address: coyle@ims.uaf.edu (K.O. Coyle).

a b s t r a c t

The GLOBEC program was tasked with understanding the mechanistic links between climate forcing andthe ocean-ecosystem response on the northern Gulf of Alaska (GOA) shelf. To address this task, sampleswere collected five to six times times annually along the Seward Line between 1998 and 2004. However,interpreting Seward-Line field observations in space and time is complicated by the complex circulationon the GOA shelf. The Alaska Current/Alaskan Stream and Alaska Coastal Current produce eddies andmeanders which mix the iron-limited small-cell oceanic community with the iron-rich large-cell coastalcommunity. Thus observations at any point in space and time are the result of the degree of mixing of theoceanic and coastal water masses. The ROMS circulation model with an embedded ecosystem model wasused to extend GLOBEC observations in space and time on the GOA shelf. The timing of the spring bloomin simulations was related to shallowing of the pycnocline. The spring bloom began in late March–Aprilon the inner shelf and in May on the mid and outer shelf. The simulations suggest that the magnitude ofshelf production is a balance between the amount of iron from freshwater runoff and nitrate, with ironlimitation on the outer shelf and adjacent ocean and nitrate limitation on the inner shelf. Simulated shelf-break eddies form near Yakutat, have elevated iron concentrations relative to surrounding waters, andpropagate westward, influencing production and nitrate concentrations on the outer shelf and in theadjacent ocean during spring and summer. Simulated primary production in the Seward Line regionwas about 100–130 g cm�2 y�1, but production of up to 300 g cm�2 y�1 is predicted for regions in LowerCook Inlet and around Kodiak.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Potential impacts of climate change have become central polit-ical, social and economic issues. As the growing human populationplaces increasing demands on renewable resources, concern overthe effects of climate on renewable resources has become the focusof many environmental studies. In the ocean, climate change is ex-pected to change temperature and precipitation patterns, poten-tially altering primary production in marine ecosystems, affectingspecies composition, abundance and biomass of the constituentmarine organisms, especially those in polar and sub-polar regions.In response to these concerns, the United States Global Ocean Eco-system Dynamics program (GLOBEC) was undertaken to collectfield observations and generate models to improve our under-

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standing of the mechanistic links between climate and the ecosys-tem response in four regions including the northern Gulf of Alaskashelf (Fogarty and Powell, 2002). This work focuses on the GLOBECobservational and modeling results from the Gulf of Alaska (GOA)shelf.

The GOA climate is characterized by energetic storm systemsassociated with the Aleutian Low (Weingartner et al., 2005); thesestorms are constrained by coastal mountains, causing uplift of themoist air and elevated precipitation. Much of the precipitation issequestered on the continent as snow and ice in winter but dis-charged into the coastal ocean in summer and fall as the snow packmelts and summer rains add to the runoff. The amount of runoffcan be massive, averaging about 24,000 m3 s�1 (Royer, 1982).Freshwater discharge into the coastal ocean is apparently a criticalsource of iron to the shelf ecosystem (Wu et al., 2009). Concentra-tions of dissolved iron in coastal surface waters range from 0.5 to4.1 nM, but dissolution of leachable particulate iron, present at

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 17

concentrations of 5 nM to 1 lM, may provide a relatively constantsupply of iron to coastal waters (Lippiatt et al., 2010) during peri-ods of elevated runoff. Since the northern GOA is an iron-limitedsystem (Martin et al., 1989; Strom et al., 2000), iron from freshwa-ter discharge is thought to be critical to production on the GOAshelf.

In addition to precipitation, the cyclonic winds associated withstorms promote onshore Ekman transport, causing coastal down-welling which is particularly intense during winter (Weingartneret al., 2005). Given the persistent downwelling, one would expectthe GOA shelf to have low annual primary production. Neverthe-less, primary production rates of up to 300 g cm�2 y�1 have beenreported on the GOA shelf (Sambrotto and Lorenzen, 1987). Thesehigh production rates have been attributed to mixing of nutrient-rich iron-poor oceanic waters with iron-rich nutrient-poor coastalwaters (Stabeno et al., 2004). The coastal GOA is characterized byintense spring phytoplankton blooms of large centric diatomsand nitrate is exhausted in surface waters on the inner shelf byMay–June (Childers et al., 2005; Strom et al., 2006). In contrast,the oceanic community is typically composed of small cells, nitrateutilization occurs much more slowly and distinct springphytoplankton blooms are not usually obvious (Booth et al.,1993; Strom et al., 2000; Harrison et al., 2004; Goes et al., 2004).Observations at any point in space and time on the GOA shelf arethe result of the mixing history of the sampled water masses andtheir constituent communities of organisms. Mixing is influencedby currents in the northern GOA. The elevated precipitation and

Fig. 1. LTOP station locations in the northern Gulf o

runoff, and coastal winds generate the Alaska Coastal Current(ACC), a westward-flowing buoyancy-driven current within about50 km of the coast (Royer, 1998; Stabeno et al., 2004). The AlaskanStream, the other major current in the northern GOA, is a westwardflowing boundary current near the shelf break with maximum flowrates of 80–100 cm s�1 (Reed, 1984). The ACC and Alaskan Streaminteract with the rough shelf and coastal topography to generateeddies and meanders, which promote advection of oceanic andcoastal water masses across the shelf (Okkonen et al., 2003). Thus,biological observations at any point in time and space on the shelfresult from active mixing of oceanic and coastal water masses andthe distinct ecological processes characteristic of the sourcewaters. The above features of the GOA shelf oceanography andecology complicate interpretation of station data in the larger con-text of the whole shelf.

The northern GOA shelf is a vast area stretching from southeastAlaska in the east to Unimak Pass in the west, a distance of over1500 km and the shelf width varies from a few kilometers in theeast to over 200 km in the west. Nevertheless, the GLOBEC pro-gram had sufficient resources to sample only one transect (theSeward Line) consistently five to six times annually (Fig. 1). Giventhe highly variable shelf topography and energetic physical ocean-ographic environment on the GOA shelf, interpretation of the GLO-BEC results in the broader spatial–temporal context of the entireshelf for a whole season is problematic. To aid in broader spa-tial–temporal interpretation of GLOBEC data, the Regional OceanModeling System (ROMS) was adapted to the Gulf of Alaska

f Alaska along the Seward Line (GAK stations).

18 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

(Haidvogel et al., 2000; Hermann et al., 2009; Dobbins et al., 2009)and an 11-compartment lower trophic level ecosystem model(GOANPZ) was embedded in ROMS (Hinckley et al., 2009). In thispaper, we outline calibration of the primary production compo-nents of the GOANPZ model using GLOBEC field observations,and application of the calibrated model to interpret GLOBEC nutri-ent-production data for 2001–2003 in the broader spatial contextof the GOA shelf between southeast Alaska and the ShumaginIslands.

In addition to the GOANPZ model used in this study, Fiechteret al. (2009) used a simpler four-compartment NPZD model withROMS on a 10 km resolution grid to examine the effects of iron lim-itation on chlorophyll concentration on the northern GOA shelf for1998–2004. In both models, an iron climatology based on observediron concentration in the GOA basin (Martin et al., 1989) was usedto initialize iron concentrations, and iron concentration was peri-odically nudged back to the climatological mean. However nudgingiron concentration to a mean value can mask the effects of seasonalfreshwater discharge and cross-shelf circulation on primary pro-duction. We therefore modified ROMS to add iron with freshwaterdischarge and adjusted iron concentrations in the forcing file toapproximate shelf iron concentrations measured in May and July(Wu et al., 2009). These modifications permitted us to explorethe potential effects of iron injection with freshwater on the distri-bution and intensity of production on the GOA shelf.

2. Methods

ROMS is a state-of-the-art, free-surface, hydrostatic primitiveequation ocean circulation model developed at Rutgers Universityand UCLA; it is a terrain-following, finite difference (Arakawa C-grid) model with the following advanced features: extensiverestructuring for sustained performance on parallel computingplatforms; high-order, weakly dissipative algorithms for traceradvection; a unified treatment of surface and bottom boundarylayers, based on the Large et al. (1994) and Styles and Glenn (bot-tom boundary layer) algorithms. Numerical details can be found inHaidvogel et al. (2000), Moore et al. (2004) and Shchepetkin andMcWilliams (2004), and on the ROMS web site. ROMS implemen-tation for the GOA has been described earlier (Haidvogel et al.,2000; Hermann et al., 2009; Dobbins et al., 2009; Fiechter et al.,2009) and a detailed outline of the scientific rational behind thestructure of the GOANPZ model has been presented (Hinckleyet al., 2009) and will not be repeated here. The model was designedto include fundamental ecosystem components as identified byfield observations while avoiding addition of components whichwould increase model complexity and computational demandsbut not substantially improve the ability of the simulations toreproduce Seward Line field observations.

The repeated model runs required to parameterize the modeland evaluate the sensitivity of model parameters would have con-sumed more time and computational resources than were avail-able if run in the fully three dimensional (3D) version. Thereforea one dimensional version (1D) of the ROMS model with embeddedGOANPZ component was run at individual Seward Line stations(Fig. 1) to generate simulations that could be directly comparedwith the observational data at the stations. Model parametersand components were then adjusted to produce simulations thatconformed as closely as possible to the field measurements. Theoptimized model was then run in 3D mode to simulate conditionson the coastal Gulf of Alaska (CGOA) shelf as a whole.

The GOANPZ model is based on the model of Hinckley et al.(2009), but parameter values have been altered and the followingmodifications of the model have been made so the simulationsmore closely resembled field observations along the Seward Line:

1. The light response curve was changed from exponential tohyperbolic tangent.

2. Nitrification was added.3. The sinking rate was modified to improve ammonium

simulations.4. Respiration was added to the phytoplankton and zooplank-

ton components. Ammonium excretion is now computedfrom respiration using the Redfield ratios.

5. A new runoff file was generated from a hydrology model forthe CGOA (see Royer et. al., NPRB project report number734). The new runoff file provides monthly estimates of run-off along the coast between Dixon Entrance and the Shuma-gin Islands from January 1931 through December 2007.

6. ROMS was modified to add iron with fresh water discharge;iron injection onto the shelf now varies by month and year,following the annual freshwater runoff cycle.

7. ROMS was modified to use SeaWiFS satellite data for PAR(photosynthetically active radiation) in the ecosystemmodel.

8. Correction of light extinction coefficients for chlorophyllconcentration was changed to that of Morel (1988).

9. Vertical resolution was increase from 30 layers to 42 layers.10. ROMS was modified to output daily primary production for

each location in the grid.

Model complexity was constrained by the following require-ments: the model must be sufficiently complex to adequately sim-ulate observed conditions along the Seward Line, but not socomplex as to exceed the computational resources available. Theultimate goal of the study was to provide spatial-temporal infor-mation on primary production and distributional information onnutrients for the GOA ecosystem based on observational data takenalong the Seward Line.

2.1. Model description

The GOANPZ model is based on equations in the models of Frost(1987, 1993) as implemented for the coastal Gulf of Alaska (CGOA)(Hinckley et al., 2009). The GOANPZ consists of an 11-compart-ment lower trophic-level ecosystem model (Fig. 2) embedded inROMS. Due to iron limitation in the Alaska Gyre and its potentialinfluence on the CGOA shelf, iron was included in the GOANPZ. Adetailed description of the model and equations are available inHinckley et al. (2009). A brief summary of model structure is pro-vided below.

Phytoplankton in the model was divided into large and smallcell components based on the observation that iron-limited com-munities off the CGOA shelf tend to be dominated by phytoflagel-lates and needle-like pennate diatoms, while iron-rich coastalenvironments have phytoplankton blooms dominated by largecells, primarily centric diatoms and dinoflagellates (Strom et al.,2006). Silicate was not modeled because nutrient measurementsindicate that silicate concentrations on the CGOA shelf are seldomlow enough to limit production (Childers et al., 2005), and whenlow silicate concentrations were observed, nitrate was exhausted.The microzooplankton were divided into large and small compo-nents, primarily to regulate carbon flow through the microzoo-plankton to the mesozooplankton components. Field dataindicate that mesozooplankton diets can be dominated by micro-zooplankton, particularly in oceanic regimes (Gifford, 1993;Gifford and Dagg, 1991), but very small microzooplankton arenot consumed by copepodid stages of copepods and by euphausi-ids. Since the model pools all life stages into a single category, foodpreference coefficients were adjusted to permit small amounts ofmaterial from the small phytoplankton and small microzooplank-ton boxes to be consumed by the two copepod boxes, thus

Fig. 2. Box diagram of the Gulf of Alaska NPZ model embedded in the ROMS generalcirculation model driven by input from the CORE1 (Version 1 Forcing for CommonOcean-ice Reference Experiments); large copepods represent Neocalanus spp.

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 19

accounting for consumption by naupliar as well as copepodidstages in the simulations. The copepod component was dividedinto Neocalanus (the large copepod component) and other cope-pods (the small component) because Neocalanus can dominatezooplankton biomass during spring but undergo ontogenetic verti-cal migrations from depths greater than 400 m to the surface inJanuary–February and migrate back down in June–July (Coyleand Pinchuk, 2003, 2005). The model incorporates the verticalmigrations, so the Neocalanus component differs from the othercopepod component in the model primarily by its simulated verti-cal migration behavior. Euphausiids are included in the model dueto their potential importance as food for commercial fish species.Mortality is a closure term for the model components. Details ofcalibrations and simulations for the zooplankton components willbe presented in a later publication.

2.2. Model grids and boundary conditions

Lateral boundary conditions for the physical model came fromSODA (Simple Ocean Data Assimilation). The model was initiallyrun on the northeast Pacific (NEP) 10 km resolution grid (Fig. 3).Biology boundary conditions for model runs on the NEP grid wereapproximated from initial conditions (Section 2.3 below). Outputfrom the NEP runs was used to generate boundary conditions forthe ecosystem component for the 3-km resolution coastal Gulf ofAlaska (CGOA) grid. The model was run using 42 layers. Attemptsto run the model using 60 layers required more computer re-sources than were available to maintain numerical stability.

2.3. Initial conditions

Initial conditions for the physical variables were taken fromSODA (Simple Ocean Data Assimilation) as outlined in Cheng

et al. (2012). Initial conditions for the ecosystem model(Fig. 4A–F) were set so that when the model was run from January1 to March 1, biological state variables would approximate fieldmeasurements collected by the GLOBEC Long Term ObservationProgram (LTOP) along the Seward Line in March. Initial iron con-centration varied with bottom depth (Fig. 4G). Iron concentrationwas set at 2.0 nM throughout the water column where bottomdepths were less than 200 m. The system was allowed to evolveduring 3D simulations without nudging the iron concentrationback to a climatological mean, but iron was added at a rate of0.7 � 102 nM per kg of freshwater discharge per second at eachlocation where fresh water was added along the coast. Additionof iron with freshwater at the above rate resulted in iron concen-trations approximately equal to the dissolved iron concentrationsreported by Wu et al. (2009) at Station GAK1for May and July2004. Neocalanus biomass was set at 0.7 lg cm�3 for depths be-tween 400 and 1000 m and zero for shallower depths (Fig. 4H).The Neocalanus biomass in the 3D simulations was programmedto ascend at 12 m d�1 for 60 days starting on day 1 of each yearand descend at 8 m d�1 starting on day 160. With this convention,all Neocalanus biomass in the simulations was out of the upper100 m by early July.

2.4. Model runs

The 1D version of the 3D ROMS-GOANPZ model was run at sta-tions along the Seward Line (Fig. 1) to permit direct comparison ofsimulations with LTOP field measurements. ROMS was modified torun in 1D on a 5 � 5 grid that was periodic in both the N–S and E–W directions, so that information passing out one side enteredimmediately via the opposite side; the 1D grid had 42 vertical lev-els, stretched in the vertical exactly as in the 3D simulations.Surface forcing (winds, freshwater, irradiance) for the 1D modelwas identical to that used by the physical oceanographic 3D model(Dobbins et al., 2009) for the particular station (latitude, longitude,and time interval) simulated. Model runs were done for 2001–2003, because the most complete field data was available for thoseyears. One dimensional model runs were done to adjust modelparameters to make simulated state variables conform as closelyas possible to GLOBEC field observations. The ROMS model withthe embedded, parameterized GOANPZ model was subsequentlyrun on the 3D CGOA grid (Fig. 3) with 42 vertical levels and 3 kmhorizontal resolution to simulate the horizontal distribution ofstate variables and production on the CGOA shelf in space andtime. All model output presented in this paper is from 3D modelruns.

Forcing files for both the 1D and 3D models were from Version 1of the Common Ocean-ice Reference Experiments (CORE1) dataset(http://data1.gfdl.noaa.gov/nomads/forms/mom4/COREv1.html). Anenhanced runoff file generated from a hydrological model (NorthPacific Research Board Project # 734) provided monthly runoffestimates by year for the CGOA grid and SeaWiFS PAR data wereused as a light source for the ecosystem model (see Section 3.2).All forcing for the NEP grid came from CORE1. The model wasrun once with high-resolution (eight times daily at 32 km resolu-tion) wind forcing from North American Regional Reanalysis data(NARR; http://nomads.ncdc.noaa.gov/data.php) to test the effectof wind resolution on simulated nitrate concentration and produc-tion. Otherwise, all wind data were from CORE1.

The model was run using two conventions: reinitialized andcontinuous. Reinitialized runs involved restarting the model onJanuary 1 of each year (2001–2003) with the biology variables re-set to initial values (Fig. 4). Continuous model runs involved initial-izing biology variables only once on January 1, 2001 and runningthe model continuously through to January 2004.

Fig. 3. Boundaries for the NEP grid and the CGOA grid.

Fig. 4. Initial conditions for ecosystem state variables: (A) Nitrate; (B) ammonium; (C) phytoplankton; (D) microzooplankton; (E) copepods; (F) euphausiids; (G) iron (grayline is bottom depth); (H) Neocalanus.

20 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

2.5. Analyses

Simulated values for comparison with field measurements wereextracted for the Julian day that the field measurement was made.Simulated and measured values were averaged by cruise and zone,and transformed where necessary to normalize the distributionand stabilize the variance for statistical inferences. Stations alongthe Seward Line were divided into three zones to aid in

interpretation of analyses. The Inner Zone (GAK1–GAK4) consistedof stations encompassing the Alaska Coastal Current and the regionof reduced salinity associated with freshwater discharge. TheTransitional Zone (GAK5–GAK9) included stations in the middleand outer shelf, where water masses could be dominated byoceanic waters, coastal waters or a mix of the two. The Outer Zone(GAK10–GAK13) consisted of stations beyond the shelf break in theAlaska Current–Alaskan Stream region.

Fig. 5. Photosynthetically active radiation (E m�2 d�1) on the Seward Line from (A)ship’s measurements and CORE1 shortwave radiation simulations and from (B)SeaWiFS satellite data for 2002 and 2003.

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 21

3. Results

Material in the results section describes alterations to the basicmodel incorporated to optimize model output to GLOBEC fieldobservations. Current optimized model equations and parametersettings are listed in Tables A1 and A2. In addition output from3D model runs are compared to field observations for 2001–2003. Parameter sensitivity and calibration results for nutrientand phytoplankton components are outlined below by parameterand model tracer.

3.1. Doubling rate

The doubling rate is computed according to:

Drate ¼ Di � 10ðDp�TÞ

where Drate is the doubling rate, Di is the doubling rate parameter,Dp is a temperature correction coefficient (set at 0.0275, Frost,1987) and T is the ambient temperature (�C). The Drate in this modelis a hypothetical maximum doubling rate chosen to yield produc-tion measurements and phytoplankton blooms approximating fieldobservations. The maximum doubling rate is used to produce ahypothetical maximum production rate Pmax

Pmax ¼ ð2Drate � 1Þ

which is then attenuated by light, nitrate, ammonium and iron tocompute phytoplankton growth in the simulations (Table A1 andEq. (3)). Frost (1987) used a Di value of 0.851 d�1 which yields a dou-bling rate of about 2.06 at 14 �C, however when a Di value of0.851 d�1 was used for both large and small phytoplankton attemperatures of 4–12 �C, simulated primary production was belowmeasured values on the Seward Line, the simulations could not pro-duce a bloom and nitrate was not exhausted from the upper mixedlayer in simulations, contrary to field observations in July and Au-gust (Childers et al., 2005). Di had to be elevated to 1.6 to generateproduction, nitrate utilization and phytoplankton carbon biomasssimilar to field estimates. High doubling rates in this model are re-quired because the attenuation factors for light, nutrients and ironare multiplicative (Table A1 and Eq. (3)). If the model is altered tocompute production from the minimum attenuation factor ratherthan multiplying them (e.g. Denman and Peña, 2002, 1999), themodel was still unable to generate a bloom with a Di value of0.851 d�1, even though production rates were over 90% of Pmax.We chose to use the multiplicative option for simulations presentedhere. Sensitivity tests with the multiplicative model indicate that Di

must be at least 1.4 for the large phytoplankton component to re-move nitrate at rates observed in the field. The failure of the modelto produce blooms with lower Di values is probably because Dp is notaccurately accounting for the ability of cold-adapted subarcticbloom species to rapidly double in cold environments. In addition,the relationships between nutrients, iron and light limitation in phy-toplankton cells are probably not simply multiplicative or minimal,but involve complex metabolic pathways not captured by the modelequations. The multiplicative option with Di set to 1.6 was able toapproximate the nitrate cycle, primary production and phytoplank-ton biomass as observed along the Seward Line in 2001–2003.

3.2. Light

Model output is very sensitive to incident radiation. Loweringor raising surface PAR resulted in decreases and increases in simu-lated annual primary production. Surface PAR also affected thedepth of the nutricline and nitrate utilization on the inner shelf.Accurate data on surface PAR is therefore critical to simulationsof the intensity and timing of phytoplankton blooms. PAR was ini-

tially estimated from CORE1 shortwave radiation data assuming aconversion factor of 0.7 (Fig. 5A). Ship’s measurements revealedlarge daily amplitudes in surface PAR, apparently due to cloudattenuation. SeaWiFS satellite measurements of surface PAR alsorevealed high amplitude variations in PAR that were not capturedby the CORE1 shortwave forcing (Fig. 5B). Field experiments ofphytoplankton production revealed that daily production is highlyinfluenced by cloud attenuation of surface PAR (Strom et al., 2010).We therefore downloaded daily SeaWiFS PAR measurements for2001–2003 from the NASA web site (ftp://oceans.gsfc.nasa.gov),regridded the data to fill in missing values in the offshore regionof the grid (Fig. 6), modified ROMS code to read in the SeaWiFSPAR data and passed it to the biology module. The algorithm forcomputing surface PAR corrects the values for cloud cover (seeFrouin et al. Algorithm to estimate PAR from SeaWiFS data: Version1.2 – Documentation http://oceancolor.gsfc.nasa.gov/DOCS/seawifs_par_wfigs.pdf).

With these modifications, CORE1 short wave radiation was usedonly for heat exchange calculations by the physical model, not forestimation of PAR. The use of measured PAR minimized potentialerrors due to problems with estimation of cloud attenuation by cli-mate model output, thus facilitating calibrations of phytoplanktonproduction in the model.

3.3. Attenuation coefficient

The simulated attenuation coefficient was computedfor each depth interval according to Morel (1988)

IðkÞ ¼ �eIwþP2

i¼1Ichl

PhðiÞccrðiÞ

� �� �0:4280@

1A, where I(k) is the extinction coeffi-

cient for each depth interval k, Iw is the extinction coefficient of

water, Ichl is the extinction coefficient of chlorophyll and PhðiÞccrðiÞ

� �ad-

justs the coefficient for the carbon biomass of each phytoplanktoncomponent (Ph(i)) converted to chlorophyll equivalents by the car-bon chlorophyll ratio (ccr(i)). This resulted in extinctioncoefficients ranging from 0.04 to 0.44, depending on the phyto-plankton concentrations at any given depth interval. Attenuation

Fig. 6. (A) SeaWiFS surface PAR image from 29-July-2001 showing a block of missing data in the offshore region of the CGOA grid (white box). (B) Log transformed meansimulated chlorophyll concentration (lg l�1) in the upper 25 m for April 10–May 31, 2002; continuous run.

22 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

coefficients from Seward Line LTOP cruises, computed using lightdata collected by a PAR sensor on the CTD starting at the depthwhere light intensity began to decline and ending at the depth ofthe 1% light level, produced water column extinction coefficientsranging from 0.06 to 0.22, with values decreasing with distancefrom shore. Higher extinction coefficients near shore wereundoubtedly the result of elevated phytoplankton biomass on theshelf and light attenuation by sediments and detritus (case 1 ver-sus case 2 waters, Babin et al., 2003). Attenuation coefficients fromSeward Line observations during summer 2003 were 0.07–0.19(Strom et al., 2010).

3.4. Carbon–chlorophyll ratios

Field measurements of carbon–chlorophyll ratios along theSeward Line varied from 20 to 80 during 2001 and 25 to 110 during2003 (Fig. 7). Phytoplankton carbon–chlorophyll ratios were deter-mined from microscopical measurements, converting measuredbiovolumes of all phytoplankton (cyanobacteria, picoeukaryotes,flagellates, diatoms and dinoflagellates) to carbon biomass usingpublished equations (methods in Frame and Lessard (2009)).Changes in the carbon chlorophyll ratio in the model altered sim-ulated production and the phytoplankton carbon biomass. Sincethe small phytoplankton community was dominant in July whenelevated ccr values were observed, we assigned a higher ccr valueto the small phytoplankton component. The model was originally

Fig. 7. Carbon–chlorophyll ratios along t

calibrated using ccr values of 30 and 60 for the large and small phy-toplankton respectively; repeated simulations varying otherparameters but maintaining ccr at 30 and 60 consistently resultedin simulated chlorophyll values below measured values by 30–50%.Simulated chlorophyll concentrations in the analysis are dailymeans for the same date that field measurements were taken. Car-bon chlorophyll ratios of 20 and 60 for large and small phytoplank-ton respectively, were used for the simulations.

3.5. Chlorophyll concentration

Log transformed measured and simulated chlorophyll concen-trations for 3D simulations with reinitialization of biological statevariables each year overlapped in about 75% of the comparisons(Fig. 8). Closest agreement between simulated and measured chlo-rophyll concentrations occurred during 2002 (Fig. 8D–F) and in thetransitional zone in 2001 (Fig. 8B). When the model was run con-tinuously without reinitializing biological variables, the peak chlo-rophyll concentrations were lower relative to the reinitializedsimulations (Figs. 8D, E, 9A and B). The lower peak value in onecase was due to a slight shift in the timing of the simulation peakrelative to the field measurement (Fig. 9I). The reinitialized simu-lation in 2003 produced a chlorophyll peak that was not observedin the field data, or in the continuously run simulation (Fig. 9J).Chlorophyll peaks are very ephemeral in the simulations. Lowersimulated chlorophyll values in April or May relative to field

he Seward Line: (A) 2001; (B) 2003.

Fig. 8. Simulated and measured log transformed mean chlorophyll concentration in the upper 30 m along the Seward Line: (A) 2001 Inner, (B) 2001 Transitional, (C) 2001Outer, (D) 2002 Inner, (E) 2002 Transitional, (F) 2002 Outer, (G) 2003 Inner, (H) 2003 Transitional, (I) 2003 Outer. Biology variables were reinitialized each year on January 1.

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 23

measurements were sometimes due to differences in timingbetween simulated chlorophyll peaks and field observations. Thespatial distribution of simulated chlorophyll concentration duringthe spring bloom was depicted in a plot of mean log-transformedchlorophyll concentrations in the upper 25 m for April 10 throughMay 31 (Fig. 6B). Elevated chlorophyll concentration in thesimulation occurred in the coastal and transitional zones off theKenai Peninsula, in Prince William Sound, Lower Cook Inlet andin patches around Kodiak Island and the shelf west of KodiakIsland.

3.6. Light curves

The light curve expressing the fraction of maximum possibleproduction at any given light level was changed from an exponen-tial (in Hinckley et al. (2009)) to a standard hyperbolic tangentfunction tanh aPAR

P�max

� �� �, where a is the slope of the light curve

(mg C (mg chlorophyll a)�1 E�1 m�2) and P�max ¼ PmaxccrðiÞ (seeStrom et al., 2010; Jassby and Platt, 1976). The model is sensitiveto a. If a is increased, light sensitivity of phytoplankton in the sim-ulations increased, total primary production increased, productionoccurred deeper in the water column and nitrate was exhausteddeeper in the water column. Alpha values for simulations wereset to measured values of a along the Seward Line in July–August2003 (converted from lg C (lg Chl a)�1 h�1 (lE m�2 s�1)�1); valueswere 2.2 and 5.6 mg C (mg chlorophyll a)�1 E�1 m�2 for large andsmall phytoplankton respectively (Strom et al., 2010). In the ab-sence of grazing, blooms of large and small phytoplankton in thesimulations could strip nitrate from surface waters in March–April.

To insure that the simulated phytoplankton were experiencingthe light regime of particles randomly mixed within the upper

mixed layer, the simulated pycnocline depth was computed (max-imum of drt

dz , where z is the water depth (m)) between 5 and 100 m,mean PAR for the depth interval between the pycnocline and sur-face was computed and PAR for all depth intervals between thepycnocline and surface was set to the mean. Typically, the pycno-cline depth was below 50 m in January–March, rapidly shoaled inApril, remained above 20–30 m between May and August, and be-gan to descend between October and December (Fig. 10). The sea-sonal cycle of simulated pycnocline depth was similar to thatcomputed from Seward Line CTD measurements.

3.7. Half saturation constants for nutrient uptake by phytoplankton

Laboratory measurements of half saturation constants (kS) fornitrate uptake by diatoms range from 0.3 to 2.4 lM (Smayda,1997). However, if kS for large phytoplankton in the simulationswas raised above 0.5 lM, the simulations were not able to removethe nitrate from the upper mixed layer as indicated from the fielddata (Childers et al., 2005). We therefore set the nitrate half satu-ration constant (k1PL) for large phytoplankton at 0.5 lM. Labora-tory measurements of diatom kS for ammonium uptake rangedfrom 0.22 to 5.5 lM (Smayda, 1997). The half saturation constantfor ammonium for the large phytoplankton box (k2PL) in the simu-lations was set at 0.8 lM. The ammonium half saturation constantdoes not appear to have a strong influence on the model output.Laboratory measurements of flagellate kS for nitrate uptake rangedfrom 0.1 to 3.0 lM and 0.1 to 2.3 lM for ammonium (for dinoflag-ellates they were as high as 20 lM) (Smayda, 1997). For small phy-toplankton, we used a k1PS of 0.8 lM for nitrate and k2PS of 0.2 lMfor ammonium in the simulations. Half saturation constants usedfor the model are within the range of experimental values and

Fig. 9. Simulated and measured log transformed mean chlorophyll concentration in the upper 30 m along the Seward Line: (A) 2002 Inner Zone, (B) 2002 Transitional Zone,(C) 2002 Outer Zone, (D) 2003 Inner Zone, (E) 2003 Transitional Zone, (F) 2003 Outer Zone. Simulation was run continuously without reinitializing biological variables. Timeseries of simulated and mean measured log transformed chlorophyll concentration for reinitialized and continuous model runs: (H) 2002 Inner Zone; (I) 2002 TransitionalZone; (J) 2003 Outer Zone.

Fig. 10. Mean daily simulated depth of the pycnocline (maximum of drt/dz) atstation GAK6 on the northern GOA shelf in 2002, open circles = measured valuesfrom the CTD.

24 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

were selected for this model because they resulted in simulatednutrient, production and biomass values similar to field observa-tions. No measurements of half saturation constants for uptakeor growth were made on the GOA phytoplankton community dur-ing the GLOBEC study.

3.8. Nitrification

Nitrification was added to the original GOANPZ model to moreaccurately simulate regeneration as a nutrient source. Nitrification

was modeled according to Kawamiya et al. (2000) with a depthcorrection according to Fennel et al. (2006) based on light penetra-tion computed from the extinction coefficient: Nitrification ¼ðkN0 expðkNT TÞNH4Þ I�I0

kIþI�I0

� �, where I is the irradiance at depth

(W m�2), I0 is the irradiance of threshold inhibition (W m�2), andkI is the irradiance where inhibition is half (W m�2), kN0 is the nitri-fication rate at 0 �C (lM d�1), kNT is a temperature correction coef-ficient for nitrification, T is temperature in �C and NH4 is theammonium concentration (lM). With kN0 ¼ 0:15, kNT ¼ 0:0694and I0 ¼ 0:0095 W m�2 (Fennel et al., 2006) a layer of elevatedammonium concentration was generated near the seasonal pycno-cline as observed in the data, provided the sinking rate of detrituswas sufficiently low.

3.9. Detritus

Detritus is produced in the model from fecal material and bio-mass of dead animal matter with losses due to sinking and degra-dation. Sinking is simulated by an additional vertical advectionterm using monotonic continuous conservative parabolic splines.Vertical derivatives are estimated at grid box interfaces, then con-verted into flux-integrated values. Total primary production is sen-sitive to the sinking rate of detritus, because detritus mineralizes toammonium and nitrate and is therefore a source of recycled nutri-ents. Temperature-dependent particle mineralization was simu-

lated according to Kawamiya et al. (2000): @D@t ¼

PV0expðPVT

TÞðDnÞn ,

where PV0 is the particulate organic nitrogen decomposition to

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 25

NH4 at 0 �C, and PVT is the temperature coefficient for decomposi-tion to ammonium, D is the detritus concentration and n is thenitrogen carbon ratio. High sinking rates, which essentially elimi-nate recycled production from detritus, depress primary produc-tion in the simulations. If all detritus is suspended at thepycnocline (wD ¼ 0), the simulated primary production is elevated,with late summer and fall simulated primary production on the or-der of 1000 mg cm�2 d�1 above observed values. Therefore, if sim-ulated maximum ammonium concentrations were under 2.5 lM,the sinking rate was adjusted using a heuristic equation based onthe assumed particle density and simulated water density:

w ¼ w 26:25�rtðkÞ0:03rtðkÞ

, where rtðkÞ is the water density at depth interval

k. With the particle density set at 26.25r(t) units, the particles tendto collect below the pycnocline, resulting in simulated primaryproduction closer to observed values. This modification of sinkingrates is a rough correction for having a single particle density fordetritus in the model, whereas in the actual ocean there is a spec-trum of particle densities and sinking rates. The nitrification andsinking rates were adjusted to approximate measured ammoniumconcentration on the shelf (Childers et al., 2005). Ammonium fromzooplankton respiration is an additional source of ammonium forthe model.

3.10. Iron concentration

The GOANPZ model simulated iron limitation by attenuatingphytoplankton production by iron concentration (Eq. (3)). Iron-attenuated primary production was constrained by the require-ment that the large phytoplankton must grow faster than the smallphytoplankton under nutrient and light saturation at high ironconcentrations, but must grow much slower than the small phyto-plankton under iron limitation. These requirements were met byusing lower half saturation constants for iron uptake by small phy-toplankton relative to large phytoplankton (Fig. 11 and Table A2).Recent measurements indicate that ACC waters have seasonallyelevated amounts of leachable particulate iron which dissolves tosupply a constant 2 nM source of dissolved iron to the ACC (Lippi-att et al., 2010) when particulate iron concentration is high. There-

fore the iron limitation term was multiplied by kfeþ22

� �to saturate

iron uptake when iron concentrations are about 2 nM and kfe wasdivided by iron concentration when iron concentration exceeded2 to insure that the term approximated 1 at higher ironconcentrations.

The amount of iron introduced with fresh water was set byrepeatedly running 3D simulations while adjusting iron input withfreshwater until simulated iron concentrations at station GAK1 inMay and July were approximately equal to the measured concen-

Fig. 11. Iron attenuation term (IronLim) modified to make iron limitation saturateat iron concentrations of about 2 nM.

trations in May and July respectively. Measured concentrations ofdissolved iron at GAK1were about 0.75 nM in May and 2.0 nM inJuly (Wu et al., 2009). Elevated iron concentrations in July relativeto May were most likely due to the seasonal cycle of freshwaterrunoff, which is minimal in February–March and increases to max-imum values in October (Weingartner et al., 2002). Measured con-centrations of dissolved iron between about 100 km and the end ofthe Seward Line were under 0.5 nM (Wu et al., 2009), indicatingthe potential for iron limitation on the middle and outer shelf.

The half saturation constants for iron uptake were set by run-ning the 3D simulations continuously for 2001 and 2002 withoutresetting the biology variables. When half saturation constantswere lowered from 0.2 and 0.4 nM to 0.1 and 0.2 nM for smalland large phytoplankton respectively, the mean nitrate concentra-tion on the shelf in March was lowered by <1 lM (Table 1). If halfsaturation constants are lowered below 0.1 and 0.2, nitrate concen-tration in the upper 25 m in the oceanic and shelf regions ap-proaches zero during the production season, and lowerconcentrations are observed on the shelf the following March.

If nutrients are averaged from the pycnocline to the surface (asdescribed in Section 3.6), mean nitrate concentrations in the upper25 m are elevated on the outer shelf during June and July (Fig. 12A–C) relative to the simulation with normal mixing. In contrast, ironconcentrations are lower in regions of elevated nitrate concentra-tion (Fig. 12D–F). Mean iron concentrations on the inner shelf tendto be lower with elevated mixing because the phytoplankton com-ponents in the simulations removed iron at a faster rate in re-sponse to elevated nitrate fluxes above the pycnocline. However,mean nitrate concentrations do not increase above the pycnoclineon the inner shelf because in the presence of enough iron, the phy-toplankton components can remove the nitrate at approximatelythe same rate that it is mixed upward. The phytoplankton compo-nents cannot remove excess nitrate from elevated vertical mixing(averaging) on the outer shelf because they are iron limited; there-fore the nitrate tends to accumulate in the upper 25 m. The simu-lations suggest the presence of a fairly delicate balance betweeniron and nitrate limitation, which varies depending on the location,circulation and runoff.

3.11. Nitrate limitation

The model is sensitive to nitrate concentration, which isthought to be the limiting nutrient on the inner shelf in the simu-lations and in the field (Childers et al., 2005). Comparisons weredone of measured and simulated nitrate concentrations along theSeward Line for 2001 through 2003 on model runs with reinitial-ization of biology variables in January (Fig. 13). Confidence inter-vals overlapped in about 75% of the comparisons. Nitrateconcentration in all zones was significantly lower in simulationsrelative to measured values in May 2001 (Fig. 13A–C), indicatingearlier or more rapid nitrate utilization in the model than in thefield. Confidence intervals overlapped in the Inner and Transitionalzones in April, May, July and August 2003 but differed in Marchand October (Fig. 13G and H). Simulated and measured nitrate

Table 1Mean annual production (g C m�2 y�1) and mean nitrate concentration in the upper25 m (lM) during March in the Western Gulf Polygon (Fig. 18A).

Treatment 2001 2002 2003

NO3 Prod. NO3 Prod. NO3 Prod.

Reinitialized biology variables 17.3 164 16.7 153 17.4 160Continuous; forcing unmodified 17.3 164 14.6 137 9.5 116Continuous; modified runoff 17.3 164 14.8 123 12 100Continuous; NARR winds 17.1 152 13.7 116 10.5 106Continuous; kFe = 0.2 and 0.1 17.3 169 14.0 129 10.0 111

Fig. 12. Mean nitrate concentration (lM) in the upper 25 m, iron concentrations (nM) in the upper 30 m and anomalies for June and July 2001 resulting from elevatingvertical mixing in the ecosystem model: (A) Nitrate – normal mixing; (B) nitrate – elevated mixing; (C) nitrate anomalies (elevated – normal mixing); (D) iron – normalmixing; (E) iron – elevated mixing; (F) iron anomalies (elevated – normal mixing).

26 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

concentrations were much more divergent in the Outer Zone afterMay (Fig. 14I). The rapid decline in measured nitrate concentration

Fig. 13. Mean simulated and measured nitrate concentration in the upper 25 m alongTransitional, (C) 2001 Outer, (D) 2002 Inner, (E) 2002 Transitional, (F) 2002 Outer, (G) 2

between July and August was associated with the propagation ofan eddy westward across the outer end of the Seward Line. The

the Seward Line by zone for reinitialized model runs: (A) 2001 Inner, (B) 2001003 Inner, (H) 2003 Transitional, (I) 2003 Outer.

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 27

low simulated value in July was associated with a decaying eddy.Simulated and measured nitrate concentrations were similar inthe Inner and Transitional Zones in 2002 (Fig. 13D and E). The highsimulated nitrate concentrations in the outer zone in July–Augustoccurred after water from a decaying eddy had moved westwardpast the Seward Line (Fig. 13F).

When the model was run continuously without reinitializingbiology variables in January, confidence intervals for simulatedand measured nitrate concentrations overlapped in 2002(Fig. 14A–C). However, simulated nitrate concentrations in 2003were significantly lower than measured values, particularly inthe Inner Zone in March and April (Fig. 14D), but were similarfor other months. While confidence intervals overlapped in theTransitional and Outer zones in March, April, May and July, thesimulated means were consistently lower, suggesting a generallow bias in the simulation relative to the field measurements,but simulated nitrate concentrations were significantly elevatedover measured values in August and October (Fig. 14E and F) whenan actual eddy was crossing the Seward Line.

The ability of the simulation to reproduce the nitrate cycle asdepicted in field measurements was related to circulation pat-terns advecting oceanic and coastal water masses across theshelf. Iron-rich coastal water occurs in the Inner Zone in the sim-ulations in 2002 and was mixed nearly completely across theSeward Line to the shelf break on May 3, the period when fieldsampling was done (Fig. 15A and D). However, by the next sam-pling period during the third week of July, iron poor water in thesimulation was pushed inward and covered about half the Sew-ard Line (Fig. 15C). This circulation pattern coincided with a largeeddy in the simulation just to the east of the Seward Line. Meannitrate concentration in the upper 25 m was lower in the eddyrelative to surrounding water due to phytoplankton uptake inthe presence of elevated iron concentrations in the eddy(Fig. 15B and E). Nitrate in the upper mixed layer was nearly ex-hausted in the eddy by July (Fig. 15F) and mean iron concentra-tion was substantially reduced due to phytoplankton uptake(Fig. 15C). However, the eddy never actually crossed the SewardLine in the continuous simulation.

Although eddies were present in both the simulations and thefield data, their locations and westward propagation rates differed.Satellite height anomaly data indicated that the position and west-

Fig. 14. Mean simulated and measured nitrate concentration in the upper 25 m along th2002 Outer, (D) 2003 Inner, (E) 2003 Transitional, (F) 2003 Outer.

ward propagation rates of actual eddies varied by year. In 2001 aneddy was east of the Seward Line in April and May (Fig. 15G), butwas crossing the Seward Line by the end of May and was east of theline by July. The eddy in 2002 was off the Seward Line in April–May(Fig. 15H) but began moving westward in mid May and was east ofthe line by the end of May. The eddy in 2003 was east of theSeward Line in April–May (Fig. 15I) and did not begin crossingthe line until August. In contrast, the eddies in the simulation prop-agated westward crossing the Seward Line in May and decayed, orthey remained just east of the Seward Line between May andNovember.

The high nitrate concentration along the Seward Line in thefield data relative to the simulations in May 2001 (Fig. 13A andB) appears to be at least partially related to a circulation patternadvecting oceanic water onto the shelf. Salinity sections alongthe Seward Line in 2001 indicate that oceanic water had beenpushed onto the shelf clear into at least 70 km (Fig. 16B). This oce-anic water was associated with elevated nitrate concentrations(Fig. 16A). In contrast, oceanic and coastal water were mixed acrossthe shelf in 2002 (Fig. 16D), and nitrate was exhausted in the sur-face layer out to 140 km (Fig. 16C). Thus, the ability of the ecosys-tem model to accurately reproduce the nitrate cycle on the shelf ishighly dependent on the ability of the ROMS circulation model toreproduce the cross-shelf advection and mixing of oceanic andcoastal waters as observed in the field data.

Nitrate concentration in the Transitional and Outer Zonesalong the Seward Line can change rapidly in response to circula-tion. The higher simulated nitrate concentration relative to thefield measurement in July 2002 (Fig. 13E) was due to a changein the water masses in the Transitional and Outer zones of theSeward Line as an eddy in the reinitialized simulation movedwestward and decayed, resulting in a rapid increase in simulatednitrate concentration as iron-depleted oceanic water moved west-ward into the Seward Line region between the end of June andearly July (Fig. 17B and C); the same eddy in the continuousrun remained just east of the Seward Line for the entire produc-tion season (Fig. 15B, C, E and F). The lower March nitrate concen-trations in the Inner Zone during the continuous model run(Fig. 14D) relative to the reinitialized run in 2003 (Fig. 13G)was due to elevated runoff in the forcing file between July 2002and March 2003. Reinitializing the iron and nitrate concentration

e Seward Line for continuous model runs: (A) 2002 Inner, (B) 2002 Transitional, (C)

Fig. 15. Mean iron concentration (nM) in the upper 30 m (A–C) and mean nitrate concentration (lM) in the upper 25 m (D–F) in 2002 for a continuous model run. (A) May 3;(B) June 4; (C) July 20; (D) May 3; (E) June 4; (F) July 20; Sea surface height anomalies from satellites (straight line is Seward Line): (G) April 15, 2001; (H) May 15, 2002; (I)May 14, 2003. Straight black line is the Seward Line (A–F), curved black line is the shelf break. Red arrows: eddies in the simulation.

Fig. 16. Nitrate and Salinity sections along the Seward Line. (A and B) May 2001; (C and D) May 2002.

28 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

in January essentially elevated shelf iron and nitrate concentra-tions in the reinitialized model run relative to the continuousmodel run (nitrate and iron are artificially injected into the reini-

tialized model run, partially erasing the effects of runoff and cir-culation between July 2002 and January 2003 on shelf nutrientconcentrations).

Fig. 17. Mean nitrate concentration in the upper 25 m along the Seward Line. A: 2002 Inner Zone; (B) 2002 Transitional Zone; (C) 2002 Outer Zone; (D) 2003 Inner Zone; (E)2003 Transitional Zone; (F) 2003 Outer Zone. Gray line: reinitialized model run; Black line: Continuous model run.

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 29

The effect of elevated runoff on the continuous model runs wasdemonstrated by running the simulation a second time but substi-tuting runoff between July 2002 and March 2003 with runoff be-tween July 2001 and March 2002 (Fig. 18B). Mean ironconcentration on the shelf (Western Gulf Polygon; Fig. 18A) inthe simulation with lower runoff declined by about 0.4 nM andmean nitrate concentration during March and July increased byabout 2 lM (Fig. 18C and D; solid lines). The mean nitrate concen-tration was elevated not only in the upper 25 m but down to 100 mdepth (Fig. 18D; dashed lines). Nitrate concentrations were higher

Fig. 18. Effect of runoff in the Western Gulf Polygon on mean nitrate and iron conceconcentration. (E) salinity. Black line: simulation with unmodified runoff. Gray line: sim2002–March 2003.

both in March, indicating a greater mean nitrate reservoir on theshelf before the bloom, but also in June–July, indicating that phyto-plankton in the simulations did not use all of the excess nitrateduring the production season when runoff was modified(Fig. 18D). Mean salinity on the shelf was higher by about 0.2 inthe low runoff simulation (Fig. 18E). The mean anomalies (the dif-ference between the modified (low runoff) and the unmodifiedbaseline (high runoff) simulations) for April and May illustratethe spatial effects of lower runoff during the spring bloom(Fig. 19A–C). Nitrate concentrations in the simulations with lower

ntration: (A) Western Gulf Polygon, (B) runoff, (C) iron concentration, (D) nitrateulation from substituting runoff in forcing file from July 2001–March 2002 into July

Fig. 19. Mean nitrate (lM), iron (nM) and salinity anomalies (test variable–baseline variable) from running the simulation with runoff from 2001 to 2002 substituted forrunoff from 2002 to 2003. Test variable = continuous run with modified runoff; baseline variable = continuous run with normal runoff. (A) Nitrate; (B) iron; (C) salinity. Effectof pycnocline depth on initiation of the simulated bloom. (D) Pycnocline depth at start of bloom; (E) changes in pycnocline depth from 5 days before bloom to the bloom date;(F) year day of bloom.

30 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

runoff (modified) were elevated by 1–5 lM in nearly all regions,with the exception of a small pocket on the mid-shelf off the KenaiPeninsula (Fig. 19A). Elevated nitrate concentrations were ob-served clear into the coast off the eastern Kenai Peninsula. In con-trast, iron concentrations in the modified simulation were loweredby 0.1 to over 0.5 nM, with the greatest differences on the innershelf (Fig. 19B). The geographic pattern of differences followedthe pattern of salinity anomalies, with greatest differences on theinner shelf where salinity anomalies were highest (Fig. 19C). Thus,runoff has a strong influence on both the distribution and meanconcentrations of iron and nitrate on the western Gulf of Alaskashelf in these simulations.

3.12. Bloom timing

The start of the spring bloom along the Seward Line is generallyrelated to a decline in the pycnocline depth. The pycnocline depthin this analysis is defined as the depth of maximum dq/dz between5 m and 100 m depth, where q is the water density and z is thewater depth. The simulated pycnocline depth in the Inner Zoneduring winter tended to oscillate (Fig. 20A, D and G) in responseto salinity. The spring bloom usually occurred in April–May whenthere was a substantial decline in the mixed depth (Fig. 20B, C, E, F,H and I). Defining the start of the spring bloom for this study as thefirst day of the year that primary production exceeded1 g cm�2 d�1, we plotted the pycnocline depth at the start of thebloom, the change in pycnocline depth between 5 days beforethe bloom and the start of the bloom, and the Julian day that thespring bloom started (Fig. 19D–F). The mean pycnocline depth onthe shelf at the start of the bloom was 20 m and the mean depth5 days before the bloom was 46 m. In most locations the depthof the pycnocline was about 20 m at the start of the bloom(Fig. 19D) but was deeper by about 50 m or more 5 days beforethe start of the bloom (Fig. 19E). Thus, the spring bloom in mostcases in these simulations coincided with a fairly rapid shallowing

of the pycnocline, however the timing of the bloom varied by loca-tion. The spring bloom on the inner shelf tended to occur betweenJulian days 70 and 100 (mid March through April) but was delayedinto May or even June on the outer shelf and in a pocket near thewestern grid boundary (Fig. 19F).

After the pycnocline shallows in May, nitrate concentration onthe outer shelf tends to follow an opposite trend to iron concentra-tion (Fig. 21A–C). An iron-rich water mass associated with an eddyin the 2002 reinitialized simulation crossed the outer end of theSeward Line in late April–June and was accompanied by an in-crease in iron concentration and a bloom (Fig. 21B). Production de-clined as nitrate was rapidly depleted from the upper 25 m. Aseddy waters propagated westward beyond the Seward Line, ironconcentration abruptly declined toward the end of June, and ni-trate concentration increased in the Outer Zone. A similar patternoccurred in the 2003 reinitialized simulation, when iron concentra-tion increased and nitrate concentration declined in the Outer Zonein June–July (Fig. 21C). The decline in nitrate concentration in lateJune–early July 2003 was not associated with a bloom along theSeward Line; the production drawing down the nitrate had oc-curred before the water mass crossed the Seward Line. The effectsof water masses of varying iron concentration on the nitrate con-centration in the upper 25 m can also be seen in the TransitionalZone, where nitrate concentration increased and again declinedas an iron-poor water mass propagated through the region inJuly–August (Fig. 21D). Intense blooms capable of stripping nitratefrom the upper 25 m of the water column do not occur in the ab-sence of elevated iron concentrations (�0.75 to >1 nM) in thesesimulations when iron half saturation constants are set to 0.2and 0.4 for small and large phytoplankton respectively.

3.13. Primary production

Primary production measurements were made at stationsGAK1, 4, 9 and 13. Highest production was observed on the inner

Fig. 20. Simulated primary production (g C m�2 d�1) and pycnocline depth (m) for the Seward Line: (A) Inner Zone 2001, (B) Transitional Zone 2001, (C) Outer Zone 2001, (D)Inner Zone 2002, (E) Transitional Zone 2002, (F) Outer Zone 2002, (G) Inner Zone 2003, (H) Transitional Zone 2003, (I) Outer Zone 2003. Biological state variables werereinitialized on January 1 of each simulation year.

Fig. 21. Mean nitrate concentration in the upper 25 m, mean iron concentration and primary production in the upper 30 m in the Outer and Transitional Zones along theSeward Line. (A) 2001 Outer; (B) 2002 Outer; (C) 2003 Outer; (D) 2002 Transitional. Biology variables were initialized on January 1 of 2001, 2002 and 2003.

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 31

32 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

shelf (stations GAK1, 4), lowest production occurred near the shelfbreak or in the oceanic regime (GAK9, 13) (Figs. 22 and 23). Max-imum primary production measurements occurred in late March,April and May, with maximum values on the order of 4 g cm�2 d�1

occurring in May (Figs. 22 and 23). Simulated primary productionalso peaked in April–May with maximum values of around6 g cm�2 d�1, but the simulated peaks were occasionally offsettemporally from the maximum measured values. The simulationstended to underestimate post bloom production in July–Augustrelative to field measurements. Model runs done without resettingbiological variables resulted in substantial reductions in primaryproduction relative to runs where biological variables were reini-tialized at the start of each year, although several peak simulatedproduction values in 2002 coincided with maximum measuredproduction values (Fig. 23A–C). Both measured and simulatedproduction tended to be lower in 2003 relative to other years.Production on the shelf ranged from <100 g cm�2 y�1 to 200–300 g cm�2 y�1, with maximum values in Lower Cook Inlet andoff Kodiak Island (Fig. 24A–C). Even when overall production waslower, regions of elevated production occurred in Lower Cook Inlet,off Kodiak Island and in some shelf regions west of Kodiak Island.When the simulation was run continuously without reinitializingbiological variables at the start of each year, the production onthe western Gulf of Alaska shelf was lower, particularly in 2003(Fig. 24E); positive anomalies occurred only near the shelf break.Production anomalies during 2002 were less consistent(Fig. 24D), positive anomalies occurred off the Kenai Peninsula, inLower Cook Inlet and in Shelikof Strait.

Mean production in the Western Gulf Polygon (Fig. 18A) de-clined when the model was run continuously without reinitializingthe biology variables at the start of each year of the simulation(Table 1). Production declined by about 10% during the 2002 sim-ulation and by about 28% in the 2003 simulation. The declines inproduction were accompanied by substantial declines in mean ni-trate concentration in the upper 25 m in the Western Gulf Polygonin March: by about 13% and 45% for 2002 and 2003 simulationsrespectively. These declines appeared to be related to substantial

Fig. 22. Simulated and measured primary production along the Seward Line from 2001 thin the figure titles. Biological state variables were reinitialized at the start of each simu

increases in runoff in the forcing file for July–February 2002–2003 relative to 2001–2002. However, when runoff for July–Febru-ary 2002–2003 was substituted with runoff for 2001–2002 (lowerrunoff) (Fig. 18B), production declined relative to the reinitializedsimulation by 38%, an additional 10% over declines from the con-tinuous simulation with normal (higher) runoff. Nevertheless, ni-trate concentration increased with lower runoff by over 14% ofthe values for the continuous simulation using unmodified (higher)runoff (Table 1), but mean iron concentration in the Western GulfPolygon declined in response to the modified (lower) runoff(Fig. 18C). Mean annual simulated shelf production in this modelis a balance between nitrate and iron availability as modulatedby the ROMS circulation model in response to forcing, in this caserunoff. When runoff for 2002–2003 was substituted for runoff from2001–2002, production was lower over most of the outer shelf andtransitional regions, but was higher on the inner shelf (Fig. 24F).

The wind resolution files from CORE1 have 2.5� resolution orabout 280 km. Running the model with high resolution NARR windforcing files (eight times daily at 32 km resolution) lowered meanproduction on the western shelf by about 7% in the 2001 modelrun, and by about 15% and 8% for continuous model runs in 2002and 2003 respectively. Corresponding mean nitrate concentrationson the western shelf in the upper 25 m in May declined usingNARR winds by about 1% and 6% in 2001 and 2002 respectively,but increased by about 10% in 2003 (Table 1).

3.14. Eddy statistics in the 3D simulation

Eddies in the simulations form near Yakutat and propagatewestward along the shelf break. The westward propagation of ed-dies in the simulations can be followed in a time series of dailyplots of mean iron concentration in the upper 30 m; three repre-sentative plots are shown in Fig. 15A–C. When the eddies crossedthe Seward Line as they did in the reinitialized model runs (notshown), they affected iron concentration and nitrate concentrationduring the production season at stations in the Outer and Transi-tional Zones. Due to the elevated iron in the eddies, nitrate is

rough 2003. Annual simulated primary production, station name and year are listedlation on January 1 of the respective years.

Fig. 23. Simulated and measured primary production along the Seward Line from 2001 through 2003. The annual simulated primary production, station name and year arelisted in the figure titles. The simulation was run continuously from 2001 to 2003 without reinitializing biological state variables.

Fig. 24. Simulated primary production (g C m�2 y�1) in the upper 30 m on the western Gulf of Alaska shelf. (A) 2001, (B) 2002, (C) 2003; production anomalies (continuous–reinitialized); (D) 2002; (E) 2003; (F) production anomalies, modified versus unmodified runoff (modified–unmodified), 2003.

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 33

drawn down relative to the surrounding areas by higher produc-tion in the eddies. When the eddies cross the Seward Line or de-cayed near the Seward Line during the production season, theirpresence can appear as a sudden change in the nitrate concentra-tion at stations on the outer half of the line (Fig. 21B–D).

Because of the potential importance of eddies to nitrate avail-ability and phytoplankton production on the GOA shelf, we exam-ined eddy statistics in the 3D simulations and compared them withsatellite observations. The satellite sea surface height (SSH) datawere downloaded from the AVISO website (http://www.aviso.oce-anobs.com/en/data/index.html), and we used the weekly griddedSSH anomalies with 1/3 deg by 1/3 deg horizontal resolution. Toensure a fair comparison, the simulated SSH data were mappedonto the same horizontal and temporal grids as the satellite obser-vations before computing EKE (Eddy Kinetic Energy). EKE was cal-culated as EKE ¼ ðu02þv 02Þ

2 , where u0 ¼ � gf

dðssh0 Þdy and v 0 ¼ g

fdðssh0 Þ

dx are thegeostrophic velocity anomalies associated with ssh0, ssh0 is the

deviation of ssh from the long term multi-year mean of either mod-el or satellite data, g is the acceleration of gravity and f is the cori-olis parameter. Since satellite observations and runs of thecirculation component of the ROMS model were available from2000 to 2005, we based our calculation on this longer time period.Simulations, observations and published results (Ladd, 2007) showthat EKE in the GOA peaks in three regions: off Baranov Island(Sitka), off Kayak Island in the Northern GOA (NGOA) and off Ko-diak Island (Fig. 25a). In addition, simulations on the CGOA gridand observations show a spatially averaged EKE of 55 cm2/s2 and60 cm2/s2 respectively over the entire GOA domain, with localmaxima reaching 160 cm2/s2 (Fig. 25a and b); the spatially aver-aged EKE for the NEP grid was 28 cm2/s2, less than half of the mea-sured value. Although the CGOA simulations captured the meanamplitude of the EKE, the model did not reproduce the exact timeand location of specific eddies as observed in SSH anomalies be-cause eddies are generated by nonlinear, stochastic processes of

Fig. 25. Eddy kinetic energy off the northern Gulf of Alaska shelf break: mean values (2000–2005) from (a) Sea surface height measurements; (b) CGOA simulations; (c) NEPsimulations; (d) Temporal record from boxed enclosures off Sitka, the northern GOA (NGOA) and Kodiak.

34 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

ocean instability. Therefore, the spatial mean EKE time series ofobservations and CGOA simulations have similar amplitudes butare temporally uncorrelated (Fig. 25d). EKE in the 10 km resolutionNEP simulations was much less, sometimes only 10% of the CGOAsimulation and satellite observations (Fig. 25c and d). Thus, the EKEstatistics indicate that the model run on the 3-km CGOA grid pro-duced eddies of similar magnitude and at similar locations as ob-served from satellite data, but the model could not simulate theexact timing and magnitude of specific eddies. Since the timingand rate of nitrate utilization appears to be influenced by thesemesoscale circulation patterns, simulations will not consistentlyreproduce the exact seasonal timing of nitrate drawdown andexhaustion as observed in field measurements from year to yearbecause the physical model is currently unable to simulate the ex-act timing, location and intensity of eddies, meanders and fila-ments, or their speed of propagation.

4. Discussion

Unique features of the GOA environment impose specificrequirements on any model simulating the ecosystem responseto climate forcing. The GOA climate is characterized by energetic

storms which produce massive amounts of seasonal runoff(Weingartner et al., 2005; Royer, 1982). Freshwater discharge intothe coastal ocean is apparently a critical source of iron to the shelfecosystem (Wu et al., 2009). Since the northern GOA is an iron-lim-ited system (Martin et al., 1989; Strom et al., 2000), iron fromfreshwater discharge is critical to production on the GOA shelf. Inaddition to precipitation, the cyclonic winds associated withstorms promote onshore transport and coastal downwelling whichis particularly intense during winter (Weingartner et al., 2005). Theelevated runoff and coastal winds generate the Alaska Coastal Cur-rent (ACC) (Royer, 1998; Stabeno et al., 2004). The other major cur-rent in the northern GOA, the Alaska Current–Alaskan Stream(ACAS), flows westward near the shelf break (Reed, 1984). TheACC and ACAS interact with the rough shelf and coastal topographyto generate eddies and meanders, which promote mixing of oce-anic and coastal water masses across the shelf (Okkonen et al.,2003). The ROMS circulation model must incorporate all of theseenvironmental components to accurately simulate interannual dif-ferences in the ecosystem response to climate forcing.

While the model does simulate the ACAS and ACC, and it also pro-duces eddies, the simulations do not reproduce the specific shelf-break eddies at the same locations and times as observed from

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 35

satellite altimetry data. These shelf-break eddies originate nearYakutat, entrain iron-rich coastal waters, and propagate westwardalong the shelf break (Janout et al., 2009; Ladd et al., 2009). As theymove westward, these eddies affect cross-shelf advection and watermass properties (Okkonen et al., 2003), thus influencing production,species composition and abundance of lower-trophic level compo-nents in the ecosystem on the adjacent shelf and in the GOANPZmodel. The potential influence of eddies was illustrated by the CTDsection from May 2001 (Fig. 16A and B), when oceanic water waspushed clear into 75 km on the Seward Line. The effect of eddieson the circulation model is illustrated by daily time series of meaniron concentrations in the upper 30 m; three representative framesfrom a 2002 simulation show an eddy to the east of the Seward Lineassociated with advection of iron-poor oceanic water shoreward(Fig. 15A–C). These masses of iron-poor water propagate westwardwith the prevailing circulation causing rapid changes in the nitrateand iron concentrations in the Transitional and Outer Zones alongthe Seward Line (Fig. 17B, C and F). Although actual eddies can varymarkedly in size and propagation rate, eddies in the simulationstended to either cross the Seward Line in May and decay (reinitial-ized runs), or they remained just east of the Seward Line betweenApril and October–November (continuous runs). The timing andpropagation rate of eddies off the shelf break can be corrected in sim-ulations by assimilating satellite altimetry data and correcting thevelocities and vertical density distribution using adjoint techniques.This method was used successfully by Fiechter et al. (2011) on asmall 10-km resolution simulation but is impractical for large,high-resolution grids such as the CGOA because of the large amountof computer resources required.

Running the simulations with reinitialized biological variableseach January resulted in elevated primary production for 2002and 2003 relative to continuous model runs (Table 1). The initialcondition for nitrate was generated from mean values measuredalong the Seward Line but use of the same mean value as initialconditions for each year of the simulations was apparently mask-ing interannual differences in nitrate supply to the shelf by artifi-cially adding nitrate each winter. Mean measured nitrateconcentration in the upper 25 m along the Seward Line in Marchvaried from a minimum of about 7 lM in 2004 to a maximum ofabout 18 lM in 2002, with most values above 15 lM. HoweverMarch nitrate concentrations along the Seward Line are not neces-sarily reasonable surrogates for prebloom nitrate concentrations atother locations on the shelf. Continuous simulations indicated thatuse of mean Seward Line nitrate values was inflating the spring ni-trate reservoir and elevating simulated production estimates.

The simulations are sensitive to freshwater discharge. This sen-sitivity is appropriate, given the large volume of freshwater sea-sonally discharged from the continent into the adjacent ocean.When discharge is elevated, the simulations indicate that the innershelf is flooded by iron-rich coastal waters, but onshore advectionof nitrate is inhibited, leading to declines in mean nitrate concen-tration and mean productivity on the shelf. The only source of ironin this model after initiation of biological state variables is runoff.The only source of nitrate on the shelf in these simulations isadvection from the adjacent ocean. In contrast to iron, nitratewas not added with freshwater discharge, nor was nitrate diluted.ROMS does not actually add water volume in response to dis-charge, but only changes the salinity. Thus, lower prebloom nitrateconcentrations on the inner shelf in the 2003 simulation (Fig. 14D)were due to alterations in horizontal and/or vertical circulation,not due to dilution of nitrate with fresh water. Seasonal processesother than those affected by runoff were influencing shelf nitrateconcentrations in 2003 because the modified simulation with2002 runoff raised mean prebloom nitrate concentrations from9.5 to 12 lM but prebloom nitrate concentration in 2002 was high-er, about 14.6 lM (Fig. 18D). The shelf is a sink for particulate

carbon and nitrogen because detritus sinks from the system, solosses in shelf nitrogen due to particle sinking must be replacedfrom offshore by shoreward advection of nitrate to prevent de-clines in the shelf prebloom nitrate reservoir during continuousmodel runs. Mean nitrate concentrations on the shelf in January2003 and 2004 were nearly identical, suggesting that circulationin the continuous simulation was restoring prebloom nitrate con-centrations in 2004 to prebloom concentrations in 2003(Fig. 18D). The only nitrate source to the CGOA grid was fluxthrough the southern and western boundaries. Nitrogen fixationand nudging of nitrate or iron were not done in these simulations.Long-term model runs might require addition of nitrate to replacenitrogen and/or iron lost from sinking, particularly on the shelfwhere production is elevated.

Production in the model does not strictly follow nitrate avail-ability because production is also iron limited. Iron is added toGOA shelf waters primarily through freshwater runoff (Lippiattet al., 2010; Wu et al., 2009; Stabeno et al., 2004), which carriesa large suspended load of sediments into the adjacent ocean(Milliman and Syvitski, 1992). Recent measurements indicate thatACC waters have seasonally elevated amounts of leachable partic-ulate iron which supply a 2 nM source of dissolved iron to the ACC(Lippiatt et al., 2010). The iron response in the model was thereforeadjusted to saturate at 2 nM. Increasing nitrate by lowering runoffdid not elevate mean shelf production in the 2003 simulationbecause iron injection into the coastal ocean with freshwater dis-charge was reduced, thus lowering the mean shelf iron concentra-tion and primary production, particularly on the outer and middleshelf. Iron measurements indicate low iron concentrations acrossthe shelf into approximately 100 km along the Seward Line (Wuet al., 2009). However, simulations indicate that iron-rich coastalwater can be advected further outward, or iron-poor oceanic watercan be advected inward, which can change the concentrations ofiron and nitrate at point locations in a matter of days. When runoffwas lowered, simulated production on the inner shelf increased inresponse to elevated nitrate concentrations, even though iron con-centration was substantially reduced; production increased be-cause reduced runoff did not lower iron concentrations on theinner shelf sufficiently to limit production. However, productionon the middle and outer shelf was reduced in response to loweriron concentrations so the overall change in shelf production wasnegative. The simulations suggest that nudging iron concentrationto a climatological mean or use of forcing files which do not reflectinterannual and seasonal cycles of freshwater discharge will maskinterannual differences in simulated production by climate forcingwith this model.

Lowering iron half saturation constants in the continuous sim-ulations did not consistently alter shelf nitrate concentration.However, if iron half saturation constants were lowered suffi-ciently for phytoplankton in the model to remove nitrate fromthe upper 25 m in the ocean basin during summer (kFe below 0.1and 0.2 for small and large phytoplankton respectively), mean ni-trate concentration in the upper 25 m over the basin and shelf dur-ing the following March was reduced. Apparently, winterupwelling generated by the circulation model was insufficient tocompletely resupply the euphotic zone with nitrate when phyto-plankton growth was not iron limited. Shelf upwelling has beenattributed to Ekman pumping from positive wind stress curl onthe shelf generated by winter winds (Fiechter and Moore, 2009).Comparison of synthetic aperture radar data with data fromNCEP/NCAR Reanalysis (Kalnay et al., 1996) indicated that duringperiods of southerly or easterly along shore winds, the NCEP/NCARwind fields underestimate actual wind speeds by an average of 50%because the resolution of NCEP winds is about 2.5�, which is insuf-ficient to resolve the effects of coastal topography on winds(Stabeno et al., 2004). Wind fields driving our ROMS simulations

36 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

come from CORE1 files with 2.5� resolution which cannot repro-duce the coastal jet winds responsible for generating upwellingon the mid shelf (Stabeno et al., 2004). However, a continuousmodel run with NARR winds having 32 km resolution did notsubstantially increase mean prebloom nitrate concentrations inthe upper 25 m on the shelf or increase simulated production(Table 1). Thus, resolution of the wind files in these simulationsdid not substantially affect production or nitrate supply to theeuphotic zone.

The bloom on the GOA shelf in the simulations was associatedwith shallowing of the pycnocline. Observations indicate thatbloom timing on the shelf is related to the onset of stratificationin late April–early May due to heating from seasonal increases inshortwave radiation and to freshwater discharge (Henson, 2007).Stratification presumably permits positive growth by phytoplank-ton when the mixing depth is less than the critical depth (Sverd-rup, 1953). However, recent analysis of Atlantic data indicatethat positive phytoplankton growth occurs under very low lightconditions in winter and that blooms result not from changes inlight experienced by the phytoplankton but from a cessation ofdilution of phytoplankton by vertical mixing as the seasonalpycnocline is established (Behrenfeld, 2010). The response of thephytoplankton to stratification and elevated light is probably nota simple balance between respiration losses and a constant photo-synthetic response to light by phytoplankton. The use of a constanta for each size category of phytoplankton in the model is a simpli-fication of reality. The phytoplankton community on the GOA shelfactually consists of numerous species of phytoflagellates, cyano-bacteria, diatoms and dinoflagellates (Strom et al., 2006), eachadapted to optimize growth under different conditions. Large cen-tric diatoms (Thalassiosira and Chaetoceros) dominate the phyto-plankton community on the shelf during the spring bloom. Theouter shelf community is a mix of small phytoplankton cells andneedle-shaped pennates (Cylindrotheca, Thalassionema, Pseudo-nitzschia) and the community on the shelf following the springbloom is often dominated by a number of species of autotrophicflagellates and Synechococcus. These species groups undoubtedlyhave different physiological responses to prevailing light and nutri-ent levels. The marked changes in a observed during sequentialsampling in Auke Bay during the spring bloom (Ziemann et al.,1990) provides further evidence that single parameter values forphytoplankton size categories in NPZ models are unlikely to con-sistently reproduce field observations.

A number of attempts have been made to generate more inclu-sive models of phytoplankton physiology and biochemistry(Armstrong, 2006; Baklouti et al., 2006; Gruber et al., 2006; Flynn,2001). These usually involve application of submodels to follownutrient and carbon pools inside the cells and to model photosys-tem II and respiration in the mitochondria explicitly. While theaddition of such submodels may create a more realistic phyto-plankton response to light and nutrient pools, they also substan-tially add to the complexity of the model, the volume ofparameter space that must be searched during optimization, andthe computational requirements of the simulations, particularlyif the NPZ model is embedded in a general circulation model.Although diatoms can dominate the shelf phytoplankton commu-nity at certain times, the GOANPZ model in its current iteration in-cludes only ammonium and nitrate as macro-nutrient statevariables. We have not included silicate at this point because sili-cate does not usually appear at limiting concentrations. Neverthe-less, there is some evidence that silicate may occasionally limitdiatom growth on the inner shelf where iron limitation is seldomobserved (Strom et al., 2006) and possibly also in the central eastPacific gyre (Harrison et al., 2004). Incorporation of silicate intothe model would necessitate introduction of dissolved and partic-ulate silicate state variables, and also probably a separate model

box for diatoms (Fujii et al., 2007), substantially increasing modelcomplexity and parameter space.

Mean log-transformed chlorophyll concentrations in the upper30 m from direct field measurements and model output had a sim-ilar range of values (0.1–0.9 lg l�1), but maximum values fromfield measurements were underestimated by the model about25% of the time. Lower simulated chlorophyll values relative tomeasured values were sometimes due to differences in timing be-tween simulated chlorophyll peaks and field measurements. Directcomparison of chlorophyll output from the model and satellitemeasurements are more difficult to interpret because the satellitedata are limited to surface observations on clear days and are bestinterpreted as composites producing mean monthly climatologies(Brickley and Thomas, 2004). Nevertheless, SeaWiFS chlorophyllmeasurements (Fig. 3 in Brickley and Thomas, 2004) and simula-tions (Fig. 6B) do show a similar spatial pattern of elevated chloro-phyll concentrations on the shelf relative to the adjacent oceanbasin, but model estimates of mean chlorophyll tend to underesti-mate satellite values. Mean simulated chlorophyll concentration inthe upper 25 m on the western shelf during the spring bloom wasabout 1.7 lg l�1. Satellite mean estimates for May were about2.5 lg l�1 for the shelf (Brickley and Thomas, 2004).

A model may generate reasonable chlorophyll concentrationsbut be incapable of generating reasonable upper trophic level bio-mass if the simulations consistently underestimate primary pro-duction. Chlorophyll measurements can be high with lowproduction if the grazing rates, phytoplankton mortality or sinkingrates of phytoplankton and detritus are underestimated. The mag-nitude of primary production in the simulations is related not somuch to chlorophyll concentration but more to the integratednutrients and light supplied by the circulation model and forcingfiles at specific grid locations for the whole season.

The model tended to produce production peaks of similar magni-tude to measured values, particularly in the Inner and TransitionalZones in continuous model runs during 2002, but summer produc-tion tended to be underestimated. Averaging nitrate from the pycno-cline to the surface was a crude method of increasing summerproduction in 3D runs, but it resulted in nitrate accumulation inthe upper mixed layer in the Transitional Zone, a condition not ob-served in the field data. Nitrate accumulation in the simulationwas due to iron limitation, as elevated production stripped iron fromthe euphotic zone. Underestimation of summer production in unal-tered simulations is probably due to an underestimation of verticalmixing near the pycnocline by the circulation model. Simulationsindicate that summer production is a balance between the amountof nitrate moving upward through the pycnocline and the amountof iron available for phytoplankton growth. Summer productionwas increased in 1D simulations by artificially increasing the verticaldiffusion coefficient but nudging vertical diffusion created numeri-cal instabilities in 3D simulations. Simulated primary productionranged from 100 to 300 g cm�2 y�1; a similar range was reportedfrom field observations (Sambrotto and Lorenzen, 1987).

The highly dynamic environment on the GOA shelf complicatesinterpretation of field data. If field measurements of primary pro-duction are integrated to estimate annual production along the Sew-ard Line, values of up to 350 g cm�2 y�1 would result (Fig. 23B forexample). The simulation generated a production estimate of114 g cm�2 y�1 because the production peak captured by the fieldmeasurement is quite narrow in the simulation; thus the simula-tions indicate that the field observations are subject to serious tem-poral aliasing. In addition, the simulations indicate that productionmeasurements are spatially dependent. Measurements along theSeward Line are not necessarily characteristic of values measuredin Lower Cook Inlet or off Kodiak. Thus, interpretation of field mea-surements is also susceptible to spatial aliasing. Given the limita-tions imposed by cloud cover on satellite observations and

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 37

logistics on field observations, the skill of the numerical model insimulating the actual spatial and temporal magnitude and distribu-tion of model state variables for the shelf or gulf as a whole is difficultto thoroughly evaluate. As additional field measurements are gener-ated, the model will undoubtedly require much refinement. Never-theless, these types of models provide a formal method to place fieldmeasurements in a broader spatial–temporal context than just thetime and place where the measurement was taken.

5. Conclusions

(1) Timing of the spring bloom on the inner shelf is driven pri-marily by physical factors affecting pycnocline depth.

(2) Nitrate is utilized in the coastal zone first; nitrate depletionpropagates from the inner shelf to beyond the shelf break inApril–August. Nitrate concentration in eddies is elevated inearly spring but is quickly consumed in the presence ofhigher iron concentration in eddy waters as stratificationdevelops. Therefore advection of eddy water across anobservation point may result in increases or decreases innitrate, depending on the season.

(3) Mean simulated primary production rates from 90 to130 g cm�2 y�1 are observed along the Seward Line, withmaximum values in the mid shelf. Mean simulated primaryproduction on the western GOA shelf is on the order of 100–140 g cm�2 y�1, with maximum values of up to300 g cm�2 y�1 in lower Cook Inlet, over Portlock Bank,and in regions around Kodiak Island and on the shelf tothe west of Kodiak.

(4) Simulated production on the shelf is influenced by runoff,which injects iron into the adjacent ocean and influences cir-culation patterns moving nitrate onto the shelf. Altering run-off can raise or lower mean shelf production, depending onhow changes in runoff affect the balance of iron and nitratein various regions on the shelf.

(5) Reinitialization of nitrate and iron concentration in Januarycan alter the amount and distribution of iron and nitrateon the shelf from what is supplied by runoff and the circula-tion model component, thus altering simulated production.The simulations suggest that interannual differences in shelf

Table A1GOANPZ model equations. Advection and diffusion terms are not included. PS = small phEup = Euphausiids, MZS and MZL = small and large microzooplankton.

State variable Equation

(1) Nitrate@NO3

@t¼�n PS�PMAX tanh

a�PARP�max

� �� �NO3e�WPSNH4

k1PSþNO3

� �k

��

þPL�PMAX tanha�PARP�max

� �� �NO3e�WPL NH4

k1PLþNO3

� �Fe

kfePLþ

�(2) Ammonium

@NH4

@t¼ �n PS � PMAX tanh

a � PARP�max

� �� �NH4

kPS þNH4

� ����

þ PL � PMAX tanha � PARP�max

kFePL þ FeFe

� �� �NH4

kPL þ NH

�(3) Phyto-plankton(i) where

i = PS or PL @PhðiÞ@t

¼ PhðiÞ � Pmax tanha � PARP�max

� �� �NO3e

�wPðiÞNH4

kNO3PðiÞ þNO3

!

�XTotPðiÞ

j¼1 eZði;jÞ QT�Q10Zði;jÞT

1010

!fpZði;jÞ

PhðiÞ

fZðiÞ þPTotPðjÞ

k¼1 fpPrðj;kÞP

0B@

0B@

�MAX mPhðiÞmin;mPhðiÞmax

� mPhðiÞmax�mPhðiÞmin

� �� �

Ph(i) is the phytoplankton componentTotPðiÞ is the total number of predators taking Ph(i)

Z(j) is predator(j) feeding on Ph(i)

production are affected more by fall-winter runoff and circu-lation patterns moving nutrients onto the shelf than by pro-cesses during the production season.

(6) Eddies in the simulations have elevated iron concentrationsoriginating from shelf sources. The eddies originate nearYakutat, propagate westward along the shelf break at vary-ing speeds and affect nitrate and iron concentrations as theypass points along the outer shelf and adjacent ocean.

(7) Mean EKE in simulations on the 3 km grid was approxi-mately equal to mean EKE from satellite-measured SSHanomalies, while mean EKE in simulations on the 10 km res-olution NEP grid was approximately half of the mean fromSSH anomalies. These results suggest that higher resolutiongrids are substantially better at resolving mesoscale eddiesand other circulation features affecting production on theshelf and in the adjacent ocean.

Acknowledgements

The modeling work was funded by North Pacific Research Board(Award 614). Data from the Seward Line was collected with sup-port from National Science Foundation and National Oceanic andAtmospheric Administration (Award OCE 1019078) and by theNorth Pacific Research Board’s Seward Line Long Term ObservationProgram (Awards 520, 603 and 708). Support for this work alsocame from the US GLOBEC lower trophic level synthesis program(NEP Phase IIIb-CGOA; Award 0639449) and GLOBEC Pan-regionalSynthesis program (Award 0814395). Super computing supportwas provided by the Arctic Region Supercomputing Center at Uni-versity of Alaska Fairbanks and computing and programming aidwas provided by Elizabeth Dobbins and Georgina Gibbson. Moor-ing data was provided by Phyllis Stabeno through Dave Kachel.CTD data was provided by Tom Weingartner. We thank all of theseindividuals and organizations for their help on and support for thisresearch.

Appendix A. Appendix

See Tables A1 and A2.

ytoplankton, PL = large phytoplankton, D = detritus, C = copepods, NA = Neocalanus,

Fe

fePSþFe

�kfePSþ2

2

� �

Fe

�kfePLþ2

2

� ��þnitr

FekfePS þ Fe

�kfePS þ 2

2

� �

4

�Fe

kfePL þ Fe

� �kfePL þ 2

2

� ��þ regen � degrad � D

�� nitr þ Ei

þ NH4

kNH4PðiÞ þ NH4

!!Fe

kFePðiÞ þ Fe

!kFePðiÞ þ 2

2

!

rðj;kÞ

1CAZðjÞ

1CA

NO3

NOcritPhðiÞ

!PhðiÞ �wPhðiÞPhðiÞ � RðiÞ

(continued on next page)

Table A1 (continued)

State variable Equation

TotPðjÞ is the total number of prey species for Z(j)

j = predator species j of phytoplankton ik = prey species k of predator j

(4) Zooplankton(i) wherei = MZS, MZL, C, NC orEup.

@ZðiÞ@t¼ cðiÞeZðiÞ Q

T � Q10ZðiÞT

1010ZðiÞ

0BB@

1CCA

PTotPj¼1 fpZði;jÞ

PrðjÞ� �

fZðiÞ þPTotPðjÞ

j¼1 fpPrðj;kÞPrðj;kÞ

0B@

1CAZðiÞ �

XTotPðiÞj¼1 eZðjÞ Q

T�Q10Zði;jÞT10

10ZðjÞ

!fpZði;jÞ

ZðiÞ

fZðjÞ þPTotPðjÞ

k¼1 fpPrðj;kÞPrðj;kÞ

0B@

1CAZðjÞ

0B@

1CA� RðiÞ �MðiÞZ

2ðiÞ

(5) Detritus

@D@t¼ ð1� cMZSÞeMZS Q

Temp�Q10MZST10

10MZS

� �fpPSMZSPS

fMZS þ fpPSMZSPS

� �MZS

þ ð1� cMZLÞeMZL QTemp�Q10MZLT

1010MZL

� �fpPSMZSPSþ fpPLMZLPL

fMZL þ fpPSMZSPSþ fpPLMZLPL

� �MZL

þ ð1� cCÞeC QTemp�Q10CT

1010C

� �fpPSMZSPSþ fpPLCPLþ fpMZSCMZSþ fpMZLCMZL

fC þ fpPSMZSPSþ fpPLCPLþ fpMZSCMZSþ fpMZLCMZL

� �C

þ ð1� cNCÞeNC QTemp�Q10NCT

1010NC

� �fpPSMZSPSþ fpPLNCPLþ fpMZSNCMZSþ fpMZLNCMZL

fNC þ fpPSMZSPSþ fpPLNCPLþ fpMZSNCMZSþ fpMZLNCMZL

� �NC

þ ð1� cEupÞeEup QTemp�Q10NCT

1010Eup

� �fpPLEupPLþ fpMZSEupMZSþ fpMZLEupMZLþ fpCEupC

fEup þ fpPLEupPLþ fpMZSEupMZSþ fpMZLEupMZLþ fpCEupC

!Eup

þMAXðmPSmin;mPSmax � ðmPSmax �mPSminÞÞNO3

NOcritPS

� �PS

þMAXðmPLmin;mPLmax � ðmPLmax �mPLminÞÞNO3

NOcritPL

� �PL

þmpredCC2 þmpredNCNC2 þmpredEupEup2 � degrad � D�wD � D

(6) Iron

@Fe@t¼ fec � PS � PMAX tanh

a � PARP�max

� �� �NO3e�WPSNH4

k1PS þ NO3

� �þ NH4

kPS þ NH4

� �� �Fe

kfePS þ Fe

� �kfePS þ 2

2

� �

þ fec � PL � PMAX tanha � PAR

P�max

� �� �NO3e�WPLNH4

k1PL þ NO3

� �þ NH4

k2PL þ NH4

� �� �Fe

kfePL þ Fe

� �kfePL þ 2

2

� �

Subequations(7) Maximum carbon-

specific photosyntheticrate (mg C/mg C/day)

Pmax = (2D � 1)

(8) Maximum chlorophyll a-specific

P�max ¼ PmaxccrPðiÞ

(9) photosynthetic rate (mgChl/mgC/day)

(10) Doubling rate (D) D ¼ DiðiÞð10Þ0:0275T

(11) Sinking rate (w) Sinking ¼ ðwXÞ @X@z

where X = PS, PL or D(12) Senescence of small

and largemX ¼ MAXðmin mX;max mX � ðmax mX �min mXÞ � NO3

NcritXÞ

Phytoplankton where X = PhS or PhL(13) Nitrification nitr ¼ ðkN0 expðkNT TÞNH4Þ I�I0

kIþI�I0

� �where I = irradiance at depth (W m�2), I0 = irradiance of threshold inhibition

(W m�2), kI = irradiance where inhibition is half (W m�2), kN0 = nitrification rate at 0 �C (lM d�1),kNT = temperature correction coefficient for nitrification, T = temperature (�C) and NH4 = ammonium concentration (lM)

Respiration Ri ¼ ðbmðiÞeðktbmðiÞ

T�TrefðiÞÞÞBi where Ri is the respiration rate of species i (mg C m�3 d�1), bm(i) = basal metabolism,

ktbm(i) = temperature coefficient and Tref(i) = reference temperature (�C) for species i and T = ambient temperature (�C), Bi = biomass ofspecies i (mg C m�3).

Excretion Ei ¼ Rin where Ri = respiration for species i and n = nitrogen carbon ratio

38 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

Table A2Parameters for the biological model. PS = small phytoplankton, PL = large phytoplankton, D = detritus, C = copepods,NA = Neocalanus, Eup = Euphausiids, MZS and MZL = small and large microzooplankton.

Parameter Description Value

dt Time step (d�1) 1D simulations 0.0093dt Time step (d�1) 3D simulations 0.0017kext Extinction coefficient (due to seawater) (m�1) 0.035kp Extinction coefficient (due to phytoplankton) (m�1) 0.035DiS Doubling rate parameter for PS 1.6DiL Doubling rate parameter for PL 1.6DpS Doubling rate exponent for PS 0.0275DpL Doubling rate exponent for PL 0.0275n Nitrogen:carbon ratio (mmol N/mg C) 0.0126aPS Slope of P–I curve for PS (mg C/mg chl-a/Em�2) 5.6kfePS Half-saturation constant for iron limitation by PS (lmol Fe/m3) 0.2wPS Coefficient of NO3 limitation for PS 3.0k1PS Half-saturation constant for NO3 limitation for PS (mmol N/m3) 0.8k2PS Half-saturation constant for NH4 limitation for PS (mmol N/m3) 0.2aPL Slope of P–I curve for PL (mg C/mg chl-a/Em�2) 2.2kfePL Half-saturation constant for iron limitation for PL ((lmol Fe/m3)) 0.4wPL Coefficient of NO3 limitation for PL 0.3k1PL Half-saturation constant for NO3 limitation for PL (mmol N/m3) 0.5k2PL Half-saturation constant for NH4 limitation for PL (mmol N/m3) 0.8eMZS MZS maximum specific ingestion rate (mg C/mg C/d) 2.0eMZL MZL maximum specific ingestion rate (mg C/mg C/d) 5.0eC C maximum specific ingestion rate (mg C/mg C/d) 0.5eNC NC maximum specific ingestion rate (mg C/mg C/d) 0.5eEup Eup maximum specific ingestion rate (mg C/mg C/d) 0.234Q10MZS Q10 for MZS growth rate 2.3Q10MZL Q10 for MZL growth rate 2.0Q10C Q10 for C growth rate 1.37Q10NC Q10 for NC growth rate 1.75Q10Eup Q10 for Eup growth rate 2.25Q10MZST Temperature coefficient for Q10 for MZS growth rate (�C) 5.0Q10MZLT Temperature coefficient for Q10 for MZL growth rate (�C) 5.0Q10CT Temperature coefficient for Q10 for C growth rate (�C) 5.0Q10NCT Temperature coefficient for Q10 for NC growth rate (�C) 5.0Q10EupT Temperature coefficient for Q10 for Eup growth rate (�C) 8.0fMZS Half-saturation constant for MZS grazing (mg cm�3) 20.0fMZL Half-saturation constant for MZL grazing (mg cm�3) 40.0fC Half-saturation constant for C grazing (mg cm�3) 40.4fNC Half-saturation constant for NC grazing (mg cm�3) 55.0fEup Half-saturation constant for Eup grazing (mg cm�3) 45.0fpPSMZS Feeding preference of MZS for PS 1.0fpPLMZL Feeding preference of MZL for PL 1.0fpPSMZL Feeding preference of MZL for PS 1.0fpMZSMZL Feeding preference of MZL for MZS 1.0fpPSC Feeding preference of C for PS 0.5fpPLC Feeding preference of C for PL 1.0fpMZSC Feeding preference of C for MZS 0.5fpMZLC Feeding preference of C for MZL 1.0fpPSNC Feeding preference of NC for PS 0.5fpPLNC Feeding preference of NC for PL 1.0fpMZSNC Feeding preference of NC for MZS 0.5fpMZLNC Feeding preference of NC for MZL 1.0fpPLEup Feeding preference of Eup for PL 1.0fpMZLEup Feeding preference of Eup for MZL 0.5fpCEup Feeding preference of EUP for C 0.5mPSmin Minimum daily linear mortality rate for PS (d�1) 0.01mPSmax Maximum daily linear mortality rate for PS (d�1) 0.085NOcritPS Critical NO3 for PS mortality (mg C/m�3) 0.6mPLmin Minimum daily (linear) mortality rate for PL (d�1) 0.01mPLmax Maximum daily (linear) mortality rate for PL (d�1) 0.085NOcritPL Critical NO3 for PL mortality (mg C/m�3) 0.6mpredMZS Daily (nonlinear) mortality for MZS (d�1) 0.001mpredMZL Daily (nonlinear) mortality for MZL (d�1) 0.001mpredC Daily (nonlinear) mortality for C (d�1) 0.00075mpredNC Daily (nonlinear) mortality for NC (d�1) 0.00075cMZS Growth efficiency for MZS 0.7cMZL Growth efficiency for MZL 0.7cC Growth efficiency for C 0.7cNC Growth efficiency for NC 0.7wPS Sinking rate for PS (m/s) 0.0respPS Basal metabolic rate (d�1) for PS 0.08respPL Basal metabolic rate (d�1) for PL 0.1respMZS Basal metabolic rate (d�1) for MZS 0.6respMZL Basal metabolic rate (d�1) for MZL 1.4

(continued on next page)

K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41 39

Table A2 (continued)

Parameter Description Value

respC Basal metabolic rate (d�1) for C 0.03respN Basal metabolic rate (d�1) for N 0.03respEup Basal metabolic rate (d�1) for Eup 0.02wPL Sinking rate for PL (m/s) 0.1wD Sinking rate for D (m/s) 10.0regen Regeneration rate for ammonium 0.4degrad Degradation rate for detritus 0.04ccrPS Carbon-to-chlorophyll ratio for PS (mg C/mg Chl) 60.0ccrPL Carbon-to-chlorophyll ratio for PL (mg C/mg Chl) 20.0fec Iron to Carbon Ratio (nmol Fe/mg C) 1.667d-4

40 K.O. Coyle et al. / Progress in Oceanography 103 (2012) 16–41

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