Partial decoupling of primary productivity fromupwelling in the California Current systemLionel Renault1*, Curtis Deutsch2, James C. McWilliams1, Hartmut Frenzel2, Jun-Hong Liang3,4
and François Colas5
Coastal winds and upwelling of deep nutrient-rich water alongsubtropical eastern boundaries yield some of the ocean’smost productive ecosystems1. Simple indices of coastal windstrength have been extensively used to estimate the timingand magnitude of biological productivity on seasonal andinterannual timescales2 and underlie the prediction that an-thropogenic climate warming will increase the productivity bymaking coastal winds stronger3–6. The e�ect of wind patternson regional net primary productivity is not captured by suchindices and is poorly understood. Here we present evidence,using a realistic model of the California Current system andsatellite measurements, that the observed slackening of thewinds near the coast has little e�ect on near-shore phyto-plankton productivity despite a large reduction in upwellingvelocity. On the regional scale the wind drop-o� leads tosubstantially higher production even when the total upwellingrate remains the same. This partial decoupling of productivityfrom upwelling results from the impact of wind patterns onalongshore currents and the eddies they generate. Our resultsimply that productivity in eastern boundary upwelling systemswill be better predicted from indices of the coastal wind thataccount for its o�shore structure.
Upwelling indices are based on a large-scale pressure-gradient es-timate of the wind field, but the spatial structure of the surface windsin eastern boundary upwelling systems (EBUS) is complex, and sois the oceanic response. Alongshore winds are typically strongestoffshore, becoming weaker towards the coast owing to orography,surface roughness, and air–sea interaction7. The nearshore drop-offin winds diminishes coastal upwelling, spreading it over a broaderoffshore region with slower vertical velocities (‘Ekman pumping’).It can also modulate the mean current structure8. The partition ofthe total wind-driven upwelling between rapid coastal and sloweroffshore components has been suggested to influence the uppertrophic levels of the ecosystem9,10. However, the impact of the winddrop-off on mesoscale activity and total net primary productivity(NPP) at a regional scale has not yet been assessed11.
To investigate the influence of the coastal wind drop-off on NPP,we conducted simulations of the California Current system with anoceanic circulation and biogeochemical model (see SupplementaryMethods). The model is forced by realistic climatological surfaceand open boundary conditions in three simulations that differ onlyin the cross-shore gradient of alongshorewind, themain componentof mean wind stress curl (Fig. 1a). A base case (‘uniform’) isconstructed from satellite scatterometer wind data, with a simple
extrapolation from its reliable offshore measurements across itsblind zone to the shoreline11. Two additional simulations areconducted with wind stress reduced by 60% at the coast, consistentwith observations for the upwelling season, that is, spring andsummer7. The cross-shore wind tapering distance is applied overwidths of 25 km (‘sharp’) and 80 km (‘wide’) (Fig. 1a), which spanthe variation of the drop-off scale7. Neither ‘sharp’ nor ‘wide’ profilescan be considered the most realistic because the real drop-off scaleis not uniform7. Model solutions are analysed along the centralCalifornia coast, between 38◦N and 43◦N and within 100 km fromshore, where the alongshore wind, eddy kinetic energy (EKE), andbiological productivity are all relatively high (see SupplementaryFig. 1). Analyses are carried out for the spring season becausethe reversal of coastal winds during this season initiates thephytoplankton bloom timing12,13, and an accumulation of surfacenutrients during that season ensures that higher productivitypersists into summer.
Consistent with Ekman theory, a stronger drop-off diminishesthe horizontal transport of surface water and thus the upwellinginto the photic zone near the coast (Fig. 1b). Despite the weakercoastal upwelling, rates of NPP integrated over the photic zone(0–70m, Fig. 2d) and within 20 km of the coast do not decreaseand even slightly increase (Fig. 1c). In all cases, horizontal transportis constrained by the same wind stress at 100 km, so that offshoreEkman pumping compensates for differences in coastal upwelling,to maintain a similar total upwelling mass flux. Nevertheless, theintegratedNPPover the photic zone (0–70m) significantly increasesby 30% when using a broader wind drop-off (‘wide’, Fig. 1c). Theincreases in NPP are even larger for regions closer to the shore(a 36% increase from 40 km) and in surface waters (0–10m: a75% increase from 0–100 km; not shown). These results imply thatdespite being limited by N supply, NPP is not simply related to thestrength ofwind-driven upwelling, either at the coast or on a broaderregional scale.
To identify the oceanic wind response that decouples NPP fromupwelling rates, we computed the nutrient budget of the photic zone(0–70m) within 100 km of the shore along the central Californiacoast (38◦–43◦N). Although themodel accounts for multiple poten-tially limiting nutrients, the reservoir and flux of nitrate (NO3
−) arewhat limit overall production in the photic zone. Its budget can beexpressed (see Supplementary Methods) as:
∂N∂t=Fmean+Feddy− J (N )
1Department of Atmospheric and Oceanic Sciences, UCLA, 405 Hilgard Avenue, California 90095-1565, USA. 2University of Washington, School ofOceanography, Box 357940 Seattle, Washington 98195-7940, USA. 3Department of Oceanography and Coastal Sciences, Louisiana State University,Baton Rouge, Louisiana 70803, USA. 4Center for Computation and Technology, Louisiana State University, Baton Rouge, Louisiana 70803, USA. 5Institutde Recherche pour le développement (IRD), UMR LOCEAN, IRD/Sorbonne Universités (UPMC Univ Paris 06)/CNRS/MNHN, 4 Place Jussieu,Paris Cedex 75252, France. *e-mail: [email protected]
Figure 1 | Impact of wind drop-o� on total upwelling and NPP. a, Coastalwind profile factor applied to the wind product. b, Mean vertical velocity at70 m depth between 38◦ N and 43◦ N during spring (April–June). The totalupwelling rate integrated over a distance of 100 km o�shore is indicated inthe legend. c, NPP integrated over the photic zone (0–70 m depth) andbetween 38◦ N and 43◦ N during spring. The total NPP (integrated over adistance of 100 km o�shore) is indicated in the legend. The shaded areasrepresent the standard deviations. The means and standard deviations areestimated using 8 years of simulations.
where N represents nitrate concentration and (∂N/∂t) is thebuildup of nitrate inventory change over time according to theimbalance between total physical nitrate transport, due to boththe time-averaged flow (Fmean) and its fluctuations (Feddy), and thetotal nitrate uptake by the ecosystem, J (N ). The biological uptakeof nitrate approximates the net community production of organicnitrogen (dissolved and particulate) that is exported to depth.
During spring, when winds become upwelling favourable, thephysical nitrate supply (Fmean+Feddy) exceeds the rate of ecosystemproduction in all cases, leading to a significant buildup of thenitrate reservoir (Fig. 2a). The larger integrated NPP in cases witha wind drop-off (‘sharp’ and ‘wide’) is reflected in J (N ) and themore rapid accumulation of surface nutrients (∂N/∂t) ensuresthat higher productivity persists into summer. In the case of abroader wind drop-off (‘sharp’ and ‘wide’), both the increase ofJ (N ) and (∂N/∂t) is due to the higher physical nutrient delivery.As the boundary layer is shallower than 70m (estimated from theK-profile parameterization, not shown), the effect of wind shape onnutrient supplymust be due to changes in advective transport ratherthan mixing.
A broader wind drop-off alters significantly themean alongshorecurrents, especially the coastal undercurrent, which transports
high-nutrient tropical water poleward. Consistent with Sverdrupdynamics, a stronger wind stress curl in ‘sharp’ and ‘wide’ yieldsa stronger, shallower poleward flow8 that induces a weaker meansouthward flow between 0–70m depth (Fig. 2b), and a highernitrate transport than in ‘uniform’. For the coastal region (0–20 km),the reduction of the vertical velocity from ‘uniform’ to ‘wide’ (by54%, Fig. 1b) is offset by the stronger alongshore undercurrentthat brings higher nutrient water below the photic layer. However,the increase of nitrate transport does not continue from ‘sharp’to ‘wide’, in spite of a further strengthening and shoaling of themean undercurrent. Thus, the change in the mean undercurrentonly partially explains the larger advective supply rate induced bya broader wind drop-off. Moreover, the effect of wind shape on thealongshore nutrient supply is offset by changes in nutrient transportto the east, such that horizontal nutrient fluxes in the photic layeras a whole are insensitive to the wind drop-off (Fig. 2a). Hence, thenitrate supply by mean vertical velocities is also roughly insensitiveto the wind drop-off (Supplementary Fig. 8). Together, these resultsimply a major role for nutrient transport by fluctuating componentsof the circulation.
Although the wind structure has a relatively small effect onFmean, it exerts a strong indirect influence on the mesoscale eddiesthat also have a net nutrient transport (Feddy). A broader winddrop-off in ‘sharp’ and ‘wide’ weakens the vertical shear of thealongshore current below the thermocline, flattens the isopycnaltilt, and reduces the EKE (Fig. 2c). The rate at which EKE isconverted from eddy potential energy by baroclinic instability isdiagnosed from the integrated eddy vertical buoyancy flux, whichis reduced under the wind drop-off (Supplementary Fig. 6). Inthe EBUS, eddies have been shown to reduce NPP by subductingnutrients along isopycnal surfaces that plunge below the euphoticlayer offshore, termed ‘eddy quenching’14. Indeed, all our cases showa negative (downward) eddy nitrate flux during spring (Fig. 2d),except in the first 10 km nearshore, where the upwelling prevails. Bydiminishing the mesoscale eddy activity, a broader wind drop-off in‘sharp’ and ‘wide’ weakens significantly this removal of nitrate fromthe photic zone and thus allows its more complete consumption.This inverse relationship between ‘eddy quenching’ and the winddrop-off accounts for the minimal response of NPP to winds nearthe coast, and the shallow subduction of nitrate contributes to thehigher NPP offshore under a strong wind drop-off14,15.
Ekman theory andmodel simulations predict that thewind stressmagnitude is the main driver of productivity and, further, that winddrop-off modulates NPP by being negatively correlated with EKE,but positively correlated with phytoplankton growth and biomass.We tested these predictions using satellite data for chlorophyll a (aproxy for NPP, from SeaWiFS), wind stress (fromQuikSCAT), windstress curl (a measure of the wind drop-off), and the EKE (from theArchiving, Validation, and Interpretation of SatelliteOceanographicdata (AVISO)) (Fig. 3a–c). The scatterometer blind zone near thecoast allows only a partial sampling of the wind drop-off profile16;therefore, a positive wind curl anomaly can be interpreted as abroader wind drop-off more fully sampled by QuikSCAT, and anegative anomaly implies a sharper wind drop-off, more of whichoccurswithin the blind zone. The offshorewind stress gives a reliablemetric for the total upwelling.
Over the period of overlapping satellite records (2000–2009),interannual fluctuations in the wind stress, wind stress curl,EKE and chlorophyll are significantly correlated. Consistent withearlier studies17, the mean upwelling is the main driver of theproductivity. However, years with a larger wind stress curl generallyhave smaller EKE and larger chlorophyll a (Fig. 3d). The formerlink implicates eddy modulation by wind-induced changes inthe unstable alongshore currents, and the latter link supportseddy quenching of NPP. The importance of the wind drop-off inmodulating total NPP is most evident in years when anomalies
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Figure 2 | Wind drop-o� control of the NPP by modulation of the eddy physical fluxes. a, Nutrient budget during spring between 38◦ N and 43◦ N from70 m depth to the surface from the three model experiments. Storage is ∂N/∂t. Uptake is J(N). Transport is F. b, Alongshore current during spring, averagedover a cross-shore band 100 km wide and 70 m in depth. The dashed black lines indicate the 38◦–43◦ N region. c, Mean surface EKE, averaged over across-shore band 100 km wide. The dashed black lines indicate the 38◦–43◦ N region. d, Mean vertical eddy nutrient supply during spring averagedbetween 38◦ N to 43◦ N. The black lines indicate the corresponding mean simulated euphotic depth. d, cross-shore distance. The error bars (a) and shadedareas (b,c) represent the standard deviations estimated using 8 years of simulation.
Figure 3 | An upwelling index that considers wind structure, and perhaps eddy activity, would better predict interannual NPP variations. a, Mean windstress curl from QuikSCAT during spring. Mean wind stress magnitude is superimposed with dashed black contours. The solid black contour indicates thelocation where the indices and the budget shown in Fig. 2 are computed. b, EKE from AVISO. c, Chlorophyll a (Chl-a) from SeaWiFS. d, Indices of variabilityin wind stress, wind stress curl, EKE and chlorophyll a. The indices are computed by subtracting the mean value over the area indicated by the solid blackcontour in a–c from 2000–2009 in spring, and dividing the resulting anomalies by the largest magnitude over the time period. The correlations among theindices are listed, and they all are significant at the 95% level. The years are divided into two categories: when the mean upwelling and wind drop-o� e�ectare reinforcing; and when they are counteracting. Similar results are found for the summer season.
NATURE GEOSCIENCE | VOL 9 | JULY 2016 | www.nature.com/naturegeoscience
in wind stress curl counteract, rather than reinforce, the changesin coastal upwelling (Fig. 3d). Such counteracting years occur inroughly half the years in the available time series, when strongercoastal winds are associated with weaker wind stress curl, or viceversa. In three out of four such years in the satellite record, theNPP anomaly is in the direction predicted by the wind drop-off, andopposite to what would be expected by the anomaly in coastal wind.Satellite estimates thus are consistent with the model predictionsand suggest that relatively complex indices, not solely based onsimple wind time series, will be needed to predict interannual NPPvariations in eastern boundary upwelling systems.
This consistency between satellite remote sensing and regionalmodelling experiments supports a new eddy-mediated link betweenthe coastal wind pattern and biological productivity, at least forthe California Current system. The same mechanisms are likelyto be present in other EBUS, albeit to varying degrees that reflectdifferences in wind structure, ocean stratification, and nutrientlimitation factors. For example, the steeper coastal orography ofthe Andes in South America may induce a broader wind drop-off7,which may explain the weak nearshore generation of EKE18, thusyielding less eddy quenching of nutrients and a more productivesystem17. Similarly, the density stratification has a strong influenceon baroclinic energy conversion and EKE levels, so the wind drop-off effect can only partially explain the EKE difference betweenthe EBUS.
As the mean upwelling is the main driver of the productivity,indices based on large-scale winds remain useful to predict theoverall tendencies of coastal marine productivity. However, ourfindings help explain residual interannual variations of NPP in theEBUS and demonstrate the need for better predictors than indicesbased on large-scale winds alone19. Predicting how productivityin EBUS will react to future climate change will require regionalatmospheric and/or coupled models that adequately resolve thewind drop-off profile and the ocean–atmosphere interactions20, aswell as changes in the oceanic state thatmodulate its effects on eddy-driven nutrient supply.
MethodsMethods, including statements of data availability and anyassociated accession codes and references, are available in theonline version of this paper.
Received 6 August 2015; accepted 25 April 2016;published online 30 May 2016
References1. Carr, M. E. & Kearns, E. J. Production regimes in four Eastern Boundary
Current systems. Deep-Sea Res. II 50, 3199–3221 (2003).2. Bograd, S. J. et al . Phenology of coastal upwelling in the California Current.
Geophys. Res. Lett. 36, 1602 (2009).3. Bakun, A. Global climate change and intensification of coastal ocean upwelling.
Science 247, 198–201 (1991).4. Wang, D., Gouhier, T. C., Menge, B. A. & Ganguly, A. R. Intensification and
spatial homogenization of coastal upwelling under climate change. Nature 518,390–394 (2015).
5. Sydeman, W. J. et al . Climate change and wind intensification in coastalupwelling ecosystems. Science 345, 77–80 (2014).
6. Bakun, A. et al . Anticipated effects of climate change on coastal upwellingecosystems. Curr. Clim. Change Rep. 1, 85–93 (2015).
7. Renault, L., Hall, A. & McWilliams, J. C. Orographic shaping of US West Coastwind profiles during the upwelling season. Clim. Dynam. 46, 273–289 (2016).
8. Song, H., Miller, A. J., Cornuelle, B. D. & Di Lorenzo, E. Changes in upwellingand its water sources in the California Current System driven by different windforcing. Dyn. Atmos. Oceans 52, 170–191 (2011).
9. Rykaczewski, R. R. & Checkley, D. M. Influence of ocean winds on the pelagicecosystem in upwelling regions. Proc. Natl Acad. Sci. USA 105,1965–1970 (2008).
10. Jacox, M. G., Moore, A. M., Edwards, C. A. & Fiechter, J. Spatially resolvedupwelling in the California Current System and its connections to climatevariability. Geophys. Res. Lett. 41, 3189–3196 (2008).
11. Capet, X. J., Marchesiello, P. & McWilliams, J. C. Upwelling response to coastalwind profiles. Geophys. Res. Lett. 31, 13 (2004).
12. Barth, J. A. et al . Delayed upwelling alters nearshore coastal ocean ecosystemsin the northern California current. Proc. Natl Acad. Sci. USA 104,3719–3724 (2007).
13. Thomas, A. C. & Brickley, P. Satellite measurements of chlorophylldistribution during spring 2005 in the California Current. Geophys. Res. Lett.33, L22S05 (2006).
14. Gruber, N. et al . Eddy-induced reduction of biological production in easternboundary upwelling systems. Nature Geosci. 4, 787–792 (2011).
15. Nagai, T. et al . Dominant role of eddies and filaments in the offshore transportof carbon and nutrients in the California Current System. J. Geophys. Res. 120,5318–5341 (2015).
16. Renault, L. et al . Impact of atmospheric coastal jet off central Chile on seasurface temperature from satellite observations (2000–2007). J. Geophys. Res.114, C08006 (2009).
17. Chavez, F. P. & Messié, M. A comparison of eastern boundary upwellingecosystems. Prog. Oceanogr. 83, 80–96 (2009).
18. Colas, F., McWilliams, J. C., Capet, X. & Kurian, J. Heat balance and eddies inthe Peru-Chile current system. Clim. Dynam. 39, 509–529 (2012).
19. García-Reyes, M., Largier, J. L. & Sydeman, W. J. Synoptic-scale upwellingindices and predictions of phyto- and zooplankton populations. Prog.Oceanogr. 120, 177–188 (2014).
20. Renault, L. et al . Modulation of wind-work by oceanic current interaction withthe atmosphere. J. Phys. Oceanogr. 46, 1685–1704 (2016).
AcknowledgementsWe appreciate support from the Office of Naval Research (N00014-12-1-0939), NationalScience Foundation (OCE-1419450 and OCE-1419323), Bureau of Ocean EnergyManagement, and California Ocean Protection Council, as well as computing resourcesfrom the Extreme Science and Engineering Discovery Environment and on theYellowstone cluster (ark:/85065/d7wd3xhc) provided by NCAR’s Computational andInformation Systems Laboratory, sponsored by the National Science Foundation.
Author contributionsL.R., J.C.M. and C.D. conceived and designed the experiments; L.R. performed theexperiments; L.R., C.D., J.C.M., H.F. and F.C., analysed the data; L.R., H.F. and J.-H.L.contributed materials/analysis tools; L.R., C.D. and J.C.M. co-wrote the paper.
Additional informationSupplementary information is available in the online version of the paper. Reprints andpermissions information is available online at www.nature.com/reprints.Correspondence and requests for materials should be addressed to L.R.
Competing financial interestsThe authors declare no competing financial interests.
NATURE GEOSCIENCE DOI: 10.1038/NGEO2722 LETTERSMethodsModel configuration. The oceanic simulations were performed with the RegionalOceanic Modeling System (ROMS)21. ROMS is a free-surface, terrain-followingcoordinate model with split-explicit time stepping and Boussinesq and hydrostaticapproximations. The model extends from 142.1◦W to 114.4◦W and from 23.9◦ Nto 50.0◦ N (see Supplementary Fig. 1). The model grid has 627× 377 points with ahorizontal resolution of 4 km and has 42 vertical levels. The vertical grid is stretchedfor increased resolution of the surface and bottom boundary layers. The bottomtopography is derived from an SRTM30 database22. The boundary conditionalgorithm consists of a modified Flather-type scheme for the barotropic mode23and Orlanski-type scheme for the baroclinic mode (including temperature andsalinity; ref. 24). The simulation is forced at the surface by the QuikSCAT-baseddaily product described in ref. 25 (based on the SCOW climatology). Heat andfreshwater atmospheric forcing are from the Comprehensive Ocean–AtmosphereData Set26. The freshwater atmospheric forcing has an additional restoring term toprevent surface salinity from drifting away from climatological values. This weakrestoring is towards climatological monthly surface salinity from the World OceanAtlas27. A flux correction term is included in the atmospheric heat forcing to allowfeedback from the ocean to the atmosphere following the formulation of ref. 28. Asin ref. 29, initial and boundary information are taken from a 12 km Pacificclimatological solution and the model is run for ten years. Ref. 25 has fullinformation about a similar Pacific simulation at coarser resolution.
The Biogeochemical Elemental Cycling30 model is coupled to ROMS. Itincludes multiple limiting nutrients (N, P, Fe and Si) and three phytoplanktonfunctional groups (diatoms, diazotrophs, and small phytoplankton) that representthe biogeographical variability of different oceanic biomes, for example, highlyproductive coastal regimes versus the oligotrophic open ocean areas of thesubtropical gyres. It includes the dissolved iron cycle, including inputs of iron fromsediments and from atmospheric dust deposition. The degree of realism of thesimulation here is similar to the results of ref. 14.
A set of three experiments has been carried out. The only difference betweenthem is the coastal wind profile used to force the model (Fig. 1a). ‘uniform’ is thecontrol run, the QuikSCAT SCOW wind is interpolated onto the ROMS grid, andthe missing coastal values are tapered using a simple extrapolation11. As QuikSCATmonitors only partially the wind drop-off in this region, we consider thisexperiment as without wind drop-off. ‘sharp’ and ‘wide’ add a wind drop-off usingthe factors shown in Fig. 1a. As a result, ‘sharp’ and ‘wide’ have wind reduction by60% and a wind drop-off length of 25 km and 80 km, respectively. Note, as shownby ref. 7, the wind drop-off is not uniform and presents latitudinal variation both inlength and wind reduction. The values chosen for this study are in the range ofvalues found in that former study. However, for the purpose of this study, idealizedexperiments using such a wind modification allow us to assess how the coastalwind shape controls mesoscale activity and NPP.
In this study, the winter, spring, summer and autumn seasons correspondto the months (January–March), (April– June), (July–September) and(October–December), respectively.
Budget analysis. To analyse the role of eddies in the nutrient evolution, wedecompose the advective flux of any nutrient concentration C into time-mean andfluctuation (eddy) components:
UC= U C+U ′C ′ (1)
where U denotes the three-dimensional velocity. As in ref. 31, where an analysis ismade for C equal to the buoyancy, the ‘instantaneous’ simulation outputs are 2-dayaverages, and the mean is defined as a multi-year seasonal average. In the followingwe denote the alongshore and cross-shore currents as v and u and the offshoredistance as d . The balance equation for any C is
∂C∂t=∇ ·K ·∇(C)−∇h ·uhC−∇v ·wC− J (C) (2)
where K is the eddy kinematic diffusivity tensor, ∇ is the three-dimensionalgradient operators, and uh, and w are the horizontal and vertical velocities, andJ (C) is the biogeochemical source minus sink term.
We present the budget for inorganic nitrogen, because it is the nutrient thatultimately limits biological productivity at a regional scale in the CaliforniaCurrent. We further restrict our analysis to nitrate (NO3
−), because it is by far thelargest reservoir and physical supply of inorganic nitrogen (compared withammonium transport and concentration, which are both very small). Nitrate alsohas the advantage of not being sensitive to the internal ecosystem nutrienttransformations. The only sinks of nitrate are from phytoplankton uptake, and itsonly biological source is nitrification, which is inhibited by light in the photic zone.The biological uptake of nitrate in the model is thus equal to ‘new production’, andbalances the portion of NPP that is exported to depth.
From equation (2) a mean-seasonal balance is estimated for the 38◦–43◦ N×100 km cross-shore× 70m depth region, which includes both the turbulent surface
boundary layer and euphotic zone in spring. By 70m depth the mixing termbecomes negligible. Equation (2) will be first analysed in the reduced form,
dNdt=Ftot− J (N ) (3)
where N represents nitrate concentration, Ftot is the total physical nitrate transport,and J (N ) the total nitrate uptake by the ecosystem. This budget analysis is thenfurther decomposed between mean transport (Fmean) and eddies (Feddies) followingequation (1),
∂N∂t=Fmean+Feddy− J (N ) (4)
and between horizontal (Fhor) and vertical transport (Fver),∂N∂t=Fhor(mean)+Fhor(eddy)+Fver(mean)+Fver(eddy)− J (N ) (5)
Data availability and description. QuikSCAT wind stress. The near-surfaceatmospheric circulation over the ocean is described through daily QuikSCAT zonaland meridional wind components, obtained from Centre ERS d’Archivage et deTraitement on a 0.25◦×0.25◦ resolution grid32. This product is built from bothascending and descending passes from discrete observations (available inJPL/PO.DAAC Level 2B product) over each day. Standard errors are also computedand provided as complementary gridded fields.
SeaWiFS chlorophyll a. Surface chlorophyll concentrations were estimated fromSeaWiFS data33 for the 2000–2009 period. We used Level 3 (9 km) monthlycomposites obtained from the Distributed Active Archive Center at NASAGoddard Space Flight Center.
AVISO sea level anomalies. The sea level anomalies come from the Archiving,Validation, and Interpretation of Satellite Oceanographic data (AVISO)multimission mapped altimetry product34. We use the Delayed Time 2014 version,in which data from at least two (up to four) simultaneous satellite altimetermissions were merged and mapped onto a 0.25◦ Mercator grid at daily intervals forthe period October 1992–December 2013; the 1993–1999 mean was removed ateach grid point. The surface geostrophic currents are computed by using the sealevel anomalies and the eddy kinetic energy (EKE) is defined as 1/2(u′2+v ′2)where u′ and v ′ are velocity perturbations relative to a seasonal time-mean (samemethod is applied when estimating the EKE from the model).
California Cooperative Oceanic Fisheries Investigation. Large-scale systematichydrographic sampling of the California Current system was initiated in 1949 bythe California Cooperative Oceanic Fisheries Investigations (CalCOFI)programme. Since 1950, stations have been repeatedly occupied at varying intervalsbased on a geographically fixed grid. In this study, lines 60 (off Point Reyes;∼38◦N)and 67 (∼37◦N), which have enough data to estimate a seasonal climatology ofrespectively chlorophyll a and temperature, are used to validate the simulations.
Satellite analysis. Indices of wind stress, wind stress curl, EKE and chlorophyll a asestimated over the black rectangle in Fig. 3 during spring are computed using thefollowing method: the long-time mean value over the black rectangle during springover the period 2000–2009 is calculated first. Then, the indices are calculated bycomputing the anomalies of spring rectangle values with respect to the long-timemean value and are finally normalized by the largest magnitude over thetime period.
Mean variability of chlorophyll a, wind stress and EKE.Model solutions areanalysed along the central California coast during spring, between 38◦ N and 43◦ Nand within 100 km from shore. The spring season is chosen because the reversal ofcoastal winds during this season initiates the phytoplankton bloom timing and anaccumulation of surface nutrients during that season will ensure that higherproductivity persists into summer. Additionally, during spring, as illustrated inSupplementary Fig. 1, the alongshore wind stress, EKE and biological productivityare all relatively high.
Model evaluation and eddy buoyancy fluxes. To illustrate the realism of thesimulations, Supplementary Figs 2–6 depict some basic diagnostics of both physicaland biogeochemical fields. Supplementary Fig. 2 shows the sea surface temperaturemean from ‘uniform’ and in situ observations (World Ocean Atlas27). Thesimulated sea surface temperature mean and variability are fairly reproduced by themodel, which clearly shows the upwelling signature. Supplementary Fig. 3represents simulated and observed vertical distributions of temperature duringspring along CalCOFI line 67 (which starts around 37◦ N). It indicates that thesimulated vertical gradients are captured well. However, the onshore slope isslightly overestimated, particularly in the nearshore region (Supplementary Fig. 2).Supplementary Fig. 4a shows the EKE computed from ‘uniform’ using low-pass
filtered (7-day averaging and Gaussian spatial filter with 30-km half-width)geostrophic velocities. The realism of the EKE indicates the ability of the model toreproduce the mesoscale activity (AVISO EKE is shown in Supplementary Fig. 4b)but also the mean current because mesoscale eddies arise from mean currentsinstabilities. Recently, ref. 20 showed that current feedback to the atmospheredampens the EKE. Here, the offshore EKE is overestimated. The absence of currentfeedback in the model (fluxed-forced) induces an overestimation of the eddy life,allowing eddies to propagate further offshore.
Supplementary Fig. 5 depicts the mean chlorophyll a from ‘uniform’ andSeaWiFS during spring. As expected, the coastal upwelling region is marked byhigh concentrations of chlorophyll a. Model-simulated chlorophyll a and theobservations have similar agreements and disagreements as in ref. 14. There is anoverall tendency for the model to be biased low. The largest underestimationoccurs in the nearshore areas. Offshore, a low bias is found that is likely to be due tothe absence of picoplankton (which grow under oligotrophic conditions) in themodel. Finally, Supplementary Fig. 6 shows simulated (from ‘uniform’ and ‘sharp’)and observed vertical distributions of chlorophyll a during spring along CalCOFIline 60 (starting around 38◦ N). ‘uniform’ underestimates the coastal chlorophyll aconcentration. By using a broader wind drop-off (here ‘sharp’, a similar increase isfound in ‘wide’), the chlorophyll a concentration increases (see main paper),becoming more realistic with respect to the observations. This also illustrates thesensitivity of the simulated chlorophyll a to the coastal wind shapes.
A broader wind drop-off diminishes the southward surface current, strengthensthe undercurrent (Fig. 2b), and even can induce a surfacing of the undercurrent in‘wide’ (not shown). This is consistent with Sverdrup dynamics in response to winddrop-off: a positive wind stress curl produces a barotropic poleward flow that addsup to the coastal undercurrent35,36. As a result, the undercurrent strength is largerwith a broader wind drop-off. Note, the surfacing of the undercurrent in ‘wide’ isnot realistic (not shown); an overestimation of the wind drop-off length can inducesuch a feature. A broader wind drop-off not only changes the undercurrent depthand intensity but also induces a different vertical shear of the alongshore current.From ‘uniform’ to ‘wide’, the vertical shear diminishes below the thermocline,stabilizing the water column. This is confirmed by Supplementary Fig. 7, whichdepicts the mean vertical buoyancy flux w ′b′ from all of the experiments duringspring. Negative values are important as they indicate regions where eddies actlocally contrary to the baroclinic instability theoretical expectation of positiveconversion of available potential to kinetic energy. In the stratified interior, eddybuoyancy flux acts to balance the effect of upwelling-favourable winds, that is, toflatten the tilted upper thermocline31. As a result, the offshore eddy restratificationflux weakens in the progression from ‘uniform’ to ‘wide’, and there is a similarweakening in the eddy destratification flux near the coast. By inducing a weakervertical shear of the alongshore current, a broader drop-off weakens the energyflux associated with upper ocean baroclinic instability and then reduces theEKE (Fig. 2c). A broader wind drop-off, by reducing the coastal windstress, also weakens the coastal wind work35, acting again towards a reduction ofthe EKE.
Supplementary Fig. 8 depicts a dissolved inorganic nitrogen budget of thephotic zone (0–70m) within 20 km of the shore along the central California coast(38◦–43◦ N) during spring (N , nitrate concentration). The effect of the reduction ofthe vertical velocities from ‘uniform’ to ‘wide’ (by 54%, Fig. 1b) is damped by theeffect of changes of alongshore current on the nitrate reservoir below the photiclayer. ‘wide’ leads to a more effective coastal mean upwelling because the meannitrate supply by mean vertical and horizontal velocities is roughly insensitive to thewind drop-off (Supplementary Fig. 8), whereas the opposing eddy flux is decreased.
Code availability.We have opted not to make the computer code associated withthis paper available because we use in-house versions of ROMS at UCLA and UW;similar ROMS versions are available through Rutgers (www.myroms.org) andROMS-AGRIF (http://www.romsagrif.org).
Data availability. The simulation outputs that support the findings of this studyare available on request from the corresponding author (L.R.). The data are notpublicly available owing to the large size of the model output files.
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