The tattered curtain hypothesis revised: Coastal ... - Wiley Online Library

ORIGINAL ARTICLE

The tattered curtain hypothesis revised: Coastal jets limit cross-shelf larval transport Cheryl S. Harrison 1 and David A. Siegel 2

Abstract Exchange and retention of coastal waters modulate dispersal of marine larvae, affecting marine ecosystem dynamics. A hypothesis was put forward in the 1980s describing the coastal upwelling front as a “tattered curtain” that retains larvae. This front was envisioned to be broken up by squirts and eddies, hitting the coast under upwelling relaxation events. Here we revise this hypothesis by using an idealized ocean model of an eastern boundary upwelling current, and an idealized particle/larvae model appropriate for shelf-spawning benthic species. Modeled larval settlement patterns were controlled by retention in the core of the upwelling jet, bounded by regions of high-velocity shear on the flanks of the jet. Squirts, filaments, poleward-moving eddies, and meanders modulated settlement patterns locally, while dense packets moved equatorward within the upwelling jet. Correlation between settlement (i.e., particles 20–40 d old !10 km from shore) and wind was low for a lagged wind product (rZ 0:33) and moderate for a 20-d integrated wind product (rZ 0:62). We determined that it is not upwelling relaxation but sustained, moderate upwelling that can result in a highly retentive jet that entrains larvae and acts as a barrier to cross-shelf transport; however, the amount of retention is highly variable. Settlement was low after strong, persistent upwelling completely tattered the jet. Jet cores in general should act as important retentive transport barriers across diverse coastal systems, a view supported by dynamical theory, modeling studies, and larval recruitment observations. Keywords: upwelling, Lagrangian transport, coastal retention, larval transport

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College of Earth, Ocean and Atmospheric Science, Oregon State University, Corvallis, Oregon 97330, USA

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Earth Research Institute and Department of Geography, University of California, Santa Barbara, California 93106, USA Correspondence to Cheryl S. Harrison, [email protected]

Introduction Ecological Background [1] One of the central problems in marine ecology is understanding the temporal and spatial dynamics affecting the transport of coastally spawned species. Coastal upwelling ecosystems, though representing only a small extent of the ocean surface, contain a disproportionate number of the ocean’s fisheries (Ryther 1969; Stock et al. 2014). Near the coast, recently upwelled water brings nutrients to the surface where they can be utilized by phytoplankton, providing

energy to coastal ecosystems. Many coastal benthic species have a pelagic larval or juvenile stage. A common reproductive strategy is to spawn thousands to millions of larvae that drift or swim with the currents for days to months until gaining competency to recruit back to suitable habitat, forming the next generation. This strategy arose in the Cambrian and is utilized across diverse species, from barnacles to groundfish, animals inhabiting the kelp beds, rocky substrates, and intertidal habitats along the coastal shelf and slope (Thorson 1950;

Limnology and Oceanography: Fluids and Environments † 4 (2014): 50–66 † DOI 10.1215/21573689-2689820 q 2014 by the Association for the Sciences of Limnology and Oceanography, Inc.

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Strathmann 1985). Many commercially exploited benthic species renew their populations slowly and are overfished to the point of being threatened or endangered. Understanding the life cycle dynamics of these fisheries, especially the poorly understood pelagic stage, is important for appropriate management (e.g., Roughgarden et al. 1988; Botsford et al 2009; Rassweiler et al. 2012). Retention over the shelf during the larval competency phase keeps larvae close to suitable habitat, and a growing number of observations suggest that this limited dispersal due to retention is much more common than previously thought (Swearer et al. 1999; Warner and Cowen 2002; Shanks and Shearman 2009). However, the degree to which physical processes determine settlement patterns remains poorly understood. [2] The distribution of benthic larvae near suitable habitat after some time in the plankton plays a large role in recruitment success to the adult stage. The “tattered curtain” hypothesis proposed a link between the observed recruitment pulses of barnacle larvae on the Central California coast and the dynamics of the upwelling front, where larvae were thought to converge (Roughgarden et al. 1988, 1991). The distance of this front from the shore was thought to be determined largely by the strength of the upwelling winds, carrying larvae far from shore when upwelling was strong and back toward shore during relaxation, resulting in a large settlement pulse when the front collides with the coast. This hypothesis was thought to be especially applicable to the Central California coast, where the shelf is narrow and the upwelling front is often found close to shore. [3] The details of this hypothesis are best appreciated in the original text (Roughgarden et al. 1991): We imagine, somewhat simplistically, the upwelling front as a “tattered curtain” hanging before an open window. It is tattered because it is punctured by offshore squirts such as those typical of Point Arena, Point Reyes, and Point Sur. It does not move as rigid iron curtain in response to the upwelling strength, but instead is pushed and pulled locally, billowing in toward shore in some spots while remaining far from shore at others. Thus, during a short period of relaxation, small parts of the upwelling front may touch the coast leading to local recruitment. But during an extended relaxation, long sections of the front collide with the shore leading to nearly synchronous recruitment across long stretches of the coast. . . . Furthermore, when eddies and meanders disrupt a simple linear geometry for the front, mixed signals result. . . . We term this overall hypothesis the “tattered curtain” hypothesis.

[4] A number of more recent studies have demonstrated a relationship among upwelling winds, larval distributions, fronts, and settlement events for a wide range of coastal species and in regions far beyond the California coast (see Shanks and Shearman 2009; Woodson et al. 2012). However, the upwelling-relaxation implies settlement mechanism, as well as “supply side” ecology in general (the idea that ocean transport determines larval recruitment patterns), has come under scrutiny. Limited dispersal is more common than previously assumed (Sponaugle et al. 2002; Swearer et al. 2002; Levin 2006), and observations along the California coast in particular demonstrate that some species recruit under strong upwelling conditions (Morgan et al. 2009; Shanks and Shearman 2009). A hypothesis often invoked to explain these observations is that larval behavior keeps larvae close to the coast, limiting offshore transport (e.g., Batchelder et al. 2002), in accordance with the idea that small-scale behavior can exert a large control on larval distributions in general (Metaxas and Saunders 2009; McManus and Woodson 2012). Confirming this alternate hypothesis requires that, in the absence of larval behavior, upwelling winds result in offshore transport, which we find is not always the case. The systematic examination of interactions between upwelling forcing and mechanisms leading to larval retention over the shelf is the goal of this modeling study. Physical Background [5] In the coastal ocean, jets are a common occurrence, with the most dominant jets along western boundary currents (i.e., Gulf Stream, Kuroshio). Eastern boundary currents, such as the California Current system (CCS), feature a number of surface and subsurface jets, including the upwelling jet, a geostrophic response to the upwelling of the pycnocline associated with the “peeling back” of the surface ocean by offshore Ekman transport (Brink 1983; Hill et al. 1998). Here there is a change in water density moving offshore from colder, denser, recently upwelled water near the coast to warmer, lighter water offshore, that is, the upwelling front or the “curtain” in the tattered curtain. This density gradient results in a change in sea surface height and thus a pressure force toward the coast. When the flow is in

q 2014 by the Association for the Sciences of Limnology and Oceanography, Inc. / e-ISSN 2157-3689

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geostrophic balance, the Coriolis force balances this pressure gradient, and flow is along lines of constant sea surface height moving toward the equator, resulting in an equatorward upwelling jet. The strength of this jet is directly proportional to the density gradient across the upwelling front, so in highly stratified regions the jet is faster (Allen et al. 1995). Observations indicate that instabilities in the coastal jet commonly result in eddies, meanders, upwelling filaments (which often terminate in eddies), and squirts, which are wider offshore excursions of the upwelling jet featuring fast cross-shelf velocities (Strub et al. 1991; Haynes et al. 1993; Marchesiello et al. 2003). All of these features have been found to affect larval distribution patterns in both observations (Larson et al. 1994; Bećogneé et al. 2009; Woodson et al. 2012) and models (Siegel et al. 2008; Harrison et al. 2013). [6] Despite the complexity of upwelling circulation, simplified analytical results suggest that the distance of the upwelling front from shore is linearly proportional to the time-integrated upwelling wind intensity (cf. Austin and Barth 2002), in accordance with Roughgarden’s (1988, 1991) hypothesis. This result has been somewhat supported in observations, as well as in observed and modeled larval distributions and settlement patterns (Austin and Barth 2002; Kim and Barth 2011). These studies suggest that a “settlement index” analogous to an upwelling index may be tractable. Here we use a numerical model of an idealized upwelling system to examine the feasibility of constructing such an index. Methods [7] Ocean model development was done in the Regional Ocean Modeling System (ROMS), a finite-difference, hydrostatic, primitive equation, community ocean model that uses terrain-following sigma coordinates in the vertical direction and a split-explicit time-stepping routine to represent the barotropic and baroclinic modes (Shchepetkin and McWilliams 2005). The three-dimensional model setup was designed to simulate mesoscale processes affecting Lagrangian transport along the Central California coast (Mitarai et al. 2008; Siegel et al. 2008; Harrison et al. 2013). Bathymetry and forcing were adapted from observations at CalCOFI line

70 (California Oceanic Cooperative Fisheries Investigations; e.g., Chelton 1984; Lynn and Simpson 1987) off the coast of Big Sur, California, during a typical July, the height of the upwelling season for this region (spring–fall). The model featured a straight coastline with a free-slip boundary condition. Minimum depth was set to 10 m and maximum depth to 500 m, with a narrow shelf (w10 km), as typical for this region (Fig. 1). There was no alongshore variation in bathymetry. Horizontal resolution of 2 km and 20 sigma levels were used in the vertical, with increased vertical resolution near the top and bottom of the domain and near the coast. The alongshore domain was periodic with a length of 256 km, necessitating use of an f-plane for the Coriolis parameter. The extent of the cross-shelf domain was 288 km, with open boundary conditions at the offshore boundary (Marchesiello et al. 2001). [8] The model was initialized using CalCOFI climatological data and nudged at the open western boundary toward the climatology, which creates a realistic average vertical temperature profile after the 2-yr spin-up (Mitarai et al. 2008, their figure 1). A climatological alongshore pressure gradient was estimated using mean dynamic height differences between hydrographic sections off Point Arena and Point Conception (CalCOFI lines 60 and 80; Lynn and Simpson 1987) and imposed within the model as a body force that decreases with depth. Surface wind stress was modeled as a statistically stationary Gaussian random process with magnitudes and decorrelation time scales taken from buoy wind data (National Data Buoy Center

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layer offshore preferentially, and sensitivity analysis 2 has shown that small 0 changes in vertical position –2 did not significantly alter –4 settlement here or in other –6 models of the region (Pfeiffer-Herbert et al. –8 2007; Mitarai et al. 2008). –10 20 40 60 80 100 120 140 160 180 0 [10] For the analysis, Time (d) we focused on particles Fig. 2 Temporal variation in wind forcing through a model run. Winds were predominantly equatorward (down) and with 20- to 40-d residence upwelling favorable with brief periods of relaxation (red; up), with a small cross-shelf component (horizontal component). time, or pelagic larval duration (PLD), a common stations 46028, 46012, and 46042). The wind field was PLD for many shelf-spawning fishes (Love et al. 2002; assumed to vary on spatial scales much larger than the Shanks and Eckert 2005). Note that whereas many shelfmodel domain while wind magnitude decreases toward spawning fish species have a longer PLD than used here, the shore (i.e., positive wind stress curl), consistent with later larval stages are not well approximated by passive observations along the California coast (Pickett and particles as they can modify their retention, presumably Paduan 2003; Capet et al 2004). The resulting wind with behavior (Larson et al. 1994); our particle model product allows an ensemble of runs forced by time vary- addresses the early larval stage, before larval behavior ing wind fields (e.g., Fig. 2) that capture the statistics keeps larvae on the shelf. The total number of particles and temporal patterns driving upwelling dynamics increases with time; the number of 20- to 40-PLD paralong the Central California coast (see Mitarai et al. ticles is constant after day 40, as particles age in and out 2008 for more model details). Each of the 28 model of this age class at constant rates (Mitarai et al. 2008, ensemble runs lasts for 180 d. their fig. 4). When these 20- to 40-PLD particles were [9] Because we focused on the role of abiotic found over the shelf, that is, less than 10 km from shore, affects of ocean currents on dispersal, larvae were ideal- we determined that they had “settled,” by which we ized as simple passive particles released over the shelf. mean potential settlement, as we did not model life hisThe particle model roughly approximates early non- tory, mortality in the plankton, survival to maturity, or swimming stages of shelf-spawning fishes such as rock- spatial fecundity (e.g., Watson et al. 2010; Rassweiler fish or, alternately, intertidal larvae that have made it out et al. 2012). We define a wide settlement area as we of the inner shelf (!1–2 km offshore), a region not well assume that physical processes (e.g., waves, tides, etc.), represented in our model (Nickols et al. 2012; Romero which are not modeled here, along with late-stage behavet al. 2013). Following Harrison et al. (2013), Mitarai ior contribute to settlement after larvae are retained et al. (2008), and Siegel et al. (2008), particles were over the shelf. released on a regular 2-km grid over the shelf (!150 m deep; 640 particles) each day for 170 d and tracked Results with a second-order Runge–Kutta integration algorithm Modeled Patterns of Coastal Retention and Settlement using ROMS model surface velocities saved every 6 h. [11] Inspection of model fields over the ensemble of Whereas surface-following particles were employed as a runs demonstrated that particles were often retained numerical simplification, both early rockfish (Moser in the core of the upwelling jet during settlement events. and Boehlert 1991; Larson et al. 1994) and early inter- For example, the upwelling jet followed the coast rectitidal (cf. Pfeiffer-Herbert et al. 2007; Tapia et al. 2010) linearly for a significant alongshore extent (O100 km) larvae are often observed to inhabit the surface mixed but broke up in a fast offshore squirt and recirculation Windspeed (m s–1)

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region toward the north of the domain at w225 km alongshore in run 111 (Fig. 3). Squirts occurred even under wind forcing that was uniform alongshore and in the absence of coastal topography (see Strub et al. 1991). Modeled particle densities were high within the core of the jet for larvae of all ages (Fig. 3D). The upwelling jet retained 20- to 40-PLD larvae over the shelf within the settlement zone (!10 km from shore, inshore of dashed line in Fig. 3E), so that settlement was coherent for a wide alongshore region.

[12] Transects of the model surface fields shown in Fig. 3 provide information on the spatial relationships between the velocity field, velocity gradient, surface temperature gradient, and particle densities (Fig. 4). Velocity across the upwelling jet was approximately Gaussian (Figs. 3C, 4A). The core of the jet, marked by the zero crossing of the velocity gradient (Fig. 4B), corresponds to the maximum SST (sea surface temperature) gradient as predicted by geostrophic balance (Fig. 4C). The jet core was also coincident with the region of highest particle density (Fig. 4D), which A B 15.5 35 250 250 was bounded by lines of maximal velocity shear. C 30 12 This pattern of particle 15 200 200 70 10 retention by the upwell25 60 ing jet core was evident 8 14.5 150 150 20 50 through the ensemble of 6 28 runs. 40 15 4 14 100 100 [13] Visualization 30 2 of settlement patterns 10 20 0 through time for run 111 13.5 50 50 30 20 10 0 5 Across shore (km) is shown in a Hovmo¨ller plot in Fig. 5A, where the 0 0 13 0 number of 20- to 40100 50 0 100 50 0 2 2 PLD particles is summed 250 E 250 D 1.8 1.8 between 0 and 10 km offshore for every 1 km 1.6 1.6 200 200 alongshore each day 1.4 1.4 and plotted against time. 1.2 1.2 150 150 Settlement was semico1 1 herent along the coast, 0.8 0.8 with moderate levels 100 100 tending to occur simul0.6 0.6 taneously (blue) and 0.4 0.4 50 50 more sparse, high-magni0.2 0.2 tude events interspersed 0 0 (green–red). Settlement 50 0 100 50 0 100 Across shore (km) Across shore (km) often ceases coherently all across the domain Fig. 3 Surface fields for run 111, day 125, on a subset of the model domain. The model coast is on the right; poleward is (e.g., at day 140), often up. Particles released over the shelf (inshore of the dashed line in D and E) are retained in the coastal upwelling jet, associated with an upwelling front. A — Sea surface temperature (SST) and sea surface height (SSH) contours, shown coincident with very with 0.5-cm intervals. B — Surface velocity magnitude and direction. C — Close-up view of the upwelling jet velocity strong upwelling events field. D — Particle density for all residence time, binned to 1 km2. E — Particle density for 20- to 40-d residence time (Fig. 5B). Coherent settle(see Suppl. Movies S1 and S2 for visualization through time). A transect along the horizontal line at 50 km alongshore is ment patterns were shown in Fig. 4. 2

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km alongshore. From day 80 to day 85 (Fig. 6B,E) eddies and their associ0 0 ated breaks in retention have moved northward, –0.5 while the squirt has nar–5 –5 rowed. The effects of these structures are vis–10 –1.5 20 10 30 20 10 30 ible in Fig. 5A as poleAcross shore (km) Across shore (km) ward-moving regions of C D 10 0.1 no settlement (yellow ar20-40 d rows). These poleward 8 All 0.05 moving eddies were seen 6 throughout the ensemble 0 4 of runs and exhibited –0.05 a range of propagation 2 speeds. –0.1 0 10 30 20 10 30 20 [15] After extended Across shore (km) Across shore (km) strong upwelling-favorable winds, patterns of Fig. 4 Transects across the upwelling jet for model day 125 of run 111 (Fig. 3), where the jet was parallel to the coast: alongshore velocity v (A), cross-shore gradient of alongshore velocity dv=dx (B), across-shore sea surface temperature (SST) coastal particle retention gradient (C), and particle density (D). Blue solid lines identify maximum cross-shore velocity shear. Maximum jet velocity (the moved offshore, coincijet core) is denoted by a dashed blue line (identified by zero crossing of dv=dx). Particle density is high between maximum dent with cooler temshear regions and is higher toward the center of the jet, shown for all ages (D; black dashed line) and 20- to 40-d residence peratures along the coast time (D; black solid line). (Figs. 5B, 6C,F). Once the jet had moved far offshore and broken up, a broken up by regions of low or no settlement propagat- period of weaker upwelling was needed before the jet ing poleward through time (Fig. 5A, yellow arrows), reformed and settlement resumed (Fig. 5A; Supplemenwhile dense packets moved equatorward (black arrows). tal Movies S1, S2). For example, in model run 111, by For example, the jet meander in Fig. 3D and E, at w200 day 125 (Fig. 3) the same pattern of a coastal jet broken km alongshore on day 125 appeared as a region of no up by a large squirt seen earlier in the run (Fig. 6) settlement propagating poleward from day 110 to day emerged and continued for some time. These patterns 135 (Fig. 5A). This jet meander grew in time, eventually of semicoherence in settlement were similar throughout joining with the large squirt to the north (see Sup- all model runs (cf. Fig. 7) and indicate that particle plemental Movies S1, S2). During this same period, retention was associated with a wide region within the dense packets moved south within the jet during a upwelling jet and was not limited to the region along the time of overall high settlement (Fig. 5A, black arrows). upwelling front. [14] Particle distributions along with model SST and SSH (sea surface height) fields during three days of Statistical Correlation between Wind and Settlement run 111 illustrate the time evolution of flow and settle- [16] A relevant question is whether upwelling relaxation ment (Fig. 6; Supplemental Movies S1, S2). On day 80 leads to delivery of particles back to the coast. The three (Fig. 6A,D) particle retention in the coastal jet was inter- largest settlement periods in model run 111 occurred rupted by meanders around smaller eddies (w10 km during different wind conditions (Fig. 5B). The first, diameter) and broken by a wide squirt centered at 125 centered at day 40, peaked after an upwelling relaxation Day 125 y = 50 km 0.5 dv/dx (cm s–1 km–1)

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of strong upwelling and decrease in settlement. 250 A These wind–settlement 40 relationships suggest ex200 tended strong upwelling 30 breaks patterns of coastal 150 particle retention and 20 thus settlement. 100 [17] The alongshore 10 spatial coherence of settle50 ment is also evident in run 0 141, again broken up by 0 20 40 60 80 100 120 140 160 180 poleward-propagating fea5 3000 B tures and dense packets (and antipackets, i.e., 0 2000 low-particle-density regions) traveling equatorward (Fig. 7A,B). Of the –5 1000 two high-settlement periods, the first peaked –10 0 about 5 d after upwelling 0 20 40 60 80 100 120 140 160 180 winds relaxed (day 40), Time (d) and the second extended Fig. 5 Settlement patterns and wind for run 111. A — Hovmo¨ller plot of settlement density (defined as particles with over a long period co20- to 40-d residence time within !10 km of shore and summed across-shelf ) through the model run (horizontal axis). inciding with upwelling Black arrows indicate packets of high particle density moving within the jet; yellow arrows, low settlement patterns caused by poleward propagating coherent structures that break up and modulate the jet (see also Figs. 3 and 6). B — Alongshore of variable intensity, not wind (blue) and number of settled particles (black) through the model run. Negative (equatorward) wind is upwelling clearly related with any favorable. relaxation event. Again, settlement ceased after strong periods of upwelling (e.g., around day 50). event on day 42, but settlement was occurring many Upwelling relaxation around day 130 consisted of two days before this event and continued after relaxation nearby periods of downwelling-favorable winds without ended. The second and largest settlement period, cen- any large settlement events afterward. This relaxation tered at day 87, peaked after a large wind relaxation, event occurred after a long period of upwelling-favorwith a 4-d lag between the peak relaxation and peak able winds (days 75–128). settlement times. However, moderate settlement again [18] The highest level of settlement of the 28 occurred well before the relaxation event. The next high- model runs lasted for a 40-d period, peaking after a settlement period (days 110–140) was long and occurred period of moderate upwelling, and was coherent along during intermittent upwelling-favorable winds. Notably, a significant stretch of the coast (run 113; Fig. 7C,D). there was only a small pulse of settlement after the two Again, upwelling relaxation events had a moderate corwind relaxation events between days 100 and 110. Low relation with settlement intensity; wind relaxation on settlement occurred after prolonged strong upwelling day 80, after a 20-d period of high-intensity upwelling, winds, that is, around day 100 and from day 140 to was followed by a moderate amount of settlement. A day 180, with a few days of lag between the initiation subsequent relaxation event at day 100, after another Number of settled particles

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lags with similar correlation, consistent with the hypothesis that upwelling intensity over some period of time is important in determining particle retention (or lack of retention). To examine this, we calculated the timeintegrated alongshore wind, integrating from the day in question backward in time, and correlated this wind product with the settlement time series. In other ðwords, we constructed the wind product WT ðtÞZ t

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Fig. 6 Jet retention patterns: particles and SST/SSH for days 80 (A, D), 85 (B, E), and 100 (C, F) in model run 111. Particles are colored by residence time and position: blue particles are all releases; black are 20- to 40-d-olds; green are 20- to 40-d-olds and within 10 km of the coast, that is, settling particles. SSH contour interval is 0.5 cm (black lines in D–F). Particles within the coastal jet move equatorward, while meanders in the coastal jet propagate poleward. During a period of moderate integrated upwelling (A, D), offshore transport is focused in a wide filament (at w125 km alongshore), while nearby the jet remains close to the coast. After strong persistent upwelling over days 90–100 (see Fig. 5B), particles have moved off-shelf (C). See Supplemental Movies S1 and S2 for particle visualization through time.

20-d period of strong upwelling, had no associated settlement. A third, more pronounced relaxation event occurred after day 160, and in this case the settlement patterns followed the wind pattern with a 2- to 5-d lag. [19] To explore the statistical relationship between wind and settlement, we calculated both the cross-correlation at a series of lags between wind and settlement, and correlation between the settlement time series and a temporally integrated wind product. There was very little correlation for zero lag, and the cross-correlation between wind and settlement peaks at r ¼ 0.44 for a 3-d lag for run 111 (Fig. 8A), consistent with the large pulse of settlement after the upwelling relaxation around day 84. However, there was also a larger broad period of

vðt 0 Þdt 0 , where v is the alongshore wind speed, t

the model day, T the integration window size; WT has units of m d s–1. This resulted in a peak correlation of rZ 0:78 for T Z 20 d (Fig. 8B), much higher than the lagged wind result (r! 0:44). As in the case of the lagged correlation, there were a wide range of averaging times with significant correlation. [20] The dependence of the correlation on the averaging window size can be observed when the wind product and the settlement time series have been normalized (Fig. 8). For the shorter averaging time (T Z 5 d; Fig. 8C) the integrated wind product predicted the short settlement pulse following the relaxation event and some of the variability in the second settlement event, but did not generally do well in predicting times of low or no settlement (e.g., from day 100 to day 110 and from day 140 onward). However, the 20-d averaged wind product (W20 ), corresponding to the maximum correlation, did well in predicting periods of low settlement, as well as the broader, lower settlement period from day 116 to day 140 (Fig. 8D). The result that W20 correlates with settlement patterns is consistent with the observed low particle retention after long periods of strong upwelling and more retention during times of moderate sustained upwelling (as opposed to during relaxation events). [21] Combined correlation statistics for the ensemble of 28 runs show similar results, with a low correlation in the lagged result (rZ 0:33 for a 2-d lag) and a peak correlation for a 20-d integrated wind (Fig. 9). The maximum correlation (rZ 0:62) is moderate given the high number of data points (nZ 3948). This correlation is less than for run 111 (Fig. 8), but it had a similar relationship between correlation and the averaging window T. Note that the peak in the ensemble case is sharper (compare Figs. 8B, 9B), suggesting a


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product W20 (Fig. 10) shows strong, persistent 40 200 upwelling (W20 ! –90), 30 150 was associated with low settlement for all runs, 20 100 and there was always 10 50 some settlement under low integrated upwelling 0 0 20 40 60 80 100 120 140 160 180 (W20 O –50), though the 5 3000 B magnitude was stochastic. [22] Because of the moderate correlation 0 2000 level for a wide range of integrated wind conditions, as well as the high 1000 –5 variability in the amount of settlement for the 0 –10 most significantly corre0 20 40 60 80 100 120 140 160 180 lated wind product W20 250 60 C (Fig. 10), we determined 200 50 that settlement was only 40 partially predictable by an 150 upwelling wind product. 30 100 This follows from the 20 inherent nonlinearity of 50 10 the ocean in its response 0 to time-varying forcing. 0 20 40 60 80 100 120 140 160 180 5000 5 However, we can predict D the end-member behavior 4000 for this model: (1) after 0 times of low-integrated up3000 welling, some amount of 2000 potential settlement (with –5 a high degree of variabili1000 ty) is expected; (2) during extended heavy upwelling, –10 0 0 20 40 60 80 100 120 140 160 180 settlement will be low; and Time (d) (3) it is not upwelling Fig. 7 Settlement patterns and wind for run 141 (A, B) and for the high-settlement outlier run 113 (C, D). Model fields relaxation that predictably are as shown in Fig. 5. Note the change in scale for the number of settling particles (in C and D, maximum is O4000 on results in settlement but day 141). See text for interpretation. moderate, sustained upwelling that results in a stronger dependence on the wind integration window highly retentive jet that entrains coastal particles and size over the ensemble than in the single run. A scatter acts as a significant barrier to cross-shelf transport. plot of the number of settlers and the integrated wind 250

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Fig. 8 Wind-settlement correlation statistics for run 111. A — Cross-correlation between number of settled particles and alongshore wind peaks at rZ 0:44 for a 3-d lag. B — Correlation is higher using an integrated alongshore wind WT , where T is the integration window size, peaking at rZ 0:78 for T Z 20 d. C and D — Some settlement events were better predicted by a short integration time (C), while extended periods of low settlement were better predicted by longer wind integration (D). Here both wind and settlement time series are normalized for the correlation by their respective means and so are dimensionless.

Discussion: The Tattered Curtain Hypothesis Jets as Barriers to Transport [23] The results of this study indicate that upwelling jets in coastal systems control settlement patterns, and high particle densities are the result of particle retention in the core of the jet. This view is consistent with modern perspectives on Lagrangian transport dynamics and recent developments in applied dynamical systems theory (e.g., Samelson 1992; Beron-Vera et al. 2010). [24] To understand how jets affect coastal particle (Lagrangian) transport, consider an idealized jet:

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[26] Trajectories near jet cores have been shown to be remarkably stable to perturbations in analytical (e.g., Samelson 1992; Rypina et al. 2007), experimental (e.g., Beron-Vera et al. 2010), and observational studies (Rypina et al. 2011). These jet cores, where the flow is shearless, remain a significant barrier to transport when regions on either side of the jet become more turbulent due to breaking waves or other disturbances. Thus, jets in general are expected to be robust barriers to transport both because of the change in momentum needed to move in and out of them and because of their stability of their cores under perturbation.

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Moving Beyond the Upwelling Index Fig. 10 A — Scatter plot of the number of settling particles versus the maximally correlated integrated wind product [27] Jet instabilities were W20 . The outlier case run 113 (Fig. 8) is plotted in red. B — Density plot of the same data. Settlement is predictably low to common in our model, nonexistent under extended strong upwelling conditions ðW20 !K90Þ. Under moderate levels of integrated upwelling ðW20 OK50Þ, settlement is always present but highly variable in magnitude. Between these two end-members, settlement and the system tended to is highly variable, suggesting a systemic nonlinearity (and thus unpredictability). organize into regions of coherent alongshore flow broken up by areas of which is maximized in the same location as the velocity persistent swift offshore transport (i.e., squirts or filapffiffiffi shear, at xZGð 2=2ÞL. Thus, packets of material depos- ments). The upwelling jet never manifests as a simple, ited at the shear zones will stretch out over time as rectilinear feature, suggesting that we should consider material closer to the jet center is transported faster how both the jet and its instabilities affect local particle than material toward the flanks of the jet. The relative retention patterns. [28] In a tattered curtain, a breeze will flow prefdispersion has a minimum at x0 Z 0, so material near the center of the jet will remain coherent, staying togeth- erentially through rips in the curtain, unimpeded by the curtain’s weight. Similarly, swift offshore transport in er as it moves along within the core of the jet. W20

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UE vVG =vx C fUE Z Ty =r;

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coastal upwelling systems should occur preferentially in regions where the upwelling jet is already weakened by instabilities, that is, the openings in the jet. In the case of the tattered curtain, resistance is applied by the weight of the curtain: the heavier the curtain, the more resistance to the wind. By analogy for the case of coastal upwelling under geostrophic balance, resistance is applied by coastal velocity shear, with more resistance for stronger shear, that is, a faster alongshore jet. To see this relationship, consider idealized Ekman mass transport in an upwelling system. [29] Ekman transport is calculated from an alongshore momentum balance after assuming that the horizontal velocity can be decomposed into geostrophic ðuG Þ and Ekman ðuE Þ components, that is, uZ uG C uE , and that all (nonlinear) advection terms are negligible (e.g., Gill 1982; Brink 1983). In upwelling systems this results in the alongshore momentum balance fUE Z Ty =r, where UE is the offshore Ekman velocity integrated over the depth in which the winds act (the Ekman boundary layer thickness), Ty is the wind stress at the ocean surface in the alongshore direction, f is the Coriolis parameter, and r is a scaling density. Values of UE are often reported as the “upwelling index” (see Schwing et al. 1996). [30] In the presence of a steady upwelling jet, offshore advection of alongshore momentum becomes important, and the momentum balance must be modified to include nonlinear advection of the geostrophic vorticity by the Ekman flow (Stern 1965; Niiler 1969; Thomas and Lee 2005):

ð7Þ

When vVG =vx is positive (on the inshore flank of the upwelling jet; Fig. 11), the strength of the offshore transport is reduced by the magnitude of the velocity shear (Brink 1987), as has been recently observed off the Central California coast (Woodson 2013). [31] Meanders or breaks in the upwelling jet (where vVG =vx is locally small and thus UE stronger), the “tatters” in the curtain, can be triggered by spontaneous instability, local topography, or interactions with the energetic offshore eddy field generated by previous instabilities (Strub et al. 1991; Barth 1994; Durski and Allen 2005). These mechanisms are inherently complex, involving nonlinear interactions, and so exhibit a large degree of variability and unpredictability, as seen here. [32] Particle retention in the upwelling jet and its spatiotemporal complexity suggest that a local index of jet position and strength, rather than a regional windbased index, may be a better predictor of settlement timing and intensity. High-shear areas on the flanks of the upwelling jet in our study were found to coincide at times with collinear backward and forward Lagrangian shear lines identified with the finite-time Lyapunov exponent metric (Beron-Vera et al. 2010; Harrison and Glatzmaier 2012; Harrison et. al. 2013). This follows from the high relative dispersion in both backward and forward time for high-velocity shear areas (Fig. 11); such features are found farther offshore along the main jet of the CCS (Harrison and Glatzmaier 2012) and so were expected for this region. However, collinear Lagrangian shear lines in our model runs were not found to be reliable markers of the boundaries of high-particle-density regions, perhaps due to the short time scales of jet edge stability. Similarly, jet cores identified by low values of the finite-time Lyapunov exponents, indicating minima of relative dispersion approximating stable tori (Beron-Vera et al. 2010;


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Rypina et al. 2011), were not predictably evident at the jet core; this may be a function of the Lagrangian particle integration time (Beron-Vera et al. 2010) or the sensitivity of this method (Harrison and Glatzmaier 2012). Another possible explanation is that because the jet is highly interactive with coastal eddies, and inherently unstable itself (Fig. 6; Barth 1994; Durski and Allen 2005), there is a significant amount of exchange in and out of the jet (Samelson 1992). Such “lobe” exchange is highly complex in this system (Harrison et al. 2013), most likely due to the broad spectrum of forcing frequencies or the inherent nonlinearity of this system. Further investigation is needed to determine how useful these dynamical system techniques will be, given realistic flows, or if Eulerian methods are better suited for jet detection. Links to Previous Studies [33] In this study, settlement (i.e., larvae !10 km from shore) is indeed stochastic due to nonlinear dynamics of the upwelling jet and its interaction with offshore features, as well as the prevalence of packets of dense material that appear to be folded into the jet intermittently (Mitarai et al. 2008; Siegel et al. 2008; Harrison et al. 2013). This indicates that determining detailed retention patterns in similarly turbulent systems (e.g., Mitarai et al 2009) will require a “weather prediction” approach involving regional data assimilative modeling (e.g., Roughan et al. 2011), or a significantly sized data set of coastal remote sensing observations that can be used to perform Lagrangian tracking experiments and estimate local settlement events. [34] Some systems may be more predictable. The winds used to force our model feature the regular strong upwelling off the Point Sur area, much higher than the other regions of the CCS (Pickett and Paduan 2003). Upwelling regions with consistently weaker upwelling winds exhibit more linear, predictable coastal jets (e.g., on the well-studied Oregon shelf; Austin and Barth 2002; Kim and Barth 2011). Our finding that moderate upwelling is associated with high particle retention suggests that settlement would be strongly modulated by the upwelling jet in these regions and may be more deterministic than stochastic for these more regular flows (Kim and Barth 2011). Results from the long-

term PISCO (Partnership for the Interdisciplinary Study of Coastal Oceans) study of barnacle and mussel recruitment along the US West Coast (Broitman et al. 2008) may be consistent with this idea. In that data set, intertidal larval recruitment was largely synchronous along the central Oregon coast, where the upwelling jet is a semipermanent feature, and more stochastic in more energetic regions. Larval transport modeling results for Oregon (Kim and Barth 2011) show results similar to those here: upwelling relaxation is poorly correlated with settlement events, and an integrated wind product is more predictive. Kim and Barth (2011) found a moderate correlation that peaked for an 8-d integrated wind product and settlement of 15- to 35-PLD particles (for a single model run), indicating that correlated wind products will depend on the region and PLD of interest. [35] Similar to the Oregon system, the Peru–Chile upwelling system features a straight coastline and a series of nearshore coastal jets, both equatorward at the surface and poleward below. Here upwelling winds are persistently strong in some regions for most of the year. A modeling study of this system (Aiken et al. 2011) showed that increased upwelling winds from a modeled global warming scenario decreased retention for neutrally buoyant larvae, consistent with our results. Aiken et al. (2011) also found an increase in settlement for vertically migrating larvae, which were retained in the global warming–intensified poleward undercurrent until settling age. Thus, even under persistently strong upwelling conditions, larval behavior coupled with retention in coastal jets (in this case the undercurrent) may increase larval settlement, consistent with observations of high settlement during upwelling on the California coast (Morgan et al. 2009; Shanks and Shearman 2009). [36] In upwelling systems with wider shelves, as in the Iberian, Canary, and Beneguela regions, upwelling can be centered far offshore near the shelf break (Estrade et al. 2008). This can create a series of geostrophically balanced alongshore jets, poleward onshore of the upwelling center and equatorward offshore. Hence, where larvae are retained may depend on their release location: larvae released very close to shore could get caught in the poleward jet (along with river output), while larvae spawned on the outer shelf or slope could


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be retained in the more offshore equatorward jet. This could lead to strong retention for nearshore spawning species in the poleward coastal jet, as has been seen in the Iberian upwelling system in both observational and modeling studies (Santos et al. 2004; Domingues et al. 2012). The Iberian nearshore jet is strongly coherent along the coast, reverses with variability of the largescale wind forcing, and again exhibits a high degree of predictability (Domingues et al. 2012). Similarly, but perhaps for different dynamical reasons, the CCS features coastal poleward flows during upwelling relaxation and low upwelling times of year (Roughan et al. 2006; Pringle and Dever 2009; Drake et al. 2011). Poleward flows such as this have been observed to affect intertidal settlement in the CCS (Wing et al. 1995; Dudas et al. 2009). These studies suggest that a variety of coastal jets will exert an important hydrodynamic control on retention and settlement across a wide range of regions. [37] Truly realistic modeling of coastal larval transport poses considerable difficulties, especially for intertidal organisms. From an ocean modeling perspective, representations of physical processes considered to be important, such as tides (Osborne et al. 2011), surf zone dynamics (Wilson et al. 2013), internal waves (Walter et al. 2012), and submesoscale horizontal and vertical mixing (Badin et al. 2011), are all areas of active research. Little is known about the effects of these processes on transport. On the biology side, larval behavior and what cues trigger it are difficult to observe, much less parameterize for modeling. Additionally, there is difficulty in accurately modeling life history effects, such as feeding success and mortality, which depend on complex food web interactions. Progress is being made on both of these biological modeling issues, including systematic sensitivity studies of the effects of behavior (Drake et al. 2013), release depth (Simons et al. 2013), and development of end-to-end ecosystem models that simulate a wide range of trophic levels (Cury et al. 2008). Further research is needed to determine the individual and synergistic effects of adding physical and biological complexity to idealized models such as the one used here.

Significance to Aquatic Environments [38] The role of physical processes in determining coastal transport of planktonic organisms, particularly larvae of coastally spawning species, has long been an important question in marine ecology. Here we examine the hypothesis that upwelling winds control coastal retention using a modeling study. Specifically, we tested the hypothesis that strong upwelling implies offshore transport and low retention, and that upwelling relaxation implies delivery of material back the coast, that is, larval settlement. Using an ensemble of 28 model runs and particles representing surface following plankton with no behavior capability, we found that upwelling relaxation is poorly correlated with settlement (determined by model larvae approaching within 10 km of shore; rZ 0:33) and that a 20-d integrated wind statistic is most correlated with settlement (rZ 0:62), with a large degree of scatter in the data. This result indicates that even without behavior or life history (feeding, mortality, etc.), coastal larval density is only partially explained by winds. [39] We also note that settlement is strongly controlled by an alongshore jet associated with the upwelling front. This jet retains coastally released material and is often present in one section of the coast when regions nearby experience swift offshore transport by squirts or upwelling filaments. A wide range of observations and other modeling studies support our findings, suggesting that coastal jets could exert a strong control on the transport of plankton and larvae. Jets cores in general are expected to be robust to strong forcing and so should represent a strong local barrier to offshore transport for other applications, such as pollutant dispersal and search and rescue efforts. Acknowledgments DAS was supported in part by National Science Foundation grants OCE-030844 and OCE-1155813. Thanks to Eli Silver, Ken Brink, and Roger Samelson for their comments on a previous version of the manuscript and to Satoshi Mitarai, Bob Warner, Irina Rypina, Scott Durski, and John Allen for the insightful conversations. Art Miller and an anonymous reviewer, along with the L&O:F&E editors, were helpful in revising the manuscript.


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Supplemental Movie S1 Density of 20–40-d-old particles through time for model run 111; animation for panel shown in Fig. 3E. Particle density is shown on a log scale. The vertical dashed line is 10 km from the coastal boundary on the right, denoting both the region within which particles were released and where “settlement” is defined (particles 20–40 d old and !10 km from shore). Supplemental Movie S2 Particles are colored by their ages: blue for all ages, black for 20–40-d-old particles (potential settlers), and green for 20–40-d-old old particles within the zone where settlement is defined (!10 km from shore). Bibliography Aiken, C. M., S. A. Navarrete, and J. L. Pelegrı´. 2011. Potential changes in larval dispersal and alongshore connectivity on the central Chilean coast due to an altered wind climate. J. Geophys. Res. 116(G4), G04026. doi:10.1029/2011JG001731. Allen, J. S., P. A. Newberger, and J. Federiuk. 1995. Upwelling circulation on the Oregon continental shelf. Part I: Response to idealized forcing. J. Phys. Oceanogr. 25: 1843–1866. doi:10 .1175/1520-0485(1995)025!1843:UCOTOCO2.0.CO;2. Austin, J. A., and J. A. Barth. 2002. Variation in the position of the upwelling front on the Oregon shelf. J. Geophys. Res. 107 (C11), C113180. doi:10.1029/2001JC000858. Badin, G., A. Tandon, and A. Mahadevan. 2011. Lateral mixing in the pycnocline by baroclinic mixed layer eddies. J. Phys. Oceanogr. 41: 2080–2100. doi:10.1175/JPO-D-11-05.1. Barth, J. A. 1994. Short-wave length instabilities on coastal jets and fronts. J. Geophys. Res. 99(C8): 16095–16115. doi:10.1029/94 JC01270. Batchelder, H., C. Edwards, and T. Powell. 2002. Individual-based models of copepod populations in coastal upwelling regions: Implications of physiologically and environmentally influenced diel vertical migration on demographic success and nearshore retention. Prog. Oceanogr. 53: 307–333. doi:10 .1016/S0079-6611(02)00035-6. Bećogneé, P., M. Moyano, C. Almeida, J. M. Rodrı´guez, E. FraileNuez, A. Hernańdez-Guerra, and S. Hernańdez-Leoń. 2009. Mesoscale distribution of clupeoid larvae in an upwelling filament trapped by a quasi-permanent cyclonic eddy off northwest Africa. Deep Sea Res. Part I Oceanogr. Res. Pap. 56: 330–343. doi:10.1016/j.dsr.2008.10.008. Beron-Vera, F. J., M. J. Olascoaga, M. G. Brown, H. Koçak, and I. I. Rypina. 2010. Invariant-tori-like Lagrangian coherent structures in geophysical flows. Chaos 20: 017514. doi:10.1063/1 .3271342.

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Received: 1 July 2013 Amended: 7 December 2013 Accepted: 17 January 2014
