Physical and biogeochemical variability in Todos Santos Bay, northwestern Baja California, derived from a numerical NPZD model

Journal of Marine Systems 183 (2018) 63–75

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Physical and biogeochemical variability in Todos Santos Bay, northwestern Baja California, derived from a numerical NPZD model

T

Jorge Cruz-Rico, David Rivas* Departamento de Oceanografía Biológica, CICESE, Carr. Ensenada-Tijuana #3918, Zona Playitas, Ensenada 22860, Baja California, Mexico

A R T I C LE I N FO

A B S T R A C T

Keywords: Numerical oceanic model NPZD biogeochemical model Physical-biogeochemical coupling Interannual variability Baja California Todos Santos Bay

A physical-biogeochemical Nitrate-Phytoplankton-Zooplankton-Detritus (NPZD) numerical model is used to study the variability of coastal phytoplankton biomass in northwestern Baja California and the Todos Santos Bay (TSB), a region of high socioeconomic importance located in the southern California Current System. The model reproduces adequately the most important oceanographic features of the study area, like the coastal chlorophylla (Chl-a) maxima and thermal gradients in the regions of enhanced coastal upwelling. The variability of Chl-a in the TSB is influenced by the activity of El Niño-Southern Oscillation (ENSO) and decadal modes of the Pacific, e.g., the Pacific Decadal Oscillation (PDO) and the North Pacific Gyre Oscillation (NPGO). From de multi-year model simulation (2004–2011), this large-scale influence is remarkable in two contrasting anomalous years. The year 2006 was anomalously warm and with low Chl-a levels, associated with warm phases of ENSO and PDO and a weakening of the NPGO. These climatic anomalies caused a strong stratification and weak upwelling around the TSB, which induced a poor nutrient input into the Bay and a deep and weak subsurface Chl-a maximum (SCM) during summer. The year 2011, on the other hand, was a cold year with enhanced upwelling during the spring, associated with cold phases of ENSO and PDO and an intensification of the NPGO. These conditions also caused a weak stratification and an intense nutrient transport into the TSB and hence a shallower and stronger SCM.

1. Introduction The marine ecological systems exhibit a significant variability commonly modulated by climatic forcings, in diverse time and spatial scales. For example, during a warm phase of the Pacific Decadal Oscillation (PDO) low biological productivity occurs along the U.S. west coast, but during a cold phase the productivity between north and south is reversed, due to the change in the north–south transports (Francis and Hare, 1994; Beamish, 1993; Barnett et al., 1999; Bond and Harrison, 2000). In the southern California Current System (CCS), changes in the regional salinity, phytoplankton chlorophyll-a (Chl-a), and zooplankton volume off Baja California have been also attributed to the phase change of the PDO (Gaxiola-Castro et al., 2008). On the other hand, long-term observations of upper ocean salinity and nutrients in the Alaskan Gyre show significant decadal variations that are in phase with variations observed in the southern CCS, which are linked to the North Pacific Gyre Oscillation (NPGO) (Di Lorenzo et al., 2009). The NPGO has been also related to the nearshore winds and the onset of the upwelling season along the U.S. west coast (Chenillat et al., 2012; Chenillat et al., 2013). Also, the variations of the thermocline depth,

*

Corresponding author. E-mail address: [email protected] (D. Rivas).

https://doi.org/10.1016/j.jmarsys.2018.04.001 Received 27 January 2017; Received in revised form 20 March 2018; Accepted 2 April 2018 Available online 07 April 2018 0924-7963/ © 2018 Elsevier B.V. All rights reserved.

intensity of the upwelling, and sea level height are associated with the ENSO activity. During the occurrence of this phenomenon, transports of warmer and saltier waters from the south induce a warming of the surface levels and a raise of the sea surface height in the southern CCS. This increases the stratification of the water column and hence the deepening of the thermocline and the reduction of the transport of nutrients to the euphotic zone. As a result there is a decrease in the concentration of Chl-a (Chavez et al., 2002; Durazo and Baumgartner, 2002). The purpose of this paper is to analyze the conditions and mechanisms associated with the variability of the phytoplankton biomass in the Todos Santos Bay (TSB), in northwestern Baja California Peninsula, a region of high socioeconomic importance in the southern CCS. Unlike the central and northern portions of the CCS, many of the oceanographic/atmospheric phenomena affecting the southern CCS remain to be elucidated. This task is achieved using a physical-biological numerical model. Since their origins a few decades ago (e.g., Wroblewski, 1977), the physical-biogeochemical numerical models are increasingly used as a tool to understand the ecological processes involved in plankton

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modeling applications (e.g., Rivas and Samelson, 2011). For the open boundary data we used daily velocity, sea level, temperature, and salinity fields for the period 2003–2011 from the 1/8° version of the Naval Coastal Ocean Model (NCOM; Barron et al., 2006). On the model's surface, wind stress forcing for 2003–2011 was taken from the 6-hourly data of the Cross-Calibrated Multi-Platform (CCMP) Ocean Surface Wind Vector Analyses (Atlas et al., 2011), applying dragcoefficient parameterizations proposed by Smith (1988). The CCMP product was available at the National Aeronautics and Space Administration (NASA) Physical Oceanography Distributed Active Archive Center (PODAAC) website: https://podaac.jpl.nasa.gov/CrossCalibratedMulti-Platform-OceanSurfaceWind-VectorAnalyses. As in the climatological simulation mentioned above, monthly climatological values for surface heat and freshwater fluxes were used, but linear correction schemes were applied for deviations from the climatological mean state.

dynamics (e.g., Edwards et al., 2000; Batchelder et al., 2002; Baird et al., 2006; Powell et al., 2006; Fiechter et al., 2009). With the start of three-dimensional coupled models, Powell et al. (2006) used a four component ecosystem model [nitrate (NO3), phytoplankton (P), zooplankton (Z), and detritus (D)] implemented for the central portion of the CCS, which reproduced the spatial and temporal variability of the concentration of nutrients and phytoplankton associated with mesoscale physical processes such as coastal upwelling and eddies, as well as the variability associated with finer scale structures in which physical transports are closely linked to biological functions. More recently, models have been adapted to the needs of solving ecological processes in a specific region. For example, Fiechter et al. (2009) used the numerical model of Powell et al. (2006) but they included iron as an important (limiting) element in the phytoplankton growth to study the conditions of primary production in the northwestern coast of the Gulf of Alaska. Their model provided an insight of the importance of the limitation of the micro-nutrients and macro-nutrients on the continental shelf and in the deep ocean, with the shelf slope acting as a transition zone where the availability of nitrate and iron significantly impact the phytoplankton growth. The rest of the paper is organized as follows. Section 2 describes the model setup, its forcing and boundary data, and the parameters involved in the biogeochemical processes; a comparison between the model outputs and satellite observations is also included. Section 3 describes the results of this study, including an analysis of the interannual variability of the temperature and Chl-a around the TSB, and a comparison of the oceanic conditions within the Bay in two contrasting anomalous years. Section 4 discusses some implications of this study. Finally, Section 5 summarizes the main results of this work.

2.1.2. Todos Santos Bay model A finer scale model for the TSB was also implemented. Most of the details used in this finer-scale configuration are the same as those described in the previous section, unless otherwise indicated. The model domain extends from 32.1°N to 31.6°N and from 116.6°W to 117.0°W (Fig. 1). The horizontal resolution is ∼300 m, and the vertical resolution is given by 20 sigma levels. As in the regional model (previous section), the TSB model was integrated for a climatological year and for the period from 2003 to 2011, but only the last eight years were used for this analysis. The regional model provides initial conditions and boundary data for the TSB model. Daily averages of the regional model outputs were used at the TSB model's open boundaries. A sponge layer was also used in this domain, but with a width of 6 grid points. Within this layer, viscosity and diffusivity coefficients decreased linearly from 10 m2 s−1 at the boundaries to the value of 1 m2 s−1 at the interior. No nudging layer was used in this domain. In contrast with the 4-to-1 downscaling in the nesting from the 1/8° NCOM to the regional model (Section 2.1.1), in this finer-scale model a 10-to-1 downscaling is successfully achieved. Although this downscaling seems to be excessively large, the high-frequency (daily) input of the boundary data helps to make the model stable enough at its open boundaries, needing only the sponge layer mentioned above to prevent any instability growth within this layer.

2. Model setup 2.1. Ocean circulation model This study is based on the Regional Ocean Modeling System (ROMS) [e.g., Haidvogel et al., 2008] version 3.6. This was implemented for a fine scale domain for TSB which is nested (in a one-way, offline fashion) to a lower-resolution, larger-scale regional model for Baja California. These two models are described below. 2.1.1. Regional model The regional model configuration is based on that used by Rivas and Samelson (2011) for a domain focused on the Oregon coast. The model is configured for a spherical coordinated domain extending from 26.8°N to 35.5°N and from 113.7°W to 123.5°W (Fig. 1), with a spatial resolution is 1/30° (∼3 km) (a horizontal grid of 307 × 295 points) and a vertical resolution of 31 sigma levels. The model grid was prepared using the software described by Penven et al. (2008). Nudging time scales of 6 and 300 d were used for conditions of active (input) and passive (output) at the model's open western, northern and southern boundaries. Nudging was also applied inside the model domain in order to avoid spurious signals related to the boundary. This nudging decreases from its value at the boundary (mentioned above) to zero inside in the first 20 points of the grid of each open boundary. The model also includes a sponge layer within the first 20 points adjacent to each open boundary, within which both the viscosity and diffusivity increase linearly from inner values (mentioned above) to 100 m2 s−1 at the boundaries. The model was integrated for a full year with the climatological forcing and boundary data obtained by the software described by Penven et al. (2008), for spin-up purposes. After this, the model was integrated for the years 2003 to 2011; the year 2003 was also considered as a spin-up period for the next eight years (2004–2011), which were used for the analysis in this paper. The two-year spin-up period is long enough for the model to reach a stable state. Indeed, shorter spinup periods have been successfully used in other similar numerical-

2.2. Biogeochemical ecosystem model Biological dynamics is modeled using a biogeochemical model based on nitrogen (Powell et al., 2006), the NPZD model. The total nitrogen at any point is partitioned between dissolved nitrogen (N), phototrophic phytoplankton (P), the herbivorous zooplankton (Z), and particulate nitrogen (D: Detritus). Major biological processes included in the model are the photosynthetic growth and uptake of nitrogen by phytoplankton, grazing on phytoplankton by zooplankton, mortality of both phytoplankton and zooplankton, and sinking and remineralization of detritus (Powell et al., 2006). This model also includes advection and mixing (horizontal and vertical) of the four biological components (N, P, Z, D), using the velocity field and diffusivities provided every time step by the hydrodynamic model. The time step of the physical model is enough to resolve the biological processes, and both models run as one (Powell et al., 2006). The parameters used in the NPZD model are identical to those used by Powell et al. (2006), with a few exceptions (uptake half saturation kN, zooplankton grazing rate Rm, zooplankton mortality ζd, remineralization δ, and detrital sinking rate wd) which are identical to those used by Macías et al. (2012) for the southern California Current System. The model was initialized with uniform fields in P, Z, and D (0.08, 0.06 and 0.04 mM m−3, respectively), and a non-uniform field in N taken from monthly climatology of nitrate (Garcia et al., 2010), 64

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Fig. 1. (a) Domain and bathymetry (m) of the regional model. The black square indicates the Todos Santos Bay (TSB) model domain. The purple box indicates an alongshore region where variables are analyzed. The green dots correspond to 8 hydrographic casts during the period from 8 May to 9 June, 2009 (see Section 2.3). (b) Domain and bathymetry (m) of the TSB model domain. The dashed region along the domain's open boundaries corresponds to the transitional zones where the bathymetry of both domains match. Relevant locations are shown in both panels. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

beginning of the analyzed period. d) Volume-integrated assimilation of N by the phytoplankton:

available at the NASA National Oceanographic Data Center (NODC) website: https://www.nodc.noaa.gov/. These fields were also used as boundary data for the NPZD components at the model's open boundaries. As in the hydrodynamic model, the two-year spin-up period is enough to obtain stable solutions of the NPZD model. In Powell et al. (2006), the non-equilibrium initial conditions cause the NPZD model to oscillate and bloom for the first 2 months (∼60 days) but the transient behavior declines and is not seen after 120 days. All the model's NPZD variables are expressed as concentration of N, which is not necessarily convenient when compared with observations (e.g., satellite-derived Chl-a). In order to solve this issue, the phytoplankton concentration (P) is translated from its original units (mM m−3) to Chl-a units (mg m−3). This is achieved by multiplying an abundance of nitrogen-phytoplankton by a ratio of Chl:N of 1.325 gChl mol-N−1 (derived from a Redfield C:N ratio of 106:16 mol-C molN−1 and a C:Chl-a ratio of 60:1 g-C g-Chl-a−1) (Fiechter et al., 2009).

Assim =

Surface fields from the regional model were validated with satellite data. These data include the monthly composites of 4-km resolution sea surface temperature (SST) and Chl-a, taken from the Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) - Aqua product. This product is available at the NASA OceanColor Web: http://oceancolor. gsfc.nasa.gov. Sea surface height (SSH) from a 1/4° altimeter gridded product of absolute dynamic topography (Rio et al., 2014) was also included in the validation. Monthly mean fields were calculated from the daily SSH for the period 2004–2011. Vertical thermal structure from the regional model was validated with in situ hydrographic observations. These observations consisted of a set of 8 consecutive vertical temperature profiles from a profiling float drifting close to the Baja California shelf in the period from 8 May to 9 June, 2009. These data were available at the NASA NODC website (https://www.nodc.noaa.gov/). The temperature values were interpolated onto a 20 m (length) by 5 m (depth) vertical grid using an objective mapping, similar to the one described in Thomson and Emery (2014). A horizontal length scale of 30 km and a vertical length scale of 30 m, and a signal-to-noise ratio of 0.05 were used in the mapping. Three-dimensional structures of temperature and phytoplankton structures of the TSB model were validated with in situ hydrographic data. These data were taken from 26 hydrographic casts carried out during an oceanographic campaign in the TSB in 23–24 April 2007 (García-Mendoza et al., 2009). Surface and cross-sectional mappings of temperature and fluorescence were done using the objective mapping method mentioned above. A 300 m by 300 m grid and a length scale of 15 km were used for the horizontal mapping, and a 20 m (length) by 5 m (depth) grid and length scales of 15 km (horizontal) and 20 m (vertical) were used for the vertical mapping.

(1)

where A is the cross-sectional area of either the southern or the northern TSB's entrance, and v is the velocity normal to such an area A and positive toward the TSB's interior. b) Transport of the biogeochemical property B:

TB =

∫A vB dA,

(2)

where B can Chl-a, P or N. c) Volume-integrated phytoplankton anomaly:

P* =

∫V (P − P0) dV ,

(4)

2.3. Satellite imagery and ancillary data

a) Transport of volume:

∫A v dA,

UP dV ,

where U is given by Eq. (7) in Powell et al. (1996).

2.2.1. Physical-biogeochemical transports In this paper, the effects of the interannual variability of the largescale patterns on the TSB's dynamics are analyzed. As part of this analysis, a physical-biogeochemical budget is quantified in order to diagnose the environmental conditions affecting the TSB during two contrasting anomalous years. This budget includes the following processes.

Tr =

∫V

(3)

where V is the TSB's volume, and P0 is the value of P at the 65

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To quantify the ability of the model to reproduce the variability in monthly mean SST, Chl-a and SSH, model-data correlations and standard deviation (SD) ratios are presented in Fig. 3 as Taylor diagrams (Taylor, 2001). The SST is the variable that is best represented by the model (Fig. 3a): the correlations and SD ratios are close to 1 for the absolute values, and the correlations are around 0.6 and the SD ratios are 0.4–0.7 for the anomalies (i.e., after removing the mean seasonal cycle). The Chl-a is reasonably well reproduced over the shelf (correlations ∼ 0.74 for absolute values and 0.4 for the anomalies; SD ratios ∼ 1.5) but it is remarkably overestimated in the rest of the domain (Fig. 3b). The SSH also shows correlations over 0.5 (Fig. 3c), with some underestimation over the shelf (SD ratios ∼ 0.7) in contrast with the rest of the domain (SD ratios ∼ 1). The discrepancies over the shelf may be related to a limitation of the SSH product to reproduce the nearcoastal variability. Among the three variables, the Chl-a is also the one that presents the highest RMSD values. In addition to the validation of the model surface fields, the model vertical structure was also compared to observations. Fig. 4 shows a vertical cross-section of the temperature observed in the 8 hydrographic casts off Baja California coast (see Fig. 1a and Section 2.3), compared to the model's mean temperature for the same period. The model's temperatures are ∼1° lower at the surface (Fig. 4c), consistent with the model-satellite comparison shown above. The model also overestimates the thickness of the mixing layer and the thermocline, and presents a more pronounced vertical thermal gradient down to 150-m depth. Below 150 m, the vertical gradient is similar in the model and the observed temperatures, but the model is ∼3 °C colder. The model also presents the sloping isotherms close to the bottom (Fig. 4), which is not observed in the hydrographic section probably because its spatial resolution is not enough to resolve this region.

2.4. Climatic teleconnection indices Four climatic indices were compared to the modeled SST in the TSB in order to explore a possible relation between this variable and the large-scale climatic patterns (Section 3.2). The first index is the PDO, defined as the leading principal component of North Pacific (northward of 20°N) monthly SST variability (Mantua et al., 1997). This index was available through the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) website: http://research.jisao.washington.edu/ pdo/. The second index is the NPGO, defined as the 2nd dominant mode of SSH variability in the Northeast Pacific (Di Lorenzo et al., 2008). This index was available at http://www.o3d.org/npgo/. The third index is the Multivariate El Niño-Southern Oscillation (ENSO) Index (MEI), a multivariate measure of the ENSO signal in the first principal component of six main observed variables over the tropical Pacific: sea level pressure, zonal and meridional components of the surface wind, sea surface temperature, surface air temperature, and cloudiness (Mazzarella et al., 2010). The highest values of MEI values represent the warm ENSO phase (El Niño) while its lowest values represent the cold ENSO phase (La Niña) (Mazzarella et al., 2010). The last index is the Outgoing Longwave Radiation (OLR), essential to understand the radiative energy budget within energy balance models and higher-order general circulation models (Zhang et al., 2017). The OLR has been widely used as a proxy for tropical convection and precipitation (e.g., Chiodi and Harrison, 2010; Joseph et al., 1994), and to study climate feedback and processes (Susskind et al., 2012; Zhang et al., 2016). The MEI and the OLR were provided by the NOAA-Earth System Research Laboratory (ESRL) Physical Science Division (PSD) through its website: http://www.esrl.noaa.gov/psd/data/gridded/. 3. Results

3.1.2. Hydrographic structure within the TSB A comparison between the model and hydrographic observations in 23–24 April 2007 (Fig. 5) shows that the northern coast is characterized by colder waters with higher Chl-a (fluorescence in the observations), especially in Salsipuedes Bay and Punta San Miguel, result from a local upwelling. The TSB's eastern portion, however, is characterized by warmer waters with lower Chl-a, most probably as a result of an entrapment of the upwelling waters that results in a water-mass modification. As in the case of the regional model, at the surface the TSB model is ∼1 °C colder than the observations. The root-mean-squared error (RMSE) of the model SST is 2.5 °C. The model-data correlations are 0.26 (p = 0.000) for the temperature and 0.04 (p = 0.009) for the Chl-a. The differences between the model and the observations point out the imperative need for a downscaling of the wind product, since the topography around the TSB, unrepresented in the 1/4° wind product, may have a major influence in the direction and magnitude of the local winds. The vertical structure also presents remarkable differences between the model and the hydrographic observations(Fig. 6). As in the case of the temperature cross-section shown in Fig. 4, the TSB model is colder and overestimates the thickness of the mixing layer and is characterized by a deeper thermocline (Fig. 6a–b). Linked to the depth of the thermocline is the subsurface chlorophyll maximum (SCM), which is evidenced by the highest values of fluorescence (Fig. 6c) at 15–20-m depth. The model presents a deeper SCM localized at 20–30-m depth (Fig. 6d). The root-mean-squared error (RMSE) of the model temperature is 1.8 °C. The model-data correlations are 0.84 (p = 0.000) for the temperature and 0.20 (p = 0.000) for the Chl-a, which indicate that the model partly reproduces the vertical structure shown in the observations.

3.1. Model validation 3.1.1. Regional fields Surface fields from the regional model were compared to those obtained from the satellite products (Section 2.3). Fig. 2 shows the climatologies (average 2004–2011) of SST, surface Chl-a and SSH during the spring (April–June), period when the highest Chl-a concentrations are usually observed, as consequence of enhanced coastal upwelling (Espinosa-Carreón et al., 2004). The model shows the patterns typical in spring, such as lower SST and higher Chl-a within the upwelling regions along most of the Baja California Peninsula coast (Fig. 2a–b), which persist all year round with greater intensity during spring-summer for the northern region of Baja California (Durazo et al., 2010). Compared to the satellite data, the model is ∼1 °C colder but the horizontal gradients are consistent with those data, and also the model shows the typical north–south gradient associated with the latitudinal differences in surface heating due to solar radiation (Espinosa-Carreón et al., 2004). The model Chl-a is also in good agreement with that of the satellite data (Fig. 2c–d). The model accurately reproduces the coastal Chl-a where the near-coastal upwelling is vigorous. In some regions the model overestimates the Chl-a concentration. This may be associated with some hydrodynamic issue, like an overestimated upwelling signal due to excessively strong winds, or may be a consequence of the values assigned to parameters related to the absorption rate, mortality, and grazing, which remain to be explored and estimated for our study area. The model SSH is qualitatively consistent with the satellite-derived SSH (Fig. 2e–f). Both fields show their lower values over the shelf and near the coast, consistent with a response of the SSH to the seasonal maximum of upwelling (Espinosa-Carreón et al., 2004; Durazo et al., 2010). The observed (satellite-derived) SSH shows a steeper offshore increase. This is an evidence of stronger and perhaps more persistent mesoscale activity seaward of the shelf, especially west of Vizcaíno Bay and the TSB. However, as in the SST, the region around the TSB is reproduced adequately by the model.

3.2. Interannual variability Fig. 7a–b shows a comparison between the temporal evolution of monthly mean anomalies of SST and Chl-a from the model and from the 66

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Fig. 2. Spring climatology (period 2004–2011) of sea surface temperature (SST) (a–b), surface chlorophyll-a concentration (Chl-a) (c–d), and sea surface height (SSH) (e–f) from satellite observations (first column) and from the regional numerical model (second column).

the frequent cloudiness present in that region. This issue may cause bias in the calculations of the monthly composites which would result in errors in the magnitude of the peaks in the series. To analyze the variability of the regional upwelling regime, a monthly coastal upwelling index (CUI) was calculated with the model's wind stress in the upwelling region shown in Fig. 1a. This calculation was done according to the definition used by Bakun (1973) and Schwing et al. (1996), assuming a coast oriented 28° clockwise from the north. As a reference, we compared our model's CUI to the extensively used North America CUI provided by the NOAA Pacific Fisheries Environmental Laboratory (PFEL; https://www.pfeg.noaa.gov/). The resulting correlations were r = 0.71 (p = 0.000) at 27°N, r = 0.61 (p = 0.000) at 30°N and r = 0.69 (p = 0.000) at 33°N. Other CUI-related indices were tested in the analysis, namely those proposed by Bograd et al. (2009), but given the seasonality of the regional winds (upwelling-favorable all year round) they do not provide significant information additional to that provided by the monthly mean CUI

satellite sensor, for the upwelling box shown in Fig. 1a. As summarized in Fig. 3a, the satellite-derived SST and Chl-a anomalies are reasonably well reproduced by the regional model, showing correlations of r = 0.56 (p = 0.000) and r = 0.41 (p = 0.000), respectively. This variability is partly present in the SST within the TSB, with correlations of r = 0.38 (p = 0.000) in the SST and r = 0.31 (p = 0.002) in the Chla. The SST anomaly is about ± 1.5 °C (Fig. 7a) and the Chl-a anomaly is about ± 3 mg m−3. Two different periods can be defined in the series, the first one from year 2004 to 2006 is characterized by with a higher frequency of positive SST anomalies and negative Chl-a anomalies, that result in a positive trend in the SST and a negative trend in the Chl-a. The second period from year 2007 to 2011 is characterized by a higher frequency of negative SST anomalies and positive Chl-a anomalies, with a negative trend of SST and a positive trend of Chl-a. Some of the Chl-a peaks present in the observations are not consistent with those in the model. Nonetheless, it is important to keep in mind that the satellite observations in the vicinity of TSB usually have numerous gaps given 67

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Fig. 3. Taylor diagrams for spatially averaged, monthly mean (a) SST, (b) Chl-a, and (c) SSH during the period 2004–2011. Radial distance represents the ratio of simulated to observed standard deviations and azimuthal angle represents model-observation correlation. All the correlation calculations presented a p < 10−3. Observations coincide with the location defined by standard deviation ratio and correlation equal to one (black dots). Interior dashed contours show the root-meansquare deviation (RMSD). In all the diagrams the series were separated in three regions: all the domain, the continental shelf (arbitrarily defined within the 250-m isobath) and the along-shore box shown in Fig. 1; for each region, absolute values (dots) and anomalies (crosses) were included.

Fig. 4. Vertical cross-section of (a) observed and (b) modeled temperature (°C) along the L-shaped transect depicted by the 8 hydrographic casts shown in Fig. 1a. The vertical purple line corresponds to the vertex of the transect. Dotted vertical lines in panel (a) correspond to the observation points. (c) Mean vertical profiles of the temperatures shown in panels (a) and (b). Error bars correspond to standard deviations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 68

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Fig. 5. Horizontal distributions at 5-m depth of temperature (°C) from (a) hydrographic observations (Section 2.3) and from (b) the model, and (c) observed fluorescence (relative units) and (d) model Chl-a (mg m−3), in 23–24 April 2007. The black dots in panels (a) and (c) indicate the observations points. The gray straight line indicates a transect where the water column properties are diagnosed.

Fig. 6. Vertical cross-sections of temperature (°C) from (a) observations and from (b) the model, and (c) observed fluorescence (relative units) and (d) model Chl-a (mg m−3) along the transect shown in Fig. 5, in 23–24 April 2007. Dotted vertical lines in panels (a) and (c) correspond to the observation points. 69

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Fig. 8. Annual mean values of modeled SST anomaly (normalized by its standard deviation) in the TSB, Pacific Decadal Oscillation (PDO), Outgoing Longwave Radiation (OLR), Multivariate ENSO Index (MEI), and North Pacific Gyre Oscillation index (NPGO) for the period from year 2004 to 2011. Correlations between the series are shown.

Fig. 7. Temporal evolution of monthly mean anomalies of (a) SST, (b) Chl-a, and (c) CUI spatially averaged within the along-shore box shown in Fig. 1a, for the period 2004–2011. Panels (a) and (b) compare series from the MODIS-satellite (black line), the regional model (blue line), and the TSB model (red line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

whereas in the last period the PDO was more negative and the NPGO was positive (Fig. 8). The most contrasting years in the series are the year 2006, characterized by the highest (least negative, closest to zero) PDO and the lowest (most negative) NPGO, and the year 2011, characterized by the lowest (most negative) PDO and one of the highest values of NPGO. Then, these two contrasting years exemplify the differences in the large-scale environmental pattern affecting Baja California and hence they are the focus of the analysis in the following sections.

anomalies. Fig. 7c shows the model's CUI anomalies. The first 2 years (2004–2005) are characterized by mostly negative CUI anomaly, whereas last 2 years (2010–2011) mostly by positive ones; in the rest of the years (2006–2009) the number of negative and positive anomalies are comparable. As expected, the CUI anomaly is negatively correlated with the SST anomaly (BC: r = −0.72, p = 0.000; TSB: r = −0.52, p = 0.000) and positively correlated with the Chl-a anomaly (BC: r = 0.70, p = 0.000; TSB: r = 0.57, p = 0.000). Extended periods of negative CUI anomaly are associated with positive SST anomaly and negative Chl-a anomaly, e.g. 2004 to mid-2006, and vice versa, e.g. 2010–2011. One of the highest SST anomalies (∼2 °C) occurs in mid2006, which coincides with a persistent condition of remarkably negative CUI anomaly and the most negative Chl-a anomaly (∼−5 mg m−3). In late 2006 a period of positive CUI anomaly starts and the SST decreases to reach a nearly null anomaly in early 2007, and a small positive Chl-a anomaly occurs. From early 2010 through 2011 the highest CUI anomalies occurred (Fig. 7c), associated with the lowest (most negative) SST anomalies and positive Chl-a anomalies, especially in the first half of 2011, when the model presents the highest Chl-a values (> 5 mg m−3). The variability observed in the TSB's SST is influenced by large-scale oceanic and atmospheric patterns. In order to explore this notion, Fig. 8 shows a comparison between the annual mean values of the TSB-model SST shown in Fig. 7a and the teleconnection indices described in Section 2.4. The correlations suggest some relation of all the indices with the SST in the TSB, but not all of them are significant. The PDO and the NPGO present the highest magnitudes of correlation which are also significant: RPDO−SST = 0.80 (p = 0.018) and RNPGO−SST = −0.76 (p = 0.028). The OLR and the MEI present also relatively high correlations (ROLR−SST = −0.53 and RMEI−SST = 0.64) but they are not statistically significant (p-values of 0.174 and 0.089, respectively). No significant correlations were found for the Chl-a (not shown). In the analyzed period there are differences between the first 3 years (2004–2006) and the rest of the series (2007–2011). In the initial period the PDO was closer to zero and the NPGO was mostly negative,

3.3. Extreme years In order to analyze the processes and environmental conditions associated with variations in the Chl-a field in the TSB, two contrasting extreme years were selected, 2006 and 2011, as an approach to the analysis of the interannual variability. As shown in the previous sections, these contrasting years are characterized by the largest SST and Chl-a anomalies (in the period 2004–2011) in the regional fields as well as in the TSB interior (Fig. 7). In the regional-scale anomalies, the year 2006 was characterized by a downwelling favorable condition (Fig. 9a) which induced a warming (∼1 °C anomaly) and a Chl-a deficit along the Baja California coast (Fig. 9b–c). These anomalies are consistent with the oceanic conditions observed off Baja California during a warm phase of the PDO (e.g., Mantua et al., 1997) and during a negative phase of the NPGO (Di Lorenzo et al., 2008). In the year 2011 the oceanic conditions are nearly the opposite, with enhanced upwelling conditions (Fig. 9d) which induced a ∼−1 °C anomaly around the TSB (Fig. 9e) and an Chl-a surplus in this region (Fig. 9f). During the spring, when the upwelling conditions are intense and persistent, the near-coastal equatorward flow associated with the upwelling front over the Baja California shelf drives an inflow into the TSB's interior (Fig. 10a, d) that probably increases the mass transport into the TSB through its northwestern entrance. This inflow occurs as a surface-intensified incoming current with its core close to Todos Santos Island, flowing over a reversal outflow with its deeper core in the northern coast (Punta San Miguel). In 2006 this surface-intensified inflow is narrower (Fig. 10a) than that in 2011 (Fig. 10d). The relatively weak wind stress in 2006 induced a net transport [Eq. (1)] of 5.5 × 103 m3 s−1 into the TSB, about 30% lower than that in 2011 (7.9 × 103 m3 s−1). The Chl-a transport [Eq. (2)] across the 70

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Fig. 9. Spring mean anomalies of wind stress (a, d; in Pa), SST (b, e; in °C), and Chl-a (c, f; in mM m−3). Upper panels (a–c) correspond to the year 2006, bottom panels (d–f) correspond to the year 2011. The black box indicates the TSB domain.

Fig. 10. Spring-mean model velocity at 5-m depth (a, d; in cm s−1), and cross-sections of transports of Chl-a (b, e; in mM s−1) and NO3 (c, f; in mM s−1) in the transect at the northern entrance of the TSB indicated by the red line in panels (a) and (d). Upper panels (a–c) correspond to the year 2006, bottom panels (d–f) correspond to the year 2011. Positive transports correspond to inflow into the TSB; the zero contour (black dashed line) is shown. The dashed brown lines in the cross-sections indicate the 11 °C isotherm as a proxy for the thermocline depth. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 71

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transports in 2011. These transport fluctuations are 5–9 times larger in 2011 compared to those in 2006. The net transports entering the TSB through its northern entrance are mostly compensated by an outflow through its southern entrance, and vice versa, consistent with Mateos et al. (2009). Our results suggest that the differences between the northern and the southern transports (i.e., an imbalance) are mainly associated with the biological activity within the TSB. The enhanced net nutrient transport in 2011 caused a remarkable increase in the phytoplankton concentration (Fig. 11d), whereas in 2006 this net nutrient transport was not enough to overcome or at least compensate the phytoplankton loss by processes like grazing and/or senescence. As a consequence, the phytoplankton shows a remarkable decrease in May and June (Fig. 11d), although the phytoplankton may also decrease because in June there is a net phytoplankton transport outside the TSB. In 2011 the nutrient-transport through the northern entrance was mostly larger than the nutrient-transport through the southern entrance, whereas in 2006 it occurred only in the second and third weeks of April, for the rest of the period the transports through both mouths seem to be balanced. These differences imply that part of the transported nutrients remain within the TSB (the outflowing waters had a lower nutrient concentration) and are mostly assimilated by the phytoplankton. With the exception of mid-April, when the nutrient assimilation was similar in both years, in 2011 the assimilation was about 2 orders of magnitude larger than that in 2006 (Fig. 11e), which explains the higher values of phytoplankton in 2011. As for the transports, in 2011 there is a linear relation between the CUI and the TSB's volumeintegrated phytoplankton. In the TSB the CUI fluctuations induce delayed fluctuations in the phytoplankton field, since these variables have a maximum correlation (0.42, p = 0.000) at a lag of 12 days. In 2006, on the other hand, the volume-integrated phytoplankton and the CUI showed a lower and insignificant correlation (0.21, p = 0.070), also at lag of 12 days. The contrasting conditions described above also influence the vertical structure of Chl-a. In spring only minor differences in value and depth of the SCM are observed between 2006 and 2011, it is in summer when the major differences occur. In summer 2006 a weak and relatively deep SCM occurred (Fig. 12), product of the low input of NO3/ Chl-a in the previous months and a deeper thermocline (∼28 m depth in Fig. 10b–c) pushing the Chl-a to deeper levels. In 2011, when the NO3/Chl-a input was remarkably stronger in the previous months, together with a shallower thermocline (∼9-m depth in Fig. 10e–f), the formation of an intense and shallower SCM is favored (Fig. 12).

Fig. 11. Temporal evolution of (a) CUI over the TSB's domain, transports of (b) NO3 and (c) phytoplankton into the TSB [Eq. (2)], (d) volume-integrated phytoplankton anomaly in the TSB's interior [Eq. (3)], and (e) volume-integrated N assimilation in the TSB's interior [Eq. (4)] during the spring (April–June) for the years 2006 (red lines) and 2011 (blue lines). In panels (b) and (c), thick and thin lines correspond to transports through the TSB's northern and southern entrance, respectively; the southern transports were multiplied by − 1 for better comparison. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

northern TSB entrance in 2006 is partitioned in an inflow at the southern portion (next to the Island) and an outflow at the northern portion (next to Punta San Miguel) (Fig. 10b), resulting in a net transport of + 2.1 × 103 mg s−1 into the Bay. This amount is only a small fraction (4%) of the Chl-a transport in 2011 (+ 51.3 × 103 mg s−1), when the inflow next to the island is remarkably more intense (Fig. 10e). Unlike the Chl-a transport, the NO3 transport [Eq. (2)] occurs as an inflow that occupies most of the water column, partly compensated by an outflow within a bottom layer (Fig. 10c, f). In 2006 the near-bottom outflow exceeds the upper inflow, resulting in a NO3 transport of − 2.9 × 103 mM s−1 out of the TSB; as in the case of the Chl-a transport, this amount is only about 6% of the NO3 transport (+ 52.8 × 103 mM s−1) into the TSB in 2011. In a time-dependent perspective, in spring (April–June) 2006 only a few short (2–3 days) events of intensified winds occurred, like those in mid-April, late May and mid-June (Fig. 11a), whereas in 2011 the intensified winds occurred in longer periods, especially those in late April–early May and late May–late June (Fig. 11a). This difference between both years is evident in the values of the third quartile of the CUI: 32.3 m3 s−1 100 m−1 for 2006 and 56.0 m3 s−1 100 m−1 for 2011. The CUI fluctuations largely induced changes in the transports of nutrients (Fig. 11b) and phytoplankton (Fig. 11c), especially in 2011, with a delay of 2 days, as shown by calculations of lagged maximum correlation: 0.39 (p = 0.000) and 0.45 (p = 0.000) for the nutrient and phytoplankton transports in 2006, and 0.63 (p = 0.000) for both

Fig. 12. Mean vertical profiles of modeled Chl-a in the TSB's central zone, for spring (gray/black) and summer (red) for the years 2006 and 2011, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 72

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4. Discussion

representation of the SCM might be the inclusion of a component of microbial interaction. For example, Suttle (2005) and Mussap et al. (2016) report that a microbial interaction component in biochemical ecosystem models result in a better response of the processes associated with the availability and recycling of nutrients, i.e., including a microbial interaction could improve processes associated with assimilation, availability and remineralization of nutrients, which can modify the growth of Chl-a through the water column. In addition to the microbial-related processes, it may be also important to reformulate the already existent processes in the NPZD model equations. For example, Hodges and Rudnick (2004) used a twocompartment nutrient-phytoplankton model to prove that the magnitude of the deep biomass maximum is highly dependent on sinking rate and diffusivity of the organic matter than on growth and death rates, while the depth of the maximum is influenced by all these four parameters. Then, including biochemical processes like the ones mentioned above in an NPZD model can result in a significant improvement in the diagnosed phytoplankton field in deeper levels. Nevertheless, such an addition should respect the model's simplicity, reason for why such a simple model is conveniently and successfully used.

The results presented in the previous sections show that the variations of the SST within the TSB are modulated by the activity of ENSO and the decadal patterns of the PDO and NPGO. This is exemplified by the year 2006, which coincided with the phase change of the PDO (from positive to negative) and a positive anomaly of the MEI (Fig. 8). This year was characterized by a relaxation of the regional upwelling, with negative impacts in the higher trophic levels reported for the northern CCS (Peterson et al., 2006; Goericke et al., 2007). This negative anomaly in the wind pattern is also associated with an intense positive anomaly of the high-pressure system west of California and a negative NPGO anomaly (Di Lorenzo et al., 2008), which corresponds to an unusual period of low productivity observed in most of the CCS (Bjorkstedt et al., 2011). These conditions modify the regional circulation features like the subsurface countercurrent (Pérez-Brunius et al., 2006), and hence the transport of nutrients into the TSB. The negative Chl-a anomalies off Baja California were affected by “bottom up” and “top down” ecological processes, both coupled with the warm phase of the PDO and negative anomalies in salinity (Gaxiola-Castro et al., 2008). The relatively fresher water present in the region causes subsurface conditions that obstruct the vertical transport of nutrients into the euphotic zone, limiting the growth of cells in the nanophytoplankton and microphytoplankton (mainly diatoms) and favoring the dinoflagellates during the winter 2006 (Gaxiola-Castro et al., 2008). In the year 2011, on the other hand, the anomaly in the wind pattern was positive favoring the upwelling events during spring (Bjorkstedt et al., 2012), which induced a higher input of nutrients into the TSB. The SST in this year is low, in accordance with a negative phase of the PDO with a larger transport of cold and nutrient-rich water from the northern portion of the CCS, all of this caused by the strong anomaly in the anticyclonic winds over the North Pacific (Bjorkstedt et al., 2012), which was more intense during the transition between spring and summer. Di Lorenzo et al. (2008) suggest that the variability in the salinity, nutrients, and Chl-a in the northeastern Pacific is associated with changes in the NPGO, hence this index is a strong indicator of fluctuations in the mechanisms of the dynamics of planktonic ecosystems of the North Pacific. An intensification of the Gyre induces a transport of subarctic cold and nutrient-rich water and an intensification of the coastal upwelling, which leads to an increased supply of nutrients to the euphotic zone and thus an increased Chl-a in the CCS (Di Lorenzo et al., 2008). Another important regulatory component of the climate system is the OLR, which might be another factor driving the response of the SST in our study area. An example of this teleconnection is found in Chelliah and Arkin (1992), who reported a significant relationship between interannual variability of OLR anomalies and the El Niño-3 index, which is part of the MEI and is calculated for the same domain. Furthermore, Cho et al. (2012) found a relationship between the observed variability in the cloud-induced OLR and the SST in the Pacific Warm Pool, demonstrating the importance of the contribution of solar radiation on the SST in the Pacific. The OLR-SST correlation found for the TSB showed a relatively high but not significant value (− 0.53, p = 0.174) (Fig. 8). However, the same calculation for the regional SST shows a higher and significant value (− 0.72, p = 0.043). On the other hand, the model reproduces the seasonality of the Chla (e.g., Delgadillo-Hinojosa et al., 2015) and the value and depth of the SCM. However, the model's ability to reproduce the Chl-a distribution in deeper levels is limited, since the Chl-a profile shows a too weak exponential decay toward the deeper levels beneath the SCM (Fig. 12). Cullen (2015) argues that the phenomenon of SCM is not only a unique ecological response to environmental conditions, but it is also a response to a wide range of processes and interactions present in the stratified surface waters. This means that it is necessary to include in the model mechanisms that contribute to a better simulation of the Chla at deeper levels. An improvement for a NPZD-like model for a better

5. Conclusions In general the physical-biological numerical model described in this paper was able to simulate qualitatively the oceanographic conditions observed in the TSB. A simple ecological approach like the NPZD model is useful for a conceptual understanding of the biogeochemical processes associated with circulation patterns in the region of the TSB. The seasonal variability of the Chl-a in the TSB is regulated by the seasonal flows and the along-coast winds (which favors coastal upwelling and hence the vertical export of nutrients from deeper levels to the euphotic zone). These flows drive the transports and residence time of nutrients and phytoplankton within the TSB. The interannual variability of Chl-a in the TSB is regulated by the activity of the decadal modes of the Pacific. These large scale patterns can modify the wind regimes and circulation in the CCS, modifying also the water characteristics in our study area. Those flows responsible for nutrient transports into the TSB, like the near-coastal poleward jet associated with the upwelling front and the subsurface countercurrent, may be affected in their position, magnitude, and timing. According to the model results, a warm year such as 2006 is characterized by low Chl-a concentrations and a weak and deep SCM during spring and summer. These conditions are related to warm phases of the MEI and the PDO, a weakening of the NPGO, and a relaxation of the coastal winds (decrease of the coastal upwelling). On the other hand, a cold year such as 2011 is characterized by high Chl-a concentrations and an intense and shallow SCM. These conditions are related to cold phases of the MEI and the PDO, as well as an intensification of the NPGO. Thus, all the physical and biological anomalies successfully captured in our simulations show that a simple NPZD model is useful for the conceptual understanding of ecological processes at lower trophic levels in Baja California and the TSB. This paper also marks the beginning of systematic implementation of subsequent, more detailed models for those regions. Acknowledgments We thank the reviewers for their critical comments and suggestions to earlier versions of this manuscript. Comments and suggestions from Ernesto García-Mendoza and Paula Pérez-Brunius enriched this work and are thankfully acknowledged. This study was funded by Mexico's CONACYT through a graduate scholarship to JCR, who also received funding from Project UC MEXUS #CN-14-106. DR was supported by this project and by CICESE through the internal project 625118. Part of the computational equipment was acquired with funding from Project CONACYT CB-2009-128940-F. We thank the computational technical 73

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support by Julián Delgado Jiménez, and the help in the ROMS implementation by Rocío Mancilla Rojas. The NCOM outputs were kindly provided by Paul Turner. The hydrographic data for the TSB were provided by Ernesto García-Mendoza.

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