The California Current System (CCS) is characterized by dynamic coastal upwelling that profoundly influences plankton community structure and diversity and that leads to the emergence of large diatoms nearshore. This study presents a high-resolution biogeochemical model of the CCS, coupling the Darwin ecosystem model with the Regional Ocean Modeling System (ROMS). The model is configured to capture key plankton patterns in the CCS, including chlorophyll distribution, cross-shore phytoplankton community composition, and ecosystem size structure. We show that the emergence of large diatoms in nutrient-rich nearshore waters requires both high nutrient availability and robust grazing pressure, a result that aligns with ecological theory and observational studies. The model also shows size quantization patterns in the plankton biomass spectra, which dynamically adjust to changing environmental conditions. Our results highlight the importance of the interplay between bottom-up and top-down controls in shaping the planktonic ecosystem, with a specific emphasis on the factors leading to the emergence of large diatoms in the CCS. These findings provide insights into the mechanisms underlying plankton diversity and community structure in coastal upwelling systems, which we can build upon to improve our understanding of ecosystem responses to environmental changes in the CCS and other highly productive coastal regions.

Coastal embayments in nearshore upwelling systems (“upwelling bays”) play a disproportionately large role in regional oceanography. In these systems, local diurnal wind forcing and thermal gradients associated with an upwelling shadow front that forms between warmer sheltered waters inside the bay and colder recently upwelled waters outside the bay strongly influence local temperature dynamics. Despite their importance to local ecosystems, there are no long-term studies that assess the heat budget inside upwelling bays. In this study, we analyzed approximately two years of water temperature throughout the water column using an autonomous profiler in a small upwelling bay in central California (San Luis Obispo Bay). We coupled these measurements with local meteorological data and in-situ temperature measurements made outside the bay to investigate how local diurnal wind forcing and the presence of the upwelling shadow front modify the diurnal heat budget inside the bay. Over the study period, strong seasonality in regional upwelling coincided with changes in local wind forcing, front strength (bulk temperature difference between inside and outside the bay), and temperature structure, from which we identified various forcing regimes to quantify the diurnal heat budget. During the non-upwelling season when the front was largely absent, the heat budget was primarily a balance between changes in heat content and surface heat fluxes, regardless of local wind forcing strength. This was also the primary balance during the upwelling season when local winds were weak. In contrast, during the upwelling season when the upwelling shadow front was persistent and the local winds were strong, increasing front strength led to increasingly large residual heat fluxes, which were interpreted to be due to advection (advective fluxes not calculated directly). These results highlight the importance of both local wind forcing and frontal intensity on the heat budget inside the upwelling bay, with significant implications for residence time, temperature dynamics, and local biogeochemistry in similar systems that are ubiquitous globally.

The central California coast between San Francisco Bay (SFB) and Monterey Bay (MB) is an upwelling-dominated marine ecosystem with a coastal population of 8.5 million. Terrestrial nutrients enter the ocean via three primary pathways, representing natural and anthropogenic sources: (a) SFB net export across the Golden Gate Strait, (b) coastal rivers, and (c) municipal wastewater discharged to ocean outfalls. The consequences of these inputs on primary production, acidification, and hypoxia remain poorly understood. Here, we investigate these effects with a submesoscale-resolving biogeochemical ocean model. Simulations suggest that while terrestrial nutrient inputs collectively affect a broad region, stronger impacts occur in nearshore waters, increasing dissolved inorganic nitrogen by 11.4%, primary production by 6.5%, and chlorophyll concentration by 4.5% along a 15-km coastal band. While exchanges from the SFB dominate these effects, all sources, including coastal rivers and ocean outfalls, produce distinct, localized footprints. Subsurface oxygen and pH decline due to terrestrial nutrient loading, but these changes are small relative to vigorous upwelling and circulation. The resulting nutrient enrichment could promote conditions favorable for diatom growth, including toxigenic species such as Pseudo-nitzschia spp., creating an environment predicted to elevate the risk of domoic acid (DA) events. Model results indicate that chlorophyll concentrations exceed the threshold associated with elevated DA risk on 10%-45% more days under nutrient-enriched conditions. These findings highlight the need for expanded observational and modeling efforts to better understand the ecological consequences of terrestrial nutrient pathways and their anthropogenic contributions along the central California coast.

Ocean acidification monitoring and carbon accounting require accurate estimates of marine carbonate system variables, particularly in dynamic coastal regions where observations remain sparse. This study presents an approach to improving carbonate system state estimates in the California Current System through the assimilation of underwater glider observations with both dynamical and statistical models. We implement a 4D-Var data assimilation system that jointly assimilates physical variables, chlorophyll, and glider-based pH and alkalinity data into a regional coupled physical-biogeochemical model. In our experiments, the assimilation of physical variables and chlorophyll alone has limited impact on pH and other carbonate system estimates, while the joint assimilation including pH and alkalinity variables successfully improves these estimates. Cross-validation experiments further demonstrate that the joint assimilation typically also improves estimates near the observation network, although downstream advection of increments can occasionally degrade results. We also show that hybrid estimates that combine the output of the dynamical, physical ocean model with a statistical model produce accurate carbonate system estimates without requiring a biogeochemical model. This finding suggests that physical ocean models and data assimilation systems can obtain reasonable carbonate system estimates by combining statistical methods with model estimates of temperature and salinity. Our carbonate system data assimilation setup relies on the combined assimilation of pH and alkalinity data to obtain reliable state estimates. Because alkalinity is not yet routinely measured by gliders, we utilize statistically estimated alkalinity values and examine the limitations of this approach in our study.

Of the seven species of Pacific rockfish declared overfished in the California Current in 2008, yelloweye rockfish (Sebastes ruberrimus) is the only remaining overfished rockfish species. Part of the original rebuilding plan included designation of a yelloweye rockfish conservation area, a rocky reef closed off the Central Coast of Oregon that is closed to bottom fishing. The yelloweye rockfish conservation area’s ability to help rebuild the population, is predicated on the theory that demersal rockfishes are relatively sedentary. However, in the years since being declared overfished, acoustic tagging studies suggested yelloweye rockfish did not remain in the yelloweye rockfish conservation area. However, where they went remained a mystery. In this paper we describe the use of pop-off satellite tags to conduct a mark-recapture study of 11 yelloweye rockfish tagged within the Stonewall Bank Yelloweye Rockfish Conservation Area. We used back-in-time particle tracking coupled with an ocean circulation model in an attempt to increase the precision in the location at which each tag shed off the fish, and further validated that location by associating it to the underlying seafloor habitat type. Ten out of eleven tags were shed from the fish while it was outside the Stonewall Bank Yelloweye Rockfish Conservation Area’s boundary. While most fish remained within 50 km of the Stonewall Bank Yelloweye Rockfish Conservation Area, one tagged fish swam to an offshore reef off Central Washington (~40 km from the shore). Backtracked locations were more likely over rock than the initial satellite transmission, indicating the method was effective at identifying tag shed locations. We found no relationship between days at large, fish sex or length and the distance between release site and shed location. Our work supports a growing body of work that suggests yelloweye rockfish have less site fidelity than previously hypothesized.

A descriptive analysis of remotely-sensed surface chlorophyll-a within the Gulf of the Farallones and nearby coastal waters occupying portions of three NOAA National Marine Sanctuaries along the central California coast is presented. The seasonal cycle from a 25-year chlorophyll-a record reveals elevated levels near the mouth of San Francisco Bay at the Golden Gate, with broad spatial extent climatologically from April through November. A 19-year time series of normalized water-leaving radiance at the 555 nm band (nLw555) was used to estimate the presence of waters representing the San Francisco Bay plume. Although the plume shows its largest spatial extent in winter, decreasing during spring and summer, chlorophyll-a was enhanced within plume waters relative to non-plume waters during all months; however, was not statistically different during upwelling months (April-June). Linear correlations between chlorophyll-a and a 20-year record of wind stress, a 10-year record of surface currents, and a 20-year record of sea surface temperatures reveal consistent, coherent regional spatial patterns. Weighted averages confirm that poleward winds and surface currents result in enhanced chlorophyll-a in nearshore waters north of the Golden Gate and around Point Reyes. Periods of equatorward winds and surface transport exhibit elevated chlorophyll-a and temperature south of the Golden Gate, offshore of Half Moon Bay, and are associated with nearby onshore currents. Correlations of plume concentrations (nLw555 >= 12 W m-2 mu m-1 sr-1) with wind stress, however, do not show the same coherent patterns as with chlorophyll-a, and turbid plume waters are largely confined to the Gulf of the Farallones. These analyses suggest that surface chlorophyll-a within the inner Gulf of the Farallones close to San Francisco Bay is significantly influenced by outflow from the Bay, but the greater Gulf of Farallones is more strongly influenced by upwelling and relaxation effects.

Introduction: Species distribution models (SDMs) are increasingly used in fisheries science to understand species' spatial patterns and improve stock assessments. Method: This study developed SDMs for eulachon smelt (Thaleichthys pacificus), a threatened, demersal forage fish often caught as bycatch in the United States West Coast ocean shrimp trawl fishery. Using ten years of observer data (2012-2021, n=19,749 with 25.4% being zeros), the study assessed the influence of static (e.g., substrate) and dynamic (e.g., ocean temperature, currents) environmental variables on eulachon abundance. Results: The best-performing SDM included near-bottom temperature and current data, outperforming SDMs using only surface variables. Eulachon abundance peaked at similar to 150 m depth, especially over gravel substrates, and during nighttime. Although none of the SDM-based abundance indices significantly correlated with the Columbia River stock assessment index, bottom-based SDMs showed stronger alignment with the stock assessment (R=0.61, p=0.08 & R=0.62, p=0.07). Discussion: Management implications are significant. Gravel habitats were associated with higher eulachon bycatch, and vessels can use bottom-typing tools to avoid them. Also, delaying the season opener could reduce bycatch, as eulachon catch was reduced by 0.0109 mt per trawl over the interquartile range of day of the year. These findings can inform Best Management Practices (BMPs), which have historically led to regulatory changes such as the adoption of excluder grates and LED lights. Overall, incorporating near-bottom oceanographic data greatly enhanced predictive performance, especially for demersal species like eulachon. These mechanistic SDMs can be projected forward, aiding future management amid changing ocean conditions. While some discrepancies remain, this approach offers promising insights for adaptive fishery management and conservation of imperiled species like eulachon.

Ocean boundary currents are complex and highly variable systems that play key roles in connecting the open and coastal ocean through cross-slope circulation and upwelling of nutrient-rich water. The structure, strength, and variability of boundary currents are associated with a broad range of spatial and temporal scales. For that reason, long-term boundary current monitoring is challenging and requires the use of complementary observing platforms and sensors coupled with numerical simulations. The Ocean Observations Physics and Climate Panel Boundary Systems Task Team recently held a virtual dialogue series to discuss six mature boundary current monitoring systems. The goal of the series was to examine strategies for developing a conceptual design for sustained observing activities applicable to a wide range of boundary current systems. This article provides a brief overview of the six systems, including users and the observational and modeling components needed to achieve scientific, operational, and societal goals. Ocean observing best practices and recommendations are shared to provide guidance for the coordination and sustainability of observing systems at ocean boundaries and to strengthen and integrate partnerships across and within the global observing networks.

Ocean observing systems in coastal, shelf and marginal seas collect diverse oceanographic information supporting a wide range of socioeconomic needs, but observations are necessarily sparse in space and/or time due to practical limitations. Ocean analysis and forecast systems capitalize on such observations, producing data-constrained, four-dimensional oceanographic fields. Here we review efforts to quantify the impact of ocean observations, observing platforms, and networks of platforms on model products of the physical ocean state in coastal regions. Quantitative assessment must consider a variety of issues including observation operators that sample models, error of representativeness, and correlated uncertainty in observations. Observing System Experiments, Observing System Simulation Experiments, representer functions and array modes, observation impacts, and algorithms based on artificial intelligence all offer methods to evaluate data-based model performance improvements according to metrics that characterize oceanographic features of local interest. Applications from globally distributed coastal ocean modeling systems document broad adoption of quantitative methods, generally meaningful reductions in model-data discrepancies from observation assimilation, and support for assimilation of complementary data sets, including subsurface in situ observation platforms, across diverse coastal environments.

Assimilating biogeochemical (BGC) data into ocean models using traditional four-dimensional variational data assimilation (4D-Var) includes the technical challenge of constructing tangent-linear (TLM) and adjoint (ADJ) models corresponding to the non-linear BGC model. This hurdle can be time-consuming, particularly for nonlinear BGC models experiencing active development, with regular updates to functional types or representation of key BGC processes. We evaluate two alternate approaches that greatly simplify TLM and ADJ construction and eliminate the need for code updates when the underlying, non-linear BGC model changes. One form of model-reduced data assimilation represents BGC interactions as a linear combination of orthogonal modes; we use empirical orthogonal functions and refer to this as the BioEOFs method. The second approach treats BGC variables as passive tracers that experience advection and diffusion by TLM and ADJ physics over an assimilation cycle, but are biochemically inactive. We evaluate the efficacy of these methods in a realistically configured, data-assimilative implementation of the Regional Ocean Modeling System with a simple nutrient- phytoplankton-zooplankton-detritus (NPZD) BGC model for the U.S. west coast. In a series of sequential data-assimilative model cycles, observations of temperature, salinity, sea-level anomaly, and phytoplankton biomass constrain the modeled state over the period spanning January-June 2019. We perform the assimilation in both physical and logarithmic-transformed space and compare results of the two approximate methods to traditional 4D-Var using TLM and ADJ models with BGC processes corresponding explicitly to the BGC model. While the full-adjoint 4D-Var results in the best correction to phytoplankton fields for analyses and forecasts, the BioEOFs and passive-tracer approaches also significantly reduce the model-observation misfit compared to a non-assimilative run and do so at reduced computational expense. The passive tracer performs generally better than BioEOFs, whose corrections exhibit large-scale structure across the domain. While the most accurate state estimate will result from development and application of the full TLM and ADJ, the log-transformed passive-tracer approach may be a viable alternative for performing BGC 4D-Var for state estimation when a full-adjoint option is not yet available.

Ocean forecasting is now widely recognized as an important approach to improve the resilience of marine ecosystems, coastal communities, and economies to climate variability and change. In particular, regionally tailored forecasts may serve as the foundation for a wide range of applications to facilitate proactive decision making. Here, we describe and assess ~30 years of retrospective seasonal (1–12 month) forecasts for the California Current System, produced by forcing a regional ocean model with output from a global forecast system. Considerable forecast skill is evident for surface and bottom temperatures, sea surface height, and upper ocean stratification. In contrast, mixed layer depth, surface wind stress, and surface currents exhibit little predictability. Ocean conditions tend to be more predictable in the first half of the year, owing to greater persistence for forecasts initialized in winter and dynamical forecast skill consistent with winter/spring influence of the El Niño–Southern Oscillation (ENSO) for forecasts initialized in summer. Forecast skill above persistence appears to come through the ocean more than through the atmosphere. We also test the sensitivity of forecast performance to downscaling method; bias correcting global model output before running the regional model greatly reduces bias in the downscaled forecasts, but only marginally improves prediction of interannual variability. We then tailor the physical forecast evaluation to a suite of potential ecological applications, including species distribution and recruitment, bycatch and ship-strike risk, and indicators of ecosystem change. This evaluation serves as a template for identifying promising ecological forecasts based on the physical parameters that underlie them. Finally, we discuss suggestions for developing operational forecast products, including methodological considerations for downscaling as well as the respective roles of regional and global forecasts.

When the barotropic tide encounters variable bathymetry, fluctuating flow along a topographic slope generates baroclinic tides, or internal tides. There is growing evidence that these internal tides can affect primary production in the euphotic zone, though the dominant mechanisms are unclear. Internal tides move passive phytoplankton through an exponentially varying light field, enhancing primary production near the base of the euphotic zone. In addition internal tides also increase primary production through vertical nutrient advection into the euphotic zone. Topographically generated internal tides can be separated into two regimes: 1) the often highly nonlinear near-field regime where tidal beams are observed and 2) the more linear far-field regime. This study examines the primary production response to these internal tide processes using the Regional Ocean Modeling System (ROMS) coupled to a simple Nutrient, Phytoplankton, Zooplankton, Detritus (NPZD) model configured for an oligotrophic system with the nutricline positioned below 50 m depth. These idealized simulations generate internal tide beams with an oscillating, horizontal body force at the M2 tidal frequency that is applied to domains with a bathymetric step and uniform stratification. Sensitivity of the primary production response to the energy content of the tidal beam is obtained by adjusting the height and slope of the bathymetric step. Simulation results reveal that primary production intensifies along tidal beams due to the local enhancement of parcel vertical displacement (light effect) and nutrient advective flux divergence (nutrient effect). In the near-field regime across the range of step heights and slopes in this study, the nutrient effect is an order of magnitude larger and explains 92% of the variance in primary production versus only 14% for the light effect. The geometry of the generating feature sets the kinematics of the tidal beam. The light effect is limited in the euphotic zone across our domains because realized changes in light experienced over a tidal cycle are small relative to the amount of light available at a particular depth. In contrast, the magnitude of the nutrient effect increases more substantially with tidal beam energy.

Advanced marine ecosystem models can contain more than 100 biogeochemical variables, making data assimilation for these models a challenging prospect. Traditional variational data assimilation techniques like 4dVar rely on tangent linear and adjoint code, which can be difficult to create for complex ecosystem models with more than a few dozen variables. More recent hybrid ensemble-variational data assimilation techniques use ensembles of model forecasts to produce model statistics and can thus avoid the need for tangent linear or adjoint code. We present a new implementation of a four-dimensional ensemble optimal interpolation (4dEnOI) technique for use with coupled physical-ecosystem models. Our 4dEnOI implementation uses a small ensemble, and spatial and variable covariance localization to create reliable flow-dependent statistics. The technique is easy to implement, requires no tangent linear or adjoint code, and is computationally suitable for advanced ecosystem models. We test the 4dEnOI implementation in comparison to a 4dVar technique for a simple marine ecosystem model with 4 biogeochemical variables, coupled to a physical circulation model for the California Current System. In these tests, our 4dEnOI reference implementation performs similarly well to the 4dVar benchmark in lowering the model observation misfit. We show that the 4dEnOI results depend heavily on covariance localization generally, and benefit from variable localization in particular, when it is applied to reduce the coupling strength between the physical and biogeochemical model and the biogeochemical variables. The 4dEnOI results can be further improved by small modifications to the algorithm, such as multiple 4dEnOI iterations, albeit at additional computational cost.

Forecast Sensitivity-based Observation Impacts (FSOI) in an analysis–forecast​ system of the California Current System (CCS) are quantified using an adjoint-based approach. The analysis–forecast system is based on the Regional Ocean Modeling System (ROMS) and a 4-dimensional variational (4D-Var) data assimilation approach. FSOI was applied to four different metrics of forecast skill that target important features of the CCS circulation along the central California coast. A particular focus of the FSOI analysis is the impact of assimilation of measurements of the radial component of surface currents from a network of high frequency (HF) radars since this is a new data stream in the near-real-time system considered here. On average, ∼50–60% of all observations assimilated into the model yielded improvements in the forecast skill. Conversely, the remaining ∼40–50% of data degrade the forecasts, in line with similar findings in numerical weather prediction systems. Much of the improvement in forecast skill arises from remotely sensed observations, including HF radar data; on average only ∼50% of in situ measurements contribute to a reduction in forecast error. This is partly due to the large volume of remote sensing observations compared to in situ observations. However, in situ observations are an order of magnitude more impactful than remotely sensed data when viewed in terms of the average impact per observation.

The saddle-point formulation of weak constraint 4-dimensional variational (4D-Var) data assimilation has been developed for the Regional Ocean Modeling System (ROMS), and is applied here to the California Current System (CCS). Unlike the conventional primal and dual forcing formulation of weak constraint 4D-Var, the saddle-point formulation can be efficiently parallelized in time, leading to a substantial decrease in wall-clock run time. The performance of the ROMS saddle-point 4D-Var algorithm is assessed here and compared to that of the dual forcing formulation which is the current standard in ROMS. While the rate of convergence of the saddle-point formulation is slower than the dual forcing formulation, the increase in computational speed due to time-parallelization can more than compensate for the additional inner-loop iterations required by the saddle-point algorithm in the CCS configuration considered here. Further gains in performance can be achieved by running the 4D-Var inner-loop iterations at reduced resolution and/or reduced arithmetic precision. The results presented here indicate that in high performance computing environments with large numbers of available compute cores, the saddle-point formulation of 4D-Var could significantly reduce the run-time compared to the dual forcing formulation for large data assimilation problems.

High-resolution simulations by the Regional Ocean Modeling System (ROMS) were used to investigate the dispersal of the San Francisco Bay (SFB) plume over the northern-central California continental shelf during the period of 2011 to 2012. The modeled bulk dynamics of surface currents and state variables showed many similarities to corresponding observations. After entering the Pacific Ocean through the Golden Gate, the SFB plume is dispersed across the shelf via three pathways: (i) along the southern coast towards Monterey Bay, (ii) along the northern coast towards Point Arena, and (iii) an offshore pathway restricted within the shelf break. On the two-year mean timescale, the along-shore zone of impact of the northward-dispersed plume is about 1.5 times longer than that of the southern branch. Due to the opposite surface Ekman transports induced by the northerly or southerly winds, the southern plume branch occupies a broader cross-shore extent, roughly twice as wide as the northern branch which extends roughly two times deeper due to coastal downwelling. Besides these mean characteristics, the SFB plume dispersal also shows considerable temporal variability in response to various forcings, with wind and surface-current forcing most strongly related to the dispersing direction. Applying constituent-oriented age theory, we determine that it can be as long as 50 days since the SFB plume was last in contact with SFB before being flushed away from the Gulf of the Farallones. This study sheds light on the transport and fate of SFB plume and its impact zone with implications for California’s marine ecosystems.

Pseudo-nitzschia species are one of the leading causes of harmful algal blooms (HABs) along the western coast of the United States. Approximately half of known Pseudo-nitzschia strains can produce domoic acid (DA), a neurotoxin that can negatively impact wildlife and fisheries and put human life at risk through amnesic shellfish poisoning. Production and accumulation of DA, a secondary metabolite synthesized during periods of low pri-mary metabolism, is triggered by environmental stressors such as nutrient limitation. To quantify and estimate the feedbacks between DA production and environmental conditions, we designed a simple mechanistic model of Pseudo-nitzschia and domoic acid dynamics, which we validate against batch and chemostat experiments. Our results suggest that, as nutrients other than nitrogen (i.e., silicon, phosphorus, and potentially iron) become limiting, DA production increases. Under Si limitation, we found an approximate doubling in DA production relative to N limitation. Additionally, our model indicates a positive relationship between light and DA pro-duction. These results support the idea that the relationship with nutrient limitation and light is based on direct impacts on Pseudo-nitzschia biosynthesis and biomass accumulation. Because it can easily be embedded within existing coupled physical-ecosystem models, our model represents a step forward toward modeling the occur-rence of Pseudo-nitzschia HABs and DA across the U.S. West Coast.

Marine populations are often typified by large annual variations in the number of larvae that return to the adult population. The Dungeness crab Cancer ( Metacarcinus ) magister is an important economic and ecological species along the western seaboard of the continental USA. Research suggests larval returns of Dungeness crabs vary annually by a factor of 1000, strongly influencing the population dynamics of the species. To understand how hydrographic conditions affect population dynamics, a light trap in Coos Bay, Oregon, was monitored daily during the recruitment season (April to September) from 1997 to 2001 and from 2006 to the present. Using an individual-based biophysical model, we tested the hypothesis that more Dungeness crab larvae recruit during negative-phase Pacific Decadal Oscillation (PDO). The model uses the Regional Oceanic Modeling System to simulate circulation in the California Current and an offline Lagrangian particle-tracking algorithm (Larval TRANSport Lagrangian Model, LTRANS) to model larval dispersal. We validated our model by comparing the model data to the light trap data. Our findings support the hypothesis that more megalopae (pelagic postlarvae) recruit during the negative phase of the PDO. In addition, megalopae appear to spend longer in the water column during positive-phase PDO as a result of faster development rates likely due to warmer seawater temperature. Lastly, our model suggests that the population experiences more self-recruitment than previously thought, albeit not to an extent to suggest there are multiple metapopulations.