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Rainfall in southern California is highly variable, with some fluctuations explainable by climate patterns. Resulting runoff and heightened streamflow from rain events introduces freshwater plumes into the coastal ocean. Here we use a 105-year daily sea surface salinity record collected at Scripps Pier in La Jolla, California to show that El Niño Southern Oscillation and Pacific Decadal Oscillation both have signatures in coastal sea surface salinity. Averaging the freshest quantile of sea surface salinity over each year’s winter season provides a useful metric for connecting the coastal ocean to interannual winter rainfall variability, through the influence of freshwater plumes originating, at closest, 7.5 km north of Scripps Pier. This salinity metric has a clear relationship with dominant climate phases: negative Pacific Decadal Oscillation and La Niña conditions correspond consistently with lack of salinity anomaly/ dry winters. Fresh salinity anomalies (i.e., wet winters) occur during positive phase Pacific Decadal Oscillation and El Niño winters, although not consistently. This analysis emphasizes the strong influence that precipitation and consequent streamflow has on the coastal ocean, even in a region of overall low freshwater input, and provides an ocean-based metric for assessing decadal rainfall variability.

 

As harmful algae blooms are increasing in frequency and magnitude, one goal of a new generation of higher spectral resolution satellite missions is to improve the potential of satellite optical data to monitor these events. A satellite-based algorithm proposed over two decades ago was used for the first time to monitor the extent and temporal evolution of a massive bloom of the dinoflagellate Lingulodinium polyedra off Southern California during April and May 2020. The algorithm uses ultraviolet (UV) data that have only recently become available from the single ocean color sensor on the Japanese GCOM-C satellite. Dinoflagellates contain high concentrations of mycosporine-like amino acids and release colored dissolved organic matter, both of which absorb strongly in the UV part of the spectrum. Ratios <1 of remote sensing reflectance of the UV band at 380 nm to that of the blue band at 443 nm were used as an indicator of the dinoflagellate bloom. The satellite data indicated that an observed, long, and narrow nearshore band of elevated chlorophyll-a (Chl-a) concentrations, extending from northern Baja to Santa Monica Bay, was dominated by L. polyedra. In other high Chl-a regions, the ratios were >1, consistent with historical observations showing a sharp transition from dinoflagellate- to diatom-dominated waters in these areas. UV bands are thus potentially useful in the remote sensing of phytoplankton blooms but are currently available only from a single ocean color sensor. As several new satellites such as the NASA Plankton, Aerosol, Cloud, and marine Ecosystem mission will include UV bands, new algorithms using these bands are needed to enable better monitoring of blooms, especially potentially harmful algal blooms, across large spatiotemporal scales. 

In recent years, harmful algal blooms (HABs) have increased in their severity and extent in many parts of the world and pose serious threats to local aquaculture, fisheries, and public health. In many cases, the mechanisms triggering and regulating HAB events remain poorly understood. Using underwater microscopy and Residual Neural Network (ResNet‐18) to taxonomically classify imaged organisms, we developed a daily abundance record of four potentially harmful algae (Akashiwo sanguinea,Chattonellaspp.,Dinophysisspp., andLingulodinium polyedra) and major grazer groups (ciliates, copepod nauplii, and copepods) from August 2017 to November 2020 at Scripps Institution of Oceanography pier, a coastal location in the Southern California Bight. Random Forest algorithms were used to identify the optimal combination of environmental and ecological variables that produced the most accurate abundance predictions for each taxon. We developed models with high prediction accuracy forA. sanguinea(),Chattonellaspp. (), andL. polyedra(), whereas models forDinophysisspp. showed lower prediction accuracy (). Offshore nutricline depth and indices describing climate variability, including El Niño Southern Oscillation, Pacific Decadal Oscillation, and North Pacific Gyre Oscillation, that influence regional‐scale ocean circulation patterns and environmental conditions, were key predictor variables for these HAB taxa. These metrics of regional‐scale processes were generally better predictors of HAB taxa abundances at this coastal location than the in situ environmental measurements. Ciliate abundance was an important predictor ofChattonellaandDinophysisspp., but not ofA. sanguineaandL. polyedra. Our findings indicate that combining regional and local environmental factors with microzooplankton populations dynamics can improve real‐time HAB abundance forecasts.

 

The large data sets provided byin situoptical microscopes are allowing us to answer longstanding questions about the dynamics of planktonic ecosystems. To deal with the influx of information, while facilitating ecological insights, the design of these instruments increasingly must consider the data: storage standards, human annotation, and automated classification. In that context, we detail the design of the Scripps Plankton Camera (SPC) system, anin situmicroscopic imaging system. Broadly speaking, the SPC consists of three units: (1) an underwater, free‐space, dark‐field imaging microscope; (2) a server‐based management system for data storage and analysis; and (3) a web‐based user interface for real‐time data browsing and annotation. Combined, these components facilitate observations and insights into the diverse planktonic ecosystem. Here, we detail the basic design of the SPC and briefly present several preliminary, machine‐learning‐enabled studies illustrating its utility and efficacy.