Phytoplankton biomass in Monterey Bay, California is typically dominated by diatoms, but it shifted to dinoflagellates twice in the past 18 years (2004–2007, 2017–2018), which was associated with increased harmful algal blooms. Located within the central California Current System (CCS), Monterey Bay is strongly influenced by cycles of upwelling‐favorable winds and their relaxation or reversal. Both dinoflagellate‐dominated periods were linked to a negative North Pacific Gyre Oscillation (NPGO) and increased river discharge, but each had a different relationship with upwelling. To examine the connection between large‐scale and local forcings underlying floristic shifts in the phytoplankton assemblage, an Imaging FlowCytobot (IFCB) was deployed within the Monterey Bay upwelling shadow for a full year. A machine learning classifier differentiating IFCB images of the phytoplankton assemblage was developed. Despite anomalously strong upwelling in 2018, dinoflagellates comprised 57% of the annual phytoplankton‐specific biomass. During upwelling, dinoflagellates appear to have accumulated at convergent fronts, while during relaxation these frontal populations were transported to the nearshore where they seeded local blooms. Frequent upwelling‐relaxation cycles and local wind anomalies generated an unusually retentive circulation pattern in the upwelling shadow, producing a warm and stratified bloom incubator. Thus, local features and forcings (upwelling shadow, winds, river discharge) modified the effects of regional‐ and basin‐scale oceanographic variability (regional upwelling, NPGO), altering local phytoplankton patterns. As North Pacific decadal variability and CCS upwelling intensity increase under climate warming, dinoflagellates may become more common in some CCS regions, due to the enhancement or mitigation of large‐scale trends by local forcings.

[1] Owing to the difficulties inherent in measuring trace metals and the importance of iron as a limiting nutrient for biological systems, the ability to monitor particulate iron concentration remotely is desirable. This study examines the relationship between labile particulate iron, described here as weak acid leachable particulate iron or total dissolvable iron, and easily obtained bio-optical measurements. We develop a bio-optical proxy that can be used to estimate large-scale patterns of labile iron concentrations in surface waters, and we extend this by including other environmental variables in a multiple linear regression statistical model. By utilizing a ratio of optical backscatter and fluorescence obtained by satellite, we identify patterns in iron concentrations confirmed by traditional shipboard sampling. This basic relationship is improved with the addition of other environmental parameters in the statistical linear regression model. The optical proxy detects known temporal and spatial trends in average surface iron concentrations in Monterey Bay. The proxy is robust in that similar performance was obtained using two independent particulate iron data sets, but it exhibits weaker correlations than the full statistical model. This proxy will be a valuable tool for oceanographers seeking to monitor iron concentrations in coastal regions and allows for better understanding of the variability of labile particulate iron in surface waters to complement direct measurement of leachable particulate or total dissolvable iron.Citation: McGaraghan, A. R., and R. M. Kudela (2012), Estimating labile particulate iron concentrations in coastal waters from remote sensing data,