Upwelling of nutrient rich waters along continental shelves generates highly productive marine ecosystems affectingplanktonic communities from coastal to offshore domains. Methods to constrain pelagic productivity are often based on different physiological or ecosystem processes, hence describe different biogeochemically important processes. Here, we present a multi-method process-oriented investigation of diverse productivity measures in the California Current Ecosystem (CCE) Long-Term Ecological Research study region, a complex physical environment. The data are from seven multi-day deployments over two field expeditions (spring 2016 and summer 2017) and cover a transition region from high to low productivity. Employing a Lagrangian study design, we aimed to follow the water parcels over several days, comparing 24 h in-situ measurements (C and NO, uptake, sediment trap export, dilution estimates of phytoplankton growth and microzooplankton grazing) with high-resolution productivity measurements by Fast Repetition Rate Fluorometry (FRRF) and Equilibrium Inlet Mass Spectrometry (EIMS). Our results show the importance of accounting for temporal and fine spatial scale variability when estimating ecosystem production. FRRF and EIMS measurements resolved diel patterns in gross primary and net community production. Diel productivity changes agreed well with comparable more traditional measurements. While differences in productivity metrics calculated over different time intervals were considerable, as those methods rely on different base assumptions, our data can be used to explain ecosystem processes which would otherwise have gone unnoticed. The processes resolved from this method comparison can help to further our understanding of the coupling and decoupling of surface productivity and potential carbon burial in coastal and offshore ecosystems.

The objective of this study is to understand relations between multiple physical and environmental factors and red  tide, which is a common name for harmful algal blooms occurring along coastal regions worldwide. Large concentrations of Karenia brevis, a toxic mixotrophic dinoflagellate, make up the red tide along the West Florida Shelf (WFS) in the Gulf of Mexico. Besides being toxic, red tide causes unpleasant odor and scenery, which result in multiple environmental and socioeconomic impacts and public health issues.  Understanding the physical and biogeochemical processes that control the occurrence of red tide is important for studying the impact of climate change on red tide frequency, and accordingly assessing the future environmental and socioeconomic impacts of red tide under different mitigation techniques and climate scenarios. We use observation and reanalysis data in the WFS to train machine learning (ML) models to predict red tide, as a classification problem of large bloom or no bloom. We develop the ML model using seasonal input data of Peace River and Caloosahatchee River outflow, alongshore and offshore wind speed, and Loop Current position. The Loop Current, which is a warm ocean current that enters and loops through the Gulf of Mexico before exiting to join the Gulf Stream, can be detected from sea surface height. In addition to the observation and reanalysis data, these variables can be simulated by the Earth system models (ESMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), especially by the high-resolution models of the High Resolution Model Intercomparison Project (HighResMIP) of CMIP6. This is needed to understand the frequency and future trends of red tide under different Shared Socioeconomic Pathways (SSPs) of CMIP6. In this preliminary study, we investigate the impact of different choices regarding ML model selection and training dataset on the accuracy of red tide prediction, and the physical interpretation of the results. We also discuss the validation of ESMs data for predictive modeling, and ensemble methods for improving predictive performance. The study provides several insights that can be useful for predicting the future trends of red tide under SSPs using CMIP6 data.