Doctoral Dissertation Research: Resolving Novel Arctic Late Season Phytoplankton Blooms

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Warming temperatures and decreased sea ice cover in the Pacific and Central Arctic have led to changes in annual growth patterns of marine algae (phytoplankton), most notably permitting novel growth to occur during the fall season. Phytoplankton serve as the foundation of the Arctic marine food web, and disruptions in the timing of growth can be detrimental to current ecosystems. Limitations of satellite remote sensing such as the inability to detect phytoplankton bloom activity throughout the water column, under ice, and in cloudy conditions dictate the need for shipboard based measurements to provide more information on bloom dynamics. This project refines a novel methodology to estimate the growing stage of phytoplankton blooms throughout the water column using satellite imagery as a reference. Estimated summer bloom stages will be used along with atmospheric and oceanic conditions to identify the drivers of fall phytoplankton blooms to better estimate where they might occur. This information will be beneficial for predicting late season biological hotspots for conservation, scientific, and marine subsistence impacts.Repeat cruise measurements will be used to map modeled bloom stages throughout the water column in the summer and fall throughout the Pacific Arctic and northward to the ice edge. This research will also develop two models to (1) identify immediate drivers of observed late bloom growth and (2) identify midsummer preconditioning scenarios for fall blooms and validate model predictions with in situ observations. This research utilizes two NSF-funded cruises: the Distributed Biological Observatory in July 2013–2022 and the Synoptic Arctic Survey in fall 2022. Phytoplankton chlorophyll-a and pheophytin (degraded chlorophyll) concentrations will be collected on the 2022 cruises. NASA MODIS-Aqua satellite chlorophyll-a and in situ pheophytin proportions will be used to develop bloom stage models that distinguish growing versus senescent blooms. The bloom stage model will be applied to measurements collected throughout the water column to extract more information from cruise samples, broaden the context of observations, and understand bloom progression. The fall bloom driver and preconditioning models will be developed using a machine learning regression approach. Variables that will be examined include remotely sensed and modelled datasets of high wind speed events, wind direction, sea ice breakup and freeze-up dates, and in situ measurements of nutrients, temperature, salinity, and stratification.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.