Improving carbon export estimates through the paired use of autonomous platforms and remote sensing algorithms near Ocean Station Papa

Jacqueline Long1, Andrea J Fassbender2, William Haskell1 and Margaret L Estapa3, (1)Monterey Bay Aquarium Research Institute, Moss Landing, CA, United States, (2)NOAA Pacific Marine Environmental Laboratory, Seattle, WA, United States, (3)Skidmore College, Saratoga Springs, NY, United States
Abstract:
Satellites have the ability to collect near-global estimates of ocean net primary production (NPP) and export efficiency (e-ratio) on a daily basis; however, they are often limited to clear-sky conditions and observations from the first optical depth in the water column, missing a seasonally varying portion of the euphotic zone. Because satellite ocean color observations are near-surface estimates, they require assumptions to be made about the depth structures of phytoplankton biomass and growth rate in their derivation of NPP. High-resolution (≤5m) water column observations from biogeochemical profiling floats could thus be a useful tool for evaluating the fidelity of remote sensing approaches for NPP and e-ratio by applying algorithms that are commonly used with satellite observations to the float data. Building from prior work in the Subtropical North Atlantic [1], we apply NPP and e-ratio algorithms to observations from historical and active biogeochemical profiling floats in the Subarctic Northeast Pacific Ocean, near Ocean Station Papa (OSP). Nearly a decade of collocated, ~weekly NPP and e-ratio estimates from the floats and satellites are compared and patterns of discrepancy are evaluated. The algorithms are also used to calculate carbon export (equivalent to NPP e-ratio) from float data for comparison with independent carbon export and e-ratio estimates derived from chemical and bio-optical sensors on the floats. Results from this study expand the in situ observational record of NPP, e-ratio, and export throughout the region and provide context for the 2018 NASA EXPORTS cruise.