Sinking and suspended particles in seawater play a central role in biological and chemical cycling. Particles can be carried to the oceans from land by wind or river flows, stirred up from the ocean bottom by currents, formed from chemical reactions, and generated by organisms. Particles play multiple roles in biogeochemical cycling: by forming and dissolving, by absorbing or releasing material, and by providing food for organisms. Understanding how much particulate material is in seawater at any given time and place is therefore important to understanding ocean biology and elemental cycling. Historically, particle concentration and composition have been measured by filtering seawater and measuring the material collected on the filter. Particle concentrations can also be estimated by optical instruments, much the same way that headlights reflect off snowflakes in the air. In this project, the team will compare data from both approaches and develop methods to convert between them. The project will produce calibrated global data products for the biogeochemistry and ecology communities, strengthening the scientific basis for monitoring ocean conditions and marine resources. The project will support a postdoctoral researcher, a graduate student, and undergraduate students.
Advances in optical methods of particle detection, and widespread deployment of ship-based optical profiling instruments, such as the Underwater Vision Profiler (UVP), and autonomous platforms with optical backscatter sensors through the Biogeochemical (BGC) Argo program, have resulted in optical detection of large and small particles at far higher spatial and temporal resolution than can be achieved through direct sampling and analysis. The proposed work will calibrate the optical measurements against in-situ pump data to map geochemical parameters of interest such as particulate organic matter (POM) and total suspended particulate matter (SPM) at the global scale. The project will consist of four parts: 1) expand a global compilation of size-fractionated SPM concentrations and composition data (POM, calcium carbonate, opal, lithogenic particles), 2) develop optical calibrations that vary geographically and by depth to convert particle backscatter coefficient from BGC-Argo to small-particle (1-51 micron) POM and SPM and to convert particle size distribution and particle biovolume from the UVP to large-particle (>51 micron) POM and SPM, 3) apply these calibrations to the global BGC-Argo and UVP datasets to estimate small and large POM and SPM where there are backscatter and biovolume measurements from those sensors, and 4) gap-fill the calibrated optical datasets using machine learning to create a global, 3-D gridded climatological dataset of small and large POM and SPM in the upper 2000 meters of the ocean.
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.