Monique Messié of the Monterey Bay Aquarium Research Institute (MBARI), alongside collaborators at Florida State University, has developed a novel methodology for predicting ocean carbon export by integrating satellite-derived wind and current data with existing phytoplankton productivity estimates. Published recently in Geophysical Research Letters, this approach addresses limitations in current satellite-based models by accounting for subsurface carbon transport processes. By leveraging ocean color data and hydrodynamic information, the team aims to improve global estimates of carbon sequestration, a critical factor in understanding and modeling Earth’s changing climate and oceanic carbon cycle.
Ocean Carbon Cycling and Its Importance
The ocean plays a vital role in Earth’s carbon cycle, absorbing roughly 30% of atmospheric carbon dioxide. Understanding how carbon moves from the surface to the deep ocean – a process called carbon export – is crucial for predicting future climate change. Direct measurement of this export is incredibly difficult, relying heavily on complex ocean models and limited observational data. Researchers at MBARI are tackling this challenge by developing new methods to estimate carbon export from space, leveraging satellite data to fill critical gaps in our understanding.
Existing models often struggle to accurately represent carbon export because they don’t fully account for the time and location disconnect between phytoplankton growth and carbon sinking. MBARI researchers have pioneered a new approach: a Lagrangian growth-advection model. This model tracks plankton blooms using satellite-derived data on ocean currents and winds, instead of relying solely on ocean color. This allows for a more accurate depiction of carbon transport, even without direct ocean color data.
This innovative modeling technique has demonstrated performance comparable to traditional methods relying on direct carbon measurements or complex ocean color analysis. The team’s findings, published in Geophysical Research Letters, show promise for creating a more comprehensive, space-based system for monitoring ocean carbon cycling. Code and data are openly available, furthering collaborative research and potentially improving global climate models.
New Method for Tracking Carbon Export
Researchers at MBARI have developed a novel method for tracking ocean carbon export—the process of transferring carbon from the surface to the deep sea—using satellite data on winds and currents. Unlike traditional models reliant on ocean color to estimate phytoplankton productivity, this new approach maps plankton succession and movement alongside surface circulation patterns. Validated against long-term monitoring data from Station M, the model demonstrates comparable accuracy, offering a crucial, independent way to assess carbon sequestration without relying on direct color measurements.
This innovative technique employs a “Lagrangian growth-advection” model, essentially tracking how plankton blooms are carried by currents after initial growth. By incorporating the spatial and temporal lag between production and export, and acknowledging the role of zooplankton, the model provides a more holistic view of carbon transfer. Results, recently published in Geophysical Research Letters, show strong performance in estimating carbon export off the California coast – a highly productive region crucial for understanding global carbon cycling.
The implications of this work are significant for climate modeling. Accurately quantifying ocean carbon export is essential for understanding Earth’s changing climate and predicting future carbon levels. MBARI is making both the model code and supporting data publicly available via GitHub and Zenodo, fostering collaboration and building upon this advancement. Further research, including machine learning applications to refine catchment area estimates, will explore unexplained carbon pulses observed at Station M.
Lagrangian Growth-Advection Model Explained
MBARI researchers have developed a novel Lagrangian growth-advection model to estimate ocean carbon export – the process of transferring carbon from surface waters to the deep sea. Unlike traditional methods relying on ocean color to measure phytoplankton, this model tracks plankton blooms and their movement via oceanic currents. By integrating satellite data on winds and currents, it accounts for the lag between biological production and actual carbon sinking, addressing a key limitation of previous satellite-based algorithms.
This new approach focuses on how carbon is transported, not just where it’s produced. The Lagrangian framework maps plankton succession and export onto surface circulation patterns following coastal upwelling events, like those in the California Current. Importantly, the model’s performance rivaled those dependent on direct carbon measurements or ocean color data. This signifies a significant step towards accurately estimating carbon export entirely from space, crucial for global climate modeling.
The findings, published in Geophysical Research Letters, demonstrate that robust carbon export estimates are achievable without relying on ocean color. Researchers are now leveraging this model to investigate unexplained pulses of carbon observed at MBARI’s deep-sea monitoring station, Station M. Openly available code and data via GitHub and Zenodo encourage further development and application within the marine research community, furthering understanding of the ocean’s role in the global carbon cycle.
MBARI’s Research & Data Accessibility
MBARI researchers are pioneering a new method for tracking ocean carbon export – the process of transferring carbon from the surface to the deep sea – using satellite data on winds and currents. This innovative approach bypasses reliance on ocean color, traditionally used to estimate phytoplankton productivity. Instead, the team developed a Lagrangian growth-advection model that maps plankton succession and movement alongside surface currents, providing a more accurate picture of carbon pathways. This is crucial as the ocean’s ability to sequester carbon significantly impacts climate change modeling.
This new model addresses a key limitation of existing methods: the time and space lag between surface phytoplankton blooms and carbon reaching the seafloor. By integrating data on ocean circulation, researchers can now better estimate carbon export without direct measurements of sinking particles. Validation against long-term monitoring data from MBARI’s Station M showed comparable results to traditional methods, demonstrating the model’s effectiveness. The team’s findings were recently published in Geophysical Research Letters.
MBARI is committed to open science, making the code and data for this Lagrangian model freely available via platforms like GitHub and Zenodo. This accessibility empowers the broader marine science community to build upon their work and refine oceanographic models. Funding for this research came from the David and Lucile Packard Foundation and the U.S. National Science Foundation, highlighting a collaborative approach to understanding and monitoring Earth’s vital carbon cycle.
