Gauging ocean Organic Carbon fluxes using Autonomous Robotic Technologies
Climate change driven by CO2 emissions from human activities is a significant challenge facing mankind. An important component of Earth’s carbon cycle is the ocean’s biological carbon pump; without it atmospheric CO2 would be ~50% higher than it is now.
The biological carbon pump consists of sinking organic matter which is remineralised back into CO2 in the deep ocean. The depth at which remineralisation occurs is the main factor affecting the amount of organic carbon stored in the ocean.
Currently we do not understand how or why remineralisation depth varies in time, which limits our ability to make robust predictions of how the future carbon cycle, and hence our climate, will change into the future. This is mainly due to the challenges of measuring remineralisation depth using conventional methods– a barrier which autonomous underwater vehicles, such as gliders, are able to overcome by providing high frequency data over long periods.
In GOCART, glider deployments will be used to establish the characteristics and significance of temporal variability in organic carbon flux and remineralisation depth. This will give new insights into the factors driving variability in remineralisation depth, ultimately leading to development of a new model parameterisation incorporating temporal variability.
GOCART represents a significant advance in quantifying temporal variability in remineralisation depth, which is key to reducing uncertainty in model predictions of ocean carbon storage, and yet currently almost entirely unknown.
Phase 1 will involve deploying gliders in the Southern Ocean near South Georgia in collaboration with the COMICS programme.
The GOCART project led by Stephanie Henson at the National Oceanography Centre is an ERC Consolidator Grant project. The aim is to investigate temporal variability in the biological carbon pump and will run from September 2017-August 2022.
This project has recieved funding from the European Research Council (ERC) under the Horizon 2020 research and innovation programme (grant agreement number 724416).