Near-real-time monitoring of global ocean carbon sink

TL;DR

This paper presents a near-real-time, machine learning-based framework for monitoring and assessing the global ocean carbon sink, providing timely data to inform climate change mitigation strategies.

Contribution

It introduces an integrated machine learning methodology and a near-real-time dataset for global ocean carbon sink monitoring, advancing current assessment capabilities.

Findings

Development of a near-real-time global ocean carbon sink dataset

Implementation of a machine learning framework for carbon sink assessment

Creation of a visualization platform for data accessibility

Abstract

Mitigation of climate change will highly rely on a carbon emission trajectory that achieves carbon neutrality by the 2050s. The ocean plays a critical role in modulating climate change by sequestering CO2 from the atmosphere. Relying on the multidisciplinary cutting-edge methodologies and technologies, the near-real-time monitoring of global ocean carbon sinks from January 2022 to July 2023 aims to provide the world's latest assessment of monthly and gridded global ocean carbon sinks based on machine learning and other data science technologies. The project will help us find a robust route to deal with climate change, which will significantly promote the ocean carbon sinks research and will be of great interest for policy makers, researchers, and the public. This research aims to build up an integrated machine learning framework and methodology for assessing global ocean carbon neutral process; development of near-real-time dataset; development of visualization platform; research papers published in international prestigious journals; an executive report openly accessible to policy makers and the public.