EGU24-14839, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Near-real-time monitoring of global ocean carbon sink based on CNN

Piyu Ke1, Xiaofan Gui2, Wei Cao2, Dezhi Wang3, Ce Hou4, Lixing Wang1, Xuanren Song1, Yun Li5, Biqing Zhu6, Jiang Bian2, Stephen Sitch7, Philippe Ciais8, Pierre Friedlingstein7, and Zhu Liu1
Piyu Ke et al.
  • 1Tsinghua University, Department of Earth System Science, China (
  • 2Microsoft research
  • 3School of Mathematics and Statistics, Lanzhou University, Lanzhou, China
  • 4Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
  • 5Department of architecture, faculty of engineering science, KU Leuven, Leuven, Belgium
  • 6Integrated Assessment and Climate Change Research Group and Exploratory Modeling of Human-natural Systems Research Group, International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
  • 7Department of Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK
  • 8Laboratoire des Sciences du Climate et de l’Environnement LSCE, Orme de Merisiers 91191 Gif-sur-Yvette, France

The ocean plays a critical role in modulating climate change by absorbing atmospheric CO2. Timely and geographically detailed estimates of the global ocean-atmosphere CO2 flux provide an important constraint on the global carbon budget, offering insights into temporal changes and regional variations in the global carbon cycle. However, previous estimates of this flux have a 1 year delay and cannot monitor the very recent changes in the global ocean carbon sink. Here we present a near-real-time, monthly grid-based dataset of global surface ocean fugacity of CO2 and ocean-atmosphere CO2 flux data from January 2022 to July 2023, which is called Carbon Monitor Ocean (CMO-NRT). The data have been derived by updating the estimates from 10 Global Ocean Biogeochemical Models and 8 data products in the Global Carbon Budget 2022 to a near-real-time framework. This is achieved by employing Convolutional Neural Networks and semi-supervised learning methods to learn the non-linear relationship between the estimates from models or products and the observed predictors. The goal of this dataset is to offer a more immediate, precise, and comprehensive understanding of the global ocean-atmosphere CO2 flux. This advancement enhances the capacity of scientists and policymakers to monitor and respond effectively to alterations in the ocean's CO2 absorption, thereby contributing significantly to climate change management.

How to cite: Ke, P., Gui, X., Cao, W., Wang, D., Hou, C., Wang, L., Song, X., Li, Y., Zhu, B., Bian, J., Sitch, S., Ciais, P., Friedlingstein, P., and Liu, Z.: Near-real-time monitoring of global ocean carbon sink based on CNN, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14839,, 2024.