Tools
Tools
Denvil-Sommer, Anna 
ORCID: https://orcid.org/0000-0002-9124-2827, Buitenhuis, Erik T. ORCID: https://orcid.org/0000-0001-6274-5583, Kiko, Rainer, Lombard, Fabien, Guidi, Lionel and Le Quéré, Corinne ORCID: https://orcid.org/0000-0003-2319-0452
(2023)
Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning.
Geoscientific Model Development, 16 (10).
2995–3012.
ISSN 1991-9603
Preview |
PDF (gmd-16-2995-2023)
- Published Version
Available under License Creative Commons Attribution. Download (10MB) | Preview |
Abstract
Understanding the relationship between surface marine ecosystems and the export of carbon to depth by sinking organic particles is key to representing the effect of ecosystem dynamics and diversity, and their evolution under multiple stressors, on the carbon cycle and climate in models. Recent observational technologies have greatly increased the amount of data available, both for the abundance of diverse plankton groups and for the concentration and properties of particulate organic carbon in the ocean interior. Here we use synthetic model data to test the potential of using machine learning (ML) to reproduce concentrations of particulate organic carbon within the ocean interior based on surface ecosystem and environmental data. We test two machine learning methods that differ in their approaches to data-fitting, the random forest and XGBoost methods. The synthetic data are sampled from the PlankTOM12 global biogeochemical model using the time and coordinates of existing observations. We test 27 different combinations of possible drivers to reconstruct small (POCS) and large (POCL) particulate organic carbon concentrations. We show that ML can successfully be used to reproduce modelled particulate organic carbon over most of the ocean based on ecosystem and modelled environmental drivers. XGBoost showed better results compared to random forest thanks to its gradient boosting trees' architecture. The inclusion of plankton functional types (PFTs) in driver sets improved the accuracy of the model reconstruction by 58 % on average for POCS and by 22 % for POCL. Results were less robust over the equatorial Pacific and some parts of the high latitudes. For POCS reconstruction, the most important drivers were the depth level, temperature, microzooplankton and PO4, while for POCL it was the depth level, temperature, mixed-layer depth, microzooplankton, phaeocystis, PO4 and chlorophyll a averaged over the mixed-layer depth. These results suggest that it will be possible to identify linkages between surface environmental and ecosystem structure and particulate organic carbon distribution within the ocean interior using real observations and to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
| Item Type: | Article |
|---|---|
| Additional Information: | Code and data availability statement: PlankTOM12 data used within this study are available at https://doi.org/10.5281/zenodo.7324781 (Denvil-Sommer, 2022a). UVP5 data can be found at https://doi.org/10.1594/PANGAEA.924375 (Kiko et al., 2021). Codes for data preparation, development of machine learning methods and tests of different driver sets, as well as codes that provide figures shown in the article, can be found at https://doi.org/10.5281/zenodo.7326992 (Denvil-Sommer, 2022b). Funding information: Anna Denvil-Sommer, Erik T. Buitenhuis and Corinne Le Quéré acknowledge support from the Royal Society (grant RP\R1\191063) and the NERC Marine Frontiers project (grant NE/V011103/1) for Corinne Le Quéré. Rainer Kiko acknowledges support via a “Make Our Planet Great Again” grant from the French National Research Agency within the “Programme d'Investissements d'Avenir” (grant no. ANR-19-MPGA-0012) and by the Heisenberg programme of the German Science Foundation under project number 469175784. |
| Uncontrolled Keywords: | sdg 13 - climate action,sdg 14 - life below water ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Jul 2024 10:31 |
| Last Modified: | 25 Sep 2024 17:56 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/95936 |
| DOI: | 10.5194/gmd-16-2995-2023 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |