Preprints
https://doi.org/10.5194/essd-2023-337
https://doi.org/10.5194/essd-2023-337
05 Oct 2023
 | 05 Oct 2023
Status: this preprint is currently under review for the journal ESSD.

CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization

Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, and Trevor Keenan

Abstract. Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing the sugars and starches needed for ecosystem metabolism. Despite the importance of GPP, however, existing estimates present significant uncertainties and discrepancies. A key issue is the underrepresentation of the CO2 fertilization effect, a major factor contributing to the increased terrestrial carbon sink over recent decades. This omission could potentially bias our understanding of ecosystem responses to climate change.

Here, we introduce CEDAR-GPP, the first global upscaled GPP product that incorporates the direct CO2 fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05º resolution from 1982 to 2020, generated using a comprehensive set of eddy covariance measurements, multi-source satellite observations, climate variables, and machine learning models. Importantly, we used both theoretical and data-driven approaches to incorporate the direct CO2 effects. Our machine learning models effectively predicted monthly GPP (R2 ~ 0.74), the mean seasonal cycles (R2 ~ 0.79), and spatial variabilities (R2 ~ 0.67). Incorporation of the direct CO2 effects substantially improved the models’ ability to estimate long-term GPP trends across global flux sites. While the global patterns of annual mean GPP, seasonality, and interannual variability generally aligned with existing satellite-based products, CEDAR-GPP demonstrated higher long-term trends globally after incorporating CO2 fertilization, particularly in the tropics, reflecting a strong temperature control on direct CO2 effects. CEDAR-GPP offers a comprehensive representation of GPP temporal and spatial dynamics, providing valuable insights into ecosystem-climate interactions. The CEDAR-GPP product is available at https://doi.org/10.5281/zenodo.8212707 (Kang et al., 2023).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, and Trevor Keenan

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-337', Anonymous Referee #1, 08 Nov 2023
    • AC1: 'Reply on RC1', Yanghui Kang, 28 Feb 2024
  • RC2: 'Comment on essd-2023-337', Songhan Wang, 12 Nov 2023
    • AC3: 'Reply on RC2', Yanghui Kang, 28 Feb 2024
  • RC3: 'Comment on essd-2023-337', Anonymous Referee #3, 04 Dec 2023
    • AC2: 'Reply on RC3', Yanghui Kang, 28 Feb 2024
Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, and Trevor Keenan

Data sets

CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, Trevor Keenan https://doi.org/10.5281/zenodo.8212707

Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, and Trevor Keenan

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Short summary
CEDAR-GPP provides spatiotemporally upscaled estimates of Gross Primary Productivity, uniquely incorporating the direct effect of elevated atmospheric CO2 on global photosynthesis. This dataset was produced by upscaling global eddy covariance measurements with multi-source satellite observations and climate data using machine learning. Available at monthly and 0.05° resolution from 1982 to 2020, CEDAR-GPP offers critical insights into ecosystem-climate interaction.
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