CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilization
<p>Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing carbohydrates needed for ecosystem metabolism. Despite the importance of GPP, however, existing estimates present significant uncertai...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-06-01
|
| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/3009/2025/essd-17-3009-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <p>Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing carbohydrates 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 CO<span class="inline-formula"><sub>2</sub></span> 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.</p>
<p>Here, we introduce CEDAR-GPP, the first global machine-learning-upscaled GPP product that incorporates the direct CO<span class="inline-formula"><sub>2</sub></span> fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05° 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 CO<span class="inline-formula"><sub>2</sub></span> effects. Our machine learning models effectively predict monthly GPP (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">∼</span> 0.72), the mean seasonal cycles (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">∼</span> 0.77), and spatial variabilities (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">∼</span> 0.63) based on cross-validation at flux sites. After incorporating the direct CO<span class="inline-formula"><sub>2</sub></span> effects, the predicted long-term GPP trend across global flux towers substantially increases from 3.1 to 4.5–5.4 gC m<span class="inline-formula"><sup>−2</sup></span> yr<span class="inline-formula"><sup>−1</sup></span>, which aligns more closely with the 7.7 gC m<span class="inline-formula"><sup>−2</sup></span> yr<span class="inline-formula"><sup>−1</sup></span> trend detected from eddy covariance data. While the global patterns of annual mean GPP, seasonality, and interannual variability generally align with existing satellite-based products, CEDAR-GPP demonstrates higher long-term trends globally after incorporating CO<span class="inline-formula"><sub>2</sub></span> fertilization and reflected a strong temperature control on direct CO<span class="inline-formula"><sub>2</sub></span> effects. The estimated global GPP trend is 0.57–0.76 PgC yr<span class="inline-formula"><sup>−1</sup></span> from 2001 to 2018 and 0.32–0.34 PgC yr<span class="inline-formula"><sup>−1</sup></span> from 1982 to 2018. Estimating and validating GPP trends in data-scarce regions, such as the tropics, remains challenging, underscoring the importance of ongoing ground-based monitoring and advancements in modeling techniques. 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 <a href="https://doi.org/10.5281/zenodo.8212706">https://doi.org/10.5281/zenodo.8212706</a> (Kang et al., 2024).</p> |
|---|---|
| ISSN: | 1866-3508 1866-3516 |