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!
|
| _version_ | 1849429836532023296 |
|---|---|
| author | Y. Kang Y. Kang Y. Kang M. Bassiouni M. Bassiouni M. Gaber M. Gaber X. Lu X. Lu T. F. Keenan T. F. Keenan |
| author_facet | Y. Kang Y. Kang Y. Kang M. Bassiouni M. Bassiouni M. Gaber M. Gaber X. Lu X. Lu T. F. Keenan T. F. Keenan |
| author_sort | Y. Kang |
| collection | DOAJ |
| description | <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> |
| format | Article |
| id | doaj-art-2ab7580823514da2b5123db42b3aa656 |
| institution | Kabale University |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj-art-2ab7580823514da2b5123db42b3aa6562025-08-20T03:28:13ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-06-01173009304610.5194/essd-17-3009-2025CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilizationY. Kang0Y. Kang1Y. Kang2M. Bassiouni3M. Bassiouni4M. Gaber5M. Gaber6X. Lu7X. Lu8T. F. Keenan9T. F. Keenan10Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USAClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USADepartment of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USADepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USAClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USADepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USADepartment of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, 1350, DenmarkDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USAClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USADepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USAClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA<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>https://essd.copernicus.org/articles/17/3009/2025/essd-17-3009-2025.pdf |
| spellingShingle | Y. Kang Y. Kang Y. Kang M. Bassiouni M. Bassiouni M. Gaber M. Gaber X. Lu X. Lu T. F. Keenan T. F. Keenan CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilization Earth System Science Data |
| title | CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilization |
| title_full | CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilization |
| title_fullStr | CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilization |
| title_full_unstemmed | CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilization |
| title_short | CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO<sub>2</sub> fertilization |
| title_sort | cedar gpp spatiotemporally upscaled estimates of gross primary productivity incorporating co sub 2 sub fertilization |
| url | https://essd.copernicus.org/articles/17/3009/2025/essd-17-3009-2025.pdf |
| work_keys_str_mv | AT ykang cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT ykang cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT ykang cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT mbassiouni cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT mbassiouni cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT mgaber cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT mgaber cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT xlu cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT xlu cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT tfkeenan cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization AT tfkeenan cedargppspatiotemporallyupscaledestimatesofgrossprimaryproductivityincorporatingcosub2subfertilization |