A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021
<p>Insufficient spatiotemporal coverage of observations of the surface partial pressure of CO<span class="inline-formula"><sub>2</sub></span> (<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula&q...
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| Language: | English |
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Copernicus Publications
2025-01-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/43/2025/essd-17-43-2025.pdf |
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| author | Z. Wu Z. Wu W. Lu A. Roobaert L. Song X.-H. Yan W.-J. Cai |
| author_facet | Z. Wu Z. Wu W. Lu A. Roobaert L. Song X.-H. Yan W.-J. Cai |
| author_sort | Z. Wu |
| collection | DOAJ |
| description | <p>Insufficient spatiotemporal coverage of observations of the surface partial pressure of CO<span class="inline-formula"><sub>2</sub></span> (<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>) has hindered precise carbon cycle studies in coastal oceans and justifies the development of spatially and temporally continuous <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> data products. Earlier <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> products have difficulties in capturing the heterogeneity of regional variations and decadal trends of <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> in the North American Atlantic Coastal Ocean Margin (NAACOM). This study developed a regional reconstructed <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> product for the NAACOM (Reconstructed Coastal Acidification Database-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>, or ReCAD-NAACOM-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>) using a two-step approach combining random forest regression and linear regression. The product provides monthly <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> data at 0.25° spatial resolution from 1993 to 2021, enabling investigation of regional spatial differences, seasonal cycles, and decadal changes in <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>. The observation-based reconstruction was trained using Surface Ocean CO<span class="inline-formula"><sub>2</sub></span> Atlas (SOCAT) observations as observational values, with various satellite-derived and reanalysis environmental variables known to control sea surface <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> as model inputs. The product shows high accuracy during the model training, validation, and independent test phases, demonstrating robustness and a capability to accurately reconstruct <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> in regions or periods lacking direct observational data. Compared with all the observation samples from SOCAT, the <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> product yields a determination coefficient of 0.92, a root-mean-square error of 12.70 <span class="inline-formula">µ</span>atm, and an accumulative uncertainty of 23.25 <span class="inline-formula">µ</span>atm. The ReCAD-NAACOM-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> product demonstrates its capability to resolve seasonal cycles, regional-scale variations, and decadal trends of <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> along the NAACOM. This new product provides reliable <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> data for more precise studies of coastal carbon dynamics in the NAACOM region. The dataset is publicly accessible at <span class="uri">https://doi.org/10.5281/zenodo.14038561</span> (Wu et al., 2024a) and will be updated regularly.</p> |
| format | Article |
| id | doaj-art-3699acc0bbd4442f937a8e2fb79f1a06 |
| institution | Kabale University |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
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| series | Earth System Science Data |
| spelling | doaj-art-3699acc0bbd4442f937a8e2fb79f1a062025-01-08T10:57:15ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-01-0117436310.5194/essd-17-43-2025A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021Z. Wu0Z. Wu1W. Lu2A. Roobaert3L. Song4X.-H. Yan5W.-J. Cai6State Key Laboratory of Marine Environmental Science & College of Ocean and Earth Science, Xiamen University, Xiamen, Fujian, 361102, ChinaSchool of Marine Science and Policy, University of Delaware, Newark, Delaware 19716, USASchool of Marine Sciences, State Key Laboratory of Environmental Adaptability for Industrial Products, Sun Yat-sen University, Zhuhai, Guangdong, 519082, ChinaFlanders Marine Institute (VLIZ), Jacobsenstraat 1, Ostend, 8400, BelgiumSchool of Marine Science and Technology, Zhejiang Ocean University, Zhoushan, Zhejiang, 316022, ChinaSchool of Marine Science and Policy, University of Delaware, Newark, Delaware 19716, USASchool of Marine Science and Policy, University of Delaware, Newark, Delaware 19716, USA<p>Insufficient spatiotemporal coverage of observations of the surface partial pressure of CO<span class="inline-formula"><sub>2</sub></span> (<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>) has hindered precise carbon cycle studies in coastal oceans and justifies the development of spatially and temporally continuous <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> data products. Earlier <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> products have difficulties in capturing the heterogeneity of regional variations and decadal trends of <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> in the North American Atlantic Coastal Ocean Margin (NAACOM). This study developed a regional reconstructed <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> product for the NAACOM (Reconstructed Coastal Acidification Database-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>, or ReCAD-NAACOM-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>) using a two-step approach combining random forest regression and linear regression. The product provides monthly <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> data at 0.25° spatial resolution from 1993 to 2021, enabling investigation of regional spatial differences, seasonal cycles, and decadal changes in <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span>. The observation-based reconstruction was trained using Surface Ocean CO<span class="inline-formula"><sub>2</sub></span> Atlas (SOCAT) observations as observational values, with various satellite-derived and reanalysis environmental variables known to control sea surface <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> as model inputs. The product shows high accuracy during the model training, validation, and independent test phases, demonstrating robustness and a capability to accurately reconstruct <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> in regions or periods lacking direct observational data. Compared with all the observation samples from SOCAT, the <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> product yields a determination coefficient of 0.92, a root-mean-square error of 12.70 <span class="inline-formula">µ</span>atm, and an accumulative uncertainty of 23.25 <span class="inline-formula">µ</span>atm. The ReCAD-NAACOM-<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> product demonstrates its capability to resolve seasonal cycles, regional-scale variations, and decadal trends of <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> along the NAACOM. This new product provides reliable <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula"><sub>2</sub></span> data for more precise studies of coastal carbon dynamics in the NAACOM region. The dataset is publicly accessible at <span class="uri">https://doi.org/10.5281/zenodo.14038561</span> (Wu et al., 2024a) and will be updated regularly.</p>https://essd.copernicus.org/articles/17/43/2025/essd-17-43-2025.pdf |
| spellingShingle | Z. Wu Z. Wu W. Lu A. Roobaert L. Song X.-H. Yan W.-J. Cai A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 Earth System Science Data |
| title | A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 |
| title_full | A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 |
| title_fullStr | A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 |
| title_full_unstemmed | A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 |
| title_short | A machine-learning reconstruction of sea surface <i>p</i>CO<sub>2</sub> in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 |
| title_sort | machine learning reconstruction of sea surface i p i co sub 2 sub in the north american atlantic coastal ocean margin from 1993 to 2021 |
| url | https://essd.copernicus.org/articles/17/43/2025/essd-17-43-2025.pdf |
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