Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning
Abstract Global measurements of ocean p C O 2 are critical to monitor and understand changes in the global carbon cycle. However, p C O 2 observations remain sparse as they are mostly collected on opportunistic ship tracks. Several approaches, especially based on direct learning, have been used to u...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03959-w |
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| author | Siyeon Kim Juan Nathaniel Zhewen Hou Tian Zheng Pierre Gentine |
| author_facet | Siyeon Kim Juan Nathaniel Zhewen Hou Tian Zheng Pierre Gentine |
| author_sort | Siyeon Kim |
| collection | DOAJ |
| description | Abstract Global measurements of ocean p C O 2 are critical to monitor and understand changes in the global carbon cycle. However, p C O 2 observations remain sparse as they are mostly collected on opportunistic ship tracks. Several approaches, especially based on direct learning, have been used to upscale and extrapolate sparse point data to dense estimates using globally available input features. However, these estimates tend to exhibit spatially heterogeneous performance. As a result, we propose a physics-informed transfer learning workflow to generate dense p C O 2 estimates that are grounded in real-world measurements and remain physically consistent. The models are initially trained on dense input predictors against p C O 2 estimates from Earth system model simulation, and then fine-tuned to sparse SOCAT observational data. Compared to the benchmark direct learning approach, our transfer learning framework shows major improvements of up to 56-92%. Furthermore, we demonstrate that using models that explicitly account for spatiotemporal structures in the data yield better validation performances by 50-68%. Our strategy thus presents a new monthly global p C O 2 estimate that spans for 35 years between 1982-2017. |
| format | Article |
| id | doaj-art-beae03fcefaf43baa25dd0e5fecfbfea |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-beae03fcefaf43baa25dd0e5fecfbfea2025-08-20T02:40:21ZengNature PortfolioScientific Data2052-44632024-10-0111111310.1038/s41597-024-03959-wSpatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learningSiyeon Kim0Juan Nathaniel1Zhewen Hou2Tian Zheng3Pierre Gentine4Department of Statistics, Columbia UniversityDepartment of Earth and Environmental Engineering, Columbia UniversityDepartment of Statistics, Columbia UniversityDepartment of Statistics, Columbia UniversityDepartment of Earth and Environmental Engineering, Columbia UniversityAbstract Global measurements of ocean p C O 2 are critical to monitor and understand changes in the global carbon cycle. However, p C O 2 observations remain sparse as they are mostly collected on opportunistic ship tracks. Several approaches, especially based on direct learning, have been used to upscale and extrapolate sparse point data to dense estimates using globally available input features. However, these estimates tend to exhibit spatially heterogeneous performance. As a result, we propose a physics-informed transfer learning workflow to generate dense p C O 2 estimates that are grounded in real-world measurements and remain physically consistent. The models are initially trained on dense input predictors against p C O 2 estimates from Earth system model simulation, and then fine-tuned to sparse SOCAT observational data. Compared to the benchmark direct learning approach, our transfer learning framework shows major improvements of up to 56-92%. Furthermore, we demonstrate that using models that explicitly account for spatiotemporal structures in the data yield better validation performances by 50-68%. Our strategy thus presents a new monthly global p C O 2 estimate that spans for 35 years between 1982-2017.https://doi.org/10.1038/s41597-024-03959-w |
| spellingShingle | Siyeon Kim Juan Nathaniel Zhewen Hou Tian Zheng Pierre Gentine Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning Scientific Data |
| title | Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning |
| title_full | Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning |
| title_fullStr | Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning |
| title_full_unstemmed | Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning |
| title_short | Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning |
| title_sort | spatiotemporal upscaling of sparse air sea pco2 data via physics informed transfer learning |
| url | https://doi.org/10.1038/s41597-024-03959-w |
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