Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration Framework
Abstract The spatial contribution to the global land‐atmosphere carbon dioxide (CO2) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the para...
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American Geophysical Union (AGU)
2025-03-01
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| Series: | Journal of Advances in Modeling Earth Systems |
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| Online Access: | https://doi.org/10.1029/2024MS004479 |
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| author | H. Lee M. Jung N. Carvalhais M. Reichstein M. Forkel A. A. Bloom J. Pacheco‐Labrador S. Koirala |
| author_facet | H. Lee M. Jung N. Carvalhais M. Reichstein M. Forkel A. A. Bloom J. Pacheco‐Labrador S. Koirala |
| author_sort | H. Lee |
| collection | DOAJ |
| description | Abstract The spatial contribution to the global land‐atmosphere carbon dioxide (CO2) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the parameter and structural uncertainties and advance our understanding. Here, we develop a parsimonious diagnostic process‐based model of land carbon cycles, constraining parameters with observation‐based products. We compare CO2 flux estimates from our model with observational constraints and Trends in Net Land‐Atmosphere Carbon Exchange (TRENDY) model ensemble to show that our model reasonably reproduces the seasonality of net ecosystem exchange (NEE) and gross primary productivity (GPP) and interannual variability (IAV) of NEE. Finally, we use the developed model, TRENDY models, and observational constraints to attribute variability in global NEE and GPP to regional variability. The attribution analysis confirms the dominance of Northern temperate and boreal regions in the seasonality of CO2 fluxes. Regarding NEE IAV, we identify a significant contribution from tropical savanna regions as previously perceived. Furthermore, we highlight that tropical humid regions are also identified as at least equally relevant contributors as semi‐arid regions. At the same time, the largest uncertainty among ensemble members of NEE constraint and TRENDY models in the tropical humid regions underscore the necessity of better process understanding and more observations in these regions. Overall, our study identifies tropical humid regions as key regions for global land‐atmosphere CO2 exchanges and the inter‐model spread of its modeling. |
| format | Article |
| id | doaj-art-c19a8d9905ae4e42b33d7410a8fc33b1 |
| institution | OA Journals |
| issn | 1942-2466 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj-art-c19a8d9905ae4e42b33d7410a8fc33b12025-08-20T02:10:41ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662025-03-01173n/an/a10.1029/2024MS004479Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration FrameworkH. Lee0M. Jung1N. Carvalhais2M. Reichstein3M. Forkel4A. A. Bloom5J. Pacheco‐Labrador6S. Koirala7Max Planck Institute for Biogeochemistry Jena GermanyMax Planck Institute for Biogeochemistry Jena GermanyMax Planck Institute for Biogeochemistry Jena GermanyMax Planck Institute for Biogeochemistry Jena GermanyTechnische Universität Dresden Institute of Photogrammetry and Remote Sensing Dresden GermanyJet Propulsion Laboratory California Institute of Technology Pasadena CA USAEnvironmental Remote Sensing and Spectroscopy Laboratory (SpecLab) Spanish National Research Council (CSIC) Madrid SpainMax Planck Institute for Biogeochemistry Jena GermanyAbstract The spatial contribution to the global land‐atmosphere carbon dioxide (CO2) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the parameter and structural uncertainties and advance our understanding. Here, we develop a parsimonious diagnostic process‐based model of land carbon cycles, constraining parameters with observation‐based products. We compare CO2 flux estimates from our model with observational constraints and Trends in Net Land‐Atmosphere Carbon Exchange (TRENDY) model ensemble to show that our model reasonably reproduces the seasonality of net ecosystem exchange (NEE) and gross primary productivity (GPP) and interannual variability (IAV) of NEE. Finally, we use the developed model, TRENDY models, and observational constraints to attribute variability in global NEE and GPP to regional variability. The attribution analysis confirms the dominance of Northern temperate and boreal regions in the seasonality of CO2 fluxes. Regarding NEE IAV, we identify a significant contribution from tropical savanna regions as previously perceived. Furthermore, we highlight that tropical humid regions are also identified as at least equally relevant contributors as semi‐arid regions. At the same time, the largest uncertainty among ensemble members of NEE constraint and TRENDY models in the tropical humid regions underscore the necessity of better process understanding and more observations in these regions. Overall, our study identifies tropical humid regions as key regions for global land‐atmosphere CO2 exchanges and the inter‐model spread of its modeling.https://doi.org/10.1029/2024MS004479carbon dioxideland‐atmosphere interactionmodel‐data integrationinterannual variabilityattribution |
| spellingShingle | H. Lee M. Jung N. Carvalhais M. Reichstein M. Forkel A. A. Bloom J. Pacheco‐Labrador S. Koirala Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration Framework Journal of Advances in Modeling Earth Systems carbon dioxide land‐atmosphere interaction model‐data integration interannual variability attribution |
| title | Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration Framework |
| title_full | Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration Framework |
| title_fullStr | Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration Framework |
| title_full_unstemmed | Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration Framework |
| title_short | Spatial Attribution of Temporal Variability in Global Land‐Atmosphere CO2 Exchange Using a Model‐Data Integration Framework |
| title_sort | spatial attribution of temporal variability in global land atmosphere co2 exchange using a model data integration framework |
| topic | carbon dioxide land‐atmosphere interaction model‐data integration interannual variability attribution |
| url | https://doi.org/10.1029/2024MS004479 |
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