[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126803-en":3,"doc-seo-126803-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126803,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine learning reveals regime shifts in future ocean carbon dioxide fluxes - Inter-annual variability","Global ocean air-sea CO2 flux inter-annual variability is non-negligible, shapes the climate warming signal, and remains poorly represented in Earth System Models while experiments are computationally costly. A kernel ridge regression approach reconstructs present and future CO2 flux variability across five ESMs, identifying dissolved inorganic carbon and alkalinity as key drivers. Results show future reduced influence of dissolved inorganic carbon due to decreasing vertical gradients, enabling efficient interpretation of ESM datasets and guiding model development to better constrain CO2 flux variability.","communications earth & environment Article  \n\n| \u003Cbr>[https://doi.org/10.1038/s43247-024-01257-2](https://doi.org/10.1038/s43247-024-01257-2) |  |  |  |\n| --- | --- | --- | --- |\n| Machine learning reveals regime shiftsin future ocean carbon dioxide fluxes inter-annual variability\u003Cbr> Check for updates |  |  |  |\n| Damien Couespel Bjørnar Jensen  |  1 , Jerry Tjiputra  | 1, Klaus Johannsen1, Pradeebane Vaittinada Ayar  |  1 & |\n\n1  \nThe inter-annual variability of global ocean air-sea CO2 fluxes arenon-negligible, modulates the global warming signal, and yet it is poorly represented in Earth System Models (ESMs) . ESMs are highly sophisticated and computationally demanding, making it challenging to perform dedicated experiments to investigate the key drivers of the CO2 flux variability across spatial and temporal scales. Machine learning methods can objectively and systematically explore large datasets, ensuring physically meaningful results. Here, weshowthata kernel ridge regression can reconstruct the present and future CO2 flux variability in five ESMs. Surface concentration of dissolved inorganic carbon (DIC) and alkalinity emerge as the critical drivers, but the former is projected to play a lesser role in the future due to decreasing vertical gradient. Our results demonstrate a new approach to efficiently interpret the massive datasets produced by ESMs, and offer guidance into future model development to better constrain the CO2 flux.  \nThe ocean takes up roughly 25% of the total human-induced carbon emissions per year1, thereby mitigating the consequences of the anthropogenic perturbation on the Earth system and its climate. This estimate is based on both, global ocean biogeochemistry models and observation-based data products2. While model-based estimates rely on simulations of ocean circulation and carbon biogeochemistry [e.g.3], the observation-based estimations rely on pCO2-products that use machine learning to combine insitu observation, remote sensing and reanalysis products [e.g.4,]. Although estimates of present-day ocean carbon uptake rate and its long-term trends agree reasonably well using either tool, discrepancies in thespatio-temporal variability of the air-sea CO2 flux (CO2 flux, hereafter) remain, and these biases even appear to increase over time5. However, our ability to accurately quantify the magnitude of the ocean carbon sink and the variability of the CO2 flux across multiple timescales is crucial to project the future evolution ofthe Earth’s climate and to improve our ability to robustly detect long-term anthropogenic climate change6,7.  \nThe rate at which CO2 is taken up by the ocean correlates on longer timescales with the fairly steady increase of atmospheric CO2 concentration8, but is modulated by inter-annual variability of the CO2 flux (IAV, hereafter), regionally and globally4,9, 10. Year-to-year down to decadal variations in the IAV of the CO2 flux may be driven by modes of atmospheric variability such as the El Niño Southern Oscillation (ENSO), the  \nSouthern Annular Mode (SAM) or the North Atlantic Oscillation (NAO) that modulate the ocean circulation, thesea surface temperature, the surface winds and the biology and consequently the CO2 flux9, 11, 12. Modelling and observational studies have achieved substantial progress in quantifying IAVand identifying regional drivers over the historical and preindustrial periods (see literature review in2, 13, 14), however, part oftheIAV remains unexplained and the dominant drivers in some areas have yet to be clearly identified.  \nTraditionally, variability ofthe global ocean carbon sink was attributed to equatorial Pacific Ocean variability of the CO2 flux associated with ENSO10, 15, 16 and to variability in the Southern Ocean linked to the SAM4, 12, 17–19. In these regions, the CO2 fluxIAVis driven by the upwelling of DIC-rich waters. In the equatorial Pacific, the upwelling is modulated by the ENSO phases11, 12,20,21. In the Southern Ocean, oscillationsin the posi","cbCaivBsKSBF9FtM","https://ap.wps.com/l/cbCaivBsKSBF9FtM","pdf",3554033,1,12,"English","en",105,"# Introduction\n## Global ocean CO2 flux variability and modeling gaps\n## Climate modes as potential drivers\n## Traditional regional attribution of drivers\n## Motivation and research gap","[{\"question\":\"Why is inter-annual variability of ocean air-sea CO2 flux important?\",\"answer\":\"It is substantial and modulates the global warming signal, making long-term detection more difficult.\"},{\"question\":\"What challenge do Earth System Models face in studying CO2 flux variability?\",\"answer\":\"They are computationally demanding, which makes dedicated experiments across spatial and temporal scales difficult.\"},{\"question\":\"How does the study reconstruct present and future CO2 flux variability?\",\"answer\":\"It uses kernel ridge regression to reconstruct variability across five Earth System Models.\"}]","Machine learning reveals regime shifts in future ocean carbon dioxide fluxes - Inter-annual variability | PDF",1785934882,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-reveals-regime-shifts-in-future-ocean-carbon-dioxide-fluxes-inter-annual-variability","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-reveals-regime-shifts-in-future-ocean-carbon-dioxide-fluxes-inter-annual-variability/126803/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is inter-annual variability of ocean air-sea CO2 flux important?","Question",{"text":75,"@type":76},"It is substantial and modulates the global warming signal, making long-term detection more difficult.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What challenge do Earth System Models face in studying CO2 flux variability?",{"text":80,"@type":76},"They are computationally demanding, which makes dedicated experiments across spatial and temporal scales difficult.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study reconstruct present and future CO2 flux variability?",{"text":84,"@type":76},"It uses kernel ridge regression to reconstruct variability across five Earth System Models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]