[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121623-en":3,"doc-seo-121623-105":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},121623,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Regionalizing the sea-level budget with machine learning techniques - Research report","Sea-level change is commonly attributed to multiple drivers using a sea-level budget approach, but closure becomes difficult at finer spatial scales because observational limitations and regional processes prevent consistent attribution. This study investigates sub-basin sea-level budgets from 1993–2016 using neural network (self-organizing map) and network detection (􀀎-MAPS) methods to extract domains of coherent variability. The resulting domains improve spatial detail and enable sub-basin budget closure within uncertainty, clarifying that steric variations dominate temporal variability while mass exchange and dynamic redistribution contribute regionally.","Ocean Sci., 19, 17–41, 2023  \n[https://doi.org/10.5194/os-19-17-2023](https://doi.org/10.5194/os-19-17-2023)[ ](https://doi.org/10.5194/os-19-17-2023)© Author(s) 2023 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nRegionalizing the sea-level budget with machine learning techniques  \nCarolina M. L. Camargo 1,2 , Riccardo E. M. Riva2 , Tim H. J. Hermans 1,2 , Eike M. Schütt3 , Marta Marcos4 , Ismael Hernandez-Carrasco4 , and Aimée B. A. Slangen 1  \n1Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, The Netherlands  \n2Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands  \n3Department of Geography, Kiel University, Kiel, Germany  \n4Mediterranean Institute for Advanced Studies (IMEDEA), Spanish National Research Council – University of the Balearic Islands (CSIC-UIB), Esporles, Spain  \nCorrespondence: Carolina M. L. Camargo ([carolina.camargo@nioz.nl](carolina.camargo@nioz.nl))  \nReceived: 2 September 2022 – Discussion started: 13 September 2022  \nRevised: 1 December 2022 – Accepted: 8 December 2022 – Published: 16 January 2023  \nAbstract. Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sealevel change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (􀀎-MAPS) . The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1􀀛 uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and 􀀎 -MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in  \ncombination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.  \n1 The sea-level budget  \nSea-level change will be one of the major challenges of the coming centuries for coastal communities worldwide (FoxKemper et al., 2021) . Global mean sea-level change has been rising at a rate of 1 .6 mm yr􀀀1 since 1900 and 3 .3 mm yr􀀀1 since 1993 (Frederikse et al., 2020) . However, sea level does not change uniformly: it displays strong spatial and temporal variations (Hamlington et al., 2020) . Ocean dynamics, land ice mass changes and associated gravitational effects, vertical land movement, and the inverse barometer effect are some of the processes responsible for these regional differences (e.g. Stammer et al., 2013 ; Slangen et al., 2017) . Understanding the regional variability of the processes driving sea-level change is critical for improving our understanding of its causes, constraining sea-level projections, and better preparing for the impacts of climate change.  \nThe attribution of sea-level change to its different drivers is typically done using a sea-level budget approach (Chambers et al., 2017 ; WCRP Global Sea Level Budget Group, 2018) . ","cbCaieUuKwiXCyHn","https://ap.wps.com/l/cbCaieUuKwiXCyHn","pdf",13334182,1,25,"English","en",105,"# The sea-level budget\n## Motivation and regional complexity\n## Budget closure challenges at finer scales\n## Global mean closure versus local mismatch\n# Machine-learning regionalization of sea-level variability\n## Extracting coherent domains with SOM and 􀀎-MAPS\n## Interpreting driver contributions within domains\n## Implications for unobserved processes","[{\"question\":\"Why is the sea-level budget difficult to close on sub-basin scales?\",\"answer\":\"Finer-scale closure is hindered by limitations of the observational system and by spatial processes that contribute to regional sea-level change, leading to mismatches between measured totals and summed contributions.\"},{\"question\":\"Which machine learning techniques are used to analyze sub-basin sea-level variability?\",\"answer\":\"The study applies a neural network approach using a self-organizing map (SOM) and a network detection approach (􀀎-MAPS) to extract domains with coherent sea-level variability.\"},{\"question\":\"What do the extracted domains reveal about the main drivers of regional sea-level change?\",\"answer\":\"Steric variations dominate temporal sea-level variability and explain a significant portion of total regional change, while mass exchange has a relatively homogeneous contribution and dynamic mass redistribution is important in highly dynamic regions.\"}]","Regionalizing the sea-level budget with machine learning techniques - Research report | PDF",1785805759,63,{"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},"regionalizing-the-sea-level-budget-with-machine-learning-techniques-research-report","",{"@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/regionalizing-the-sea-level-budget-with-machine-learning-techniques-research-report/121623/",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","2026-08-04",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 the sea-level budget difficult to close on sub-basin scales?","Question",{"text":75,"@type":76},"Finer-scale closure is hindered by limitations of the observational system and by spatial processes that contribute to regional sea-level change, leading to mismatches between measured totals and summed contributions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning techniques are used to analyze sub-basin sea-level variability?",{"text":80,"@type":76},"The study applies a neural network approach using a self-organizing map (SOM) and a network detection approach (􀀎-MAPS) to extract domains with coherent sea-level variability.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the extracted domains reveal about the main drivers of regional sea-level change?",{"text":84,"@type":76},"Steric variations dominate temporal sea-level variability and explain a significant portion of total regional change, while mass exchange has a relatively homogeneous contribution and dynamic mass redistribution is important in highly dynamic regions.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]