{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T03:06:02Z","timestamp":1789182362502,"version":"build-2803163510"},"reference-count":73,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T00:00:00Z","timestamp":1783728000000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42471122"],"award-info":[{"award-number":["42471122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFF1309000"],"award-info":[{"award-number":["2024YFF1309000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Global Change Biology"],"published-print":{"date-parts":[[2026,7]]},"abstract":"ABSTRACT<\/jats:title>\n \n The isotopic composition of atmospheric carbon dioxide (\u03b4\n 13<\/jats:sup>\n C\n atm<\/jats:sub>\n ) provides insights into the terrestrial carbon cycle. However, long\u2010term global \u03b4\n 13<\/jats:sup>\n C\n atm<\/jats:sub>\n maps with both high spatial resolution and continuous temporal coverage remain scarce. Here, we present a new global terrestrial dataset of monthly \u03b4\n 13<\/jats:sup>\n C\n atm<\/jats:sub>\n isoscapes from 2001 to 2020 at 0.05\u00b0 spatial resolution, developed by integrating in\u00a0situ observations with optimized 4D CO\n 2<\/jats:sub>\n concentration fields from inversion outputs, reanalysis data, and geographic information using machine learning. Among four tested models, the Gradient Boosting Machine demonstrated the highest predictive performance under random validation (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u20090.80, RMSE\u2009=\u20090.12\u2030) and maintained robust performance across three spatially independent validation frameworks (\n R<\/jats:italic>\n 2<\/jats:sup>\n \u2009=\u20090.56\u20130.67, RMSE\u2009=\u20090.16\u2030\u20130.19\u2030). Key predictors were air temperature and atmospheric CO\n 2<\/jats:sub>\n concentration. The resulting global terrestrial isoscapes reveal strong spatial and temporal heterogeneity. Model predictions closely align with National Oceanic and Atmospheric Administration (NOAA) marine boundary layer (MBL) observations in terms of trend magnitude, seasonal amplitude (<\u20090.13\u2030 deviation), and latitude gradient (<\u20090.2\u2030). In our study, \u03b4\n 13<\/jats:sup>\n C\n atm<\/jats:sub>\n seasonal amplitude varies from 0.06\u2030 in Southern Hemisphere mid\u2010latitudes to 0.6\u2030 in Northern Hemisphere high latitudes, indicating strong hemispheric asymmetry. Moreover, over 99.8% of global terrestrial grid cells show negative trends in all seasons, with a global terrestrial average annual depletion rate of \u22120.030\u2030\u2009\u00b1\u20090.0006\u2030 year\n \u22121<\/jats:sup>\n . The trend shows stronger depletion during summer and autumn, reaching its peak in August (\u22120.035\u2030\u2009\u00b1\u20090.0012\u2030 year\n \u22121<\/jats:sup>\n ), while spring and winter seasons remain comparatively stable. This study delivers a long\u2010term, high\u2010resolution global terrestrial \u03b4\n 13<\/jats:sup>\n C\n atm<\/jats:sub>\n isoscape dataset, offering a valuable tracer for carbon cycle research and, importantly, robust data support for large\u2010scale investigations of carbon\u2013water coupling in terrestrial ecosystems.\n <\/jats:p>","DOI":"10.1111\/gcb.71002","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:14:46Z","timestamp":1783790086000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mapping Global Terrestrial Spatiotemporal Dynamics of Atmospheric\n \n CO\n 2<\/sub>\n <\/scp>\n Carbon Isoscapes for Ecological Studies"],"prefix":"10.1111","volume":"32","author":[{"given":"Hanlin","family":"Feng","sequence":"first","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China"},{"name":"College of Resources and Environment University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1271-767X","authenticated-orcid":false,"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China"},{"name":"Institute of Mountain Hazards and Environment Chinese Academy of Sciences Chengdu China"},{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe","family":"Ciais","sequence":"additional","affiliation":[{"name":"Laboratoire des Sciences du Climat et de l'Environnement, LSCE\/IPSL, CEA\u2010CNRS\u2010UVSQ Universit\u00e9 Paris\u2010Saclay Gif\u2010sur\u2010Yvette France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5104-6568","authenticated-orcid":false,"given":"Guo","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment Chengdu University of Technology Chengdu Sichuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Laboratory for Subtropical Mountain Ecology, College of Geographical Sciences Fujian Normal University Fuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Makowski","sequence":"additional","affiliation":[{"name":"Unit Applied Mathematics and Computer Science (UMR MIA\u2010PS) INRAE AgroParisTech Universit\u00e9 Paris\u2010Saclay Palaiseau France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Sciences Indiana University Indianapolis Indianapolis Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7627-8824","authenticated-orcid":false,"given":"Zheng","family":"Fu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China"},{"name":"College of Resources and Environment University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,11]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gca.2007.08.014"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.5194\/acp\u201014\u201011959\u20102014"},{"key":"e_1_2_11_4_1","doi-asserted-by":"crossref","unstructured":"Al\u2010Bayati R. M. H. Q.Adeeb A. M.Al\u2010Salihi andY. K.Al\u2010Timimi.2020.\u201cThe Relationship Between the Concentration of Carbon Dioxide and Wind Using GIS.\u201dPaper presented at the International Conference of Numerical Analysis and Applied Mathematics Icnaam 2019.","DOI":"10.1063\/5.0027402"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.5194\/bg\u20108\u20103093\u20102011"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.214"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envc.2025.101113"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1029\/2004gb002394"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00442\u2010001\u20100851\u2010y"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469\u20108137.2007.02342.x"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1023\/a:1010933404324"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2025.114843"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2024.114482"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.269.5227.1098"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1081\/qen\u2010120001878"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.pp.40.060189.002443"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1071\/pp9820121"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aac4971"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1034\/j.1600\u20100889.1999.t01\u20101\u201000005.x"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.4209\/aaqr.2018.01.0010"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/rcm.1886"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1111\/gcb.13646"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1029\/2019GB006170"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/cli10030044"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0169748"},{"key":"e_1_2_11_28_1","unstructured":"Hersbach H. B.Bell P.Berrisford et\u00a0al.2023.\u201cERA5 Monthly Averaged Data on Single Levels From 1940 to Present.\u201dhttps:\/\/doi.org\/10.24381\/cds.f17050d7."},{"key":"e_1_2_11_29_1","volume-title":"Consultants' Group Meeting on Stable Isotope Reference Samples for Geochemical and Hydrological Investigations","author":"Hut G.","year":"1987"},{"key":"e_1_2_11_30_1","unstructured":"Jacobson A. R. C.vanDinther A.Vermeulen et\u00a0al.2023.\u201cCarbonTracker CT2022.\u201dhttps:\/\/doi.org\/10.25925\/Z1GJ\u20103254."},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.5194\/cp\u201016\u2010423\u20102020"},{"key":"e_1_2_11_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2025.114613"},{"key":"e_1_2_11_33_1","first-page":"165","volume-title":"Aspects of Climate Variability in the Pacific and the Western Americas","author":"Keeling C. 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