{"refrec":{"BRefID":355615,"RR":"<b>Zeng, J.; Nojiri, Y.; Landschützer, P.; Telszewski, M.; Nakaoka, S.</b> (2014). A global surface ocean <i>f</i>CO<sub>2</sub> climatology based on a feed-forward neural network. <i>J. Atmos. Oceanic. Technol. 31(8)</i>: 1838-1849. <a href=\"https://dx.doi.org/10.1175/jtech-d-13-00137.1\" target=\"_blank\">https://dx.doi.org/10.1175/jtech-d-13-00137.1</a>","BEntID":353328,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":0,"RefStringPartII":". <i>J. Atmos. Oceanic. Technol. 31(8)</i>: 1838-1849. <a href=\"https://dx.doi.org/10.1175/jtech-d-13-00137.1\" target=\"_blank\">https://dx.doi.org/10.1175/jtech-d-13-00137.1</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Zeng, J.; Nojiri, Y.; Landschützer, P.; Telszewski, M.; Nakaoka, S.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Zeng, J. <i>et al.</i>","Englishabstract":"A feed-forward neural network is used to create a monthly climatology of the sea surface fugacity of CO<sub>2</sub> (<em>f</em>CO<sub>2</sub>) on a 1° × 1° spatial resolution. Using 127 880 data points from 1990 to 2011 in the track-gridded database of the Surface Ocean CO<sub>2</sub> Atlas version 2.0 (Bakker et al.), the model yields a global mean <em>f</em>CO<sub>2</sub> increase rate of 1.50 <em>μ</em>atm yr<sup>−1</sup>. The rate was used to normalize multiple years’ <em>f</em>CO<sub>2</sub> observations to the reference year of 2000. A total of 73 265 data points from the normalized data were used to model the global <em>f</em>CO<sub>2</sub> climatology. The model simulates monthly <em>f</em>CO<sub>2</sub> distributions that agree well with observations and yields an anthropogenic CO<sub>2</sub> update of −1.9 to −2.3 PgC yr<sup>−1</sup>. The range reflects the uncertainty related to using different wind products for the flux calculation. This estimate is in good agreement with the recently derived best estimate by Wanninkhof et al. The model product benefits from a finer spatial resolution compared to the product of Lamont–Doherty Earth Observatory (Takahashi et al.), which is currently the most frequently used product. It therefore has the potential to improve estimates of the global ocean CO<sub>2</sub> uptake. The method’s benefits include but are not limited to the following: (i) a fixed structure is not required to model <em>f</em>CO<sub>2</sub> as a nonlinear function of biogeochemical variables, (ii) only one neural network configuration is sufficient to model global <em>f</em>CO<sub>2</sub> in all seasons, and (iii) the model can be extended to produce global <em>f</em>CO<sub>2</sub> maps at a higher resolution in time and space as long as the required data for input variables are available.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"A global surface ocean <i>f</i>CO<sub>2</sub> climatology based on a feed-forward neural network","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-11 01:32:56.604418","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"Fluxes; Carbon dioxide; Climatology; Neural networks; Oceanic variability","OtherDescriptors":null,"Notes":null,"AnaPub":2014,"MonPub":null,"DateUpdate":"2022-09-23","DateCreate":"2022-09-21","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000340212500011","VABBcode":null,"OpenAcc":1,"DOI":"10.1175/jtech-d-13-00137.1"},"refs":null,"anarec":{"AnaID":355615,"PubliDate":2014,"Pagination":"1838-1849","XtraPublOfAnaID":null,"ISBN":null,"Volume":"31","Issue":"8","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":45630,"SerRR":"Journal of Atmospheric and Oceanic Technology. 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