{"refrec":{"BRefID":367383,"RR":"<b>Duke, P.J.; Hamme, R.C.; Ianson, D.; Landschützer, P.; Ahmed, M.M.M.; Swart, N.C.; Covert, P.A.</b> (2023). Estimating marine carbon uptake in the northeast Pacific using a neural network approach. <i>Biogeosciences 20(18)</i>: 3919-3941. <a href=\"https://dx.doi.org/10.5194/bg-20-3919-2023\" target=\"_blank\">https://dx.doi.org/10.5194/bg-20-3919-2023</a>","BEntID":365108,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Biogeosciences 20(18)</i>: 3919-3941. <a href=\"https://dx.doi.org/10.5194/bg-20-3919-2023\" target=\"_blank\">https://dx.doi.org/10.5194/bg-20-3919-2023</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Duke, P.J.; Hamme, R.C.; Ianson, D.; Landschützer, P.; Ahmed, M.M.M.; Swart, N.C.; Covert, P.A.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Duke, P.J. <i>et al.</i>","Englishabstract":"The global ocean takes up nearly a quarter of anthropogenic CO<sub>2</sub> emissions annually, but the variability in this uptake at regional scales remains poorly understood. Here we use a neural network approach to interpolate sparse observations, creating a monthly gridded seawater partial pressure of CO<sub>2</sub> (<i>p</i>CO<sub>2</sub>) data product from January&nbsp;1998 to December&nbsp;2019, at 1/12° x 1/12° spatial resolution, in the northeast Pacific open ocean, a net sink region. The data product (ANN-NEP; NCEI Accession 0277836) was created from <i>p</i>CO<sub>2</sub> observations within the 2021 version of the Surface Ocean CO<sub>2</sub> Atlas (SOCAT) and a range of predictor variables acting as proxies for processes affecting <i>p</i>CO<sub>2</sub> to create nonlinear relationships to interpolate observations at a spatial resolution 4 times greater than leading global products and with better overall performance. In moving to a higher resolution, we show that the internal division of training data is the most important parameter for reducing overfitting. Using our <i>p</i>CO<sub>2</sub> product, wind speed, and atmospheric CO<sub>2</sub>, we evaluate air–sea CO<sub>2</sub> flux variability. On sub-decadal to decadal timescales, we find that the upwelling strength of the subpolar Alaskan Gyre, driven by large-scale atmospheric forcing, acts as the primary control on air–sea CO<sub>2</sub> flux variability (<i>r</i><sup>2</sup>=0.93, <i>p</i>&lt;0.01). In the northern part of our study region, divergence from atmospheric CO<sub>2</sub> is enhanced by increased local wind stress curl, enhancing upwelling and entrainment of naturally CO<sub>2</sub>-rich subsurface waters, leading to decade-long intervals of strong winter outgassing. During recent Pacific marine heat waves from 2013 on, we find enhanced atmospheric CO<sub>2</sub> uptake (by as much as 45 %) due to limited wintertime entrainment. Our product estimates long-term surface ocean <i>p</i>CO<sub>2</sub> increase at a rate below the atmospheric trend (1.4 ± 0.1 µatm yr<sup>−1</sup>) with the slowest increase in the center of the subpolar gyre where there is strong interaction with subsurface waters. This mismatch suggests the northeast Pacific Ocean sink for atmospheric CO<sub>2</sub> may be increasing.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Estimating marine carbon uptake in the northeast Pacific using a neural network approach","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2025-09-25 01:36:50.712317","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"SURFACE OCEAN PCO(2);SEA CO2 FLUXES;WIND-SPEED;PARTIAL-PRESSURE;SEAWATER PCO(2);SOUTHERN-OCEAN;VARIABILITY;TRENDS;MODEL;SINK","OtherDescriptors":null,"Notes":null,"AnaPub":2023,"MonPub":null,"DateUpdate":"2025-09-18","DateCreate":"2023-09-27","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:001170669600001","VABBcode":null,"OpenAcc":1,"DOI":"10.5194/bg-20-3919-2023"},"refs":null,"anarec":{"AnaID":367383,"PubliDate":2023,"Pagination":"3919-3941","XtraPublOfAnaID":null,"ISBN":null,"Volume":"20","Issue":"18","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":68092,"SerRR":"<b>Gattuso, J.P.; Kesselmeier, J. 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