CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories (resumen)

dc.creatorBerna Peña, Lucas
dc.creatorLopez Noreña, Ana
dc.creatorPuliafito, Enrique
dc.date.accessioned2024-07-31T13:11:00Z
dc.date.available2024-07-31T13:11:00Z
dc.date.issued2024-06-22
dc.description.abstractGridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologi- cally consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific infor- mation and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1 ◦ 0.1 × ◦ ) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB- ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff (36.7 Gt) and CO2bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions gener- ally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations.es_ES
dc.description.affiliationUniversidad Tecnológica Nacional. Facultad Regional Mendoza; Argentinaes_ES
dc.description.peerreviewedPeer Reviewedes_ES
dc.formatpdfes_ES
dc.identifier.citationEarth Syst. Sci. Data, 16, 501–523, 2024es_ES
dc.identifier.doihttps://doi.org/10.5194/essd-16-5012024
dc.identifier.urihttp://hdl.handle.net/20.500.12272/11206
dc.language.isoenges_ES
dc.rightsopenAccesses_ES
dc.rights.holderUniversidad Tecnológica Nacional. Facultad Regional Mendozaes_ES
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.rights.uriCC0 1.0 Universal*
dc.rights.useCC BY (Autoría) CC BY-SA (Autoría – Compartir Igual)es_ES
dc.sourceEarth Syst. Sci. Data 16, 501–523 ( 2024)es_ES
dc.subjectCarbon dioxido, Emissions inventory,Global, Fossil fuel, Biofueles_ES
dc.titleCoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories (resumen)es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.type.versionacceptedVersiones_ES

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