Near-real-time global gridded daily CO2 emissions

Abstract

Precise and high-resolution carbon dioxide (CO 2 ) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO 2 Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO 2 emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO 2 ) retrievals. Our study on the global CO 2 emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO 2 emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO 2 emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

Carbon Monitor Cities, near-real-time daily estimates of CO2 emissions from 1500 cities worldwide

“…Carbon Monitor Cities disaggregates the Carbon Monitor national emissions to cities using the GRACED dataset developed by the Carbon Monitor team 38 , which consists of emission maps generated by spatializing and gridding the daily national emission inventories from Carbon Monitor into grid cells. This was achieved by estimating spatial distribution proxies from satellite data and existing gridded products while maintaining consistency between 'bottom-up' accounting results and the spatial sum of the gridded results.…”

“…9). While the spatial emission patterns derived from GID and EDGAR (with latest updates in 2019) cannot accurately reflect the situation in 2020 and 2021, the NRT TROPOMI NO 2 retrievals were used as a proxy for CO 2 to capture the daily variability in CO 2 emission following GRACED 38 . After several data processing steps, such as rolling-average and thresholding, the NO 2 data can reasonably indicate the spatial distribution of CO 2 sources 46 .…”

Near real-time CO2 fluxes from CarbonTracker Europe for high resolution atmospheric modeling

“…The added value of data assimilation for atmospheric CO 2 mole fractions has so far mostly been on larger scales of (sub)continents, and on the biospheric or oceanic component of the carbon cycle (Peters et al, 2007;Rödenbeck et al, 2018;Monteil et al, 2020). With the current observational network mostly away from densely populated regions, this has allowed studies of regional carbon cycle anomalies such as the 2010 wildfires in Russia (Shvidenko et al, 2011;Krol et al, 2013;Guo et al, 2017), the drought of 2018 in Europe Smith et al, 2020;Rödenbeck et al, 2020), and the COVID-19 crisis in 2020-2021 (Turner et al, 2020;Dou et al, 2021). For the 2018 drought, despite having large impacts on the European carbon cycle, quantification of the change in fluxes only became available about 2 years after the event (Smith et al, 2020;Ramonet et al, 2020;Thompson et al, 2022), mostly due to the burden of collecting and harmonising observational data and the time required to produce proper first-guess flux datasets to use in atmospheric data assimilation.…”

“…Economic slowdowns during the recent COVID-19 crisis have spurred the development of operational fossil flux estimation systems at the country level (e.g. Liu et al (2020); Dou et al (2021)), pushing the community towards more timely (near real-time) and more specific (high resolution) provision of anthropogenic and biospheric carbon exchange information.…”

“…For example, in Super et al (2020b) we used the relation between outside temperature and residential heating in an optimization of 'dynamical' fossil fuel (FF) emissions for the Dutch Rijnmond area. Similar approaches are ongoing elsewhere (Guevara et al, 2020), and activity-flux relations were used to study CO 2 exchange during COVID-19 (Liu et al, 2020;Dou et al, 2021). For biospheric fluxes the relation between short-term CO 2 flux variability and weather are already used for example in the Vegetation Photosynthesis and Respiration model (VPRM) model (Mahadevan et al, 2008), FLUXCOM (Jung et al, 2020) and in the Carbon -Tiled ECMWF Surface Scheme for Exchange processes over Land (CTES-SEL) biosphere module (Boussetta et al, 2013) of ECMWF's integrated forecast system (IFS) (Agustí-Panareda et al, 2014).…”