Global anthropogenic CO2 emissions and uncertainties as a prior for Earth system modelling and data assimilation

Autor: M. Choulga, G. Janssens-Maenhout, I. Super, E. Solazzo, A. Agusti-Panareda, G. Balsamo, N. Bousserez, M. Crippa, H. Denier van der Gon, R. Engelen, D. Guizzardi, J. Kuenen, J. McNorton, G. Oreggioni, A. Visschedijk
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Earth System Science Data, Vol 13, Pp 5311-5335 (2021)
Druh dokumentu: article
ISSN: 1866-3508
1866-3516
DOI: 10.5194/essd-13-5311-2021
Popis: The growth in anthropogenic carbon dioxide (CO2) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For an increased understanding of the CO2 emission sources, patterns, and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO2 concentration to variability in CO2 emissions by constraining the regional distribution of CO2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence and boundaries for each of these CO2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic CO2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO2 emissions to help assess and manage the uncertainty in the different emitting sectors. The postprocessor is available under https://doi.org/10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The dataset is documented and available under https://doi.org/10.5281/zenodo.3967439 (Choulga et al., 2020). While the uncertainty in most emission groups remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure), (2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect against an even redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping.
Databáze: Directory of Open Access Journals
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