The first 1-year-long estimate of the Paris region fossil fuel CO<sub>2</sub> emissions based on atmospheric inversion
The ability of a Bayesian atmospheric inversion to quantify the Paris region's fossil fuel CO<sub>2</sub> emissions on a monthly basis, based on a network of three surface stations operated for 1 year as part of the CO<sub>2</sub>-MEGAPARIS experiment (August 2010–Jul...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-11-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/16/14703/2016/acp-16-14703-2016.pdf |
| Summary: | The ability of a Bayesian atmospheric
inversion to quantify the Paris region's fossil fuel CO<sub>2</sub> emissions on
a monthly basis, based on a network of three surface stations operated for
1 year as part of the CO<sub>2</sub>-MEGAPARIS experiment (August 2010–July 2011),
is analysed. Differences in hourly CO<sub>2</sub> atmospheric mole fractions between
the near-ground monitoring sites (CO<sub>2</sub> gradients), located at the
north-eastern and south-western edges of the urban area, are used to estimate
the 6 h mean fossil fuel CO<sub>2</sub> emission. The inversion relies on the
CHIMERE transport model run at 2 km × 2 km horizontal resolution,
on the spatial distribution of fossil fuel CO<sub>2</sub> emissions in 2008 from
a local inventory established at 1 km × 1 km horizontal resolution
by the AIRPARIF air quality agency, and on the spatial distribution of the
biogenic CO<sub>2</sub> fluxes from the C-TESSEL land surface model. It corrects
a prior estimate of the 6 h mean budgets of the fossil fuel CO<sub>2</sub> emissions
given by the AIRPARIF 2008 inventory. We found that a stringent selection of
CO<sub>2</sub> gradients is necessary for reliable inversion results, due to large
modelling uncertainties. In particular, the most robust data selection
analysed in this study uses only mid-afternoon gradients if wind speeds are
larger than 3 m s<sup>−1</sup> and if the modelled wind at the upwind site is
within ±15° of the transect between downwind and upwind sites.
This stringent data selection removes 92 % of the hourly observations.
Even though this leaves few remaining data to constrain the emissions, the
inversion system diagnoses that their assimilation significantly reduces the
uncertainty in monthly emissions: by 9 % in November 2010 to 50 % in
October 2010. The inverted monthly mean emissions correlate well with
independent monthly mean air temperature. Furthermore, the inverted annual
mean emission is consistent with the independent revision of the AIRPARIF
inventory for the year 2010, which better corresponds to the measurement
period than the 2008 inventory. Several tests of the inversion's sensitivity
to prior emission estimates, to the assumed spatial distribution of the
emissions, and to the atmospheric transport modelling demonstrate the
robustness of the measurement constraint on inverted fossil fuel CO<sub>2</sub>
emissions. The results, however, show significant sensitivity to the
description of the emissions' spatial distribution in the inversion system,
demonstrating the need to rely on high-resolution local inventories such as
that from AIRPARIF. Although the inversion constrains emissions through the
assimilation of CO<sub>2</sub> gradients, the results are hampered by the improperly
modelled influence of remote CO<sub>2</sub> fluxes when air masses originate from
urbanised and industrialised areas north-east of Paris. The drastic data
selection used in this study limits the ability to continuously monitor Paris
fossil fuel CO<sub>2</sub> emissions: the inversion results for specific months such
as September or November 2010 are poorly constrained by too few CO<sub>2</sub>
measurements. The high sensitivity of the inverted emissions to the prior
emissions' diurnal variations highlights the limitations induced by
assimilating data only during the afternoon. Furthermore, even though the
inversion improves the seasonal variation and the annual budget of the city's
emissions, the assimilation of data during a limited number of suitable days
does not necessarily yield robust estimates for individual months. These
limitations could be overcome through a refinement of the data processing for
a wider data selection, and through the expansion of the observation network. |
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| ISSN: | 1680-7316 1680-7324 |