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Question

1. What are the main objectives and findings of the study on atmospheric CO2 inversions over the Paris metropolitan area?

2. What is the main principle of the Bayesian atmospheric inversion presented in Lian et al. (2022)?

3. How are CO2 concentrations linked to emissions in the Paris urban area?

4. What is the spatial and temporal resolution of Origins.earth inventory?

10. What is the capacity of the city-scale inversion system for monitoring urban fossil fuel CO2 emissions?

Accepted Answer

The main objectives of the study were to assess the ability and robustness of the inversion technique to track absolute urban CO2 emission levels and their relative changes over multiple years. The study aimed to investigate the variations in CO2 emissions at different time scales (daily, seasonal, and interannual) across an urban area. The findings revealed that the six years (2016-2021) continuous CO2 measurements in Paris provided sufficient information to assess the variations in CO2 emissions. The study also explored the potential for assimilating morning CO2 data, considering the performance of the Weather Research and Forecasting Model coupled with a chemistry transport model (WRF-Chem) in capturing the evolution of the atmospheric boundary layer (ABL) dynamics. The height of the ABL was identified as the main driver for uncertainties when assessing emissions from concentrations. The study demonstrated the capability of the urban atmospheric monitoring system to identify significant emission changes (>20%) at short-term monthly timescales, as evidenced by the estimated CO2 emission reductions during COVID-19 confinements in Paris. Overall, the study provided valuable insights into the spatial-temporal variations of CO2 concentrations over the Paris region and the effectiveness of atmospheric inversion techniques in monitoring fossil fuel CO2 emissions.

Accepted Answer

The main principle of the Bayesian atmospheric inversion presented in Lian et al. (2022) is to optimize the 6-hour mean fossil fuel CO2 emission budgets of the Greater Paris region over four time windows per day. This approach assimilates atmospheric CO2 concentration differences between pairs of stations located upwind and downwind of the city to decrease uncertainties caused by the transport of remote and natural fluxes outside the urban area. The inversion system is based on atmospheric CO2 measurements at seven in-situ stations combined with meteorological measurements, the WRF-Chem transport model run at 1 km x 1 km horizontal resolution, a near real-time fossil fuel CO2 inventory produced by Origins.earth, and the biogenic CO2 fluxes simulated by the Vegetation Photosynthesis and Respiration Model (VPRM) included in WRF-Chem. The inversion system setup is described in Lian et al. (2022) and outlined briefly below.

Accepted Answer

CO2 concentrations are significantly higher at urban stations CDS and JUS compared to peri-urban sites across all seasons. The gradients of CO2 concentrations between downwind and upwind stations are linked to emissions within the Paris urban area. The magnitude in CO2 gradients between urban and suburban areas ranges from 5~10 ppm in summer to 20~30 ppm during winter months. This variation is due to more stable atmospheric conditions, lower vertical dispersion, and shallow ABLH combined with high emissions from residential heating. The citywide CO2 gradients across the Paris 35 agglomerations have been used in previous inversion studies to estimate city-scale CO2 emissions.

Accepted Answer

The Origins.earth inventory provides a spatial resolution of 1 km x 1 km and a temporal resolution of hourly. It is a gridded map of fossil fuel CO2 emissions within a rectangular domain that encompasses most of the 5 IdF region. The inventory includes data from the year 2018 until the present time, with emissions from the year 2018 used as inputs for the simulation period from 2016 to 2017. The inventory covers more than 60 source types for carbon emission activities, grouped into six activity sectors: transportation, residential, tertiary, industry including cement, energy, and waste. The compilation method is detailed in SI Appendix (Text S1).

Accepted Answer

The annual emission budgets of the residential sector in IdF are 11.0, 10.9, 10.1, and 11.4 MtCO2 for the years 2018, 2019, 2020, and 2021 respectively. These budgets represent 32%, 34%, 35%, and 38% of the total emissions for each year. The Origins.earthinventory provides these estimates, highlighting the significant contribution of the residential sector to the overall CO2 emissions in the IdF region. The data shows a slight decrease in emissions from 2018 to 2021, indicating potential efforts to reduce the carbon footprint in the residential sector. Understanding these emission budgets is crucial for developing targeted strategies to mitigate CO2 emissions and promote sustainable practices in the IdF region.

Accepted Answer

Adding morning CO2 data and ABL height selection can improve atmospheric inversion results by enhancing the accuracy of atmospheric transport models. The quality of these models heavily relies on the selection of CO2 concentration data for assimilation. By including morning CO2 data, researchers can capture diurnal variations and better understand the vertical mixing and dilution processes associated with turbulence near the surface. Assessing ABL heights using potential temperature and aerosol-based algorithms allows for a more accurate diagnosis of atmospheric conditions. The comparison of modeled ABL heights against observations helps identify biases and discrepancies, leading to the revision of data selection criteria. By discarding data with significant errors in ABL heights and applying stricter wind speed thresholds, researchers can minimize contamination from local CO2 sources and improve the reliability of inverse emissions estimates. Additionally, conducting sensitivity tests with different inversion setups further enhances the understanding of the impact of data selection and prior emissions uncertainties on the inversion results. Overall, incorporating morning CO2 data and refining ABL height selection criteria contribute to more accurate and reliable atmospheric inversion outcomes.

Accepted Answer

Statistical comparisons between assimilated modeled and measured CO2 concentration gradients show improvements in emission estimates. The inversion leads to an overall improvement in the representation of 30 observations, both for the morning and afternoon periods. The mean bias errors (MBE) are reduced from -0.85 to -0.29 ppm in the morning and from -1.16 to -0.55 ppm in the afternoon after the inversion. The selected morning CO2 gradients correspond to approximately 24.5% of the total assimilated observation gradients. The estimates from the reference inversion, which assimilates daytime CO2 concentration gradients, are compared to estimates from sensitivity tests using only morning or afternoon CO2 data. The inversion increases the average posterior emissions per day of the week by around 9~16% and 13~23% with uncertainty reductions of 14~21% and 15~21% for the 6-11 UTC and 12-17 UTC time windows respectively. The assimilation of afternoon CO2 data has similar retrieved emissions but with a smaller uncertainty reduction, especially for the 6-11 UTC period. The inversion reduces the uncertainty in daily fossil fuel CO2 emission estimates from ~31% to ~24%+-4.5% over the Greater Paris region. However, CO2 emissions over the rest of the IDF region remain close to their prior values due to insufficient observational constraints. The inverse CO2 emissions show a fairly good agreement with the prior estimate, indicating that the Origins.earthnear-real-time inventory captures some timely emission variations related to meteorological effects and human activities. The daily CO2 emissions exhibit larger variability in the posterior estimates compared to the prior, representing both actual variability in emissions and sources of uncertainty in the inverse modeling system.

Accepted Answer

The inversion consistently points to an average 10% increase in fossil fuel emissions in winter months (January to March) compared to the prior estimates. This increase could be due to an underestimation of residential emissions in the Origins.earthinventory, which may not accurately represent wintertime energy demands from other fuel types, especially petroleum and wood burning in suburban residential areas. The estimated monthly emissions are approximately 20% larger compared to the prior estimates from April to June. Additionally, discrepancies in biogenic fluxes from the VPRM model and model-observation comparisons of CO2 concentrations at the SAC station suggest potential sources of modeling errors during the growing season.

Accepted Answer

The trend in annual fossil fuel CO2 emissions in the IdF region shows a decreasing trend over the period 2005-2021. The Mann-Kendall (MK) trend test at a 5% level of significance (p-values (0.0001) < 0.05) indicates this trend. The reduction in emissions is mainly attributed to emission reductions in the residential and industry sectors, a decrease in coal use, improvement in energy efficiency, and building renovation. The inverse annual fossil fuel CO2 emissions have been declining at a rate of ~2% per year over the four-year period from 2016 to 2019. In 2020, the COVID-19 pandemic and restrictions led to a ~13% reduction in annual emissions compared to 2019. In 2021, the emissions rose by ~5.2% compared to 2020 but remained 7.8% below the pre-COVID-19 level in 2019. The inversion results are generally in agreement with the TNO 1km inventory, with the inversion results being higher by ~5% for each year compared to the prior estimates from the Origins.earthinventory. The agreement among various estimates of annual fossil fuel CO2 emissions in the IdF region is within 10%, demonstrating robust emission estimates at the city scale. This trend provides evidence for continuous and timely monitoring of urban fossil fuel CO2 emission trends towards achieving carbon neutrality.

Accepted Answer

The city-scale inversion system demonstrated the capacity to monitor urban fossil fuel CO2 emissions effectively over a long-term period from 2016 to 2021. The system utilized a state-of-the-art inventory and high-precision continuous CO2 measurements. The results showed a decreasing trend in annual CO2 emissions in the IdF region, with an amplitude of approximately 2% per year at a 5% level of significance. The comparison of prior and posterior annual emissions with independent estimates from other inventory datasets showed less than a 10% difference, indicating the system's ability to detect emission trends and support emissions-mitigation measures. The system's high-resolution near-real-time local inventory, like the one used in Paris, is a unique feature that few cities possess. The inversion system's ability to detect emission changes based on CO2 measurements, independent of the prior inventory information, was demonstrated by comparing the posterior emissions with the prior annual total emission for the years 2016-2018. The assimilation of morning CO2 data, when the ABL heights are well reproduced by the model, could lead to an additional 11-16% uncertainty reduction in the morning fossil fuel emissions. However, the selection of morning CO2 observations only for moderate to high wind speeds might reduce the observational constraint on the emissions during stable weather periods. The uncertainties in the posterior estimates of CO2 emissions are partly caused by errors in the spatio-temporal distribution of emissions at finer scales than the targeted ones. The system mainly controls the city-scale fossil fuel CO2 emissions, but not the finer resolution in space. A sensitivity inversion test using the TNO 1km inventory as an alternative to the Origins.earthdataset for 2018 showed more fossil fuel CO2 emissions concentrated within the Paris inner city compared to the Origins.earthdataset. The inversion system was able to spatially differentiate between the Paris city center and the Greater Paris region, correcting the respective emissions differently. Improvements in urban ecosystem modeling and monitoring for a precise accounting of urban biomass in the estimates of CO2 fluxes are necessary. The study acknowledges limitations and suggests further investigation into the seasonal and daily variations of biogenic fluxes within the IdF region, especially during the cropland growing season. Improving the default VPRM model with high-resolution input datasets and high-quality diagnostic phenology, as well as measurements of carbon isotopes and tracers co-emitted with CO2, could help separate the contributions from fossil fuel and biogenic components to the total CO2 concentrations.

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Most frequently asked questions

1. What are the main objectives and findings of the study on atmospheric CO2 inversions over the Paris metropolitan area?

2. What is the main principle of the Bayesian atmospheric inversion presented in Lian et al. (2022)?

3. How are CO2 concentrations linked to emissions in the Paris urban area?

4. What is the spatial and temporal resolution of Origins.earth inventory?

5. What are the annual emission budgets of the residential sector in IdF?

6. How can morning CO2 data and ABL height selection improve atmospheric inversion results?

7. How do statistical comparisons between assimilated modeled and measured CO2 concentration gradients impact emission estimates?

8. What is the trend in fossil fuel CO2 emissions in winter months?

9. What is the trend in annual fossil fuel CO2 emissions in the IdF region?

10. What is the capacity of the city-scale inversion system for monitoring urban fossil fuel CO2 emissions?

References

Inverse Problem Theory and Methods for Model Parameter Estimation

Fully coupled “online” chemistry within the WRF model

Exploiting synergies of global land cover products for carbon cycle modeling

Assessment of ground-based atmospheric observations for verification of greenhouse gas emissions from an urban region

Observed Impacts of COVID-19 on Urban CO2 Emissions

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