A Gradient-Descent Optimization of CO2-CO-NOx Emissions over the Paris Megacity─The Case of the First SARS-CoV-2 Lockdown

Urban greenhouse gas emissions monitoring is essential to assessing the impact of climate mitigation actions. Using atmospheric continuous measurements of air quality and carbon dioxide (CO2), we developed a gradient-descent optimization system to estimate emissions of the city of Paris. We evaluated our joint CO2-CO-NOx optimization over the first SARS-CoV-2 related lockdown period, resulting in a decrease in emissions by 40% for NOx and 30% for CO2, in agreement with preliminary estimates using bottom-up activity data yet lower than the decrease estimates from Bayesian atmospheric inversions (50%). Before evaluating the model, we first provide an in-depth analysis of three emission data sets. A general agreement in the totals is observed over the region surrounding Paris (known as Île-de-France) since all the data sets are constrained by the reported national and regional totals. However, the data sets show disagreements in their sector distributions as well as in the interspecies ratios. The seasonality also shows disagreements among emission products related to nonindustrial stationary combustion (residential and tertiary combustion). The results presented in this paper show that a multispecies approach has the potential to provide sectoral information to monitor CO2 emissions over urban areas enabled by the deployment of collocated atmospheric greenhouse gases and air quality monitoring stations.

Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment

The Paris metropolitan area, the largest urban region in the European Union, has experienced two national COVID-19 confinements in 2020 with different levels of restrictions on mobility and economic activity, which caused reductions in CO₂ emissions. To quantify the timing and magnitude of daily emission reductions during the two lockdowns, we used continuous atmospheric CO₂ monitoring, a new high-resolution near-real-time emission inventory, and an atmospheric Bayesian inverse model. The atmospheric inversion estimated the changes in fossil fuel CO₂ emissions over the Greater Paris region during the two lockdowns, in comparison with the same periods in 2018 and 2019. It shows decreases by 42-53% during the first lockdown with stringent measures and by only 20% during the second lockdown when traffic reduction was weaker. Both lockdown emission reductions are mainly due to decreases in traffic. These results are consistent with independent estimates based on activity data made by the city environmental agency. We also show that unusual persistent anticyclonic weather patterns with north-easterly winds that prevailed at the start of the first lockdown period contributed a substantial drop in measured CO₂ concentration enhancements over Paris, superimposed on the reduction of urban CO₂ emissions. We conclude that atmospheric CO₂ monitoring makes it possible to identify significant emission changes (>20%) at subannual time scales over an urban region.

Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production

We constructed a near-real-time daily CO2 emission dataset, the Carbon Monitor, to monitor the variations in CO2 emissions from fossil fuel combustion and cement production since January 1, 2019, at the national level, with near-global coverage on a daily basis and the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including the hourly to daily electrical power generation data of 31 countries, monthly production data and production indices of industry processes of 62 countries/regions, and daily mobility data and mobility indices for the ground transportation of 416 cities worldwide. Individual flight location data and monthly data were utilized for aviation and maritime transportation sector estimates. In addition, monthly fuel consumption data corrected for the daily air temperature of 206 countries were used to estimate the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO2 emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as by the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 8.8% decline in CO2 emissions globally from January 1st to June 30th in 2020 when compared with the same period in 2019 and detects a regrowth of CO2 emissions by late April, which is mainly attributed to the recovery of economic activities in China and a partial easing of lockdowns in other countries. This daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making.

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2 emissions from large cities and point sources

BACKGROUND

Satellite imagery will offer unparalleled global spatial coverage at high-resolution for long term cost-effective monitoring of CO₂ concentration plumes generated by emission hotspots. CO₂ emissions can then be estimated from the magnitude of these plumes. In this paper, we assimilate pseudo-observations in a global atmospheric inversion system to assess the performance of a constellation of one to four sun-synchronous low-Earth orbit (LEO) imagers to monitor anthropogenic CO₂ emissions. The constellation of imagers follows the specifications from the European Spatial Agency (ESA) for the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) concept for a future operational mission dedicated to the monitoring of anthropogenic CO₂ emissions. This study assesses the uncertainties in the inversion estimates of emissions ("posterior uncertainties").

RESULTS

The posterior uncertainties of emissions for individual cities and power plants are estimated for the 3 h before satellite overpasses, and extrapolated at annual scale assuming temporal auto-correlations in the uncertainties in the emission products that are used as a prior knowledge on the emissions by the Bayesian framework of the inversion. The results indicate that (i) the number of satellites has a proportional impact on the number of 3 h time windows for which emissions are constrained to better than 20%, but it has a small impact on the posterior uncertainties in annual emissions; (ii) having one satellite with wide swath would provide full images of the XCO₂ plumes, and is more beneficial than having two satellites with half the width of reference swath; and (iii) an increase in the precision of XCO₂ retrievals from 0.7 ppm to 0.35 ppm has a marginal impact on the emission monitoring performance.

CONCLUSIONS

For all constellation configurations, only the cities and power plants with an annual emission higher than 0.5 MtC per year can have at least one 8:30-11:30 time window during one year when the emissions can be constrained to better than 20%. The potential of satellite imagers to constrain annual emissions not only depend on the design of the imagers, but also strongly depend on the temporal error structure in the prior uncertainties, which is needed to be objectively assessed in the bottom-up emission maps.

Evaluation of the aerosol vertical distribution in global aerosol models through comparison against CALIOP measurements: AeroCom phase II results

The ability of 11 models in simulating the aerosol vertical distribution from regional to global scales, as part of the second phase of the AeroCom model intercomparison initiative (AeroCom II), is assessed and compared to results of the first phase. The evaluation is performed using a global monthly gridded data set of aerosol extinction profiles built for this purpose from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Layer Product 3.01. Results over 12 subcontinental regions show that five models improved, whereas three degraded in reproducing the interregional variability in Z _{α0-6 km}, the mean extinction height diagnostic, as computed from the CALIOP aerosol profiles over the 0-6 km altitude range for each studied region and season. While the models' performance remains highly variable, the simulation of the timing of the Z _{α0-6 km} peak season has also improved for all but two models from AeroCom Phase I to Phase II. The biases in Z _{α0-6 km} are smaller in all regions except Central Atlantic, East Asia, and North and South Africa. Most of the models now underestimate Z _{α0-6 km} over land, notably in the dust and biomass burning regions in Asia and Africa. At global scale, the AeroCom II models better reproduce the Z _{α0-6 km} latitudinal variability over ocean than over land. Hypotheses for the performance and evolution of the individual models and for the intermodel diversity are discussed. We also provide an analysis of the CALIOP limitations and uncertainties contributing to the differences between the simulations and observations.

Surface urban heat island across 419 global big cities

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.

A static Fourier transform spectrometer for atmospheric sounding: concept and experimental implementation

Spaceborne remote sensing can be used to retrieve the atmospheric composition and complement the surface or airborne measurement networks. In recent years, a lot of attention has been placed on the monitoring of carbon dioxide for an estimate of surface fluxes from the observed spatial and temporal gradients of its concentration. Although other techniques may be used to estimate atmospheric CO(2) concentration, the most promising for the near future is the absorption spectroscopy, focusing on the CO(2) absorption lines at 1.6 and/or 2.0 microns. For this objective, the French space agency (CNES) has developed a new spectrometer concept that is sufficiently compact to be placed onboard a microsatellite platform. The principle is that of a Fourier Transform Spectrometer (FTS), although the classical moving mirror is replaced by two sets of mirrors organized in steps. The interferogram is then imaged on a CCD matrix. The concept allows a very high resolving power, although limited to narrow spectral bands, which is well suited for the observation of a few CO(2) absorption lines. The laboratory model shows that a resolving power of about 65000 is achieved with a signal to noise on the spectra around 300. A modulating plate on the light path allows an easy of the path difference. Although this component adds some complexity to the instrument, it greatly improves the information content of the measurements.

Spaceborne estimate of atmospheric CO2 column by use of the differential absorption method: error analysis

For better knowledge of the carbon cycle, there is a need for spaceborne measurements of atmospheric CO2 concentration. Because the gradients are relatively small, the accuracy requirements are better than 1%. We analyze the feasibility of a CO2-weighted-column estimate, using the differential absorption technique, from high-resolution spectroscopic measurements in the 1.6- and 2-microm CO2 absorption bands. Several sources of uncertainty that can be neglected for other gases with less stringent accuracy requirements need to be assessed. We attempt a quantification of errors due to the radiometric noise, uncertainties in the temperature, humidity and surface pressure uncertainty, spectroscopic coefficients, and atmospheric scattering. Atmospheric scattering is the major source of error [5 parts per 10 (ppm) for a subvisual cirrus cloud with an assumed optical thickness of 0.03], and additional research is needed to properly assess the accuracy of correction methods. Spectroscopic data are currently a major source of uncertainty but can be improved with specific ground-based sunphotometry measurements. The other sources of error amount to several ppm, which is less than, but close to, the accuracy requirements. Fortunately, these errors are mostly random and will therefore be reduced by proper averaging.

Comment on rayleigh-scattering calculations for the terrestrial atmosphere

It is shown that, for a given surface pressure, the atmospheric vertical temperature profile has a negligible influence on the Rayleigh optical depth. This contradicts the Bucholtz recommendation for the use of values that vary with air mass type. The influence of atmospheric water vapor amount on the Rayleigh optical depth is also investigated.