• Energy Research

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.

Abstract Resolving regional carbon budgets is critical for informing land-based mitigation policy. For nine regions covering nearly the whole globe, we collected inventory estimates of carbon-stock changes complemented by satellite estimates of biomass changes where inventory data are missing. The net land–atmospheric carbon exchange (NEE) was calculated by taking the sum of the carbon-stock change and lateral carbon fluxes from crop and wood trade, and riverine-carbon export to the ocean. Summing up NEE from all regions, we obtained a global ‘bottom-up’ NEE for net land anthropogenic CO2 uptake of –2.2 ± 0.6 PgC yr−1 consistent with the independent top-down NEE from the global atmospheric carbon budget during 2000–2009. This estimate is so far the most comprehensive global bottom-up carbon budget accounting, which set up an important milestone for global carbon-cycle studies. By decomposing NEE into component fluxes, we found that global soil heterotrophic respiration amounts to a source of CO2 of 39 PgC yr−1 with an interquartile of 33–46 PgC yr−1—a much smaller portion of net primary productivity than previously reported.

A system for measuring the two-dimensional (2-D) spatial distribution of atmospheric CO2 over complex industrial sites and urban areas on the order of 1 to 30 km2 every few minutes with a spatial resolution as high as tens of meters has been developed and demonstrated over the past 3 years. The greenhouse gas (GHG) laser imaging tomography experiment (GreenLITE™) provides improved measurement capabilities for applications ranging from automated 24/7 monitoring of ground carbon storage/sequestration (GCS) sites to long-duration real-time analyses of GHG sources and sinks in urban environments. GreenLITE combines a set of sensors based on an intensity modulated continuous wave approach with 2-D sparse tomographic reconstruction mechanisms to compute a 2-D map of CO2 concentrations over the area of interest. GreenLITE systems have recently been deployed at a number of test facilities, including a 4000-h demonstration at a GCS site in Illinois and an urban deployment in Paris, France, from November 2015 to the present. This paper describes the GreenLITE concept and the associated measurement capabilities and provides proof of concept results and analyses of observations from both short-term tests as well as longer-term industrial and urban deployments.

THE STEADY RISE IN ATMOSPHERIC LONGlived greenhouse gas concentrations is the main driver of contemporary climate change. The Mauna Loa CO2 time series (1, 2), started by C. D. Keeling in 1958 and maintained today by the Scripps Institution of Oceanography and the Earth System Research Laboratory (ESRL) of NOAA, is iconic evidence of the effect of humancaused fossil fuel and land-use change emissions on the atmospheric increase of CO2. The continuity of such records depends critically on having stable funding, which is challenging to maintain in the context of 3- to 4-year research grant funding cycles (3), and is currently threatened by the fi nancial crisis. The ESRL Global Monitoring Division maintains a network of about 100 surface and aircraft sites worldwide at which whole air samples are collected approximately every week for analysis of CO2, CH4, CO, halocarbons, and many other chemical species (4). This is complemented by high-frequency measurements at the Mauna Loa, Barrow, American Samoa, and South Pole observatories, and about 10 North American tall towers. The success of the NOAA program has inspired similar efforts in Europe (5), China (6), India (7), and Brazil (8), with the United Nations World Meteorological Organization providing guidance and precision requirements through the Global Atmosphere Watch program (9), but no funding.

Abstract. Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e., the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75∘ × 2.5∘ resolution. We conduct Observing System Simulation Experiments (OSSEs) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as “posterior uncertainty”, and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions, which are used as a prior knowledge by the inversion (called “prior uncertainty”). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic “true” emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for the UK, France, Italy, eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improves the uncertainty reduction (up to 40 to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and “true” estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model–data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.