We present the organization, instrumentation, datasets, data interpretation, modeling, and accomplishments of the multinational global atmospheric measurement program AGAGE (Advanced Global Atmospheric Gases Experiment). AGAGE is distinguished by its capability to measure globally, at high frequency, and at multiple sites all the important species in the Montreal Protocol and all the important non-carbon-dioxide (non-CO₂) gases assessed by the Intergovernmental Panel on Climate Change (CO₂ is also measured at several sites). The scientific objectives of AGAGE are important in furthering our understanding of global chemical and climatic phenomena. They are the following: (1) to accurately measure the temporal and spatial distributions of anthropogenic gases that contribute the majority of reactive halogen to the stratosphere and/or are strong infrared absorbers (chlorocarbons, chlorofluorocarbons – CFCs, bromocarbons, hydrochlorofluorocarbons – HCFCs, hydrofluorocarbons – HFCs and polyfluorinated compounds (perfluorocarbons – PFCs), nitrogen trifluoride – NF₃, sulfuryl fluoride – SO₂F₂, and sulfur hexafluoride – SF₆) and use these measurements to determine the global rates of their emission and/or destruction (i.e., lifetimes); (2) to accurately measure the global distributions and temporal behaviors and determine the sources and sinks of non-CO₂ biogenic–anthropogenic gases important to climate change and/or ozone depletion (methane – CH₄, nitrous oxide – N₂O, carbon monoxide – CO, molecular hydrogen – H₂, methyl chloride – CH₃Cl, and methyl bromide – CH₃Br); (3) to identify new long-lived greenhouse and ozone-depleting gases (e.g., SO₂F₂, NF₃, heavy PFCs (C₄F₁₀, C₅F₁₂, C₆F₁₄, C₇F₁₆, and C₈F₁₈) and hydrofluoroolefins (HFOs; e.g., CH₂ = CFCF₃) have been identified in AGAGE), initiate the real-time monitoring of these new gases, and reconstruct their past histories from AGAGE, air archive, and firn air measurements; (4) to determine the average concentrations and trends of tropospheric hydroxyl radicals (OH) from the rates of destruction of atmospheric trichloroethane (CH₃CCl₃), HFCs, and HCFCs and estimates of their emissions; (5) to determine from atmospheric observations and estimates of their destruction rates the magnitudes and distributions by region of surface sources and sinks of all measured gases; (6) to provide accurate data on the global accumulation of many of these trace gases that are used to test the synoptic-, regional-, and global-scale circulations predicted by three-dimensional models; and (7) to provide global and regional measurements of methane, carbon monoxide, and molecular hydrogen and estimates of hydroxyl levels to test primary atmospheric oxidation pathways at midlatitudes and the tropics. Network Information and Data Repository: http://agage.mit.edu/data or http://cdiac.ess-dive.lbl.gov/ndps/alegage.html (https://doi.org/10.3334/CDIAC/atg.db1001).

This paper provides a detailed description of the atmospheric conditions characterizing the high Himalayas, thanks to continuous observations begun in March 2006 at the Nepal Climate Observatory-Pyramid (NCO-P) located at 5079 m a.s.l. on the southern foothills of Mt. Everest, in the framework of ABC-UNEP and SHARE-Ev-K2-CNR projects. The work presents a characterization of meteorological conditions and air-mass circulation at NCO-P during the first two years of activity. The mean values of atmospheric pressure, temperature and wind speed recorded at the site were: 551 hPa, −3.0 °C, 4.7 m s⁻¹, respectively. The highest seasonal values of temperature (1.7 °C) and relative humidity (94%) were registered during the monsoon season, which was also characterized by thick clouds, present in about 80% of the afternoon hours, and by a frequency of cloud-free sky of less than 10%. The lowest temperature and relative humidity seasonal values were registered during winter, −6.3 °C and 22%, respectively, the season being characterised by mainly cloud-free sky conditions and rare thick clouds. The summer monsoon influenced rain precipitation (seasonal mean: 237 mm), while wind was dominated by flows from the bottom of the valley (S–SW) and upper mountain (N–NE). The atmospheric composition at NCO-P has been studied thanks to measurements of black carbon (BC), aerosol scattering coefficient, PM₁, coarse particles and ozone. The annual behaviour of the measured parameters shows the highest seasonal values during the pre-monsoon (BC: 316.9 ng m⁻³, PM₁: 3.9 μg m⁻³, scattering coefficient: 11.9 Mm⁻¹, coarse particles: 0.37 cm⁻³ and O₃: 60.9 ppbv), while the lowest concentrations occurred during the monsoon (BC: 49.6 ng m⁻³, PM₁: 0.6 μg m⁻³, scattering coefficient: 2.2 Mm⁻¹, and O₃: 38.9 ppbv) and, for coarse particles, during the post-monsoon (0.07 cm⁻³. At NCO-P, the synoptic-scale circulation regimes present three principal contributions: Westerly, South-Westerly and Regional, as shown by the analysis of in-situ meteorological parameters and 5-day LAGRANTO back-trajectories. The influence of the brown cloud (AOD>0.4) extending over Indo–Gangetic Plains up to the Himalayan foothills has been evaluated by analysing the in-situ concentrations of the ABC constituents. This analysis revealed that brown cloud hot spots mainly influence the South Himalayas during the pre-monsoon, in the presence of very high levels of atmospheric compounds (BC: 1974.1 ng m⁻³, PM₁: 23.5 μg m⁻³, scattering coefficient: 57.7 Mm⁻¹, coarse particles: 0.64 cm⁻³, O₃: 69.2 ppbv, respectively). During this season 20% of the days were characterised by a strong brown cloud influence during the afternoon, leading to a 5-fold increased in the BC and PM₁ values, in comparison with seasonal means. Our investigations provide clear evidence that, especially during the pre-monsoon, the southern side of the high Himalayan valleys represent a "direct channel" able to transport brown cloud pollutants up to 5000 m a.s.l., where the pristine atmospheric composition can be strongly influenced.

A new analytical inversion method has been developed to determine the regional and global emissions of long-lived atmospheric trace gases. It exploits in situ measurement data from three global networks and builds on backward simulations with a Lagrangian particle dispersion model. The emission information is extracted from the observed concentration increases over a baseline that is itself objectively determined by the inversion algorithm. The method was applied to two hydrofluorocarbons (HFC-134a, HFC-152a) and a hydrochlorofluorocarbon (HCFC-22) for the period January 2005 until March 2007. Detailed sensitivity studies with synthetic as well as with real measurement data were done to quantify the influence on the results of the a priori emissions and their uncertainties as well as of the observation and model errors. It was found that the global a posteriori emissions of HFC-134a, HFC-152a and HCFC-22 all increased from 2005 to 2006. Large increases (21%, 16%, 18%, respectively) from 2005 to 2006 were found for China, whereas the emission changes in North America (−9%, 23%, 17%, respectively) and Europe (11%, 11%, −4%, respectively) were mostly smaller and less systematic. For Europe, the a posteriori emissions of HFC-134a and HFC-152a were slightly higher than the a priori emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). For HCFC-22, the a posteriori emissions for Europe were substantially (by almost a factor 2) higher than the a priori emissions used, which were based on HCFC consumption data reported to the United Nations Environment Programme (UNEP). Combined with the reported strongly decreasing HCFC consumption in Europe, this suggests a substantial time lag between the reported time of the HCFC-22 consumption and the actual time of the HCFC-22 emission. Conversely, in China where HCFC consumption is increasing rapidly according to the UNEP data, the a posteriori emissions are only about 40% of the a priori emissions. This reveals a substantial storage of HCFC-22 and potential for future emissions in China. Deficiencies in the geographical distribution of stations measuring halocarbons in relation to estimating regional emissions are also discussed in the paper. Applications of the inversion algorithm to other greenhouse gases such as methane, nitrous oxide or carbon dioxide are foreseen for the future.

We have constructed an atmospheric inversion framework based on TM5-4DVAR to jointly assimilate measurements of methane and δ¹³C of methane in order to estimate source-specific methane emissions. Here we present global emission estimates from this framework for the period 1999–2016. We assimilate a newly constructed, multi-agency database of CH₄ and δ¹³C measurements. We find that traditional CH₄-only atmospheric inversions are unlikely to estimate emissions consistent with atmospheric δ¹³C data, and assimilating δ¹³C data is necessary to derive emissions consistent with both measurements. Our framework attributes ca. 85 % of the post-2007 growth in atmospheric methane to microbial sources, with about half of that coming from the tropics between 23.5^∘ N and 23.5^∘ S. This contradicts the attribution of the recent growth in the methane budget of the Global Carbon Project (GCP). We find that the GCP attribution is only consistent with our top-down estimate in the absence of δ¹³C data. We find that at global and continental scales, δ¹³C data can separate microbial from fossil methane emissions much better than CH₄ data alone, and at smaller scales this ability is limited by the current δ¹³C measurement coverage. Finally, we find that the largest uncertainty in using δ¹³C data to separate different methane source types comes from our knowledge of atmospheric chemistry, specifically the distribution of tropospheric chlorine and the isotopic discrimination of the methane sink.

We report a 40-year history of SF₆ atmospheric mole fractions measured at the Advanced Global Atmospheric Gases Experiment (AGAGE) monitoring sites, combined with archived air samples, to determine emission estimates from 1978 to 2018. Previously we reported a global emission rate of 7.3±0.6 Gg yr⁻¹ in 2008 and over the past decade emissions have continued to increase by about 24 % to 9.04±0.35 Gg yr⁻¹ in 2018. We show that changing patterns in SF₆ consumption from developed (Kyoto Protocol Annex-1) to developing countries (non-Annex-1) and the rapid global expansion of the electric power industry, mainly in Asia, have increased the demand for SF₆-insulated switchgear, circuit breakers, and transformers. The large bank of SF₆ sequestered in this electrical equipment provides a substantial source of emissions from maintenance, replacement, and continuous leakage. Other emissive sources of SF₆ occur from the magnesium, aluminium, and electronics industries as well as more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have declined steadily through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Nevertheless, there are still demands for SF₆ in Annex-1 countries due to economic growth, as well as continuing emissions from older equipment and additional emissions from newly installed SF₆-insulated electrical equipment, although at low emission rates. In addition, in the non-Annex-1 countries, SF₆ emissions have increased due to an expansion in the growth of the electrical power, metal, and electronics industries to support their continuing development. There is an annual difference of 2.5–5 Gg yr⁻¹ (1990–2018) between our modelled top-down emissions and the UNFCCC-reported bottom-up emissions (United Nations Framework Convention on Climate Change), which we attempt to reconcile through analysis of the potential contribution of emissions from the various industrial applications which use SF₆. We also investigate regional emissions in East Asia (China, S. Korea) and western Europe and their respective contributions to the global atmospheric SF₆ inventory. On an average annual basis, our estimated emissions from the whole of China are approximately 10 times greater than emissions from western Europe. In 2018, our modelled Chinese and western European emissions accounted for ∼36 % and 3.1 %, respectively, of our global SF₆ emissions estimate.

We present a global distribution of surface methane (CH₄) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH₄ (CTE-CH₄) data assimilation system. In CTE-CH₄, anthropogenic and biospheric CH₄ emissions are simultaneously estimated based on constraints of global atmospheric in situ CH₄ observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH₄ to surface in situ observations, vertical profiles of CH₄ made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH₄) from the Total Carbon Column Observing Network (TCCON), and XCH₄ from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH₄ is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH₄ yr⁻¹, with an increase of 18 Tg CH₄ yr⁻¹ from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations ( < 2 Tg CH₄ yr⁻¹ difference), and much smaller than suggested by EDGAR v4.2 FT2010 inventory (33 Tg CH₄ yr⁻¹), which was used for prior anthropogenic emission estimates. The result is in good agreement with other published estimates from inverse modelling studies (16–20 Tg CH₄ yr⁻¹). However, this study could not conclusively separate a small trend in biospheric emissions (−5 to +6.9 Tg CH₄ yr⁻¹) from the much larger trend in anthropogenic emissions (15–27 Tg CH₄ yr⁻¹). Finally, we find that the global and North American CH₄ balance could be closed over this time period without the previously suggested need to strongly increase anthropogenic CH₄ emissions in the United States. With further developments, especially on the treatment of the atmospheric CH₄ sink, we expect the data assimilation system presented here will be able to contribute to the ongoing interpretation of changes in this important greenhouse gas budget.