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Previous research has shown that stratospheric aerosol causes only a small temperature change per unit forcing because they produce stronger cooling in the tropical Indian Ocean and the western Pacific Ocean than in the global mean. The enhanced temperature change in this so-called "warm-pool" region activates strongly negative local and remote feedbacks, which dampen the global mean temperature response. This paper addresses the question of why stratospheric aerosol forcing affects warm-pool temperatures more strongly than CO 2 forcing, using idealized MPI-ESM simulations. We show that the aerosol's enhanced effective forcing at the top of the atmosphere (TOA) over the warm pool contributes to the warm-pool-intensified temperature change but is not sufficient to explain the effect. Instead, the pattern of surface effective forcing, which is substantially different from the effective forcing at the TOA, is more closely linked to the temperature pattern. Independent of surface temperature changes, the aerosol heats the tropical stratosphere, accelerating the Brewer–Dobson circulation. The intensified Brewer–Dobson circulation exports additional energy from the tropics to the extratropics, which leads to a particularly strong negative forcing at the tropical surface. These results show how forced circulation changes can affect the climate response by altering the surface forcing pattern. Furthermore, they indicate that the established approach of diagnosing effective forcing at the TOA is useful for global means, but a surface perspective on the forcing must be adopted to understand the evolution of temperature patterns. [ABSTRACT FROM AUTHOR]

Aviation emissions that are dispersed into the Earth's atmosphere affect the climate and air pollution, with significant spatiotemporal variation owing to heterogeneous aircraft activity. In this paper, we use historical flight trajectories derived from Automatic Dependent Surveillance–Broadcast (ADS-B) telemetry and reanalysis weather data for 2019–2021 to develop the Global Aviation emissions Inventory based on ADS-B (GAIA). In 2019, 40.2 million flights collectively travelled 61 billion kilometres using 283 Tg of fuel, leading to CO 2 , NO X and non-volatile particulate matter (nvPM) mass and number emissions of 893 Tg, 4.49 Tg, 21.4 Gg and 2.8 × 10 26 respectively. Global responses to COVID-19 led to reductions in the annual flight distance flown and CO 2 and NO X emissions in 2020 (- 43 %, - 48 % and - 50 % respectively relative to 2019) and 2021 (- 31 %, - 41 % and - 43 % respectively), with significant regional variability. Short-haul flights with durations < 3 h accounted for 83 % of all flights but only for 35 % of the 2019 CO 2 emissions, while long-haul flights with durations > 6 h (5 % of all flights) were responsible for 43 % of CO 2 and 49 % of NO X emissions. Globally, the actual flight trajectories flown are, on average, ∼ 5 % greater than the great circle path between the origin and destination airports, but this varies by region and flight distance. An evaluation of 8705 unique flights between London and Singapore showed large variabilities in the flight trajectory profile, fuel consumption and emission indices. GAIA captures the spatiotemporal distribution of aviation activity and emissions and is provided for use in future studies to evaluate the negative externalities arising from global aviation. [ABSTRACT FROM AUTHOR]

Air–sea carbon dioxide (CO2) flux is often indirectly estimated by the bulk method using the air–sea difference in CO2 fugacity (Δf CO2) and a parameterisation of the gas transfer velocity (K). Direct flux measurements by eddy covariance (EC) provide an independent reference for bulk flux estimates and are often used to study processes that drive K. However, inherent uncertainties in EC air–sea CO2 flux measurements from ships have not been well quantified and may confound analyses of K. This paper evaluates the uncertainties in EC CO2 fluxes from four cruises. Fluxes were measured with two state-of-the-art closed-path CO2 analysers on two ships. The mean bias in the EC CO2 flux is low, but the random error is relatively large over short timescales. The uncertainty (1 standard deviation) in hourly averaged EC air–sea CO2 fluxes (cruise mean) ranges from 1.4 to 3.2 mmolm-2d-1. This corresponds to a relative uncertainty of ∼ 20 % during two Arctic cruises that observed large CO2 flux magnitude. The relative uncertainty was greater (∼ 50 %) when the CO2 flux magnitude was small during two Atlantic cruises. Random uncertainty in the EC CO2 flux is mostly caused by sampling error. Instrument noise is relatively unimportant. Random uncertainty in EC CO2 fluxes can be reduced by averaging for longer. However, averaging for too long will result in the inclusion of more natural variability. Auto-covariance analysis of CO2 fluxes suggests that the optimal timescale for averaging EC CO2 flux measurements ranges from 1 to 3 h, which increases the mean signal-to-noise ratio of the four cruises to higher than 3. Applying an appropriate averaging timescale and suitable Δf CO2 threshold (20 µatm) to EC flux data enables an optimal analysis of K. [ABSTRACT FROM AUTHOR]

Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10 μm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67 μgm-3, 15 ppbv, and 470 ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China. [ABSTRACT FROM AUTHOR]

After carbon dioxide (CO2), aerosol black carbon (BC) is considered to be the second most important contributor to global warming. This paper presents equivalent black carbon (eBC) (derived from an optical absorption method) data collected from three sites in the interior of South Africa where continuous measurements were conducted, i.e. Elandsfontein, Welgegund and Marikana, as well elemental carbon (EC) (determined by evolved carbon method) data at five sites where samples were collected once a month on a filter and analysed offline, i.e. Louis Trichardt, Skukuza, Vaal Triangle, Amersfoort and Botsalano. Analyses of eBC and EC spatial mass concentration patterns across the eight sites indicate that the mass concentrations in the South African interior are in general higher than what has been reported for the developed world and that different sources are likely to influence different sites. The mean eBC or EC mass concentrations for the background sites (Welgegund, Louis Trichardt, Skukuza, Botsalano) and sites influenced by industrial activities and)or nearby settlements (Elandsfontein, Marikana, Vaal Triangle and Amersfoort) ranged between 0.7 and 1.1, and 1.3 and 1.4 μgm-3, respectively. Similar seasonal patterns were observed at all three sites where continuous measurement data were collected (Elandsfontein, Marikana and Welgegund), with the highest eBC mass concentrations measured from June to October, indicating contributions from household combustion in the cold winter months (June-August), as well as savannah and grassland fires during the dry season (May to mid-October). Diurnal patterns of eBC at Elandsfontein, Marikana and Welgegund indicated maximum concentrations in the early mornings and late evenings, and minima during daytime. From the patterns it could be deduced that for Marikana and Welgegund, household combustion, as well as savannah and grassland fires, were the most significant sources, respectively. Possible contributing sources were explored in greater detail for Elandsfontein, with five main sources being identified as coal-fired power stations, pyrometallurgical smelters, traffic, household combustion, as well as savannah and grassland fires. Industries on the Mpumalanga Highveld are often blamed for all forms of pollution, due to the NO2 hotspot over this area that is attributed to NOx emissions from industries and vehicle emissions from the Johannesburg-Pretoria megacity. However, a comparison of source strengths indicated that household combustion as well as savannah and grassland fires were the most significant sources of eBC, particularly during winter and spring months, while coal-fired power stations, pyrometallurgical smelters and traffic contribute to eBC mass concentration levels year round. [ABSTRACT FROM AUTHOR]

Atmospheric inversions have been used for the past two decades to derive large-scale constraints on the sources and sinks of CO2 into the atmosphere. The development of dense in situ surface observation networks, such as ICOS in Europe, enables in theory inversions at a resolution close to the country scale in Europe. This has led to the development of many regional inversion systems capable of assimilating these high-resolution data, in Europe and elsewhere. The EUROCOM (European atmospheric transport inversion comparison) project is a collaboration between seven European research institutes, which aims at producing a collective assessment of the net carbon flux between the terrestrial ecosystems and the atmosphere in Europe for the period 2006–2015. It aims in particular at investigating the capacity of the inversions to deliver consistent flux estimates from the country scale up to the continental scale. The project participants were provided with a common database of in situ-observed CO2 concentrations (including the observation sites that are now part of the ICOS network) and were tasked with providing their best estimate of the net terrestrial carbon flux for that period, and for a large domain covering the entire European Union. The inversion systems differ by the transport model, the inversion approach, and the choice of observation and prior constraints, enabling us to widely explore the space of uncertainties. This paper describes the intercomparison protocol and the participating systems, and it presents the first results from a reference set of inversions, at the continental scale and in four large regions. At the continental scale, the regional inversions support the assumption that European ecosystems are a relatively small sink (-0.21±0.2 Pg C yr -1). We find that the convergence of the regional inversions at this scale is not better than that obtained in state-of-the-art global inversions. However, more robust results are obtained for sub-regions within Europe, and in these areas with dense observational coverage, the objective of delivering robust country-scale flux estimates appears achievable in the near future. [ABSTRACT FROM AUTHOR]

This paper addresses the question of how much uncertainties in CO2 fluxes over Australia can be reduced by assimilation of total-column carbon dioxide retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite instrument. We apply a four-dimensional variational data assimilation system, based around the Community Multiscale Air Quality (CMAQ) transport-dispersion model. We ran a series of observing system simulation experiments to estimate posterior error statistics of optimized monthly-mean CO2 fluxes in Australia. Our assimilations were run with a horizontal grid resolution of 81 km using OCO-2 data for 2015. Based on four representative months, we find that the integrated flux uncertainty for Australia is reduced from 0.52 to 0.13 Pg C yr -1. Uncertainty reductions of up to 90 % were found at grid-point resolution over productive ecosystems. Our sensitivity experiments show that the choice of the correlation structure in the prior error covariance plays a large role in distributing information from the observations. We also found that biases in the observations would significantly impact the inverted fluxes and could contaminate the final results of the inversion. Biases in prior fluxes are generally removed by the inversion system. Biases in the boundary conditions have a significant impact on retrieved fluxes, but this can be mitigated by including boundary conditions in our retrieved parameters. In general, results from our idealized experiments suggest that flux inversions at this unusually fine scale will yield useful information on the carbon cycle at continental and finer scales. [ABSTRACT FROM AUTHOR]

Most atmospheric scientists agree that greenhouse gas emissions have already caused significant changes to the global climate system and that these changes will accelerate in the near future. At the same time, atmospheric scientists who - like other scientists - rely on international collaboration and information exchange travel a lot and, thereby, cause substantial emissions of CO2. In this paper, the CO2 emissions of the employees working at an atmospheric research institute (the Norwegian Institute for Air Research, NILU) caused by all types of business travel (conference visits, workshops, field campaigns, instrument maintainance, etc.) were calculated for the years 2005-2007. It is estimated that more than 90% of the emissions were caused by air travel, 3% by ground travel and 5% by hotel usage. The travelrelated annual emissions were between 1.9 and 2.4 tCO2 per employee or between 3.9 and 5.5 tCO2 per scientist. For comparison, the total annual per capita CO2 emissions are 4.5 t worldwide, 1.2 t for India, 3.8 t for China, 5.9 t for Sweden and 19.1 t for Norway. The travel-related CO2 emissions of a NILU scientist, occurring in 24 days of a year on average, exceed the global average annual per capita emission. Norway's per-capita CO2 emissions are among the highest in the world, mostly because of the emissions from the oil industry. If the emissions per NILU scientist derived in this paper are taken as representative for the average Norwegian researcher, travel by Norwegian scientists would nevertheless account for a substantial 0.2% of Norway's total CO2 emissions. Since most of the travel-related emissions are due to air travel, water vapor emissions, ozone production and contrail formation further increase the relative importance of NILU's travel in terms of radiative forcing. [ABSTRACT FROM AUTHOR]

Δ (17 O) measurements of atmospheric CO 2 have the potential to be a tracer for gross primary production and stratosphere–troposphere mixing. A positive Δ (17 O) originates from intrusions of stratospheric CO 2 , whereas values close to -0.21‰ result from the equilibration of CO 2 and water, which predominantly happens inside plants. The stratospheric source of CO 2 with high Δ (17 O) is, however, not well defined in the current models. More, and long-term, atmospheric measurements are needed to improve this. We present records of the Δ (17 O) of atmospheric CO 2 obtained with laser absorption spectroscopy from Lutjewad in the Netherlands (53°24 ′ N, 6°21 ′ E) and Mace Head in Ireland (53°20 ′ N, 9°54 ′ W) that cover the period 2017–2022. The records are compared with a 3-D model simulation, and we study potential model improvements. Both records show significant interannual variability of up to 0.3 ‰. The total range covered by smoothed monthly averages from the Lutjewad record is -0.34‰ to -0.12‰ , which is significantly higher than the range of -0.20‰ to -0.17‰ for the model simulation. The 100 hPa 60–90° N monthly-mean temperature anomaly was used as a proxy to scale stratospheric downwelling in the model. This strongly improves the correlation coefficient of the simulated and observed year-to-year Δ (17 O) variations over the period 2019–2021 from 0.40 to 0.82. As the Δ (17 O) of atmospheric CO 2 seems to be dominated by stratospheric influx, its use as a tracer for stratosphere–troposphere exchange should be further investigated. [ABSTRACT FROM AUTHOR]

Hydrofluorocarbons (HFCs) are powerful anthropogenic greenhouse gases (GHGs) with high global-warming potentials (GWPs). They have been widely used as refrigerants, insulation foam-blowing agents, aerosol propellants, and fire suppression agents. Since the mid-1990s, emissions of HFCs have been increasing rapidly as they are used in many applications to replace ozone-depleting chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) whose consumption and production have been phased out under the Montreal Protocol (MP). Due to the high GWP of HFCs, the Kigali Amendment to the MP requires the phasedown of production and consumption of HFCs to gradually achieve an 80 %–85 % reduction by 2047, starting in 2019 for non-Article 5 (developed) countries with a 10 % reduction against each defined baseline and later schedules for Article 5 (developing) countries. In this study, we have examined long-term high-precision measurements of atmospheric abundances of five major HFCs (HFC-134a, HFC-143a, HFC-32, HFC-125, and HFC-152a) at Gosan station, Jeju Island, South Korea, from 2008 to 2020. Background abundances of HFCs gradually increased, and the inflow of polluted air masses with elevated abundances from surrounding source regions were detected over the entire period. From these pollution events, we inferred regional and country-specific HFC emission estimates using two independent Lagrangian particle dispersion models and Bayesian inversion frameworks (FLEXPART-FLEXINVERT + and NAME-InTEM). The spatial distribution of the derived "top-down" (measurement based) emissions for all HFCs shows large fluxes from megacities and industrial areas in the region. Our most important finding is that HFC emissions in eastern China and Japan have sharply increased from 2016 to 2018. The contribution of East Asian HFC emissions to the global total increased from 9 % (2008–2014) to 13 % (2016–2020). In particular, HFC emissions in Japan (Annex I country) rose rapidly from 2016 onward, with accumulated total inferred HFC emissions being ∼ 114 Gg yr -1 , which is ∼ 76 Gg yr -1 higher for 2016–2020 than the "bottom-up" (i.e., based on activity data and emission factors) emissions of ∼ 38 Gg yr -1 reported to the United Nations Framework Convention on Climate Change (UNFCCC). This is likely related to the increase in domestic demand in Japan for refrigerants and air-conditioning-system-related products and incomplete accounting. A downward trend of HFC emissions that started in 2019 reflects the effectiveness of the F-gas policy in Japan. Eastern China and South Korea, though not obligated to report to the UNFCCC, voluntarily reported emissions, which also show differences between top-down and bottom-up emission estimates, demonstrating the need for atmospheric measurements, comprehensive data analysis, and accurate reporting for precise emission management. Further, the proportional contribution of each country's CO 2 -equivalent HFC emissions has changed over time, with HFC-134a decreasing and HFC-125 increasing. This demonstrates the transition in the predominant HFC substances contributing to global warming in each country. [ABSTRACT FROM AUTHOR]

We present a direct measurement of the impact of increased atmospheric CO 2 on the spectra of the Earth's longwave radiation obtained from space. The goal of this study is to experimentally confirm that the direct effects of CO 2 increase on the Earth's outgoing longwave spectra follow theoretical estimates, by developing a methodology that allows for a direct and more precise comparison between theory and observations. In this methodology, a search is performed to find selected ensembles of observed atmospheric vertical profiles of temperature and water vapor that are as close as possible to each other in terms of their values. By analyzing the spectral radiances measured from space by the Atmospheric Infrared Sounder (AIRS), corresponding to the selected ensembles of profiles, the effects of increased CO 2 on the spectra can be isolated from the temperature and water vapor effects. The results illustrate the impact of the increase in CO 2 on the longwave spectra and compare well with theoretical estimates. As far as the authors are aware, this is the first time that the spectral signature of the increase in CO 2 (isolated from temperature and water vapor changes) has been directly observed from space. [ABSTRACT FROM AUTHOR]

Devising effective national-level climate action plans requires a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India's current carbon source–sink estimates are uncertain. This study uses a high-resolution Lagrangian transport model to examine the potential of available CO 2 observations over India for inverse estimation of regional carbon fluxes. We use four different sites in India that vary in the measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2 ppm) and intra-seasonal (2 to 12 ppm) variability. Our modelling framework, a high-resolution Weather Research and Forecasting Model combined with the Stochastic Time-Inverted Lagrangian Transport model (WRF–STILT), performs better in simulating seasonal (R2=0.50 to 0.96) and diurnal (R2=0.96) variability (for the Mohali station) of observed CO 2 than the current-generation global models (CarboScope, CarbonTracker and ECMWF EGG4). The seasonal CO 2 concentration variability in Mohali, associated with crop residue burning, is largely underestimated by the models. WRF–STILT captures the seasonal biospheric variability over Nainital better than the global models but underestimates the strength of the CO 2 uptake by crops. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10 ppm), endorsing the need for accounting for emission fluxes, especially for non-background sites. Our study highlights the possibility of using the CO 2 observations from these Indian stations for deducing carbon flux information at regional (Nainital) and suburban to urban (Mohali, Shadnagar and Nagpur) scales with the help of a high-resolution model. On accounting for observed variability in CO 2 , the global carbon data assimilation system can benefit from the measurements from the Indian subcontinent. [ABSTRACT FROM AUTHOR]

This paper presents estimates of current and future global anthropogenic methane emissions, their technical mitigation potential and associated costs for the period 2005 to 2030. The analysis uses the GAINS model framework to estimate emissions, mitigation potentials and costs for all major sources of anthropogenic methane for 83 countries/regions, which are aggregated to produce global estimates. Global emissions are estimated at 323Mt methane in 2005, with an expected increase to 414Mt methane in 2030. The technical mitigation potential is estimated at 195Mt methane in 2030, whereof about 80 percent is found attainable at a marginal cost less than 20 Euro t-1 CO2eq when using a social planner cost perspective. With a private investor cost perspective, the corresponding fraction is only 30 percent. Major uncertainty sources in emission estimates are identified and discussed. [ABSTRACT FROM AUTHOR]

Major limitations of our present knowledge of the global distribution of CO2 in the atmosphere are the uncertainty in atmospheric transport mixing and the sparseness of in situ concentration measurements. Limb viewing spaceborne sounders, observing the atmosphere along tangential optical paths, offer a vertical resolution of a few kilometers for profiles, which is much better than currently flying or planned nadir sounding instruments can achieve. In this paper, we analyse the feasibility of obtaining CO2 vertical profiles in the 5-25 km altitude range from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS, launched in August 2003), high spectral resolution solar occultation measurements. Two main difficulties must be overcome: (i) the accurate determination of the instrument pointing parameters (tangent heights) and pressure/temperature profiles independently from an a priori CO2 profile, and (ii) the potential impact of uncertainties in the temperature knowledge on the retrieved CO2 profile. The first difficulty has been solved using the N2 collisioninduced continuum absorption near 4μm to determine tangent heights, pressure and temperature from the ACE-FTS spectra. The second difficulty has been solved by a careful selection of CO2 spectral micro-windows. Retrievals using synthetic spectra made under realistic simulation conditions show a vertical resolution close to 2.5 km and accuracy of the order of 2 ppm after averaging over 25 profiles. These results open the way to promising studies of transport mechanisms and carbon fluxes from the ACE-FTS measurements. First CO2 vertical profiles retrieved from real ACE-FTS occultations shown in this paper confirm the robustness of the method and applicability to real measurements. [ABSTRACT FROM AUTHOR]

In spring 2006, the European Arctic was abnormally warm, setting new historical temperature records. During this warm period, smoke from agricultural fires in Eastern Europe intruded into the European Arctic and caused the most severe air pollution episodes ever recorded there. This paper confirms that biomass burning (BB) was indeed the source of the observed air pollution, studies the transport of the smoke into the Arctic, and presents an overview of the observations taken during the episode. Fire detections from the MODIS instruments aboard the Aqua and Terra satellites were used to estimate the BB emissions. The FLEXPART particle dispersion model was used to show that the smoke was transported to Spitsbergen and Iceland, which was confirmed by MODIS retrievals of the aerosol optical depth (AOD) and AIRS retrievals of carbon monoxide (CO) total columns. Concentrations of halocarbons, carbon dioxide and CO, as well as levoglucosan and potassium, measured at Zeppelin mountain near Ny Ålesund, were used to further corroborate the BB source of the smoke at Spitsbergen. The ozone (O3) and CO concentrations were the highest ever observed at the Zeppelin station, and gaseous elemental mercury was also elevated. A new O3 record was also set at a station on Iceland. The smoke was strongly absorbing -- black carbon concentrations were the highest ever recorded at Zeppelin -- and strongly perturbed the radiation transmission in the atmosphere: aerosol optical depths were the highest ever measured at Ny Ålesund. We furthermore discuss the aerosol chemical composition, obtained from filter samples, as well as the aerosol size distribution during the smoke event. Photographs show that the snow at a glacier on Spitsbergen became discolored during the episode and, thus, the snow albedo was reduced. Samples of this polluted snow contained strongly elevated levels of potassium, sulphate, nitrate and ammonium ions, thus relating the discoloration to the deposition of the smoke aerosols. This paper shows that, to date, BB has been underestimated as a source of aerosol and air pollution for the Arctic, relative to emissions from fossil fuel combustion. Given its significant impact on air quality over large spatial scales and on radiative processes, the practice of agricultural waste burning should be banned in the future. [ABSTRACT FROM AUTHOR]

Despite the need for researchers to understand terrestrial biospheric carbon fluxes to account for carbon cycle feedbacks and predict future CO2 concentrations, knowledge of these fluxes at the regional scale remains poor. This is particularly true in mountainous areas, where complex meteorology and lack of observations lead to large uncertainties in carbon fluxes. Yet mountainous regions are often where significant forest cover and biomass are found -- i.e., areas that have the potential to serve as carbon sinks. As CO2 observations are carried out in mountainous areas, it is imperative that they are properly interpreted to yield information about carbon fluxes. In this paper, we present CO2 observations at three sites in the mountains of the western US, along with atmospheric simulations that attempt to extract information about biospheric carbon fluxes from the CO2 observations, with emphasis on the observed and simulated diurnal cycles of CO2. We show that atmospheric models can systematically simulate the wrong diurnal cycle and significantly misinterpret the CO2 observations, due to erroneous atmospheric flows as a result of terrain that is misrepresented in the model. This problem depends on the selected vertical level in the model and is exacerbated as the spatial resolution is degraded, and our results indicate that a fine grid spacing of 4 km or less may be needed to simulate a realistic diurnal cycle of CO2 for sites on top of the steep mountains examined here in the American Rockies. In the absence of higher resolution models, we recommend coarse-scale models to focus on assimilating afternoon CO2 observations on mountaintop sites over the continent to avoid misrepresentations of nocturnal transport and influence. [ABSTRACT FROM AUTHOR]

This review paper explores recent efforts to estimate state- and national-scale carbon dioxide (CO2) and methane (CH4) emissions from individual anthropogenic source sectors in the US. Nearly all state and national climate change regulations in the US target specific source sectors, and detailed monitoring of individual sectors presents a greater challenge than monitoring total emissions. We particularly focus on opportunities to synthesize disparate types of information on emissions, including emission inventory data and atmospheric greenhouse gas data. We find that inventory estimates of sector-specific CO2 emissions are sufficiently accurate for policy evaluation at the national scale but that uncertainties increase at state and local levels. CH4 emission inventories are highly uncertain for all source sectors at all spatial scales, in part because of the complex, spatially variable relationships between economic activity and CH4 emissions. In contrast to inventory estimates, top-down estimates use measurements of atmospheric mixing ratios to infer emissions at the surface; thus far, these efforts have had some success identifying urban CO2 emissions and have successfully identified sector-specific CH4 emissions in several opportunistic cases. We also describe a number of forward-looking opportunities that would aid efforts to estimate sector-specific emissions: fully combine existing top-down datasets, expand intensive aircraft measurement campaigns and measurements of secondary tracers, and improve the economic and demographic data (e.g., activity data) that drive emission inventories. These steps would better synthesize inventory and top-down data to support sector-specific emission reduction policies. [ABSTRACT FROM AUTHOR]

We investigate the use of atmospheric oxygen (O 2) and carbon dioxide (CO 2) measurements for the estimation of the fossil fuel component of atmospheric CO 2 in the UK. Atmospheric potential oxygen (APO) – a tracer that combines O 2 and CO 2 , minimizing the influence of terrestrial biosphere fluxes – is simulated at three sites in the UK, two of which make APO measurements. We present a set of model experiments that estimate the sensitivity of APO simulations to key inputs: fluxes from the ocean, fossil fuel flux magnitude and distribution, the APO baseline, and the exchange ratio of O 2 to CO 2 fluxes from fossil fuel combustion and the terrestrial biosphere. To estimate the influence of uncertainties in ocean fluxes, we compare three ocean O 2 flux estimates from the NEMO–ERSEM, the ECCO–Darwin ocean model, and the Jena CarboScope (JC) APO inversion. The sensitivity of APO to fossil fuel emission magnitudes and to terrestrial biosphere and fossil fuel exchange ratios is investigated through Monte Carlo sampling within literature uncertainty ranges and by comparing different inventory estimates. We focus our model–data analysis on the year 2015 as ocean fluxes are not available for later years. As APO measurements are only available for one UK site at this time, our analysis focuses on the Weybourne station. Model–data comparisons for two additional UK sites (Heathfield and Ridge Hill) in 2021, using ocean flux climatologies, are presented in the Supplement. Of the factors that could potentially compromise simulated APO-derived fossil fuel CO 2 (ffCO 2) estimates, we find that the ocean O 2 flux estimate has the largest overall influence at the three sites in the UK. At times, this influence is comparable in magnitude to the contribution of simulated fossil fuel CO 2 to simulated APO. We find that simulations using different ocean fluxes differ from each other substantially. No single model estimate, or a model estimate that assumed zero ocean flux, provided a significantly closer fit than any other. Furthermore, the uncertainty in the ocean contribution to APO could lead to uncertainty in defining an appropriate regional background from the data. Our findings suggest that the contribution of non-terrestrial sources needs to be better accounted for in model simulations of APO in the UK to reduce the potential influence on inferred fossil fuel CO 2 using APO. [ABSTRACT FROM AUTHOR]

We summarise current important and well-established open issues related to the depletion of stratospheric ozone and discuss some newly emerging challenges. The ozone layer is recovering from the effects of halogenated source gases due to the continued success of the Montreal Protocol despite recent renewed production of controlled substances and the impact of uncontrolled very short-lived substances. The increasing atmospheric concentrations of greenhouse gases, such as carbon dioxide, methane (CH 4) and nitrous oxide (N 2 O), have large potential to perturb stratospheric ozone in different ways, but their future evolutions, and hence impacts, are uncertain. Ozone depletion through injection of smoke particles has been observed following recent Australian wildfires. Further perturbations to the ozone layer are currently occurring through the unexpected injection of massive amounts of water vapour from the Hunga Tonga–Hunga Ha'apai volcano in 2022. Open research questions emphasise the critical need to maintain, if not expand, the observational network and to address the impending "satellite data gap" in global, height-resolved observations of stratospheric trace gases and aerosols. We will, in effect, be largely blind to the stratospheric effects of similar wildfire and volcanic events in the near future. Complex Earth system models (ESMs) being developed for climate projections have the stratosphere as an important component. However, the huge computational requirement of these models must not result in an oversimplification of the many processes affecting the ozone layer. Regardless, a hierarchy of simpler process models will continue to be important for testing our evolving understanding of the ozone layer and for providing policy-relevant information. [ABSTRACT FROM AUTHOR]

While the growth rate of atmospheric CO 2 mole fractions can be measured with high accuracy, there are still large uncertainties in its attribution to specific regions and diverse anthropogenic and natural sources and sinks. A major source of uncertainty is the net flux of carbon dioxide from the biosphere to the atmosphere, the net ecosystem exchange (NEE). There are two major approaches to quantifying NEE: top-down approaches that typically use atmospheric inversions and bottom-up estimates that rely on process-based or data-driven models or inventories. Both top-down and bottom-up approaches have known strengths and limitations. Atmospheric inversions (e.g., those used in global carbon budgets) produce estimates of NEE that are consistent with the atmospheric CO 2 growth rate at regional and global scales but are highly uncertain at smaller scales. Bottom-up data-driven models based on eddy-covariance measurements (e.g., FLUXCOM) match local observations of NEE and their spatial variability but have difficulty in accurately upscaling to a reliable global estimate. In this study, we propose combining the two approaches to produce global NEE estimates, with the goal of capitalizing on each approach's strengths and mitigating their limitations. We do this by constraining the data-driven FLUXCOM model with regional estimates of NEE derived from an ensemble of atmospheric inversions from the Global Carbon Budget 2021. To do this, we need to overcome a series of scientific and technical challenges when combining information about diverse physical variables, which are influenced by different processes at different spatial and temporal scales. We design a modeling structure that optimizes NEE by considering both the model's performance at the in situ level, based on eddy-covariance measurements, and at the level of large regions, based on atmospheric inversion estimates of NEE and their uncertainty. This resulting "dual-constraint" data-driven flux model improves on information based on single constraints (either top down or bottom up), producing robust locally resolved and globally consistent NEE spatio-temporal fields. Compared to reference estimates of the global land sink from the literature, e.g., Global Carbon Budgets, our double-constraint inferred global NEE shows a considerably smaller bias in global and tropical NEE compared to the underlying bottom-up data-driven model estimates (i.e., single constraint). The mean seasonality of our double-constraint inferred global NEE is also more consistent with the Global Carbon Budget and atmospheric inversions. At the same time, our model allows for more robustly spatially resolved NEE. The improved performance of the double-constraint model across spatial and temporal scales demonstrates the potential for adding a top-down constraint to a bottom-up data-driven flux model. [ABSTRACT FROM AUTHOR]

Current knowledge about the European terrestrial biospheric carbon sink, from the Atlantic to the Urals, relies upon bottom-up inventory and surface flux inverse model estimates (e.g. 0.27±0.16 GtC a-1 for 2000-2005 (Schulze et al., 2009), 0.17±0.44 GtC a-1 for 2001-2007 (Peters et al., 2010), 0.45±0.40 GtC a-1 for 2010 (Chevallier et al., 2014), 0.40±0.42 GtC a-1 for 2001-2004 (Peylin et al., 2013)). Inverse models assimilate in situ CO2 atmospheric concentrations measured by surface-based air sampling networks. The intrinsic sparseness of these networks is one reason for the relatively large flux uncertainties (Peters et al., 2010; Bruhwiler et al., 2011). Satellite-based CO2 measurements have the potential to reduce these uncertainties (Miller et al., 2007; Chevallier et al., 2007). Global inversion experiments using independent models and independent GOSAT satellite data products consistently derived a considerably larger European sink (1.0-1.3 GtC a-1 for 09/2009-08/2010 (Basu et al., 2013), 1.2-1.8 GtC a-1 in 2010 (Chevallier et al., 2014)). However, these results have been considered unrealistic due to potential retrieval biases and/or transport errors (Chevallier et al., 2014) or have not been discussed at all (Basu et al., 2013; Takagi et al., 2014). Our analysis comprises a regional inversion approach using STILT (Gerbig et al., 2003; Lin et al., 2003) short-range (days) particle dispersion modelling, rendering it insensitive to large-scale retrieval biases and less sensitive to long-range transport errors. We show that the satellite-derived European terrestrial carbon sink is indeed much larger (1.02±0.30 GtC a-1 in 2010) than previously expected. This is qualitatively consistent among an ensemble of five different inversion set-ups and five independent satellite retrievals (BESD (Reuter et al., 2011) 2003-2010, ACOS (O'Dell et al., 2012) 2010, UoL-FP (Cogan et al., 2012) 2010, RemoTeC (Butz et al., 2011) 2010, and NIES (Yoshida et al., 2013) 2010) using data from two different instruments (SCIAMACHY (Bovensmann et al., 1999) and GOSAT (Kuze et al., 2009)). The difference to in situ based inversions (Peylin et al., 2013), whilst large with respect to the mean reported European carbon sink (0.4 GtC a-1 for 2001-2004), is similar in magnitude to the reported uncertainty (0.42 GtC a-1). The highest gain in information is obtained during the growing season when satellite observation conditions are advantageous, a priori uncertainties are largest, and the surface sink maximises; during the dormant season, the results are dominated by the a priori. Our results provide evidence that the current understanding of the European carbon sink has to be revisited. [ABSTRACT FROM AUTHOR]

The World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) coordinates high-quality atmospheric greenhouse gas observations globally and provides these observations through the WMO World Data Centre for Greenhouse Gases (WDCGG) supported by Japan Meteorological Agency. The WDCGG and the National Oceanic and Atmospheric Administration (NOAA) analyse these measurements using different methodologies and site selection to calculate global annual mean surface CO 2 and its growth rate as a headline climate indicator. This study introduces a third hybrid method named GFIT, which serves as an independent validation and open-source alternative to the methods described by NOAA and WDCGG. We apply GFIT to incorporate observations from most WMO GAW stations and 3D modelled CO 2 fields from CarbonTracker Europe (CTE). We find that different observational networks (i.e. NOAA, GAW, and CTE networks) and analysis methods result in differences in the calculated global surface CO 2 mole fractions equivalent to the current atmospheric growth rate over a 3-month period. However, the CO 2 growth rate derived from these networks and the CTE model output shows good agreement. Over the long-term period (40 years), both networks with and without continental sites exhibit the same trend in the growth rate (0.030 ± 0.002 ppm yr -1 each year). However, a clear difference emerges in the short-term (1-month) change in the growth rate. The network that includes continental sites improves the early detection of changes in biogenic emissions. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO 2 point sources, including coal-fired power plants. In this study, we routinely made observations with the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite imaging spectrometer and the Orbiting Carbon Observatory-3 (OCO-3) instrument aboard the International Space Station at over 30 coal-fired power plants between 2021 and 2022. CO 2 plumes were detected in 50 % of the acquired PRISMA scenes, which is consistent with the combined influence of viewing parameters on detection (solar illumination and surface reflectance) and unknown factors (e.g., daily operational status). We compare satellite-derived emission rates to in situ stack emission observations and find average agreement to within 27 % for PRISMA and 30 % for OCO-3, although more observations are needed to robustly characterize the error. We highlight two examples of fusing PRISMA with OCO-2 and OCO-3 observations in South Africa and India. For India, we acquired PRISMA and OCO-3 observations on the same day and used the high-spatial-resolution capability of PRISMA (30 m spatial/pixel resolution) to partition relative contributions of two distinct emitting power plants to the net emission. Although an encouraging start, 2 years of observations from these satellites did not produce sufficient observations to estimate annual average emission rates within low (<15%) uncertainties. However, as the constellation of CO 2 -observing satellites is poised to significantly improve in the coming decade, this study offers an approach to leverage multiple observation platforms to better quantify and characterize uncertainty for large anthropogenic emission sources. [ABSTRACT FROM AUTHOR]

We present a new method for partitioning observed CO 2 enhancements (CO 2 xs) into fossil and biospheric fractions (Cff and Cbio) based on measurements of CO and δ13 CO 2 , complemented by flask-based Δ14 CO 2 measurements. This method additionally partitions the fossil fraction into natural gas and petroleum fractions (when coal combustion is insignificant). Although here we apply the method only to discrete flask air measurements, the advantage of this method (CO- and δ13 CO 2 -based method) is that CO 2 xs partitioning can be applied at high frequency when continuous measurements of CO and δ13 CO 2 are available. High-frequency partitioning of CO 2 xs into Cff and Cbio has already been demonstrated using continuous measurements of CO (CO-based method) and Δ14 CO 2 measurements from flask air samples. We find that the uncertainty in Cff estimated from the CO- and δ13 CO 2 -based method averages 3.2 ppm (23 % of the mean Cff of 14.2 ppm estimated directly from Δ14 CO 2) , which is significantly less than the CO-based method which has an average uncertainty of 4.8 ppm (34 % of the mean Cff). Using measurements of CO, δ13 CO 2 and Δ14 CO 2 from flask air samples at three sites in the greater Los Angeles (LA) region, we find large contributions of biogenic sources that vary by season. On a monthly average, the biogenic signal accounts for - 14 to + 25 % of CO 2 xs with larger and positive contributions in winter and smaller and negative contributions in summer due to net respiration and net photosynthesis, respectively. Partitioning Cff into petroleum and natural gas combustion fractions reveals that the largest contribution of natural gas combustion generally occurs in summer, which is likely related to increased electricity generation in LA power plants for air-conditioning. [ABSTRACT FROM AUTHOR]

Radiocarbon dioxide (14CO2, reported in Δ14CO2) can be used to determine the fossil fuel CO2 addition to the atmosphere, since fossil fuel CO2 no longer contains any 14C. After the release of CO2 at the source, atmospheric transport causes dilution of strong local signals into the background and detectable gradients of Δ14CO2 only remain in areas with high fossil fuel emissions. This fossil fuel signal can moreover be partially masked by the enriching effect that anthropogenic emissions of 14CO2 from the nuclear industry have on the atmospheric Δ14CO2 signature. In this paper, we investigate the regional gradients in 14CO2 over the European continent and quantify the effect of the emissions from nuclear industry. We simulate the emissions and transport of fossil fuel CO2 and nuclear 14CO2 for Western Europe using the Weather Research and Forecast model (WRF-Chem) for a period covering 6 summer months in 2008. We evaluate the expected CO2 gradients and the resulting Δ14CO2 in simulated integrated air samples over this period, as well as in simulated plant samples. We find that the average gradients of fossil fuel CO2 in the lower 1200m of the atmosphere are close to 15 ppm at a 12 km x 12 km horizontal resolution. The nuclear influence on Δ14CO2 signatures varies considerably over the domain and for large areas in France and the UK it can range from 20 to more than 500% of the influence of fossil fuel emissions. Our simulations suggest that the resulting gradients in Δ14CO2 are well captured in plant samples, but due to their time-varying uptake of CO2, their signature can be different with over 3‰ from the atmospheric samples in some regions. We conclude that the framework presented will be well-suited for the interpretation of actual air and plant 14CO2 samples. [ABSTRACT FROM AUTHOR]

The mixing height (MH) is a crucial parameter in commonly used transport models that proportionally affects air concentrations of trace gases with sources/sinks near the ground and on diurnal scales. Past synthetic data experiments indicated the possibility to improve tracer transport by minimizing errors of simulated MHs. In this paper we evaluate a method to constrain the Lagrangian particle dispersion model STILT (Stochastic Time-Inverted Lagrangian Transport) with MH diagnosed from radiosonde profiles using a bulk Richardson method. The same method was used to obtain hourly MHs for the period September/October 2009 from the Weather Research and Forecasting (WRF) model, which covers the European continent at 10 km horizontal resolution. Kriging with external drift (KED) was applied to estimate optimized MHs from observed and modelled MHs, which were used as input for STILT to assess the impact on CO2 transport. Special care has been taken to account for uncertainty in MH retrieval in this estimation process. MHs and CO2 concentrations were compared to vertical profiles from aircraft in situ data. We put an emphasis on testing the consistency of estimated MHs to observed vertical mixing of CO2. Modelled CO2 was also compared with continuous measurements made at Cabauw and Heidelberg stations. WRF MHs were significantly biased by ~10-20% during day and ~40-60%during night. Optimized MHs reduced this bias to ~5% with additional slight improvements in random errors. The KED MHs were generally more consistent with observed CO2 mixing. The use of optimized MHs had in general a favourable impact on CO2 transport, with bias reductions of 5-45% (day) and 60-90% (night). This indicates that a large part of the found CO2 model-data mismatch was indeed due to MH errors. Other causes for CO2 mismatch are discussed. Applicability of our method is discussed in the context of CO2 inversions at regional scales. [ABSTRACT FROM AUTHOR]

Interpretation of observed diurnal carbon dioxide (CO2) mixing ratios near the surface requires knowledge of the local dynamics of the planetary boundary layer. In this paper, we study the relationship between the boundary layer dynamics and the CO2 budget in convective conditions through a newly derived set of analytical equations. From these equations, we are able to quantify how uncertainties in boundary layer dynamical variables or in the morning CO2 distribution in the mixed-layer or in the free atmosphere (FA) influence the bulk CO2 mixing ratio. We find that the largest uncertainty incurred on the midday CO2 mixing ratio comes from the prescribed early morning CO2 mixing ratios in the stable boundary layer, and in the free atmosphere. Errors in these values influence CO2 mixing ratios inversely proportional to the boundary layer depth (h), just like uncertainties in the assumed initial boundary layer depth and surface CO2 flux. The influence of uncertainties in the boundary layer depth itself is one order of magnitude smaller. If we "invert" the problem and calculate CO2 surface exchange from observed or simulated CO2 mixing ratios, the sensitivities to errors in boundary layer dynamics also invert: they become linearly proportional to the boundary layer depth. We demonstrate these relations for a typical well characterized situation at the Cabauw site in The Netherlands, and conclude that knowledge of the temperature and carbon dioxide profiles of the atmosphere in the early morning are of vital importance to correctly interpret observed CO2 mixing ratios during midday. [ABSTRACT FROM AUTHOR]

In spring 2002, an atmospheric measurement site was established at the peak of Whistler Mountain in British Columbia, Canada to measure trace gases, particle chemistry and physics, and meteorology. This paper uses continuous measurements from March 2002 to December 2006 to investigate the influence of trans-Pacific transport and North American forest fires on both O3 and CO at Whistler. Annual mean mixing ratios of O3 and CO were 41 ppbv (monthly means of 35-48 ppbv) and 145 ppbv (monthly means of 113-177 ppbv) respectively with both species exhibiting an annual cycle of late-winter to early-spring maxima and summer minima. The absence of a broad summer O3 peak differs from previously-reported high altitude sites in the western US. The highest monthly-averaged O3 and CO mixing ratios relative to the 5-yr monthly means were seen in fall 2002 and spring 2003 with increased O3 and CO of 10% and 25% respectively. These increases correspond to anomalously-high values reported at other Northern Hemisphere sites and are attributed to fires in the Russian Federation. Air mass back trajectory analysis is used to associate the mean enhancements of O3 and CO with trans-Pacific transported or North American air masses relative to the Pacific background. Mean values of the enhancements for March to June in trans-Pacific air masses were 6 ppbv and 16 ppbv for O3 and CO respectively. In summers 2002-2006, higher CO and O3 mixing ratios were almost always observed in North American air masses and this relative enhancement co-varied for each year with the western US and Canada total wild- fire area. The greatest enhancements in O3 and CO were seen in 2004, a record year for forest fires in Alaska and the Yukon Territory with average O3 and CO mixing ratios 13 and 43 ppbv above background values. [ABSTRACT FROM AUTHOR]

This paper reexamines evidence for systematic errors in atmospheric transport models, in terms of the diagnostics used to infer vertical mixing rates from models and observations. Different diagnostics support different conclusions about transport model errors that could imply either stronger or weaker northern terrestrial carbon sinks. Conventional mixing diagnostics are compared to analyzed vertical mixing rates using data from the US Southern Great Plains Atmospheric Radiation Measurement Climate Research Facility, the CarbonTracker data assimilation system based on Transport Model version 5 (TM5), and atmospheric reanalyses. The results demonstrate that diagnostics based on boundary layer depth and vertical concentration gradients do not always indicate the vertical mixing strength. Vertical mixing rates are anti-correlated with boundary layer depth at some sites, diminishing in summer when the boundary layer is deepest. Boundary layer equilibrium concepts predict an inverse proportionality between CO2 vertical gradients and vertical mixing strength, such that previously reported discrepancies between observations and models most likely reflect overestimated as opposed to underestimated vertical mixing. However, errors in seasonal concentration gradients can also result from errors in modeled surface fluxes. This study proposes using the timescale for approach to boundary layer equilibrium to diagnose vertical mixing independently of seasonal surface fluxes, with applications to observations and model simulations of CO2 or other conserved boundary layer tracers with surface sources and sinks. Results indicate that frequently cited discrepancies between observations and inverse estimates do not provide sufficient proof of systematic errors in atmospheric transport models. Some previously hypothesized transport model biases, if found and corrected, could cause inverse estimates to further diverge from carbon inventory estimates of terrestrial sinks. [ABSTRACT FROM AUTHOR]

This paper presents the first global set of observations of CO2 in the mesosphere and lower thermosphere (MLT) obtained by the ACE-FTS instrument on SCISAT-I, a small Canadian satellite launched in 2003. The observations use the solar occultation technique and document the fall-off in the mixing ratio of CO2 in the MLT region. The beginning of the fall-off of the CO2, or "knee" occurs at about 78 km and lies higher than in the CRISTA-1 measurements (∼70 km) but lower than in the SABER 1.06 (∼80 km) and much lower than in rocket measurements. We also present the measurements of CO obtained concurrently which provide important constraints for analysis. We have compared the ACE measurements with simulations of the CO2 and CO distributions in the vertically extended version of the Canadian Middle Atmosphere Model (CMAM). Applying standard chemistry we find that we cannot get agreement between the model and ACE CO2 observations although the CO observations are adequately reproduced. There appears to be about a 10 km offset compared to the observed ACE CO2, with the model "knee" occurring too high. In analyzing the disagreement, we have investigated the variation of several parameters of interest (photolysis rates, formation rate for CO2, and the impact of uncertainty in turbulent eddy diffusion) in order to explore parameter space for this problem. Our conclusions are that there must be a loss process for CO2, about 2-4 times faster than photolysis that will sequester the carbon in some form other than CO and we have speculated on the role of meteoritic dust as a possible candidate. In addition, from this study we have highlighted a possible important role for unresolved vertical eddy diffusion in 3-D models in determining the distribution of candidate species in the mesosphere which requires further study. [ABSTRACT FROM AUTHOR]

Between July and November 2008, simultaneous observations were conducted by several orbiting instruments that monitor carbon monoxide in the atmosphere, among them the Infrared Atmospheric Sounding Instrument (IASI) and Measurements Of Pollution In The Troposphere (MOPITT). In this paper, the concentration retrievals at about 700 hPa from these two instruments are successively used in a variational Bayesian system to infer the global distribution of CO emissions. Starting from a global emission budget of 479 Tg for the considered period, the posterior estimate of CO emissions using IASI retrievals gives a total of 643 Tg, which is in close agreement with the budget calculated with version 3 of the MOPITT data (649 Tg). The regional totals are also broadly consistent between the two inversions. Even though our theoretical error budget indicates that IASI constrains the emissions slightly less than MOPITT, because of lesser sensitivity in the lower troposphere, these first results indicate that IASI may play a major role in the quantification of the emissions of CO. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. SCIAMACHY on ENVISAT is the first satellite instrument whose measurements are sensitive to concentration changes of the two gases at all altitude levels down to the Earth's surface where the source/sink signals are largest. We have processed three years (2003-2005) of SCIAMACHY nearinfrared nadir measurements to simultaneously retrieve vertical columns of CO2 (from the 1.58 μm absorption band), CH4 (1.66 μm) and oxygen (O2 A-band at 0.76 μm) using the scientific retrieval algorithm WFM-DOAS. We show that the latest version of WFM-DOAS, version 1.0, which is used for this study, has been significantly improved with respect to its accuracy compared to the previous versions while essentially maintaining its high processing speed (∼1 min per orbit, corresponding to ∼6000 single measurements, and per gas on a standard PC). The greenhouse gas columns are converted to dry air column-averaged mole fractions, denoted XCO2 (in ppm) and XCH4 (in ppb), by dividing the greenhouse gas columns by simultaneously retrieved dry air columns. For XCO2 dry air columns are obtained from the retrieved O2 columns. For XCH4 dry air columns are obtained from the retrieved CO2 columns because of better cancellation of light path related errors compared to using O2 columns retrieved from the spectrally distant O2 A-band. Here we focus on a discussion of the XCH4 data set. The XCO2 data set is discussed in a separate paper (Part 1). For 2003 we present detailed comparisons with the TM5 model which has been optimally matched to highly accurate but sparse methane surface observations. After accounting for a systematic low bias of ∼2% agreement with TM5 is typically within 1-2%. We investigated to what extent the SCIAMACHY XCH4 is influenced by the variability of atmospheric CO2 using global CO2 fields from NOAA's CO2 assimilation system CarbonTracker. We show that the CO2 corrected and uncorrected XCH4 spatio-temporal pattern are very similar but that agreement with TM5 is better for the CarbonTracker CO2 corrected XCH4. In line with previous studies (e.g., Frankenberg et al., 2005b) we find higher methane over the tropics compared to the model. We show that tropical methane is also higher when normalizing the CH4 columns with retrieved O2 columns instead of CO2. In consistency with recent results of Frankenberg et al. (2008b) it is shown that the magnitude of the retrieved tropical methane is sensitive to the choice of the spectroscopic line parameters of water vapour. Concerning inter-annual variability we find similar methane spatio-temporal pattern for 2003 and 2004. For 2005 the retrieved methane shows significantly higher variability compared to the two previous years, most likely due to somewhat larger noise of the spectral measurements. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. SCIAMACHY on ENVISAT is the first satellite instrument whose measurements are sensitive to concentration changes of the two gases at all altitude levels down to the Earth's surface where the source/sink signals are largest. We have processed three years (2003-2005) of SCIAMACHY nearinfrared nadir measurements to simultaneously retrieve vertical columns of CO2 (from the 1.58 μm absorption band), CH4 (1.66 μm) and oxygen (O2 A-band at 0.76 μm) using the scientific retrieval algorithm WFM-DOAS.We show that the latest version of WFM-DOAS, version 1.0, which is used for this study, has been significantly improved with respect to its accuracy compared to the previous versions while essentially maintaining its high processing speed (~1 min per orbit, corresponding to ~6000 single measurements, and per gas on a standard PC). The greenhouse gas columns are converted to dry air column-averaged mole fractions, denoted XCO2 (in ppm) and XCH4 (in ppb), by dividing the greenhouse gas columns by simultaneously retrieved dry air columns. For XCO2 dry air columns are obtained from the retrieved O2 columns. For XCH4 dry air columns are obtained from the retrieved CO2 columns because of better cancellation of light path related errors compared to using O2 columns retrieved from the spectrally distant O2 Aband. Here we focus on a discussion of the XCO2 data set. The XCH4 data set is discussed in a separate paper (Part 2). In order to assess the quality of the retrieved XCO2 we present comparisons with Fourier Transform Spectroscopy (FTS) XCO2 measurements at two northern hemispheric mid-latitude ground stations. To assess the quality globally, we present detailed comparisons with global XCO2 fields obtained from NOAA's CO2 assimilation system CarbonTracker. For the Northern Hemisphere we find good agreement with the reference data for the CO2 seasonal cycle and the CO2 annual increase. For the Southern Hemisphere, where significantly less data are available for averaging compared to the Northern Hemisphere, the CO2 annual increase is also in good agreement with CarbonTracker but the amplitude and phase of the seasonal cycle show systematic differences (up to several ppm) arising partially from the O2 normalization most likely caused by unconsidered scattering effects due to subvisual cirrus clouds. The retrieved XCO2 regional pattern at monthly resolution over various regions show clear correlations with Carbon Tracker but also significant differences. Typically the retrieved variability is about 4 ppm (1% of 380 ppm) higher but depending on time and location differences can reach or even exceed 8 ppm. Based on the error analysis and on the comparison with the reference data we conclude that the XCO2 data set can be characterized by a single measurement retrieval precision (random error) of 1-2%, a systematic low bias of about 1.5%, and by a relative accuracy of about 1-2% for monthly averages at a spatial resolution of about 7°×7°. When averaging the SCIAMACHY XCO2 over all three years we find elevated CO2 over the highly populated region of western central Germany and parts of the Netherlands ("Rhine-Main area") reasonably well correlated with EDGAR anthropogenic CO2 emissions. On average the regional enhancement is 2.7 ppm including an estimated contribution of 1-1.5 ppm due to aerosol related errors and sampling. [ABSTRACT FROM AUTHOR]

Despite the implementation of the Clean Air Action Plan by the Chinese government in 2013, the issue of increasing surface ozone (O 3) concentrations remains a significant environmental concern in China. In this study, we used an improved regional climate–chemistry–ecology model (RegCM-Chem-YIBs) to investigate the impact of anthropogenic emissions, meteorological factors, and CO 2 changes on summer surface O 3 levels in China from 2008 to 2018. Compared to its predecessor, the model has been enhanced concerning the photolysis of O 3 and the radiative impacts of CO 2 and O 3. The investigations showed anthropogenic emissions were the primary contributor to the O 3 increase in China, responsible for 4.08–18.51 ppb in the North China Plain. However, changed meteorological conditions played a crucial role in decreasing O 3 in China and may have a more significant impact than anthropogenic emissions in some regions. Changed CO 2 played a critical role in the variability of O 3 through radiative forcing and isoprene emissions, particularly in southern China, inducing an increase in O 3 on the southeast coast of China (0.28–0.46 ppb) and a decrease in southwest and central China (- 0.51 to - 0.11 ppb). Our study comprehensively analyzed O 3 variation across China from various perspectives and highlighted the importance of considering CO 2 variations when designing long-term O 3 control policies, especially in high-vegetation-coverage areas. [ABSTRACT FROM AUTHOR]

The analysis of observed atmospheric trace-gas mole fractions to infer surface sources and sinks of chemical species relies heavily on simulated atmospheric transport. The chemical transport models (CTMs) used in flux-inversion models are commonly configured to reproduce the atmospheric transport of a general circulation model (GCM) as closely as possible. CTMs generally have the dual advantages of computational efficiency and improved tracer conservation compared to their parent GCMs, but they usually simplify the representations of important processes. This is especially the case for high-frequency vertical motions associated with diffusion and convection. Using common-flux experiments, we quantify the importance of parameterized vertical processes for explaining systematic differences in tracer transport between two commonly used CTMs. We find that differences in modeled column-average CO2 are strongly correlated with the differences in the models' convection. The parameterization of diffusion is more important near the surface due to its role in representing planetary-boundary-layer (PBL) mixing. Accordingly, simulated near-surface in situ measurements are more strongly impacted by this process than are simulated total-column averages. Both diffusive and convective vertical mixing tend to ventilate the lower atmosphere, so near-surface measurements may only constrain the net vertical mixing and not the balance between these two processes. Remote-sensing-based retrievals of total-column CO2 , with their increased sensitivity to convection, may provide important new constraints on parameterized vertical motions. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO 2) and methane (CH 4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases. [ABSTRACT FROM AUTHOR]

Using co-located space-based measurements of carbon dioxide (CO2) from the Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3 (OCO-2/3) and carbon monoxide (CO) and nitrogen dioxide (NO2) from the TROPOspheric Monitoring Instrument (TROPOMI), we calculate total column enhancements for observations influenced by anthropogenic emissions from urban regions relative to clean background values. We apply this method to observations taken over or downwind of 27 large (population of >1 million) urban areas from around the world. Enhancement ratios between species are calculated and compared to emissions ratios derived from four globally gridded anthropogenic emissions inventories. We find that these global inventories underestimate CO emissions in many North American and European cities relative to our observed enhancement ratios, while smaller differences were found for NO2 emissions. We further demonstrate that the calculation and intercomparison of enhancement ratios of multiple tracers can help to identify the underlying biases leading to disagreement between observations and inventories. Additionally, we use high-resolution CO2 inventories for two cities (Los Angeles and Indianapolis) to estimate emissions of CO and NO2 using our calculated enhancement ratios and find good agreement with both a previous modelling study for the megacity of Los Angeles and California Air Resources Board (CARB) inventory estimates. [ABSTRACT FROM AUTHOR]

We present an analysis of atmospheric transport impact on estimating CO 2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO 2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO 2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO 2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between - 0.72 and 0.20 PgC yr -1 , which is larger than the a priori uncertainty of 0.47 PgC yr -1. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr -1), in comparison with 0.23 and 0.10 PgC yr -1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO 2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences. [ABSTRACT FROM AUTHOR]

Climate warming in high latitudes impacts CO2 sequestration of the northern peatlands through the changes in production and decomposition processes. The response of the net CO2 fluxes between ecosystems and the atmosphere to climate change and weather anomalies can vary across forest and non-forest peatlands. To better understand the differences in CO2 dynamics at forest and non-forest boreal peatlands induced by changes in environmental conditions, the estimates of interannual variability in the net ecosystem exchange (NEE), total ecosystem respiration (TER), and gross primary production (GPP) was obtained at two widespread peatland ecosystems – paludified spruce forest and the adjacent ombrotrophic bog in the southern taiga of west Russia using 6 years of paired eddy covariance flux measurements. Both positive and negative annual and growing season air temperature and precipitation anomalies were observed in the period of measurement (2015–2020). Flux measurements showed that, in spite of the lower growing season TER (332±17 ... 339±15 gC m -2) and GPP (442±13 ... 464± 11 gC m -2) rates, the bog had a higher CO2 uptake rates (NEE was -132±11 ... -108±6) than the forest, except for the warmest and the wettest year of the period (2020), and was an atmospheric CO2 sink in the selected years, while the forest was a CO2 sink or source, depending on the environmental conditions. Growing season NEE at the forest site was between -142±48 and 28±40 gC m -2 , TER between 1135±64 and 1366±58 gC m -2 , and GPP between 1207±66 and 1462±107 gC m -2. Annual NEE at the forest was between -62±49 and 145±41 gC m -2 , TER between 1429±87 and 1652±44 C m -2 , and GPP between 1345±89 and 1566±41 gC m -2 , respectively. Under the anomalously warm winter conditions with sparse and thin snow cover (2019/2020), the increased daily GPP, TER, and net CO2 uptake at the forest was observed, while at the bog, the changes in CO2 fluxes between the warm and cold winters were not significant. This study suggests that the warming in winter can increase the CO2 uptake of the paludified spruce forests of the southern taiga in non-growing seasons. [ABSTRACT FROM AUTHOR]

A more accurate characterization of the sources and sinks of methane (CH 4) and carbon dioxide (CO 2) in the vulnerable Arctic environment is required to better predict climate change. A large-scale aircraft campaign took place in September 2020 focusing on the Siberian Arctic coast. CH 4 and CO 2 were measured in situ during the campaign and form the core of this study. Measured ozone (O 3) and carbon monoxide (CO) are used here as tracers. Median CH 4 mixing ratios are fairly higher than the monthly mean hemispheric reference (Mauna Loa, Hawaii, US) with 1890–1969 ppb vs. 1887 ppb respectively, while CO 2 mixing ratios from all flights are lower (408.09–411.50 ppm vs. 411.52 ppm). We also report on three case studies. Our analysis suggests that during the campaign the European part of Russia's Arctic and western Siberia were subject to long-range transport of polluted air masses, while the east was mainly under the influence of local emissions of greenhouse gases. The relative contributions of the main anthropogenic and natural sources of CH 4 are simulated using the Lagrangian model FLEXPART in order to identify dominant sources in the boundary layer and in the free troposphere. On western terrestrial flights, air mass composition is influenced by emissions from wetlands and anthropogenic activities (waste management, fossil fuel industry, and to a lesser extent the agricultural sector), while in the east, emissions are dominated by freshwater, wetlands, and the oceans, with a likely contribution from anthropogenic sources related to fossil fuels. Our results highlight the importance of the contributions from freshwater and ocean emissions. Considering the large uncertainties associated with them, our study suggests that the emissions from these aquatic sources should receive more attention in Siberia. [ABSTRACT FROM AUTHOR]

The exchange ratio (ER) between atmospheric O2 and CO2 is a useful tracer for better understanding the carbon budget on global and local scales. The variability of ER (in molO2permolCO2) between terrestrial ecosystems is not well known, and there is no consensus on how to derive the ER signal of an ecosystem, as there are different approaches available, either based on concentration (ERatmos) or flux measurements (ERforest). In this study we measured atmospheric O2 and CO2 concentrations at two heights (23 and 125 m) above the boreal forest in Hyytiälä, Finland. Such measurements of O2 are unique and enable us to potentially identify which forest carbon loss and production mechanisms dominate over various hours of the day. We found that the ERatmos signal at 23 m not only represents the diurnal cycle of the forest exchange but also includes other factors, including entrainment of air masses in the atmospheric boundary layer before midday, with different thermodynamic and atmospheric composition characteristics. To derive ERforest , we infer O2 fluxes using multiple theoretical and observation-based micro-meteorological formulations to determine the most suitable approach. Our resulting ERforest shows a distinct difference in behaviour between daytime (0.92 ± 0.17 molmol-1) and nighttime (1.03 ± 0.05 molmol-1). These insights demonstrate the diurnal variability of different ER signals above a boreal forest, and we also confirmed that the signals of ERatmos and ERforest cannot be used interchangeably. Therefore, we recommend measurements on multiple vertical levels to derive O2 and CO2 fluxes for the ERforest signal instead of a single level time series of the concentrations for the ERatmos signal. We show that ERforest can be further split into specific signals for respiration (1.03 ± 0.05 molmol-1) and photosynthesis (0.96 ± 0.12 molmol-1). This estimation allows us to separate the net ecosystem exchange (NEE) into gross primary production (GPP) and total ecosystem respiration (TER), giving comparable results to the more commonly used eddy covariance approach. Our study shows the potential of using atmospheric O2 as an alternative and complementary method to gain new insights into the different CO2 signals that contribute to the forest carbon budget. [ABSTRACT FROM AUTHOR]

The global carbon cycle is experiencing continued perturbations via increases in atmospheric carbon concentrations, which are partly reduced by terrestrial biosphere and ocean carbon uptake. Greenhouse gas satellites have been shown to be useful in retrieving atmospheric carbon concentrations and observing surface and atmospheric CO 2 seasonal-to-interannual variations. However, limited attention has been placed on using satellite column CO 2 retrievals to evaluate surface CO 2 fluxes from the terrestrial biosphere without advanced inversion models at low latency. Such applications could be useful to monitor, in near real time, biosphere carbon fluxes during climatic anomalies like drought, heatwaves, and floods, before more complex terrestrial biosphere model outputs and/or advanced inversion modelling estimates become available. Here, we explore the ability of Orbiting Carbon Observatory-2 (OCO-2) column-averaged dry air CO 2 (XCO 2) retrievals to directly detect and estimate terrestrial biosphere CO 2 flux anomalies using a simple mass-balance approach. An initial global analysis of surface–atmospheric CO 2 coupling and transport conditions reveals that the western US, among a handful of other regions, is a feasible candidate for using XCO 2 for detecting terrestrial biosphere CO 2 flux anomalies. Using the CarbonTracker model reanalysis as a test bed, we first demonstrate that a well-established mass-balance approach can estimate monthly surface CO 2 flux anomalies from XCO 2 enhancements in the western United States. The method is optimal when the study domain is spatially extensive enough to account for atmospheric mixing and has favorable advection conditions with contributions primarily from one background region. We find that errors in individual soundings reduce the ability of OCO-2 XCO 2 to estimate more frequent, smaller surface CO 2 flux anomalies. However, we find that OCO-2 XCO 2 can often detect and estimate large surface flux anomalies that leave an imprint on the atmospheric CO 2 concentration anomalies beyond the retrieval error/uncertainty associated with the observations. OCO-2 can thus be useful for low-latency monitoring of the monthly timing and magnitude of extreme regional terrestrial biosphere carbon anomalies. [ABSTRACT FROM AUTHOR]

Column mixing ratio of carbon dioxide (CO2) data alone do not provide enough information for source attribution. Carbon monoxide (CO) is a product of inefficient combustion often co-emitted with CO2. CO data can then provide a powerful constraint on fire emissions, supporting more accurate estimation of biospheric CO2 fluxes. In this framework and using the chemistry transport model TM5, a CO inversion using Measurements of Pollution in The Troposphere (MOPITT) v8 data is performed to estimate fire emissions which are then converted into CO2 fire emissions (called FIREMo) through the use of the emission ratio. These optimized CO2 fire emissions are used to rebalance the CO2 net ecosystem exchange (NEEMo) and respiration (RhMo) with the global CO2 growth rate. Subsequently, in a second step, these rebalanced fluxes are used as priors for a CO2 inversion to derive the NEE and ocean fluxes constrained either by the Orbiting Carbon Observatory 2 (OCO-2) v9 or by in situ (IS) CO2 data. For comparison purpose, we also balanced the respiration using fire emissions from the Global Fire Emissions Database (GFED) version 3 (GFED3) and version 4.1s (GFED4.1s). We hence study the impact of CO fire emissions in our CO2 inversions at global, latitudinal, and regional scales over the period 2015–2018 and compare our results to the two other similar approaches using GFED3 (FIRE3) and GFED4.1s (FIRE4) fires, as well as with an inversion using both Carnegie–Ames–Stanford Approach (CASA)-GFED3 NEE and GFED3 fire priors (priorCMS). After comparison at the different scales, the inversions are evaluated against Total Carbon Column Observing Network (TCCON) data. Comparison of the flux estimates shows that at the global scale posterior net flux estimates are more robust than the different prior flux estimates. However, at the regional scale, we can observe differences in fire emissions among the priors, resulting in differences among the NEE prior emissions. The derived NEE prior emissions are rebalanced in concert with the fires. Consequently, the differences observed in the NEE posterior emissions are a result of the balancing with fires and the constraints provided by CO2 observations. Tropical net flux estimates from in situ inversions are highly sensitive to the prior flux assumed, of which fires are a significant component. Slightly larger net CO2 sources are derived with posterior fire emissions using either FIRE4 or FIREMo in the OCO-2 inversion, in particular for most tropical regions during the 2015 El Niño year. Similarly, larger net CO2 sources are also derived with posterior fire emissions in the in situ data inversion for Tropical Asia. Evaluation with CO2 TCCON data shows lower biases with the three rebalanced priors than with the prior using CASA-GFED3. However, posteriors have average bias and scatter very close each other, making it difficult to conclude which simulation performs better than the other. We observe that the assimilated CO2 data have a strong influence on the global net fluxes among the different inversions. Inversions using OCO-2 (or IS) data have similar emissions, mostly as a result of the observational constraints and to a lesser extent because of the fire prior used. But results in the tropical regions suggest net flux sensitivity to the fire prior for both the IS and OCO-2 inversions. Further work is needed to improve prior fluxes in tropical regions where fires are a significant component. Finally, even if the inversions using the FIREMo prior did enhance the biases over some TCCON sites, it is not the case for the majority of TCCON sites. This study consequently pushes forward the development of a CO– CO2 joint inversion with multi-observations for a possible stronger constraint on posterior CO2 fire and biospheric emissions. [ABSTRACT FROM AUTHOR]

There are plenty of monitoring methods to quantify gas emission rates based on gas concentration measurements around the strong sources. However, there is a lack of quantitative models to evaluate methane emission rates from coal mines with less prior information. In this study, we develop a genetic algorithm–interior point penalty function (GA-IPPF) model to calculate the emission rates of large point sources of CH 4 based on concentration samples. This model can provide optimized dispersion parameters and self-calibration, thus lowering the requirements for auxiliary data accuracy. During the Carbon Dioxide and Methane Mission (CoMet) pre-campaign, we retrieve CH 4 -emission rates from a ventilation shaft in Pniówek coal mine (Silesia coal mining region, Poland) based on the data collected by an unmanned aerial vehicle (UAV)-based AirCore system and a GA-IPPF model. The concerned CH 4 -emission rates are variable even on a single day, ranging from 621.3 ± 19.8 to 1452.4 ± 60.5 kg h -1 on 18 August 2017 and from 348.4 ± 12.1 to 1478.4 ± 50.3 kg h -1 on 21 August 2017. Results show that CH 4 concentration data reconstructed by the retrieved parameters are highly consistent with the measured ones. Meanwhile, we demonstrate the application of GA-IPPF in three gas control release experiments, and the accuracies of retrieved gas emission rates are better than 95.0 %. This study indicates that the GA-IPPF model can quantify the CH 4 -emission rates from strong point sources with high accuracy. [ABSTRACT FROM AUTHOR]

Global and regional sources and sinks of carbon across the earth's surface have been studied extensively using atmospheric carbon dioxide (CO 2) observations and atmospheric chemistry-transport model (ACTM) simulations (top-down/inversion method). However, the uncertainties in the regional flux distributions remain unconstrained due to the lack of high-quality measurements, uncertainties in model simulations, and representation of data and flux errors in the inversion systems. Here, we assess the representation of data and flux errors using a suite of 16 inversion cases derived from a single transport model (MIROC4-ACTM) but different sets of a priori (bottom-up) terrestrial biosphere and oceanic fluxes, as well as prior flux and observational data uncertainties (50 sites) to estimate CO 2 fluxes for 84 regions over the period 2000–2020. The inversion ensembles provide a mean flux field that is consistent with the global CO 2 growth rate, land and ocean sink partitioning of - 2.9 ± 0.3 (± 1 σ uncertainty on the ensemble mean) and - 1.6 ± 0.2 PgC yr -1 , respectively, for the period 2011–2020 (without riverine export correction), offsetting about 22 %–33 % and 16 %–18 % of global fossil fuel CO 2 emissions. The rivers carry about 0.6 PgC yr -1 of land sink into the deep ocean, and thus the effective land and ocean partitioning is - 2.3 ± 0.3 and - 2.2 ± 0.3, respectively. Aggregated fluxes for 15 land regions compare reasonably well with the best estimations for the 2000s (∼ 2000–2009), given by the REgional Carbon Cycle Assessment and Processes (RECCAP), and all regions appeared as a carbon sink over 2011–2020. Interannual variability and seasonal cycle in CO 2 fluxes are more consistently derived for two distinct prior fluxes when a greater degree of freedom (increased prior flux uncertainty) is given to the inversion system. We have further evaluated the inversion fluxes using meridional CO 2 distributions from independent (not used in the inversions) aircraft and surface measurements, suggesting that the ensemble mean flux (model–observation mean ± 1 σ standard deviation = - 0.3 ± 3 ppm) is best suited for global and regional CO 2 flux budgets than an individual inversion (model–observation 1 σ standard deviation = - 0.35 ± 3.3 ppm). Using the ensemble mean fluxes and uncertainties for 15 land and 11 ocean regions at 5-year intervals, we show promise in the capability to track flux changes toward supporting the ongoing and future CO 2 emission mitigation policies. [ABSTRACT FROM AUTHOR]

In this study, we employ a regional inverse modelling approach to estimate monthly carbon fluxes over the Australian continent for 2015–2019 using the assimilation of the total column-averaged mole fractions of carbon dioxide from the Orbiting Carbon Observatory-2 (OCO-2, version 9) satellite. Subsequently, we study the carbon cycle variations and relate their fluctuations to anomalies in vegetation productivity and climate drivers. Our 5-year regional carbon flux inversion suggests that Australia was a carbon sink averaging - 0.46 ± 0.08 PgC yr −1 (excluding fossil fuel emissions), largely influenced by a strong carbon uptake (- 1.04 PgC yr −1) recorded in 2016. Australia's semi-arid ecosystems, such as sparsely vegetated regions (in central Australia) and savanna (in northern Australia), were the main contributors to the carbon uptake in 2016. These regions showed relatively high vegetation productivity, high rainfall, and low temperature in 2016. In contrast to the large carbon sink found in 2016, the large carbon outgassing recorded in 2019 coincides with an unprecedented rainfall deficit and higher-than-average temperatures across Australia. Comparison of the posterior column-averaged CO2 concentration with Total Carbon Column Observing Network (TCCON) stations and in situ measurements offers limited insight into the fluxes assimilated with OCO-2. However, the lack of these monitoring stations across Australia, mainly over ecosystems such as savanna and areas with sparse vegetation, impedes us from providing strong conclusions. To a certain extent, we found that the flux anomalies across Australia are consistent with the ensemble means of the OCO-2 Model Intercomparison Project (OCO-2 MIP) and FLUXCOM (2015–2018), which estimate an anomalous carbon sink for Australia in 2016 of - 1.09 and - 0.42 PgC yr −1 respectively. More accurate estimates of OCO-2 retrievals, with the addition of ocean glint data into our system, and a better understanding of the error in the atmospheric transport modelling will yield further insights into the difference in the magnitude of our carbon flux estimates. [ABSTRACT FROM AUTHOR]

Three-hourly net ecosystem exchange (NEE) is estimated at spatial scales of 0.25 ∘ over the European continent, based on the pre-operational inverse modelling framework "CarboScope Regional" (CSR) for the years 2006 to 2019. To assess the uncertainty originating from the choice of a priori flux models and observational data, ensembles of inversions were produced using three terrestrial ecosystem flux models, two ocean flux models, and three sets of atmospheric stations. We find that the station set ensemble accounts for 61 % of the total spread of the annually aggregated fluxes over the full domain when varying all these elements, while the biosphere and ocean ensembles resulted in much smaller contributions to the spread of 28 % and 11 %, respectively. These percentages differ over the specific regions of Europe, based on the availability of atmospheric data. For example, the spread of the biosphere ensemble is prone to be larger in regions that are less constrained by CO 2 measurements. We investigate the impact of unprecedented increase in temperature and simultaneous reduction in soil water content (SWC) observed in 2018 and 2019 on the carbon cycle. We find that NEE estimates during these 2 years suggest an impact of drought occurrences represented by the reduction in net primary productivity (NPP), which in turn leads to less CO 2 uptake across Europe in 2018 and 2019, resulting in anomalies of up to 0.13 and 0.07 PgC yr -1 above the climatological mean, respectively. Annual temperature anomalies also exceeded the climatological mean by 0.46 ∘ C in 2018 and by 0.56 ∘ C in 2019, while Standardised Precipitation–Evaporation Index (SPEI) anomalies declined to -0.20 and -0.05 SPEI units below the climatological mean in both 2018 and 2019, respectively. Therefore, the biogenic fluxes showed a weaker sink of CO 2 in both 2018 and 2019 (-0.22 ± 0.05 and -0.28 ± 0.06 PgC yr -1 , respectively) in comparison with the mean -0.36 ± 0.07 PgC yr -1 calculated over the full analysed period (i.e. 14 years). These translate into a continental-wide reduction in the annual sink by 39 % and 22 %, respectively, larger than the typical year-to-year standard deviation of 19 % observed over the full period. [ABSTRACT FROM AUTHOR]

Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO 2), methane (CH 4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA's Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO 2 , CH 4 , and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO 2 , CH 4 , and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO 2 and CH 4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations. [ABSTRACT FROM AUTHOR]

Transportation represents the largest sector of anthropogenic CO 2 emissions in urban areas in the United States. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important; however, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at the abovementioned scale. The BErkeley Air Quality and CO 2 Network (BEACO 2 N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ∼ 1300 km 2 multicity spatial domain. Here, we focus on a 5 km, high-volume, stretch of highway in the San Francisco Bay Area. We show that inversion of the BEACO 2 N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO 2 emission rate of the average vehicle (in grams per vehicle kilometer) is shown to vary by as much as 27 % at different times of a typical weekday because of changes in these two factors. The BEACO 2 N-derived emission estimates are consistent to within ∼ 3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the EMission FACtor model (EMFAC) of vehicle fuel efficiency. [ABSTRACT FROM AUTHOR]