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The paper reviews and evaluates a 30-year-long atmospheric CO2 data series measured at the Hegyhátsál tall-tower greenhouse gas monitoring site, a member of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW), US National Oceanic and Atmospheric Administration (NOAA), and pan-European Integrated Carbon Observation System (ICOS) networks. The paper also gives the technical description of the monitoring system and its changes over time and introduces the environment of the station. This low-elevation (248 m above mean sea level – m a.m.s.l.), mid-continental central European site shows a 3.90 ± 0.83 µmolmol-1 offset relative to the latitudinally representative marine boundary layer reference concentration, presumably due to European net anthropogenic emissions. The long-term trend (2.20 µmolmol-1yr-1) closely follows the global tendencies. In the concentration growth rate, the ENSO effect is clearly detectable with a 6–7-month lag time. The summer diurnal concentration amplitude is slightly decreasing due to the faster-than-average increase in the nighttime concentrations, which is related to the warming climate. The warming climate also caused a 0.96 ± 0.41 dyr-1 advance at the beginning of the summer CO2 -deficit season in the first half of the measurement period, which did not continue later. The summer CO2 -deficit season was extended by 9.0 ± 6.1 d during the measurement period. [ABSTRACT FROM AUTHOR]

The extensive terrestrial ecosystems of tropical Africa are a significant store of carbon and play a key but uncertain role in the atmospheric budgets of carbon dioxide and methane. As ground-based observations in the tropics are scarce compared with other parts of the world, recent studies have instead made use of satellite observations assimilated into atmospheric chemistry and transport models to conclude that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for up to a third of global methane growth, and that the tropical Africa region dominates net carbon emission across the tropics. These studies critically rely on the accuracy of satellite datasets, such as those from the Orbiting Carbon Observatory-2 (OCO-2), the Greenhouse gases Observing SATellite (GOSAT), and the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI), along with results from atmospheric transport models, over a geographical region where there are little independent data to test the robustness of published results. In this paper we present the first ground-based observations of greenhouse gas column concentrations over East Africa, obtained using a portable Bruker EM27/SUN Fourier transform infrared (FTIR) spectrometer during a deployment covering the first few months of 2020 in Jinja, Uganda. We operated the instrument near autonomously by way of an automated weatherproof enclosure and observed total atmospheric column concentrations of the greenhouse gases carbon dioxide and methane, as well as carbon monoxide, a useful proxy for emissions from incomplete combustion processes in the region. We discuss the performance of the combined enclosure and spectrometer system that we deployed in Jinja to obtain these data and show comparisons of our ground-based observations with satellite datasets from OCO-2 and Orbiting Carbon Observatory-3 (OCO-3) for carbon dioxide and TROPOMI for methane and carbon monoxide, whilst also comparing our results with concentration data from the GEOS-Chem and Copernicus Atmosphere Monitoring Service (CAMS) atmospheric inversions that provide a means of increasing spatial and temporal coverage where satellite data are not available. For our measurement period, we find mean differences in XCO2 between OCO-2 and the EM27/SUN of - 0.29 % and between OCO-3 and the EM27/SUN of - 0.28 %. In the case of TROPOMI, the mean difference in XCH4 that we find between TROPOMI and the EM27/SUN is - 0.44 %, whilst for XCO the mean difference is - 5.65 %. In each of these cases, the mean difference observed between the satellite and ground-based column concentrations is either close to or within the precision and accuracy requirements for the respective missions. With regard to the model and reanalysis comparisons with the EM27/SUN column concentrations, we see mean differences from the EM27/SUN of a global GEOS-Chem inversion for XCO2 of - 0.08 %, a regional high-resolution GEOS-Chem inversion for XCH4 of - 0.22 %, and the CAMS global reanalysis for XCO of - 9.79 %. Our results demonstrate the value of ground-based observations of total column concentrations and show that the combined EM27/SUN and enclosure system employed would be suitable for acquisition of the longer-term observations needed to rigorously evaluate satellite observations and model and reanalysis calculations over tropical Africa. [ABSTRACT FROM AUTHOR]

VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NO x concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment. [ABSTRACT FROM AUTHOR]

The paper reviews and evaluates a 30-year-long atmospheric CO2 data series measured at Hegyhátsál tall-tower greenhouse gas monitoring site, a member of WMO GAW, NOAA, and ICOS networks (id. code: HUN). The paper also gives the technical description of the monitoring system, and that of the physical environment of the station. This low elevation (248 m above m.s.l.), mid-continental Central European site shows a 3.90±0.83 µmol mol‑1 offset relative to the latitudinally representative marine boundary layer reference concentration presumably due to the European net anthropogenic emissions. The long-term trend (2.20 µmol mol‑1 year‑1) closely follows the global tendencies. In the concentration growth rate, the ENSO effect is clearly detectable with a 6–7 months lag-time. The summer diurnal concentration amplitude is slightly decreasing due to the faster-than-average increase of the nighttime concentrations, which is related to the warming climate. The warming climate also caused a 0.96±0.41 day year‑1 advance in the beginning of the summer CO2-deficit season in the first half of the measurement period, which did not continue later. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH 4) and carbon dioxide (CO 2) , and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH 4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH 4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s -1 when the wind magnitude is less than 3.5 m s -1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH 4 and CO 2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH 4 emissions in near real time. [ABSTRACT FROM AUTHOR]

To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at 10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and 10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b). [ABSTRACT FROM AUTHOR]

Lakes and ponds experience anthropogenically forced changes that may be non-linear and sometimes initiate ecosystem feedbacks leading to tipping points beyond which impacts become hard to reverse. In many cases climate change is a key driver, sometimes in concert with other stressors. Lakes are also important players in the global climate by ventilating a large share of terrestrial carbon (C) back to the atmosphere as greenhouse gases and will likely provide substantial feedbacks to climate change. In this paper we address various major changes in lake ecosystems and discuss if tipping points can be identified, predicted, or prevented, as well as the drivers and feedbacks associated with climate change. We focus on potential large-scale effects with regional or widespread impacts, such as eutrophication-driven anoxia and internal phosphorus (P) loading, increased loading of organic matter from terrestrial to lake ecosystems (lake "browning"), lake formation or disappearance in response to cryosphere shifts or changes in precipitation to evaporation ratios, switching from nitrogen to phosphorus limitation, salinization, and the spread of invasive species where threshold-type shifts occur. We identify systems and drivers that could lead to self-sustaining feedbacks, abrupt changes, and some degree of resilience, as opposed to binary states not subject to self-propelling changes or resilience. Changes driven by warming, browning, and eutrophication can cause increased lake stratification, heterotrophy (browning), and phytoplankton or macrophyte mass (eutrophication), which separately or collectively drive benthic oxygen depletion and internal phosphorus loading and in turn increase greenhouse gas (GHG) emissions. Several of these processes can feature potential tipping point thresholds, which further warming will likely make easier to surpass. We argue that the full importance of the vulnerability of lakes to climate and other anthropogenic impacts, as well as their feedback to climate, is not yet fully acknowledged, so there is a need both for science and communication in this regard. [ABSTRACT FROM AUTHOR]

Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M (Copernicus Anthropogenic Carbon Dioxide Monitoring) satellite constellation in 2026 is expected to provide high-resolution images of CO2 (carbon dioxide) column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the low CO2 plume signal-to-noise ratio due to the variability in the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we develop a strategy employing convolutional neural networks (CNNs) to estimate the emission fluxes from a plume in a pseudo- XCO2 image. Our dataset used to train and test such methods includes pseudo-images based on simulations of hourly XCO2 , NO2 (nitrogen dioxide), and wind fields near various power plants in eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state-of-the-art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method and an absolute relative error of ∼ 20 % when only the XCO2 and wind fields are used as inputs. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs). [ABSTRACT FROM AUTHOR]

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb were to advance our knowledge of the carbon cycle, in particular, land–atmosphere fluxes of the greenhouse gases carbon dioxide (CO 2) and methane (CH 4) and the effects of these fluxes on the Earth's radiation budget. GeoCarb would retrieve column-integrated dry-air mole fractions of CO 2 (XCO2), CH 4 (XCH4) and CO (XCO), important for understanding tropospheric chemistry), in addition to solar-induced fluorescence (SIF), from hyperspectral resolution measurements in the O 2 A-band at 0.76 µ m, the weak CO 2 band at 1.6 µ m, the strong CO 2 band at 2.06 µ m, and a CH 4 /CO band at 2.32 µ m. Unlike its predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb would be in a geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high-spatial-resolution imaging capabilities would provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land–atmosphere CO 2 and CH 4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the L2 retrieval algorithms which will be followed by simulation experiments to determine an error budget for each target gas. Several sources of uncertainty will be explored, including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to forward model parameters including meteorology and spectroscopy, although there are some other instrument-related sources of uncertainty that are left out for this study, including that from "smile", the keystone effect, stray light, detector persistence, and scene inhomogeneity. The results indicate that the errors (1 σ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10 %), for XCO2 , XCH4 , and XCO , respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMSEs of 1.06 ppm for XCO2 , 8.2 ppb for XCH4 , and 2.5 ppb for XCO , respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO are dominated by measurement noise. It is important to note that the GeoCarb mission was canceled by NASA; however, the instrument is still in development and will be delivered to NASA, in full, with the hope that it will eventually be adopted in a future mission proposal. [ABSTRACT FROM AUTHOR]

A statistical model called the sea level simulator v1.0 is introduced. The model integrates mean sea level change and sea level extremes into a joint probabilistic framework that is useful for coastal spatial planning. Given a user-defined planning period, the model can estimate the flood risk as a function of height above the current mean sea level. These flood risk estimates are derived through Monte Carlo simulations of a very large number of planning periods. The derived flood risk is contingent on user-assigned probabilities for future greenhouse gas emission pathways, and the model is thus also useful for quantifying the dependence of flood risk on such pathways and their probabilities. Moreover, the simulator can quantify whether flood risk is dominated by sea level extremes or mean sea level rise and how this depends on the length of the planning period. The code, written in MATLAB, is parallelized and lightweight enough that it can be run on an ordinary PC. The code is easily adaptable to include new locations, new mean sea level projections and similar model developments. The flood risk estimates derived from the simulator are well suited to tackle adaptation and decision problems. Applications for construction of coastal protection and land development in coastal areas have been demonstrated in the past. The paper gives an in-depth technical description of the model. Example simulations from a Swedish nuclear site are also given, and the capabilities of the simulator are discussed. The main aim of the paper is to work as a technical reference for the first public release of the sea level simulator. [ABSTRACT FROM AUTHOR]

A key goal of the Paris Agreement (PA) is to reach net-zero greenhouse gas (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and gross domestic product (GDP) make this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO 2), methane (CH 4) and nitrous oxide (N 2 O) in Africa over 3 decades (1990–2018). We compare bottom-up (BU) approaches, including United Nations Convention Framework on Climate Change (UNFCCC) national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO 2 fluxes in the land use, land use change and forestry (LULUCF) sector and global atmospheric inversions. For inversions, we applied different methods to separate anthropogenic CH 4 emissions. The BU inventories show that, over the decade 2010–2018, fewer than 10 countries represented more than 75 % of African fossil CO 2 emissions. With a mean of 1373 Mt CO 2 yr -1 , total African fossil CO 2 emissions over 2010–2018 represent only 4 % of global fossil emissions. However, these emissions grew by +34% from 1990–1999 to 2000–2009 and by +31% from 2000–2009 to 2010–2018, which represents more than a doubling in 30 years. This growth rate is more than 2 times faster than the global growth rate of fossil CO 2 emissions. The anthropogenic emissions of CH 4 grew by 5 % from 1990–1999 to 2000–2009 and by 14.8 % from 2000–2009 to 2010–2018. The N 2 O emissions grew by 19.5 % from 1990–1999 to 2000–2009 and by 20.8 % from 2000–2009 to 2010–2018. When using the mean of the estimates from UNFCCC reports (including the land use sector) with corrections from outliers, Africa was a mean source of greenhouse gases of 262221863239 Mt CO 2 eq. yr -1 from all BU estimates (the subscript and superscript indicate min–max range uncertainties) and of +263717615873 Mt CO 2 eq. yr -1 from top-down (TD) methods during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of TD estimates is larger than the one of the BU estimates, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty in BU estimates. The main source of uncertainty comes from CO 2 fluxes in the LULUCF sector, for which the spread across inversions is larger than 50 %, especially in central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022). The medians of CH 4 emissions from inversions based on satellite retrievals and surface station networks are consistent with each other within 2 % at the continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH 4 emissions with BU inventories such as PRIMAP-hist. For N 2 O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N 2 O sources cannot be separated accurately from anthropogenic ones in inversions or because BU estimates ignore indirect emissions and underestimate emission factors. Future improvements can be expected thanks to a denser network of monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows us to compute budgets thanks to recombinations of those data products. Our results allow us to understand uncertainty and trends in emissions and removals in a region of the world where few observations exist and where most inventories are based on default IPCC guideline values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to the figures are available at 10.5281/zenodo.7347077 (Mostefaoui et al., 2022). [ABSTRACT FROM AUTHOR]

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian High Arctic (82 ∘ 28 ′ N, 62 ∘ 30 ′ W). In this paper, we evaluate the measurement agreement of atmospheric CO 2 , CH 4 , N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) between leading laboratories from seven independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ∼ 8000 discrete flask samples, we find that the co-located atmospheric CO 2 and CH 4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, and ECCC versus NOAA (the designated reference laboratory) are generally consistent with the WMO compatibility goals of ± 0.1 ppm CO 2 and ± 2 ppb CH 4 over the 17-year period (1999–2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N 2 O, SF 6 , and stable isotopes of CO 2 (δ13 C, δ18 O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO and SIO versus NOAA or INSTAAR (for the isotopes of CO 2) at other geographical sites suggests that the findings at Alert for CO 2 , CH 4 , N 2 O, and δ13 C–CO 2 could be extended across the CSIRO, SIO, and NOAA observing networks. The primary approach to estimate an overall measurement agreement level was carried out by pooling the differences of all individual laboratories versus the designated reference laboratory and determining the 95th percentile range of these data points. Using this approach over the entire data record, our best estimate of the measurement agreement range is -0.51 to + 0.53 ppm for CO 2 , -0.09 ‰ to + 0.07 ‰ for δ13 C, -0.50 ‰ to + 0.58 ‰ for δ18 O, -4.86 to + 6.16 ppb for CH 4 , -0.75 to + 1.20 ppb for N 2 O, and -0.14 to + 0.09 ppt for SF 6. A secondary approach of using the average of 2 standard deviations of the means for all flask samples taken in each individual sampling episode provided similar results. These upper and lower limits represent our best estimate of the measurement agreement at the 95 % confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis). [ABSTRACT FROM AUTHOR]

The extensive terrestrial ecosystems of tropical Africa are a significant store of carbon, and play a key but uncertain role in the atmospheric budgets of carbon dioxide and methane. As ground-based observations in the tropics are scarce compared with other parts of the world, recent studies have instead made use of satellite observations assimilated into atmospheric chemistry and transport models to conclude that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for up to a third of global methane growth, and that the tropical Africa region dominates net carbon emission across the tropics. These studies rely critically on the accuracy of satellite datasets such as those from OCO-2, GOSAT, and Sentinel-5P TROPOMI, along with results from atmospheric transport models, over a geographical region where there are few independent data to test the robustness of published results. In this paper we present the first ground-based observations of greenhouse gas column concentrations over East Africa, obtained using a portable Bruker EM27/SUN FTIR spectrometer during a deployment covering the first few months of 2020 in Jinja, Uganda. We operated the instrument near-autonomously by way of an automated weatherproof enclosure, and observed total atmospheric column concentrations of the greenhouse gases carbon dioxide and methane, as well as carbon monoxide, a useful proxy for emissions from incomplete combustion processes in the region. We discuss the performance of the combined enclosure and spectrometer system that we deployed in Jinja to obtain this data, and show comparisons of our ground-based observations with satellite datasets from OCO-2 and OCO-3 for carbon dioxide, and Sentinel-5P TROPOMI for methane and carbon monoxide, whilst also comparing our results with concentration data from the GEOS-Chem and CAMS atmospheric inversions that provide a means of increasing spatial and temporal coverage where satellite data are not available. For our measurement period, we find statistically significant differences at the 95% confidence level between the EM27/SUN and OCO-2 XCO2 (OCO-2 lower by a mean of 1.20ppm, standard deviation 1.05ppm), and between the EM27/SUN and Sentinel-5P XCO (Sentinel-5P lower by a mean of 3.68ppb, standard deviation 7.00ppb), whilst we found that the differ-ences between the EM27/SUN and OCO-3 XCO2 (OCO-3 lower by a mean of 1.15ppm, standard deviation 1.61ppm), and between the EM27/SUN and Sentinel-5P XCH4 (Sentinel-5P lower by a mean of 8.33ppb, standard deviation 10.5ppb), were not statistically significant. With regards to the model comparisons, we also see statistically significant differences between the EM27/SUN and a global GEOS-Chem inversion for XCO2 (GEOS-Chem lower by a mean of 0.35ppm, standard deviation 1.08ppm), between the EM27/SUN and a high-resolution GEOS-Chem inversion for XCH4 (GEOS-Chem lower by a mean of 3.80ppb, standard deviation 12.5ppb), and between the EM27/SUN and CAMS global analysis XCO (CAMS lower by a mean of 11.7ppb, standard deviation 8.94ppb). Our results demonstrate the value of ground-based observations of total column concentrations, and show that the combined EM27/SUN and enclosure system employed would be suitable for acquisition of the longer-term observations needed to rigorously evaluate satellite observations and model calculations over tropical Africa. [ABSTRACT FROM AUTHOR]

Abstract. Earth System Models (ESM) represent the time evolution of the biophysical (energy, water cycles) and biogeochemical (carbon cycle) components of the Earth. When used for near-future projections in the context of the Coupled Model Intercomparison Project (CMIP), they use as forcings the evolution of greenhouse gas and other pollutant concentrations and land-use changes simulated by an ensemble of Integrated Assessment Models (IAMs) for a combination of socio-economic pathways and mitigation targets (SSPs). More precisely, only one IAM output is used as representative of a single SSP while the inter-IAM spread is large for ammonia emissions and land-use changes, for instance. This makes the comparison of key ESM diagnostics among SSPs significantly noisy, without the capacity of disentangling SSP-driven and IAM-driven factors. In this paper, we quantify the projected change in land carbon store (CLCS) for the different SSPs with an advanced version of a land surface model embedded into IPSL-CM6 ESM. Through a set of land-only factorial simulations, we specifically aim at estimating the CLCS uncertainties associated with land-use change and nitrogen deposition trajectories. We showed that the spread of the simulated change in global land carbon store induced by the uncertainty on land-use changes is slightly larger than the one associated with the uncertainty on atmospheric CO2. Globally, uncertainty associated with N depositions is responsible for a spread in CLCS lower by a factor three, than the one driven by atmospheric CO2 or land-use changes. Our study calls for making available additional IAM scenarios for each SSP to be used in the next CMIP exercise, in order to specifically assess the IAM-related uncertainty impacts on the carbon cycle and the climate system. [ABSTRACT FROM AUTHOR]

Methane is the second most important greenhouse gas after carbon dioxide, and accounts for around 10 % of total European Union greenhouse gases emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modeling techniques appear as a powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modeling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modeling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modeling component will be based on the WRF model coupled to a lagrangian particle dispersion module. A description of the modeling tools, input data sets and one-year forward modeling evaluation from April 01, 2018 to March 31, 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modeling for Denmark, will be provided in a second paper. A good general agreement is found between the modeling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model-observation discrepancies for summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 to 2%. Domain-wide correlation coefficients and root-mean-square-errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. For winter months, otherwise, model-observation discrepancies show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 to 3%. Domain-wide correlation coefficients and root-mean-square-errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modeling results, the methane enhancement above the background concentrations came almost entirely from anthropogenicsources; however, these sources contributed with only up to 2 % to the methane total column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe, and demonstrate a huge and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

The Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse Gases Observing SATellite-2 (GOSAT-2) observes a wide spectral region of the atmosphere, from the ShortWave-InfraRed (SWIR) to the longwave Thermal InfraRed radiation (TIR) with 0.2 cm -1 spectral sampling, and the corresponding spectral resolution (full width at half maximum, FWHM) of TIR region is less than 0.27 cm -1. TANSO-FTS-2 has operated nominally since February 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration and its validation. The model applies to the version v210210 TIR products of TANSO-FTS-2 and integrates polarization sensitivity correction for the internal optics and the pointing mirror thermal emission. These correction parameters are characterized by an optimization that depends on the difference between the spectral radiance of TANSO-FTS-2 and coincident nadir observation data from the Infrared Atmospheric Sounding Interferometer (IASI) on METOP-B. To validate the updated spectral radiance product against other satellite products, temporally and spatially coincident observation points were considered for the simultaneous nadir overpass (SNO) from February 2019 to March 2021 from the Atmospheric Infrared Sounder (AIRS) on Aqua, IASI on METOP-B, and TANSO-FTS on GOSAT. The agreement of brightness temperatures between TANSO-FTS-2 and AIRS and IASI was better than 0.3 K (1 σ) from 180 to 330 K for the 680 cm -1 CO 2 spectral range. The brightness temperatures between TANSO-FTS-2 and TANSO-FTS of version v230231, which implemented a new polarization reflectivity of the pointing mirror and was released in June 2021, generally agree from 220 to 320 K. However, there is a discrepancy at lower brightness temperatures, pronounced for CO 2 spectral ranges at high latitudes. To characterize the spectral radiance bias for along-track and cross-track angles, a 2-orthogonal simultaneous off-nadir overpass (2O-SONO) is now done for TANSO-FTS-2 and IASI, TANSO-FTS-2 and AIRS, and TANSO-FTS-2 and TANSO-FTS. The 2O-SONO comparison results indicate that the TIR product for TANSO-FTS-2 has a bias that exceeds 0.5 K in the CO 2 spectral range for scenes with forward and backward viewing angles greater than 20 ∘. These multi-satellite sensor and multi-angle comparison results suggest that the calibration of spectral radiance for TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data. In addition, the paper identifies the remaining challenging issues in current TIR products. [ABSTRACT FROM AUTHOR]

Lakes experience anthropogenically-forced changes that may initiate ecosystem feedbacks, in some cases reaching tipping points beyond which impacts become hard to reverse. Lakes are also important players in the global climate by ventilating a large share of terrestrial carbon back to the atmosphere as greenhouse gases, and will likely provide substantial feedbacks to climate change. In this paper we address various major changes in lake ecosystems, and discuss if tipping points can be identified, predicted, or prevented in them, along with their associated feedbacks to climate change. Potential tipping dynamics assessed include eutrophication-driven anoxia and internal phosphorus-loading, increased loading of organic matter from terrestrial to lake ecosystems (lake "browning"), lake formation or disappearance in response to cryosphere shifts, switching from nitrogen to phosphorus limitation, salinization, and the spread of invasive species. We also address other types of abrupt, or threshold-type shifts in lakes and ponds, and conclude on which tipping points are locally or regionally relevant. We identify a key set of co-drivers that could lead to self-sustaining feedbacks, with warming, browning, and eutrophication leading to increased lake stratification, heterotrophy, and algal mass, which separately or collectively drive benthic oxygen depletion and in turn increased greenhouse gas emissions (helping to drive further warming and organic matter loading) and internal phosphorus-loading (driving further eutrophication). Several of these processes can feature tipping points, which further warming will likely make easier to reach. We argue that the full importance of the vulnerability of lakes to climate and other anthropogenic impacts, as well as their feedback to climate is not yet fully acknowledged, so there is a need both for science and communication in this regard. [ABSTRACT FROM AUTHOR]

The Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing SATellite-2 (GOSAT-2) observes a wide spectral region of the atmosphere, from the ShortWave-InfraRed (SWIR) to longwave Thermal InfraRed radiation (TIR) with 0.2cm-1 spectral intervals. The TANSO-FTS-2 has operated nominally since Feb 2019, and the atmospheric radiance spectra it has acquired have been released to the public. This paper describes an updated model for spectral radiance calibration and its validation. The model applies to the version 210210 TIR products of the TANSO-FTS-2 and integrates polarization sensitivity correction for the internal optics and the scanner mirror thermal emission. These correction parameters are characterized by an optimization which depends on the difference between the spectral radiance of the TANSO-FTS-2 and coincident nadir observation data from the Infrared Atmospheric Sounding Interferometer (IASI) on METOP-B. To validate the updated spectral radiance product against other satellite products, temporally and spatially coincident observation points were considered for Simultaneous Nadir Overpass (SNO) from February 2019 to March 2021 from the Atmospheric Infrared Sounder (AIRS) on Aqua, IASI on METOP-B, and TANSO-FTS on GOSAT. The agreement of brightness temperatures between the TANSO-FTS-2 and AIRS and IASI was better than 0.3 K (1𝜎) from 180 K to 330 K for the 680 cm-1 CO2 channel. The brightness temperatures between the TANSO-FTS-2 and TANSO-FTS of version v230231, which implemented a new polarization reflectivity of the pointing mirror and was released in June 2021, generally agree from 220 K to 320 K. However, there is a discrepancy at lower brightness temperatures, pronounced for CO2 channels at high latitudes. To characterize the spectral radiance bias for along-track and cross-track angles, a 2-Orthogonal Simultaneous Off-Nadir Overpass (2O-SONO) is now done for the TANSO-FTS-2 and IASI, the TANSO-FTS-2 and AIRS, and the TANSO-FTS-2 and TANSO-FTS. The 2O-SONO comparison results indicate that the TIR product for the TANSO-FTS-2 has a bias that exceeds 0.5 K in the CO2 channel for scenes with forward and backward viewing angles greater than 20°. These multi satellite sensor and multi-angle comparison results suggest that the calibration of spectral radiance for the TANSO-FTS-2 TIR, version v210210, is superior to that of the previous version in its consistency of multi-satellite sensor data. In addition, the paper identifies the remaining challenging issues in current TIR products. [ABSTRACT FROM AUTHOR]

The quantification of emissions of greenhouse gases and air pollutants through the inversion of plumes in satellite images remains a complex problem that current methods can only assess with significant uncertainties. The anticipated launch of the CO2M satellite constellation in 2026 is expected to provide high-resolution images of CO2 column-averaged mole fractions (XCO2), opening up new possibilities. However, the inversion of future CO2 plumes from CO2M will encounter various obstacles. A challenge is the CO2 plume low signal-to-noise ratio, due to the variability of the background and instrumental errors in satellite measurements. Moreover, uncertainties in the transport and dispersion processes further complicate the inversion task. To address these challenges, deep learning techniques, such as neural networks, offer promising solutions for retrieving emissions from plumes in XCO2 images. Deep learning models can be trained to identify emissions from plume dynamics simulated using a transport model. It then becomes possible to extract relevant information from new plumes and predict their emissions. In this paper, we employ convolutional neural networks (CNN) to estimate the emission fluxes from a plume in a pseudo XCO2 image. Our dataset used to train and test such methods includes pseudo images based on simulations of hourly XCO2, NO2 and wind fields near various power plants in Eastern Germany, tracing plumes from anthropogenic and biogenic sources. CNN models are trained to predict emissions from three power plants that exhibit diverse characteristics. The power plants used to assess the deep learning model's performance are not used to train the model. We find that the CNN model outperforms state of the art plume inversion approaches, achieving highly accurate results with an absolute error about half of that of the cross-sectional flux method. Furthermore, we show that our estimations are only slightly affected by the absence of NO2 fields or a detection mechanism as additional information. Finally, interpretability techniques applied to our models confirm that the CNN automatically learns to identify the XCO2 plume and to assess emissions from the plume concentrations. These promising results suggest a high potential of CNNs in estimating local CO2 emissions from satellite images. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH4) and carbon dioxide (CO2), and presents a use case that combines UAAS measurements and dispersion modeling to quantify CH4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations 15 in the San Joaquin Valley of California where CH4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11-m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s-1 when the wind magnitude is less than 3.5 m s-1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH4 and CO2 occurring over 10 second periods or longer. Results from the UAAS and 20 dispersion modeling use case further demonstrate that UAAS have great potential as a low-cost tool for detecting and quantifying CH4 emissions in near real-time. [ABSTRACT FROM AUTHOR]

A key goal of the Paris Agreement (PA) is to reach net-zero Greenhouse Gasses (GHG) emissions by 2050 globally, which requires mitigation efforts from all countries. Africa's rapidly growing population and GDP makes this continent important for GHG emission trends. In this paper, we study the emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) in Africa over three decades (1990-2018). We compare bottom-up approaches including UNFCCC national inventories, FAO, PRIMAP-hist, process-based ecosystem models for CO2 fluxes in the Land Use, Land Use Change and Forestry (LULUCF) sector, and global atmospheric inversions. Our database is available from Zenodo at: https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022). For inversions, we applied different methods to separate anthropogenic CH4 emissions. The bottom-up (BU) inventories show that over the decade 2010-2018, less than ten countries represented more than 75% of African fossil CO2 emissions. With a mean of 1373 MtCO2 yr-1, total African fossil CO2 emissions over 2010-2018 represent only 4% of global fossil emissions. Yet, these emissions grew by +34% from 1990-1999 to 2000-2009 and by +31% over 2000-2009 to 2010-2018, more than doubling in 30 years. This growth rate is more than twice faster than the global growth rate of fossil CO2 emissions. The anthropogenic emissions of CH4 grew by 5% from 1990-1999 to 2000-2009 and by 14.8% from 2000-2009 to 2010-2018. The N2O emissions grew by 19.5% from 1990-1999 to 2000-2009; and by 20.8% from 2000-2009 to 2010-2018. When using the mean of estimates from UNFCCC reports (including the land use sector), with corrections from outliers, Africa was a mean source of greenhouse gasses of +2622 $%&' ($() MtCO2e yr-1 from all bottom-up estimates (sub- and superscript indicating min-max range uncertainties), and of +2637%,'% -&,(MtCO2e yr-1 from top-down methods, during their overlap period from 2001 to 2017. Although the mean values are consistent, the range of top-down estimates is larger than the one of bottom up, indicating that sparse atmospheric observations and transport model errors do not allow us to use inversions to reduce the uncertainty of bottom-up estimates. A main source of uncertainty comes from CO2 fluxes in the land-use sector (LULUCF) for which the spread across inversions is larger than 50%, especially in Central Africa. Moreover, estimates from national UNFCCC communications differ widely depending on whether the large sinks in a few countries are corrected to more plausible values using more recent national sources following the methodology of Grassi et al. (2022) The median of CH4 emissions from inversions based on satellite retrievals and in situ surface networks are consistent with each other within 2% at continental scale. The inversion ensemble also provides consistent estimates of anthropogenic CH4 emissions with bottom-up inventories such as PRIMAP-hist. For N2O, inversions systematically show higher emissions than inventories, on average about 4.5 times more than PRIMAP-hist, either because natural N2O sources cannot be separated accurately from anthropogenic ones in inversions, or because bottom-up estimates ignore indirect emissions and under-estimate emission factors. Future improvements can be expected thanks to a denser network for monitoring atmospheric concentrations. This study helps to introduce methods to enhance the scope of use of various published datasets and allows to compute budgets thanks to recombinations those data products. Our results allow to understand uncertainty and trends of emissions and removals in a region of the world where few observations exist and most inventories are based on default IPCC guidelines values. The results can therefore serve as a support tool for the Global Stocktake (GST) of the Paris Agreement. The referenced datasets related to figures are available at: https://doi.org/10.5281/zenodo.7347077 (Mostefaoui et al., 2022). [ABSTRACT FROM AUTHOR]

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian high Arctic (82°28' N, 62°30' W). In this paper, we evaluate the measurement agreement of atmospheric CO2, CH4, N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) between leading laboratories from 7 independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization's (WMO) GAW greenhouse gas measurement community. Overall, based on ~8000 discrete flask samples, we find that the co-located atmospheric CO2 and CH4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, ECCC, and NOAA are generally consistent with the WMO compatibility goals of ±0.1 ppm CO2 and ±2 ppb CH4 over the 17-year period (1999 - 2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO, SIO, and NOAA at other geographical sites suggests that the findings at Alert for CO2, CH4, N2O and δ13C-CO2 could be extended across the CSIRO, SIO, and NOAA observing networks. Two approaches are carried out to determine the level of agreement as a collective for the 7 individual laboratories (1) pooling the differences of individual laboratories over the entire sampling records from a designated reference laboratory and determining the 95th percentile range of these data points and (2) averaging the 2 standard deviations (2-sigma) of the means for all flask samples taken in each individual sampling episode over the entire sampling record. For CO2, from 5691 samples, we derive a measurement agreement level of-0.51 to +0.53 ppm using the 95th percentile range of the differences from NOAA measurements. Similarly, we derive a corresponding value of ± 0.37 ppm using the mean of 2-sigma values from 923 individual weekly sampling episodes. For CO2 isotopes using INSTAAR measurements as a reference, we derive measurement agreement values of -0.09 to +0.07 and ± 0.06 ‰ for δ13C and -0.50 to +0.58 and ±0.31 %o for δ18O, for the 95th percentile ranges and the mean of the 2-sigma values, respectively. For other gases, the corresponding values for both approaches are -4.86 to +6.16 and ± 3.62 ppb for CH4, -0.75 to +1.20 and ± 0.64 ppb for N2O, and -0.14 to +0.09 and ± 0.09 ppt for SF6. These upper and lower limits represent our best estimate of the measurement agreement at the 95% confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis). [ABSTRACT FROM AUTHOR]

The Geostationary Carbon Cycle Observatory (GeoCarb) was selected as NASA's second Earth Venture Mission (EVM-2). The scientific objectives of GeoCarb are to advance our knowledge of the carbon cycle, in particular, landatmosphere fluxes of the greenhouse gases carbon dioxide (CO2) and methane (CH4), and the effects of these fluxes on the Earth's radiation budget. GeoCarb will retrieve column integrated amounts of CO2 (XCO2), CH4 (XCH4) and CO (XCO, important for understanding tropospheric chemistry), in addition to Solar-Induced Fluorescence (SIF), from hyperspectral resolution measurements in the O2 A-band at 0.76 µm, the weak CO2 band at 1.6 µm, the strong CO2 band at 2.06 µm, and a CH4/CO band at 2.32 µm. Unlike it's polar orbiting predecessors (OCO-2/3, GOSAT-1/2, TROPOMI), GeoCarb will be in a Geostationary orbit with a sub-satellite point centered over the Americas. This orbital configuration combined with its high spatial resolution imaging capabilities will provide an unprecedented view of these quantities on spatial and temporal scales accurate enough to resolve sources and sinks to improve land-atmosphere CO2 and CH4 flux calculations and reduce the uncertainty of these fluxes. This paper will present a description of the GeoCarb instrument and the L2 retrieval algorithms which will be followed by simulation experiments to determine a relatively comprehensive error budget for each target gas. Several sources of uncertainty will be explored including that from the instrument calibration parameters for radiometric gain, the instrument line shape (ILS), the polarization, and the geolocation pointing, in addition to, forward model parameters including that from meteorology and spectroscopy. The results indicate that the errors (1κ) are less than the instrument's multi-sounding precision requirements of 1.2 ppm, 10 ppb, and 12 ppb (10%), for XCO2, XCH4, and XCO, respectively. In particular, when considering the sources of uncertainty separately and in combination (all sources included), we find overall RMS errors of 1.06 ppm for XCO2, 8.2 ppb for XCH4, and 2.5 ppb for XCO, respectively. Additionally, we find that, as expected, errors in XCO2 and XCH4 are dominated by forward model and other systematic errors, while errors in XCO, like SIF, are dominated by measurement noise. [ABSTRACT FROM AUTHOR]

High-quality, long-term measurements of terrestrial trace gas emissions are important for investigations of atmospheric, geophysical and biological processes to help mitigate climate change and protect the environment and the health of citizens. High-frequency terrestrial fluxes of the radioactive noble gas 222 Rn, in particular, are useful for validating radon flux maps and used to evaluate the performance of regional atmospheric models, to improve greenhouse gas emission inventories (by the radon tracer method) and to determine radon priority areas for radiation protection goals. A new automatic radon flux system (Autoflux) was developed as a transfer standard (TS) to assist with establishing a traceability chain for field-based radon flux measurements. The operational characteristics and features of the system were optimized based on a literature review of existing flux measurement systems. To characterize and calibrate Autoflux, a bespoke radon exhalation bed (EB) facility was also constructed with the intended purpose of providing a constant radon exhalation under a specific set of controlled laboratory conditions. The calibrated Autoflux was then used to transfer the derived calibration to a second continuous radon flux system under laboratory conditions; both instruments were then tested in the field and compared with modeled fluxes. This paper presents (i) a literature review of state-of-the-art radon flux systems and EB facilities; (ii) the design, characterization and calibration of a reference radon EB facility; (iii) the design, characterization and calibration of the Autoflux system; (iv) the calibration of a second radon flux system (INTE_Flux) using the EB and Autoflux, with a total uncertainty of 9 % (k = 1) for an average radon flux of ∼ 1800 mBq m -2 s -1 under controlled laboratory conditions; and (v)​​​​​​​ an example application of the calibrated TS and INTE_Flux systems for in situ radon flux measurements, which are then compared with simulated radon fluxes. Calibration of the TS under different environmental conditions and at lower reference fluxes will be the subject of a separate future investigation. [ABSTRACT FROM AUTHOR]

The combustion of wood fuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions from households, as the emissions from wood fuel combustion can be offset by the CO2 absorbed by the growth of the forest (as a carbon sink) (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e. at a rate not exceeding the regrowth rate of the forest from which it was harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable wood fuel harvesting for use in residential food activities, by country and with global coverage. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on wood fuel use by households. We find that, in 2019, annual emissions from non-renewable wood fuel consumed for household food preparation were about 745×106 t (Mt CO2eq. yr -1) , with an uncertainty ranging from - 63 % to + 64 %. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled by countries in sub-Saharan Africa, southern Asia, and Latin America, whereas significant decreases were seen in countries in eastern Asia and South-East Asia. The Food and Agriculture Organization of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset has been developed according to the International Panel on Climate Change guidelines (IPCC, 2006), which avoid overlaps between agriculture, forestry, and other land use (AFOLU) and energy components. The aforementioned dataset relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data at 10.5281/zenodo.7310932 (Flammini et al., 2022a). [ABSTRACT FROM AUTHOR]

The Limb Imaging Fourier-Transform Spectrometer Experiment (LIFE) instrument is a balloon-borne prototype of a satellite instrument designed to take vertical images of atmospheric limb emission spectra in the 700 to 1400 cm-1 wavenumber range from the upper troposphere/lower stratosphere (UTLS) altitude region of the atmosphere. The prototype builds on the success of past and existing instruments while reducing the complexity of the imaging design. This paper details the results of a demonstration flight on a stabilized stratospheric balloon gondola from Timmins, Canada in August 2019. Retrievals 5 of vertical trace gas profiles for the important greenhouse gases H2O, O3, CH4 and N2O, as well as HNO3, are performed using an Optimal Estimation Approach and the SASKTRAN radiative transfer model. The retrieved profiles are compared to approximately coincident observations made by the ACE-FTS solar occultation satellite instrument. An evaluation of the LIFE measurements is performed and areas of improvement are identified. This work increases the overall technical readiness of the approach for future balloon, aircraft and space applications. [ABSTRACT FROM AUTHOR]

High-quality, long-term measurements of terrestrial trace gas emissions are important for investigations of atmospheric, geophysical and biological processes to help mitigate climate change, protect the environment, and the health of citizens. High-frequency terrestrial fluxes of the radioactive noble gas 222Rn, in particular, are useful for validating radon flux maps, used to evaluate the performance of regional atmospheric models, to improve greenhouse gas emission inventories (by the Radon Tracer Method) and to determine Radon Priority Areas for radiation protection goals. A new automatic radon flux system (the Autoflux) was developed as a Transfer Standard (TS) to assist with establishing a traceability chain for field-based radon flux measurements. The operational characteristics and features of the system were optimized based on a literature review of existing flux measurement systems. To characterize and calibrate the Autoflux a bespoke radon Exhalation Bed (EB) facility was also constructed with the intended purpose of providing a constant radon emanation under a specific set of controlled laboratory conditions. The calibrated Autoflux was then used to transfer the derived calibration to a second continuous radon flux system under laboratory conditions, both instruments were then tested in the field and compared with modeled fluxes. This paper presents: i) a literature review of state-of-the-art radon flux systems and EB facilities; ii) the design, characterization and calibration of a reference radon EB facility; iii) the design, characterization and calibration of the Autoflux system; iv) the calibration of a second radon flux system (INTE_Flux) using the EB and Autoflux, with a total uncertainty of 9% (k=1) for an average radon flux of ~1800 mBq m-2 s-1 under controlled laboratory conditions; and iv) an example application of the calibrated TS and INTE_Flux systems for in situ radon flux measurements which are then compared with simulated radon fluxes. Calibration of the TS under different environmental conditions and at lower reference fluxes will be the subject of a separate future investigation. [ABSTRACT FROM AUTHOR]

This paper features the new atmosphere–ocean–aerosol–chemistry–climate model, SOlar Climate Ozone Links (SOCOL) v4.0, and its validation. The new model was built by interactively coupling the Max Planck Institute Earth System Model version 1.2 (MPI-ESM1.2) (T63, L47) with the chemistry (99 species) and size-resolving (40 bins) sulfate aerosol microphysics modules from the aerosol–chemistry–climate model, SOCOL-AERv2. We evaluate its performance against reanalysis products and observations of atmospheric circulation, temperature, and trace gas distribution, with a focus on stratospheric processes. We show that SOCOLv4.0 captures the low- and midlatitude stratospheric ozone well in terms of the climatological state, variability and evolution. The model provides an accurate representation of climate change, showing a global surface warming trend consistent with observations as well as realistic cooling in the stratosphere caused by greenhouse gas emissions, although, as in previous model versions, a too-fast residual circulation and exaggerated mixing in the surf zone are still present. The stratospheric sulfur budget for moderate volcanic activity is well represented by the model, albeit with slightly underestimated aerosol lifetime after major eruptions. The presence of the interactive ocean and a successful representation of recent climate and ozone layer trends make SOCOLv4.0 ideal for studies devoted to future ozone evolution and effects of greenhouse gases and ozone-destroying substances, as well as the evaluation of potential solar geoengineering measures through sulfur injections. Potential further model improvements could be to increase the vertical resolution, which is expected to allow better meridional transport in the stratosphere, as well as to update the photolysis calculation module and budget of mesospheric odd nitrogen. In summary, this paper demonstrates that SOCOLv4.0 is well suited for applications related to the stratospheric ozone and sulfate aerosol evolution, including its participation in ongoing and future model intercomparison projects. [ABSTRACT FROM AUTHOR]

The combustion of woodfuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions in households since emissions from woodfuel combustion can be offset by the CO2 absorbed by the growth of the forest as a carbon sink (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e., at a rate not exceeding the regrowth rate of the forest from which it is harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable woodfuel harvesting for use in residential food activities. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on woodfuel use in households. We find that, in 2019, annual emissions from non-renewable woodfuel use in household food consumption were about 745 million tonnes (Mt CO2eq yr-1), with uncertainty ranging from -20 % to + 22 %, having increased 6% from 1990. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled from countries in Sub-Saharan Africa, Southern Asia, and Latin America while significant decreases were seen in countries in Eastern Asia and South-eastern Asia. The Food and Agriculture Organisation of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset is developed according to International Panel on Climate Change guidelines (IPCC, 2006), which avoids overlaps across AFOLU and energy components. It relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.7310932 (Flammini et al., 2022). [ABSTRACT FROM AUTHOR]

This paper features the new Atmosphere-Ocean-Aerosol-Chemistry-Climate Model SOCOLv4.0 and its validation. The new model was built by interactively coupling the MPI-ESM1.2 Earth System Model (T63, L47) with the chemistry (99 species) and size-resolving (40 bins) sulfate aerosol microphysics modules from the Aerosol-Chemistry-Climate Model SOCOL-AERv2. We evaluate its performance against reanalysis products and observations of atmospheric circulation, temperature, and trace gases distribution, with a focus on stratospheric processes. We show that SOCOLv4.0 captures the low and mid-latitude stratospheric ozone well in terms of the climatological state, variability and evolution. The model provides an accurate representation of climate change, showing a global surface warming trend consistent with observations as well as realistic cooling in the stratosphere caused by greenhouse gas emissions, although, as in previous model versions, a too fast residual circulation and exaggerated mixing in the surf zone are still present. The stratospheric sulfur budget for moderate volcanic activity is well represented by the model, albeit with slightly underestimated aerosol lifetime after major eruptions. The presence of the interactive ocean and a successful representation of recent climate and ozone layer trends make SOCOLv4.0 ideal for studies devoted to future ozone evolution and effects of greenhouse gases and ozone-destroying substances, as well as the evaluation of potential solar geoengineering measures through sulfur injections. Potential further model improvements could be to increase the vertical resolution, which is expected to allow better meridional transport in the stratosphere, as well as to update the photolysis calculation module and budget of mesospheric odd nitrogen. In summary, this paper demonstrates that SOCOLv4.0 is well suited for applications related to the stratospheric ozone and sulfate aerosol evolution, including its participation in ongoing and future model intercomparison projects. [ABSTRACT FROM AUTHOR]

Long, high-quality time series measurements of atmospheric greenhouse gases show interannual variability in the measured seasonal cycles. These changes can be analyzed to better understand the carbon cycle and the impact of climate drivers. However, nearly all discrete measurement records contain gaps and have noise due to the influence of local fluxes or synoptic variability. To facilitate analysis, filtering and curve-fitting techniques are often applied to these time series. Previous studies have recognized that there is inherent uncertainty associated with this curve fitting and the choice of a given mathematical method might introduce biases. Since uncertainties are seldom propagated to the metrics under study, this can lead to misinterpretation of the signal. In this study, we present a novel curve fitting method and an ensemble-based approach that allows the uncertainty of the metrics to be quantified. We apply it here to the Northern Hemisphere CO2 dry air mole fraction time series. We use this ensemble-based approach to analyze different seasonal cycle metrics, namely the onset, termination, and duration of the carbon uptake period (CUP), i.e., the time of the year when the CO2 uptake is greater than the CO2 release. Previous studies have diagnosed CUP based on the dates on which the detrended, zero-centered seasonal cycle curve switches from positive to negative (the downward zero-crossing date) and vice versa (upward zero-crossing date). However, we find that the upward zero-crossing date is sensitive to the skewness of the CO2 seasonal cycle during the net carbon release period. Hence, we propose an alternative method to estimate the onset and termination of the CUP based on a threshold defined in terms of the first-derivative of the CO2 seasonal cycle (First-derivative threshold (FDT) method). Further, using the ensemble-based approach and an additional curve fitting algorithm, we show that (a) the uncertainty of the studied metrics is smaller using the FDT method than when estimated using the timing of the zero-crossing dates, and (b) the onset and termination dates derived with the FDT-method provide more robust results, irrespective of the curve-fitting method applied to the data. The code is made freely available under a Creative Commons-BY license, along with the documentation in this paper. [ABSTRACT FROM AUTHOR]

In the last decades, several studies reported the tropics' expansion, but the rates of expansion are widely different. In this paper, data of 12 global navigation satellite systems radio occultation (GNSS-RO) missions from June 2001 to November 2020 with high resolution were used to investigate the possible widening of the tropical belt along with the probable drivers and impacts in both hemispheres. Applying both lapse rate tropopause (LRT) and cold point tropopause (CPT) definitions, the global tropopause height shows an increase of approximately 36 and 60 m per decade, respectively. The tropical edge latitudes (TELs) are estimated based on two tropopause height metrics, subjective and objective methods. Applying both metrics, the determined TELs using GNSS have expansive behavior in the Northern Hemisphere (NH), while in the Southern Hemisphere (SH) there are no significant trends. In the case of ECMWF Reanalysis v5 (ERA5) there are no considerable trends in both hemispheres. For the Atmospheric Infrared Sounder (AIRS), there is expansion in the NH and observed contraction in the SH. The variability of tropopause parameters (temperature and height) is maximum around the TEL locations in both hemispheres. Moreover, the spatial and temporal patterns of total column ozone (TCO) have good agreement with the TEL positions estimated using GNSS LRT height. Carbon dioxide (CO 2) and methane (CH 4), the most important greenhouse gases (GHGs) and the main drivers of global warming, have spatial modes in the NH that are located more poleward than that in the SH. Both surface temperature and precipitation have strong correlation with GNSS LRT height. The surface temperature spatial pattern broadly agrees with the GNSS TEL positions. In contrast, the standardized precipitation evapotranspiration index (SPEI) has no direct connection with the TEL behavior. The results illustrate that the tropics' widening rates are different from one dataset to another and from one metric to another. In addition, TEL behavior in the NH is different from that in the SH. Furthermore, the variability of meteorological parameters agrees with GNSS TEL results more than with that of other datasets. [ABSTRACT FROM AUTHOR]

Ground-based high-spectral-resolution infrared measurements are an efficient way to obtain accurate tropospheric abundances of different gaseous species, in particular greenhouse gases (GHGs) such as CO2 and CH4. Many ground-based spectrometers are used in the NDACC and TCCON networks to validate the Level 2 satellite data, but their large dimensions and heavy mass make them inadequate for field campaigns. To overcome these problems, the use of portable spectrometers was recently investigated. In this context, this paper deals with the CHRIS (Compact High-Spectral-Resolution Infrared Spectrometer) prototype with unique characteristics such as its high spectral resolution (0.135 cm-1 nonapodized) and its wide spectral range (680 to 5200 cm-1). Its main objective is the characterization of gases and aerosols in the thermal and shortwave infrared regions. That is why it requires high radiometric precision and accuracy, which are achieved by performing spectral and radiometric calibrations that are described in this paper. Furthermore, CHRIS's capabilities to retrieve vertical CO2 and CH4 profiles are presented through a complete information content analysis, a channel selection and an error budget estimation in the attempt to join ongoing campaigns such as MAGIC (Monitoring of Atmospheric composition and Greenhouse gases through multi-Instruments Campaigns) to monitor GHGs and validate the actual and future space missions such as IASI-NG and Microcarb. [ABSTRACT FROM AUTHOR]

We report in this paper the development of an embedded ultralight spectrometer (<3 kg) based on tuneable diode laser absorption spectroscopy (with a sampling rate of 24 Hz) in the mid-infrared spectral region. This instrument is dedicated to in situ measurements of the vertical profile concentrations of three main greenhouse gases – carbon dioxide (CO2), methane (CH4) and water vapour (H2O) – via standard weather and tethered balloons. The plug and play instrument is compact, robust, cost-effective, and autonomous. The instrument also has low power consumption and is non-intrusive. It was first calibrated during an in situ experiment on an ICOS (Integrated Carbon Observation System) site for several days, then used in two experiments with several balloon flights of up to 30 km altitude in the Reims region of France in 2017–2018 in collaboration with Météo-France CNRM (Centre National de Recherches Météorologiques). This paper shows the valuable interest of the data measured by the AMULSE (Atmospheric Measurements by Ultra-Light Spectrometer) instrument during the APOGEE (Atmospheric Profiles of Greenhouse Gases) measurement experiment, specifically for the vertical profiles of CO2 and CH4 , measurements of which remain very sparse. We have carried out several experiments showing that the measured profiles have several applications: the validation of simulations of infrared satellite observations, evaluating the quality of chemical profiles from chemistry transport models (CTMs) and evaluating the quality of retrieved chemical profiles from the assimilation of infrared satellite observations. The results show that the simulations of infrared satellite observations from IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) instruments performed in operational mode for numerical weather prediction (NWP) by the radiative transfer model (RTM) RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) are of good quality. We also show that the MOCAGE (Modèle de Chimie Atmosphérique de Grande Echelle) and CAMS (Copernicus Atmospheric Monitoring Service) CTMs modelled ozone profiles fairly accurately and that the CAMS CTM represents the methane in the troposphere well compared to MOCAGE. Finally, the measured in situ ozone profiles allowed us to show the good quality of the retrieved ozone profiles by assimilating ozone-sensitive infrared spectral radiances from the IASI and CrIS. [ABSTRACT FROM AUTHOR]

Ground-based high spectral resolution infrared measurements are an efficient way to obtain accurate tropospheric abundances of different gaseous species and in particular GreenHouse Gases (GHG), such as CO2 and CH4. Many ground-based spectrometers are used in the NDACC and TCCON networks to validate the Level 2 satellite data, but their large dimensions and heavy mass makes them inadequate for field campaigns. To overcome these problems, the use of portable spectrometers was recently investigated. In this context, this paper deals with the CHRIS (Compact High-spectral Resolution Infrared Spectrometer) prototype with unique characteristics such as its high spectral resolution (0.135 cm-1 non-apodized) and its wide spectral range (680 to 5200 cm-1). Its main objective is the characterization of gases and aerosols in the infrared thermal region, that's why it requires high radiometric precision and accuracy, which is achieved by performing spectral and radiometric calibrations that will be presented in this paper. Also, CHRIS's capabilities to retrieve CO2 and CH4 vertical profiles are presented through a complete information content analysis, a channel selection and an error budget estimation in the attempt to join the ongoing campaigns, such as MAGIC, to monitor the GHG and validate the actual and future space missions. [ABSTRACT FROM AUTHOR]

Wetlands are the single largest natural source of methane (CH 4), a powerful greenhouse gas affecting the global climate. In turn, wetland CH 4 emissions are sensitive to changes in climate conditions such as temperature and precipitation shifts. However, biogeochemical processes regulating wetland CH 4 emissions (namely microbial production and oxidation of CH 4) are not routinely included in fully coupled Earth system models that simulate feedbacks between the physical climate, the carbon cycle, and other biogeochemical cycles. This paper introduces a process-based wetland CH 4 model (WETMETH) developed for implementation in Earth system models and currently embedded in an Earth system model of intermediate complexity. Here, we (i) describe the wetland CH 4 model, (ii) evaluate the model performance against available datasets and estimates from the literature, and (iii) analyze the model sensitivity to perturbations of poorly constrained parameters. Historical simulations show that WETMETH is capable of reproducing mean annual emissions consistent with present-day estimates across spatial scales. For the 2008–2017 decade, the model simulates global mean wetland emissions of 158.6 Tg CH 4 yr -1 , of which 33.1 Tg CH 4 yr -1 is from wetlands north of 45 ∘ N. WETMETH is highly sensitive to parameters for the microbial oxidation of CH 4 , which is the least constrained process in the literature. [ABSTRACT FROM AUTHOR]

Parties to the Paris Agreement (PA,) outline their planned contributions towards achieving the PA temperature goal to "hold [...] the increase in the global average temperature to well below 2 ∘ C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 ∘ C" (Article 2.1.a, PA) in their nationally determined contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess i.a. whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool " NDCmitiQ " to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level and to create corresponding national and global emissions pathways. In light of the 5-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; and establishing pathways from single-year targets. For use in NDCmitiQ , we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions about economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 48.9–56.1 Gt CO2eq. AR4 for 2030 (10th/90th percentiles, median: 51.8 Gt CO2eq. AR4; excluding LULUCF and bunker fuels; submissions until 17 April 2020 and excluding the USA). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by these NDCs. Addressing all updated NDCs submitted by 31 December 2020 results in an estimated 45.6–54.1 Gt CO2eq. AR4 (median: 49.6 Gt CO2eq. AR4, now including the USA again) and increased coverage. [ABSTRACT FROM AUTHOR]

The Emissions Database for Global Atmospheric Research (EDGAR) compiles anthropogenic emissions data for greenhouse gases (GHGs), and for multiple air pollutants, based on international statistics and emission factors. EDGAR data provide quantitative support for atmospheric modelling and for mitigation scenario and impact assessment analyses as well as for policy evaluation. The new version (v4.3.2) of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of the European Union's first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol, KP). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emissions of multiple chemical compounds, GHGs as well as air pollutants, from relevant sources (fossil fuel activities but also, for example, fermentation processes in agricultural activities) are compiled following a bottom-up (BU), transparent and IPCC-compliant methodology. This paper describes EDGAR v4.3.2 developments with respect to three major long-lived GHGs (CO2 , CH4 , and N2O) derived from a wide range of human activities apart from the land-use, land-use change and forestry (LULUCF) sector and apart from savannah burning; a companion paper quantifies and discusses emissions of air pollutants. Detailed information is included for each of the IPCC-relevant source sectors, leading to global totals for 2010 (in the middle of the first KP commitment period) (with a 95 % confidence interval in parentheses): 33.6(±5.9) Pg CO 2 yr -1 , 0.34(±0.16) Pg CH 4 yr -1 , and 7.2(±3.7) Tg N 2 O yr -1. We provide uncertainty factors in emissions data for the different GHGs and for three different groups of countries: OECD countries of 1990, countries with economies in transition in 1990, and the remaining countries in development (the UNFCCC non-Annex I parties). We document trends for the major emitting countries together with the European Union in more detail, demonstrating that effects of fuel markets and financial instability have had greater impacts on GHG trends than effects of income or population. These data (10.5281/zenodo.2658138, Janssens-Maenhout et al., 2019) are visualised with annual and monthly global emissions grid maps of 0.1∘×0.1∘ for each source sector. [ABSTRACT FROM AUTHOR]

The Emissions Database for Global Atmospheric Research (EDGAR) compiles anthropogenic emissions data for greenhouse gases (GHG) and for multiple air pollutants based on international statistics and emission factors. EDGAR data provides quantitative support for atmospheric modelling and for mitigation scenario and impact assessment analyses as well as for policy evaluation. The new version v4.3.2 of the EDGAR emission inventory provides global estimates, disaggregated to IPCC-relevant source-sector levels, from 1970 (the year of EU's first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emissions of multiple chemical compounds, GHG as well as air pollutants, from relevant sources (fossil fuel activities but also, for example fermentation processes in agricultural activities) are compiled following a bottom-up (BU), fully-traceable and IPCC-based methodology. This paper describes EDGARv4.3.2 developments with respect to three major GHG (CO2, CH4, and N2O) derived from a wide range of human activities apart from the land-use, land-use change and forestry (LULUCF) sector and apart from Savannah burning; a companion paper quantifies and discusses emissions of air pollutants. Detailed information is included for each of the IPCC-relevant source-sectors, leading to global totals for 2010 (in the middle of the first KP commitment period) (with 95 % confidence interval in parentheses): 33.6 (±5.9) Pg CO2/yr, 0.34 (±0.16) Pg CH4/yr, and 7.2 (±3.7) Tg N2O/yr. We provide uncertainty factors in emissions data for the different GHGs and for three different groups of countries: OECD countries of 1990, countries with economies in transition in 1990, and the remaining countries in development (the UNFCCC non-Annex I parties). We document trends for the major emitting countries together with the European Union in more detail, demonstrating that effects of fuel markets and financial stability have had greater impacts on GHG trends than effects of income or population. These data (DOI: https://doi.org/10.2904/JRC_DATASET_EDGAR) are visualised with annual and monthly global emissions grid-maps of 0.1° ×0.1° for each source-sector; these data can be freely accessed from the EDGAR website http://edgar.jrc.ec.europa.eu/overview.php?v=432&SECURE=123. [ABSTRACT FROM AUTHOR]

Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. While the country-level scale of emissions changes can be estimated in near real time, the more detailed, gridded emissions estimates that are required to run general circulation models (GCMs) of the climate will take longer to collect. In this paper we use recorded and projected country-and-sector activity levels to modify gridded predictions from the MESSAGE-GLOBIOM SSP2-4.5 scenario. We provide updated projections for concentrations of greenhouse gases, emissions fields for aerosols, and precursors and the ozone and optical properties that result from this. The code base to perform similar modifications to other scenarios is also provided. We outline the means by which these results may be used in a model intercomparison project (CovidMIP) to investigate the impact of national lockdown measures on climate, including regional temperature, precipitation, and circulation changes. This includes three strands: an assessment of short-term effects (5-year period) and of longer-term effects (30 years) and an investigation into the separate effects of changes in emissions of greenhouse gases and aerosols. This last strand supports the possible attribution of observed changes in the climate system; hence these simulations will also form part of the Detection and Attribution Model Intercomparison Project (DAMIP). [ABSTRACT FROM AUTHOR]

The Japanese Greenhouse gases Observing SATellite-2 (GOSAT-2), in orbit since 29 October 2018, follows up the GOSAT mission, itself in orbit since 23 January 2009. GOSAT-2 monitors carbon dioxide and methane in order to increase our understanding of the global carbon cycle. It simultaneously measures carbon monoxide emitted from fossil fuel combustion and biomass burning and permits identification of the amount of combustion-related carbon. To do this, the satellite utilizes the Thermal and Near Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer-2 (TANSO-FTS-2). This spectrometer detects gas absorption spectra of solar radiation reflected from the Earth's surface in the shortwave-infrared (SWIR) region as well as the emitted thermal infrared radiation (TIR) from the ground and the atmosphere. TANSO-FTS-2 can measure the oxygen A band (0.76 µ m), weak and strong CO 2 bands (1.6 and 2.0 µ m), weak and strong CH 4 bands (1.6 and 2.3 µ m), a weak CO band (2.3 µ m), a mid-wave TIR band (5.5–8.4 µ m), and a long-wave TIR band (8.4–14.3 µ m) with 0.2 cm -1 spectral sampling intervals. TANSO-FTS-2 is equipped with a solar diffuser target, a monochromatic light source, and a blackbody for spectral radiance calibration. These calibration sources permit characterization of time-dependent instrument changes in orbit. The onboard-recalibrated instrumental parameters are considered in operational level-1 processing and released as TANSO-FTS-2 level-1 version 102102 products, which were officially released on 25 May 2020. This paper provides an overview of the TANSO-FTS-2 instrument, the level-1 processing, and the first-year in-orbit performance. To validate the spectral radiance calibration during the first year of operation, the spectral radiance of the version 102102 product is compared at temporally coincident and spatially collocated points from February 2019 to March 2020 with TANSO-FTS on GOSAT for SWIR and with AIRS on Aqua and IASI on METOP-B for TIR. The spectral radiances measured by TANSO-FTS and TANSO-FTS-2 agree within 2 % of the averaged bias and 0.5 % standard deviation for SWIR bands. The agreement of brightness temperature between TANSO-FTS-2 and AIRS–IASI is better than 1 K in the range from 220 to 320 K. GOSAT-2 not only provides seamless global CO 2 and CH 4 observation but also observes local emissions and uptake with an additional CO channel, fully customized sampling patterns, higher signal-to-noise ratios, and wider pointing angles than GOSAT. [ABSTRACT FROM AUTHOR]

The climate change problem is an actual concern of the modern world and CO2 emissions are mainly responsible for it, CO2 being the most important greenhouse gas. The worldwide effort to reduce these emissions lead to the development of CCS technologies. The increasing need for energy and our dependence on fossil fuels lead to the viable solution of reducing carbon dioxide emissions using geological storage. CO2 Capture and Storage solution was evaluated in several European projects and several European countries, including Romania. In Romania the efforts made in reducing carbon dioxide emissions lead to a general evaluation of possible geological storage sites. This evaluation resulted in calculating storage capacities in saline aquifers and hydrocarbon reservoirs.The Romanian sedimentary basins potentially containing saline formations have been combined in 4 large zones (Moesian platform and S. Carpathians foredeep, Moldavian platform and E. Carpathians foredeep, Transilvanian basin and Pannonian basin).Out of their total surface areas, the surface with sedimentary cover thinner than 800 m have been eliminated from calculations as such areas are not suitable for CO2 storage. The sectors distinguished are: Energy, Refineries, Production and processing of ferrous metals, Cement, Lime, Glass, Ceramics, Pulp and paper. From 244 industrial installations with CO2 allowances, only 63 have significant emissions (>0.1 Mt CO2).The CO2 capacity in depleted or declining hydrocarbon (oil and gas) fields was calculated by a production-based equation of the form:GCO2 = URP × B × ρ,where URP is the Ultimate Recoverable Oil (or Gas) based on sum of produced volumes and expected reserves. Romania has a history of 150 years of oil industry. It is estimated that during such a long period of time, some 720 Mt of oil and 1122 Gm3 have been extracted from its underground (Gilbert, 2007). Today it may be considered a "mature" oil and gas province with 70 - 80 percent of its resources already exploited. [ABSTRACT FROM AUTHOR]

Parties to the Paris Agreement (PA, 2015) outline their planned contributions towards achieving the PA temperature goal to hold […] the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C (Article 2.1.a, PA) in their Nationally Determined Contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess, i.a., whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool NDCmitiQ to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level, and to create corresponding national and global emissions pathways. In light of the five-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including: target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; establishing pathways from single year targets. For use in NDCmitiQ, we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale, and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions on economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 49.2-55.7 Gt CO2 eq AR4 for 2030 (10th/90th percentiles, median: 52.4 Gt CO2 eq AR4; excl. LULUCF and bunker fuels). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by current NDCs (excl. the USA). [ABSTRACT FROM AUTHOR]

The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high-precision scientific data on the carbon cycle and the greenhouse gas budget. All stations have to undergo a rigorous assessment before being labeled, i.e., receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmosphere network through the 23 stations that were labeled between November 2017 and November 2019. We describe the labeling steps, as well as the quality controls, used to verify that the ICOS data (CO 2 , CH 4 , CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the greenhouse gas data, three to four calibration gases and two target gases are measured: one target two to three times a day, the other gases twice a month. The data are verified on a weekly basis, and tests on the station sampling lines are performed twice a year. From these high-quality data, we conclude that regular calibrations of the CO 2 , CH 4 and CO analyzers used here (twice a month) are important in particular for carbon monoxide (CO) due to the analyzer's variability and that reducing the number of calibration injections (from four to three) in a calibration sequence is possible, saving gas and extending the calibration gas lifespan. We also show that currently, the on-site water vapor correction test does not deliver quantitative results possibly due to environmental factors. Thus the use of a drying system is strongly recommended. Finally, the mandatory regular intake line tests are shown to be useful in detecting artifacts and leaks, as shown here via three different examples at the stations. [ABSTRACT FROM AUTHOR]

The pre-industrial millennium is among the periods selected by the Paleoclimate Model Intercomparison Project (PMIP) for experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the fourth phase of the PMIP (PMIP4). The past1000 transient simulations serve to investigate the response to (mainly) natural forcing under background conditions not too different from today, and to discriminate between forced and internally generated variability on interannual to centennial timescales. This paper describes the motivation and the experimental set-ups for the PMIP4-CMIP6 past1000 simulations, and discusses the forcing agents orbital, solar, volcanic, and land use/land cover changes, and variations in greenhouse gas concentrations. The past1000 simulations covering the pre-industrial millennium from 850 Common Era (CE) to 1849 CE have to be complemented by historical simulations (1850 to 2014 CE) following the CMIP6 protocol. The external forcings for the past1000 experiments have been adapted to provide a seamless transition across these time periods. Protocols for the past1000 simulations have been divided into three tiers. A default forcing data set has been defined for the Tier 1 (the CMIP6 past1000) experiment. However, the PMIP community has maintained the flexibility to conduct coordinated sensitivity experiments to explore uncertainty in forcing reconstructions as well as parameter uncertainty in dedicated Tier 2 simulations. Additional experiments (Tier 3) are defined to foster collaborative model experiments focusing on the early instrumental period and to extend the temporal range and the scope of the simulations. This paper outlines current and future research foci and common analyses for collaborative work between the PMIP and the observational communities (reconstructions, instrumental data). [ABSTRACT FROM AUTHOR]

Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. While the country-level scale of emissions changes can be estimated in near-real-time, the more detailed, gridded emissions estimates that are required to run General Circulation Models (GCM) of the climate will take longer to collect. In this paper we use recorded and projected country-and-sector activity levels to modify gridded predictions from the MESSAGE-GLOBIOM SSP2-4.5 scenario. We provide updated projections for concentrations of greenhouse gases, emissions fields for aerosols and precursors, and the ozone and optical properties that result from this. The codebase to perform similar modifications to other scenarios is also provided. We outline the means by which these results may be used in a model intercomparison project (CovidMIP) to investigate the impact of national lockdown measures on climate. This includes three strands: an assessment of short-term effects (5-year period), of longer-term effects (30 years) and an investigation into the separate effects of changes in emissions of greenhouse gases and aerosols. This last strand supports possible attribution of observed changes in the climate system, hence these simulations will also form part of the Detection and Attribution Model Intercomparison Project (DAMIP). [ABSTRACT FROM AUTHOR]

Wetlands are the single largest natural source of methane (CH4), a powerful greenhouse gas affecting the global climate. In turn, wetland CH4 emissions are sensitive to changes in climate conditions such as temperature and precipitation shifts. However, biogeochemical processes regulating wetland CH4 emissions (namely microbial production and oxidation of CH4) are not routinely included in fully coupled Earth system models that simulate feedbacks between the physical climate, the carbon cycle, and other biogeochemical cycles. This paper introduces a process-based wetland CH4 model (WETMETH) developed for implementation in Earth system models and currently embedded in an Earth system model of intermediate complexity. Here we: (i) describe the wetland CH4 model; (ii) evaluate the model performance against available datasets and estimates from the literature; (iii) analyze the model sensitivity to perturbations of poorly constrained parameters. Historical simulations show that WETMETH is capable of reproducing mean annual emissions consistent with present-day estimates across spatial scales. For the 2008-2017 decade the model simulates global mean wetland emissions of 158.6 Tg CH4 yr<->1, of which 33.1 Tg CH4 yr<->1 are from wetlands north of 45° N. WETMETH is highly sensitive to parameters for the microbial oxidation of CH4, which is the least constrained process in the literature. [ABSTRACT FROM AUTHOR]

The Japanese Greenhouse gases Observing SATellite-2 (GOSAT-2), in orbit since 29 October 2018, follows up the GOSAT mission, itself in orbit since 23 January 2009. GOSAT-2 monitors carbon dioxide and methane in order to increase our understanding of the global carbon cycle. It simultaneously measures carbon monoxide emitted from fossil fuel combustion and biomass burning, and permits identification of the amount of combustion-related carbon. To do this, the satellite utilizes the Thermal and Near Infrared Sensor for Carbon Observation Fourier-Transform Spectomer-2 (TANSO-FTS-2). This spectrometer detects gas absorption spectra of solar radiation reflected from the Earth's surface in the shortwave-infrared (SWIR) region as well as the emitted thermal infrared radiation (TIR) from the ground and the atmosphere. TANSO-FTS-2 can measure the oxygen A band (0.76 um), weak and strong CO2 bands (1.6 um and 2.0 um), weak and strong CH4 bands (1.6 um and 2.3 um), a weak CO band (2.3 um), a mid-wave TIR band (5.5-8.4 um) and a long-wave TIR band (8.4-14.3 um) with 0.2 cm-1 spectral resolution. TANSO-FTS-2 is equipped with a solar diffuser target, a monochromatic light source, and a black body for spectral radiance calibration. These calibration sources permit characterization of time-dependent instrument changes in orbit. The onboard-recalibrated instrumental parameters are considered in operational level-1 processing and released as TANSO-FTS-2 level-1 version 102102 products, which were officially released on 25 May 2020. This paper provides an overview of the TANSO-FTS-2 instrument, the level-1 processing, and the first-year on-orbit performance. To validate the spectral radiance calibration during the first year of operation, the spectral radiance of the version 102102 product is compared at temporally coincident and spatially collocated points from February 2019 to March 2020 with TANSO-FTS on GOSAT for SWIR, and with AIRS on AQUA and IASI on METOP-B for TIR. The spectral radiances measured by TANSO-FTS and TANSO-FTS-2 agree within 2 % of the averaged bias and 0.5 % standard deviation for SWIR bands. The agreement of brightness temperature between TANSO-FTS-2 and AIRS, IASI, is better than 0.5 % in the range from 220 K to 320 K. GOSAT-2 not only provides seamless global CO2 and CH4 observation but also observes local emissions and uptake with an additional CO channel, fully customized sampling patterns, higher signal-to-noise ratios, and wider pointing angles than GOSAT. [ABSTRACT FROM AUTHOR]