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Methane (CH4) is an important anthropogenic greenhouse gas, and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources are responsible for a large share of global methane emissions. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, are important to support mitigating climate change. Daily global column-averaged dry air mole fractions of atmospheric methane (XCH4) are retrieved from radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board on the Sentinel-5 Precursor (S5P) satellite with a moderately high spatial resolution, enabling the detection and quantification of localized methane sources. We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly TROPOMI XCH4 dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases, we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands, and 46.5 % by unknown sources. Many of the identified PPSRs are in well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas; the Bowen Basin coal mining area in Australia; or the Pantanal Wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil- and gas-dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties in results from other studies but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil- and gas-production basins.

Global crude steel production is expected to continue to increase in the coming decades to meet the demands of the growing world population. Currently, the dominant steelmaking technology worldwide is the conventional highly CO2-intensive blast furnace–basic oxygen furnace production route (also known as the Linz–Donawitz process), which uses iron ore as raw material and coke as a reducing agent. As a result, large quantities of special gases that are rich in carbon monoxide (CO) are by-products of the various stages of the steelmaking process. Given the challenges associated with satellite-based estimates of carbon dioxide (CO2) emissions at the scale of emitting installations due to significant background levels, co-emitted CO may serve as a valuable indicator of the carbon footprint of steel plants. We show that regional CO release from steel production sites can be monitored from space using 5 years of measurements (2018–2022) from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite, benefiting from its relatively high spatial resolution and daily global coverage. We analyse all German steel plants with blast furnaces and basic oxygen furnaces and obtain associated CO emissions in the range of 50–400 kt yr−1 per site. A comparison with the respective CO2 emissions on the level of emitting installations available from emissions trading data of the European Union Emissions Trading System yields a linear relationship with a sector-specific CO/CO2 emission ratio for the analysed steelworks of 3.24 % [2.73–3.89; 1σ], suggesting the feasibility of using CO as a proxy for CO2 emissions from comparable steel production sites. An evaluation at other steel production sites indicates that the derived CO/CO2 emission ratio is also representative of other highly optimised state-of-the-art Linz–Donawitz steelworks outside Germany and that the emission ratio is potentially valuable for estimating sector-specific CO2 emissions from remotely sensed CO emissions, provided that the underlying CO emission estimate is not affected by other sources.

The Anthropogenic Carbon Dioxide Monitoring (CO2M) mission is a constellation of satellites currently planned to be launched in 2026. CO2M is planned to be a core component of a Monitoring and Verification Support (MVS) service capacity under development as part of the Copernicus Atmosphere Monitoring Service (CAMS). The CO2M radiance measurements will be used to retrieve column-averaged dry-air mole fractions of atmospheric carbon dioxide (XCO2), methane (XCH4) and total columns of nitrogen dioxide (NO2). Using appropriate inverse modelling, the atmospheric greenhouse gas (GHG) observations will be used to derive United Nations Framework Convention on Climate Change (UNFCCC) COP 21 Paris Agreement relevant information on GHG sources and sinks. This challenging application requires highly accurate XCO2 and XCH4 retrievals. Three different retrieval algorithms to derive XCO2 and XCH4 are currently under development for the operational processing system at EUMETSAT. One of these algorithms uses the heritage of the FOCAL (Fast atmOspheric traCe gAs retrievaL) method, which has already successfully been applied to measurements from other satellites. Here, we show recent results generated using the CO2M version of FOCAL, called FOCAL-CO2M. To assess the quality of the FOCAL-CO2M retrievals, a large set of representative simulated radiance spectra has been generated using the radiative transfer model SCIATRAN. These simulations consider the planned viewing geometry of the CO2 instrument and corresponding geophysical scene data (including different types of aerosols and varying surface properties), which were taken from model data for the year 2015. We consider instrument noise and systematic errors caused by the retrieval method but have not considered additional error sources due to, for example, instrumental issues, spectroscopy or meteorology. On the other hand, we have also not taken advantage in this study of CO2M's MAP (multi-angle polarimeter) instrument, which will provide additional information on aerosols and cirrus clouds. By application of the FOCAL retrieval to these simulated data, confidence is gained that the FOCAL method is able to fulfil the challenging requirements for systematic errors for the CO2M mission (spatio-temporal bias ≤ 0.5 ppm for XCO2 and ≤ 5 ppb for XCH4).

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas. Its atmospheric concentration has increased by almost 50 % since the beginning of the industrial era, causing climate change. Fossil fuel combustion is responsible for most of the atmospheric CO2 increase, which originates to a large extent from localized sources such as power stations. Independent estimates of the emissions from these sources are key to tracking the effectiveness of implemented climate policies to mitigate climate change. We developed an automatic procedure to quantify CO2 emissions from localized sources based on a cross-sectional mass-balance approach and applied it to infer CO2 emissions from the Bełchatów Power Station (Poland) using atmospheric observations from the Orbiting Carbon Observatory 3 (OCO-3) in its snapshot area map (SAM) mode. As a result of the challenge of identifying CO2 emission plumes from satellite data with adequate accuracy, we located and constrained the shape of emission plumes using TROPOspheric Monitoring Instrument (TROPOMI) NO2 column densities. We automatically analysed all available OCO-3 overpasses over the Bełchatów Power Station from July 2019 to November 2022 and found a total of nine that were suitable for the estimation of CO2 emissions using our method. The mean uncertainty in the obtained estimates was 5.8 Mt CO2 yr−1 (22.0 %), mainly driven by the dispersion of the cross-sectional fluxes downwind of the source, e.g. due to turbulence. This dispersion uncertainty was characterized using a semivariogram, made possible by the OCO-3 imaging capability over a target region in SAM mode, which provides observations containing plume information up to several tens of kilometres downwind of the source. A bottom-up emission estimate was computed based on the hourly power-plant-generated power and emission factors to validate the satellite-based estimates. We found that the two independent estimates agree within their 1σ uncertainty in eight out of nine analysed overpasses and have a high Pearson's correlation coefficient of 0.92. Our results confirm the potential to monitor large localized CO2 emission sources from space-based observations and the usefulness of NO2 estimates for plume detection. They also illustrate the potential to improve CO2 monitoring capabilities with the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) satellite constellation, which will provide simultaneously retrieved XCO2 and NO2 maps.

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite enables the accurate determination of atmospheric methane (CH4) and carbon monoxide (CO) abundances at high spatial resolution and global daily sampling. Due to its wide swath and sampling, the global distribution of both gases can be determined in unprecedented detail. The scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) has proven valuable in simultaneously retrieving the atmospheric column-averaged dry-air mole fractions XCH4 and XCO from TROPOMI's radiance measurements in the shortwave infrared (SWIR) spectral range. Here we present recent improvements of the algorithm which have been incorporated into the current version (v1.8) of the TROPOMI/WFMD product. This includes processing adjustments such as increasing the polynomial degree to 3 in the fitting procedure to better account for possible spectral albedo variations within the fitting window and updating the digital elevation model to minimise topography-related biases. In the post-processing, the machine-learning-based quality filter has been refined using additional data when training the random forest classifier to further reduce scenes with residual cloudiness that are incorrectly classified as good. In particular, the cloud filtering over the Arctic ocean is considerably improved. Furthermore, the machine learning calibration, addressing systematic errors due to simplifications in the forward model or instrumental issues, has been optimised. By including an additional feature associated with the fitted polynomial when training the corresponding random forest regressor, spectral albedo variations are better accounted for. To remove vertical stripes in the XCH4 and XCO data, an efficient orbit-wise destriping filter based on combined wavelet–Fourier filtering has been implemented, while optimally preserving the original spatial trace gas features. The temporal coverage of the data records has been extended to the end of April 2022, covering a total length of 4.5 years since the start of the mission, and will be further extended in the future. Validation with the ground-based Total Carbon Column Observing Network (TCCON) demonstrates that the implemented improvements reduce the pseudo-noise component of the products, resulting in an improved random error. The XCH4 and XCO products have similar spatial coverage from year to year including high latitudes and the oceans. The analysis of annual growth rates reveals accelerated growth of atmospheric methane during the covered period, in line with observations at marine surface sites of the Global Monitoring Division of NOAA's Earth System Research Laboratory, which reported consecutive annual record increases over the past 2 years of 2020 and 2021.

Tropical shallow-water reefs are the most diverse ecosystems in the ocean. Their persistence rests upon adequate calcification rates of the reef building biota, such as reef corals. Coral calcification is favoured in oligotrophic environments with high seawater saturation states of aragonite (Ωsw), which leads to an increased vulnerability to anthropogenic ocean acidification and eutrophication. Here we present Porites calcification records from the northern Arabian Sea upwelling zone and investigate the coral calcification response to low Ωsw and high nutrient concentrations due to seasonal upwelling. The calcification rate was determined from the product of skeletal extension rate and bulk density. Skeletal Ba/Ca and Li/Mg proxy data were used to identify skeletal portions that calcified during upwelling and non-upwelling seasons, respectively, and to reconstruct growth temperatures. With regard to sub-annual calcification patterns, our results demonstrate compromised calcification rates during the upwelling season. This is due to declining extension rates, which we attribute to light dimming caused by high primary production. Interestingly, seasonal variations in skeletal density show no relationship with temporally low Ωsw during upwelling. This suggests relatively constant, year-round saturation states of aragonite at the site of calcification (Ωcf) independent of external variability in Ωsw. Although upwelling does not affect seasonal density variability, exceptionally low mean annual density implies permanent Ωcf adjustment to the lowest sub-annual Ωsw (e.g. upwelling). In the Arabian Sea upwelling zone, the mean annual calcification rate is similar to Porites from non-upwelling regions because low skeletal density is compensated by high extension growth. Variable responses of reef coral extension to nutrients, which either exacerbate or compensate negative effects of diminished skeletal density associated with ocean acidification, may therefore be critical to the maintenance of adequate carbonate accumulation rates in coral reefs under global change.

We show new results from an updated version of the Fast atmOspheric traCe gAs retrievaL (FOCAL) retrieval method applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL was originally developed for estimating the total column carbon dioxide mixing ratio (XCO2) from spectral measurements made by the Orbiting Carbon Observatory-2 (OCO-2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species and their uncertainties within one single retrieval. The main focus of the current paper is on methane (XCH4; full-physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2, it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. For XCO2, the new FOCAL retrieval (v3.0) significantly increases the number of valid data compared with the previous FOCAL retrieval version (v1) by 50 % for GOSAT and about a factor of 2 for GOSAT-2 due to relaxed pre-screening and improved post-processing. All v3.0 FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with the Total Carbon Column Observing Network (TCCON) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes on the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are on the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data. Noting that only few XN2O measurements from satellites exist so far, the GOSAT-2 FOCAL product will be a valuable contribution in this context.

This work employs ground- and space-based observations, together with model data, to study columnar abundances of atmospheric trace gases (XH2O, XCO2, XCH4 and XCO) in two high-latitude Russian cities, St. Petersburg and Yekaterinburg. Two portable COllaborative Column Carbon Observing Network (COCCON) spectrometers were used for continuous measurements at these locations during 2019 and 2020. Additionally, a subset of data of special interest (a strong gradient in XCH4 and XCO was detected) collected in the framework of a mobile city campaign performed in 2019 using both instruments is investigated. All studied satellite products (TROPOMI, OCO-2, GOSAT, MUSICA IASI) show generally good agreement with COCCON observations. Satellite and ground-based observations at high latitudes are much sparser than at low or mid latitudes, which makes direct coincident comparisons between remote-sensing observations more difficult. Therefore, a method of scaling continuous Copernicus Atmosphere Monitoring Service (CAMS) model data to the ground-based observations is developed and used for creating virtual COCCON observations. These adjusted CAMS data are then used for satellite validation, showing good agreement in both Peterhof and Yekaterinburg. The gradients between the two study sites (ΔXgas) are similar between CAMS and CAMS-COCCON datasets, indicating that the model gradients are in agreement with the gradients observed by COCCON. This is further supported by a few simultaneous COCCON and satellite ΔXgas measurements, which also agree with the model gradient. With respect to the city campaign observations recorded in St Petersburg, the downwind COCCON station measured obvious enhancements for both XCH4 (10.6 ppb) and XCO (9.5 ppb), which is nicely reflected by TROPOMI observations, which detect city-scale gradients of the order 9.4 ppb for XCH4 and 12.5 ppb for XCO.

Abstract Understanding of emotions and intentions are key processes in social cognition at which serotonin is an important neuromodulator. Its precursor is the essential amino acid tryptophan (TRP). Reduced TRP availability leads to weaker impulse control ability and higher aggression, while TRP supplementation promotes confidence. In a double-blind placebo-controlled fMRI study with 77 healthy adults, we investigated the influence of a 4 week TRP enriched diet and an acute 5-hydroxytryptophan (5-HTP) intake on two social-cognitive tasks, a moral evaluation and an emotion recognition task. With 5-HTP, immoral behavior without negative consequences was rated as more reprehensible. Additionally, during story reading, activation in insula and supramarginal gyrus was increased after TRP intake. No significant effects of TRP on emotion recognition were identified for the whole sample. Importantly, emotion recognition ability decreased with age which was for positive emotions compensated by TRP. Since the supramarginal gyrus is associated with empathy, pain and related information integration results could be interpreted as reflecting stricter evaluation of negative behavior due to better integration of information. Improved recognition of positive emotions with TRP in older participants supports the use of a TRP-rich diet to compensate for age related decline in social-cognitive processes.

Abstract To date, only few data concerning the biologically active, free form of testosterone (FT) are available in metastatic prostate cancer (mPC) and the impact of FT on disease, therapy and outcome is largely unknown. We retrospectively studied the effect of docetaxel on FT and total testosterone (TT) serum levels in 67 mPC patients monitored between April 2008 and November 2020. FT and TT levels were measured before and weekly during therapy. The primary endpoint was overall survival (OS). Secondary endpoints were prostate-specific antigen response and radiographic response (PSAR, RR), progression-free survival (PFS), FT/TT levels and safety. Median FT and TT serum levels were completely suppressed to below the detection limit during docetaxel treatment (FT: from 0.32 to

Since 2009, the Greenhouse gases Observing SATellite (GOSAT) has performed radiance measurements in the near-infrared (NIR) and shortwave infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 have also been available. We present the first results from the application of the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL was initially developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast, it is currently being considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This particularly includes modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT and GOSAT-2 is the independent use of both S- and P-polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT's wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O), and solar-induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semi-heavy water vapour (HDO). In the case of GOSAT-2, the retrieval of nitrous oxide (XN2O) and carbon monoxide (CO) may also be possible. Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal agreement of long-term temporal variations within about 1 ppm over 1 decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a station-to-station bias of the new GOSAT-FOCAL product to the ground-based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until the end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results and also TCCON.

The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. To investigate whether a regional-scale reduction of anthropogenic CO2 emissions during the COVID-19 pandemic can be detected using space-based observations of atmospheric CO2, we have analysed a small ensemble of OCO-2 and GOSAT satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2. We focus on East China and use a simple data-driven analysis method. We present estimates of the relative change of East China monthly emissions in 2020 relative to previous periods, limiting the analysis to October-to-May periods to minimize the impact of biogenic CO2 fluxes. The ensemble mean indicates an emission reduction by approximately 10 % ± 10 % in March and April 2020. However, our results show considerable month-to-month variability and significant differences across the ensemble of satellite data products analysed. For example, OCO-2 suggests a much smaller reduction (∼ 1 %–2 % ± 2 %). This indicates that it is challenging to reliably detect and to accurately quantify the emission reduction with current satellite data sets. There are several reasons for this, including the sparseness of the satellite data but also the weak signal; the expected regional XCO2 reduction is only on the order of 0.1–0.2 ppm. Inferring COVID-19-related information on regional-scale CO2 emissions using current satellite XCO2 retrievals likely requires, if at all possible, a more sophisticated analysis method including detailed transport modelling and considering a priori information on anthropogenic and natural CO2 surface fluxes.

A reduction of the anthropogenic emissions of CO2 (carbon dioxide) is necessary to stop or slow down man-made climate change. To verify mitigation strategies, a global monitoring system such as the envisaged European Copernicus anthropogenic CO2 monitoring mission (CO2M) is required. Those satellite data are going to be complemented and validated with airborne measurements. Unmanned aerial vehicle (UAV)-based measurements can provide a cost-effective way to contribute to these activities. Here, we present the development of an sUAS (small unmanned aircraft system) to quantify the CO2 emissions of a nearby point source from its downwind mass flux without the need for any ancillary data. Specifically, CO2 is measured by an NDIR (non-dispersive infrared) detector, and the wind speed and direction are measured with a 2-D ultrasonic acoustic resonance anemometer. By means of laboratory measurements and an in-flight validation at the ICOS (Integrated Carbon Observation System) atmospheric station Steinkimmen (STE) near Bremen, Germany, we estimate that the individual CO2 measurements have a precision of 3 ppm and that CO2 enhancements can be determined with an accuracy of 1.3 % or 0.9 ppm, whichever is larger. We introduce an anemometer calibration method to minimize the effect of rotor downwash on the wind measurements. This method derives the fit parameters of a linear calibration model accounting for scaling, rotation, and a potential constant bias. For this purpose, it analyzes wind measurements taken while following a suitable flight pattern and assuming stationary wind conditions. From the calibration and validation experiments, we estimate the single measurement precision of the horizontal wind speed to be 0.40 m s−1 and the accuracy to be 0.33 m s−1. By means of two flights downwind of the ExxonMobil natural gas processing facility in Großenkneten about 40 km west of Bremen, Germany, we demonstrate how the measurements of elevated CO2 concentrations can be used to infer mass fluxes of atmospheric CO2 related to the emissions of the facility.

Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Compared to the satellite observations, the models have a bias of +25 to −20 ppmv in CMIP5 and +20 to −15 ppmv in CMIP6, with the multi-model mean biases at +10 and +2 ppmv, respectively. The derived mean atmospheric XCO2 growth rate (GR) of 2.0 ppmv yr−1 is overestimated by 0.4 ppmv yr−1 in CMIP5 and 0.3 ppmv yr−1 in CMIP6 for the multi-model mean, with a good reproduction of the interannual variability. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Any SCA derived from data with missing values can only be considered an “effective” SCA, as the missing values could occur at the peaks or troughs. The satellite data are a combined data product covering the period 2003–2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003–2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009–2014) instruments. While the combined satellite product shows a strong negative trend of decreasing effective SCA with increasing XCO2 in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the effective SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (XCO2, GR, SCA and trend in SCA). This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.

This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during 1 year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 08:30–11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 00:00–08:30 and 11:30–00:00) for each clump and for the 366 d of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the “posterior uncertainty”) of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e., the relative difference between the prior and posterior uncertainties). Beyond the one city or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC yr−1 can potentially have their emissions between 08:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within 1 year (ignoring the potential to cross or extrapolate information between 08:30–11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 08:30–11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need for systematic assessment of the sources of uncertainty in the spatiotemporally resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty in constraining the total budget of CO2 emissions from all the cities and power plants within a country or over the globe with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements.

The switch from the use of coal to natural gas or oil for energy generation potentially reduces greenhouse gas emissions and thus the impact on global warming and climate change because of the higher energy creation per CO2 molecule emitted. However, the climate benefit over coal is offset by methane (CH4) leakage from natural gas and petroleum systems, which reverses the climate impact mitigation if the rate of fugitive emissions exceeds the compensation point at which the global warming resulting from the leakage and the benefit from the reduction of coal combustion coincide. Consequently, an accurate quantification of CH4 emissions from the oil and gas industry is essential to evaluate the suitability of natural gas and petroleum as bridging fuels on the way to a carbon-neutral future. We show that regional CH4 release from large oil and gas fields can be monitored from space by using dense daily recurrent measurements of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite to quantify emissions and leakage rates. The average emissions for the time period 2018/2019 from the five most productive basins in the United States, the Permian, Appalachian, Eagle Ford, Bakken, and Anadarko, are estimated to be 3.18±1.13, 2.36±0.88, 1.37±0.63, 0.89±0.56, and 2.74±0.74 Mt yr−1, respectively. This corresponds to CH4 leakage rates relative to the associated production between 1.2 % and 1.4 % for the first four production regions, which are consistent with bottom-up estimates and likely fall below the break-even leakage rate for immediate climate benefit. For the Anadarko Basin, the fugitive emission rate is larger and amounts to 3.9±1.1 %, which likely exceeds the break-even rate for immediate benefit and roughly corresponds to the break-even rate for a 20-year time horizon. The determined values are smaller than previously derived satellite-based leakage rates for the time period 2009–2011, which was an early phase of hydraulic fracturing, indicating that it is possible to improve the climate footprint of the oil and gas industry by adopting new technologies and that efforts to reduce methane emissions have been successful. For two of the world's largest natural gas fields, Galkynysh and Dauletabad in Turkmenistan, we find collective methane emissions of 3.26±1.17 Mt yr−1, which corresponds to a leakage rate of 4.1±1.5 %, suggesting that the Turkmen energy industry is not employing methane emission avoidance strategies and technologies as successfully as those currently widely used in the United States. The leakage rates in Turkmenistan and in the Anadarko Basin indicate that there is potential to reduce fugitive methane emissions from natural gas and petroleum systems worldwide. In particular, relatively newly developed oil and gas plays appear to have larger leakage rates compared to more mature production areas.

Due to proceeding climate change, some regions such as California face rising weather extremes with dry periods becoming warmer and drier, entailing the risk that wildfires and associated air pollution episodes will continue to increase. November 2018 turned into one of the most severe wildfire episodes on record in California, with two particularly destructive wildfires spreading concurrently through the north and the south of the state. Both fires ignited at the wildland–urban interface, causing many civilian fatalities and forcing the total evacuation of several cities and communities. Here we demonstrate that the inherent carbon monoxide (CO) emissions of the wildfires and subsequent transport can be observed from space by analysing radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite in the shortwave infrared spectral range. From the determined CO distribution we assess the corresponding air quality burden in major Californian cities caused by the fires and discuss the associated uncertainties. As a result of the prevailing wind conditions, the largest CO load during the first days of the fires is found in Sacramento and San Francisco, with city area averages reaching boundary layer concentration anomalies of about 2.5 mg CO m−3. Even the most polluted city scenes likely comply with the national ambient air quality standards (10 mg CO m−3 with 8 h averaging time). This finding based on dense daily recurrent satellite monitoring is consistent with isolated ground-based air quality measurements.

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020).

Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments.The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir-viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with an unprecedented level of detail on a global scale, introducing new areas of application.Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 µm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). This algorithm is intended to be used with the operational algorithms for mutual verification and to provide new geophysical insights. We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer (FTS) measurements providing realistic error estimates of the satellite data: the XCO data set is characterised by a random error of 5.1 ppb (5.8 %) and a systematic error of 1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb (0.8 %) and a systematic error of 4.3 ppb (0.2 %). The natural XCO and XCH4 variations are well-captured by the satellite retrievals, which is demonstrated by a high correlation with the validation data (R = 0.97 for XCO and R = 0.91 for XCH4 based on daily averages).We also present selected results from the mission start until the end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4 emissions from the energy sector, which potentially allows for the advance of emission monitoring and air quality assessments to an entirely new level.

Despite its key role in climate change, large uncertainties persist in our knowledge of the anthropogenic emissions of carbon dioxide (CO2) and no global observing system exists that allows us to monitor emissions from localized CO2 sources with sufficient accuracy. The Orbiting Carbon Observatory-2 (OCO-2) satellite allows retrievals of the column-average dry-air mole fractions of CO2 (XCO2). However, regional column-average enhancements of individual point sources are usually small, compared to the background concentration and its natural variability, and often not much larger than the satellite's measurement noise. This makes the unambiguous identification and quantification of anthropogenic emission plume signals challenging. NO2 is co-emitted with CO2 when fossil fuels are combusted at high temperatures. It has a short lifetime on the order of hours so that NO2 columns often greatly exceed background and noise levels of modern satellite sensors near sources, which makes it a suitable tracer of recently emitted CO2. Based on six case studies (Moscow, Russia; Lipetsk, Russia; Baghdad, Iraq; Medupi and Matimba power plants, South Africa; Australian wildfires; and Nanjing, China), we demonstrate the usefulness of simultaneous satellite observations of NO2 and XCO2. For this purpose, we analyze co-located regional enhancements of XCO2 observed by OCO-2 and NO2 from the Sentinel-5 Precursor (S5P) satellite and estimate the CO2 plume's cross-sectional fluxes. We take advantage of the nearly simultaneous NO2 measurements with S5P's wide swath and small measurement noise by identifying the source of the observed XCO2 enhancements, excluding interference with remote upwind sources, allowing us to adjust the wind direction, and by constraining the shape of the CO2 plumes. We compare the inferred cross-sectional fluxes with the Emissions Database for Global Atmospheric Research (EDGAR), the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC), and, in the case of the Australian wildfires, with the Global Fire Emissions Database (GFED). The inferred cross-sectional fluxes range from 31 MtCO2 a−1 to 153 MtCO2 a−1 with uncertainties (1σ) between 23 % and 72 %. For the majority of analyzed emission sources, the estimated cross-sectional fluxes agree, within their uncertainty, with either EDGAR or ODIAC or lie somewhere between them. We assess the contribution of multiple sources of uncertainty and find that the dominating contributions are related to the computation of the effective wind speed normal to the plume's cross section. The flux uncertainties are expected to be reduced by the planned European Copernicus anthropogenic CO2 monitoring mission (CO2M), which will provide not only precise measurements with high spatial resolution but also imaging capabilities with a wider swath of simultaneous XCO2 and NO2 observations. Such a mission, particularly if performed by a constellation of satellites, will deliver CO2 emission estimates from localized sources at an unprecedented frequency and level of accuracy.

The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined from satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, for the years 2003 to 2016. The XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0±0.3 ppm year−1; R: 0.82). This new and independent data set confirms record-large growth rates of around 3 ppm year−1 in 2015 and 2016, which are attributed to the 2015–2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil-fuel-burning-related emissions in explaining the variance of the atmospheric CO2 growth rate. Our analysis shows that the ENSO impact on CO2 growth rate variations dominates that of human emissions throughout the period 2003–2016 but in particular during the period 2010–2016 due to strong La Niña and El Niño events. Using the derived growth rates and their uncertainties, we estimate the probability that the impact of ENSO on the variability is larger than the impact of human emissions to be 63 % for the time period 2003–2016. If the time period is restricted to 2010–2016, this probability increases to 94 %.

This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ∼ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6 h mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular, the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it.

The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e. XCH4, can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH4 show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1σ uncertainties directly from maps of annually averaged satellite XCH4. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite XCH4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003–2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5–8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR.

In geological outcrops and drill cores from reef frameworks, the skeletons of scleractinian corals are usually leached and more or less completely transformed into sparry calcite because the highly porous skeletons formed of metastable aragonite (CaCO3) undergo rapid diagenetic alteration. Upon alteration, ghost structures of the distinct annual growth bands often allow for reconstructions of annual extension ( = growth) rates, but information on skeletal density needed for reconstructions of calcification rates is invariably lost. This report presents the bulk density, extension rates and calcification rates of fossil reef corals which underwent minor diagenetic alteration only. The corals derive from unlithified shallow water carbonates of the Florida platform (south-eastern USA), which formed during four interglacial sea level highstands dated approximately 3.2, 2.9, 1.8, and 1.2 Ma in the mid-Pliocene to early Pleistocene. With regard to the preservation, the coral skeletons display smooth growth surfaces with minor volumes of marine aragonite cement within intra-skeletal porosity. Within the skeletal structures, voids are commonly present along centres of calcification which lack secondary cements. Mean extension rates were 0.44 ± 0.19 cm yr−1 (range 0.16 to 0.86 cm yr−1), mean bulk density was 0.96 ± 0.36 g cm−3 (range 0.55 to 1.83 g cm−3) and calcification rates ranged from 0.18 to 0.82 g cm−2 yr−1 (mean 0.38 ± 0.16 g cm−2 yr−1), values which are 50 % of modern shallow-water reef corals. To understand the possible mechanisms behind these low calcification rates, we compared the fossil calcification rates with those of modern zooxanthellate corals (z corals) from the Western Atlantic (WA) and Indo-Pacific calibrated against sea surface temperature (SST). In the fossil data, we found a widely analogous relationship with SST in z corals from the WA, i.e. density increases and extension rate decreases with increasing SST, but over a significantly larger temperature window during the Plio-Pleistocene. With regard to the environment of coral growth, stable isotope proxy data from the fossil corals and the overall structure of the ancient shallow marine communities are consistent with a well-mixed, open marine environment similar to the present-day Florida Reef Tract, but variably affected by intermittent upwelling. Upwelling along the platform may explain low rates of reef coral calcification and inorganic cementation, but is too localised to account also for low extension rates of Pliocene z corals throughout the tropical WA region. Low aragonite saturation on a more global scale in response to rapid glacial–interglacial CO2 cyclicity is also a potential factor, but Plio-Pleistocene atmospheric pCO2 is generally believed to have been broadly similar to the present day. Heat stress related to globally high interglacial SST only episodically moderated by intermittent upwelling affecting the Florida platform seems to be another likely reason for low calcification rates. From these observations we suggest some present coral reef systems to be endangered from future ocean warming.

Currently, 52 % of the world's population resides in urban areas and as a consequence, approximately 70 % of fossil fuel emissions of CO2 arise from cities. This fact, in combination with large uncertainties associated with quantifying urban emissions due to lack of appropriate measurements, makes it crucial to obtain new measurements useful to identify and quantify urban emissions. This is required, for example, for the assessment of emission mitigation strategies and their effectiveness. Here, we investigate the potential of a satellite mission like Carbon Monitoring Satellite (CarbonSat) which was proposed to the European Space Agency (ESA) to retrieve the city emissions globally, taking into account a realistic description of the expected retrieval errors, the spatiotemporal distribution of CO2 fluxes, and atmospheric transport. To achieve this, we use (i) a high-resolution modelling framework consisting of the Weather Research Forecasting model with a greenhouse gas module (WRF-GHG), which is used to simulate the atmospheric observations of column-averaged CO2 dry air mole fractions (XCO2), and (ii) a Bayesian inversion method to derive anthropogenic CO2 emissions and their errors from the CarbonSat XCO2 observations. We focus our analysis on Berlin, Germany using CarbonSat's cloud-free overpasses for 1 reference year. The dense (wide swath) CarbonSat simulated observations with high spatial resolution (approximately 2 km × 2 km) permits one to map the city CO2 emission plume with a peak enhancement of typically 0.8–1.35 ppm relative to the background. By performing a Bayesian inversion, it is shown that the random error (RE) of the retrieved Berlin CO2 emission for a single overpass is typically less than 8–10 Mt CO2 yr−1 (about 15–20 % of the total city emission). The range of systematic errors (SEs) of the retrieved fluxes due to various sources of error (measurement, modelling, and inventories) is also quantified. Depending on the assumptions made, the SE is less than about 6–10 Mt CO2 yr−1 for most cases. We find that in particular systematic modelling-related errors can be quite high during the summer months due to substantial XCO2 variations caused by biogenic CO2 fluxes at and around the target region. When making the extreme worst-case assumption that biospheric XCO2 variations cannot be modelled at all (which is overly pessimistic), the SE of the retrieved emission is found to be larger than 10 Mt CO2 yr−1 for about half of the sufficiently cloud-free overpasses, and for some of the overpasses we found that SE may even be on the order of magnitude of the anthropogenic emission. This indicates that biogenic XCO2 variations cannot be neglected but must be considered during forward and/or inverse modelling. Overall, we conclude that a satellite mission such as CarbonSat has high potential to obtain city-scale CO2 emissions as needed to enhance our current understanding of anthropogenic carbon fluxes, and that CarbonSat-like satellites should be an important component of a future global carbon emission monitoring system.

Stratospheric profiles of methane (CH4) and carbon dioxide (CO2) have been derived from solar occultation measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The retrieval is performed using a method called onion peeling DOAS (ONPD), which combines an onion peeling approach with a weighting function DOAS (differential optical absorption spectroscopy) fit in the spectral region between 1559 and 1671 nm. By use of updated pointing information and optimisation of the data selection as well as of the retrieval approach, the altitude range for reasonable CH4 could be broadened from 20 to 40 km to about 17 to 45 km. Furthermore, the quality of the derived CO2 has been assessed such that now the first stratospheric profiles (17–45 km) of CO2 from SCIAMACHY are available. Comparisons with independent data sets yield an estimated accuracy of the new SCIAMACHY stratospheric profiles of about 5–10 % for CH4 and 2–3 % for CO2. The accuracy of the products is currently mainly restricted by the appearance of unexpected vertical oscillations in the derived profiles which need further investigation. Using the improved ONPD retrieval, CH4 and CO2 stratospheric data sets covering the whole SCIAMACHY time series (August 2002–April 2012) and the latitudinal range between about 50 and 70° N have been derived. Based on these time series, CH4 and CO2 trends have been estimated. CH4 trends above about 20 km are not significantly different from zero and the trend at 17 km is about 3 ppbv year−1. The derived CO2 trends show a general decrease with altitude with values of about 1.9 ppmv year−1 at 21 km and about 1.3 ppmv year−1 at 39 km. These results are in reasonable agreement with total column trends for these gases. This shows that the new SCIAMACHY data sets can provide valuable information about the stratosphere.

The fast growing calcareous skeletons of zooxanthellate reef corals (z corals) represent unique environmental proxy archives through their oxygen and carbon stable isotope composition (δ18O, δ13C). In addition, the accretion of the skeleton itself is ultimately linked to the environment and responds with variable growth rates (extension rate) and density to environmental changes. Here we present classical proxy data (δ18O, δ13C) in combination with calcification records from 15 massive z corals. The z corals were sampled from four interglacial units of the Florida carbonate platform (USA) dated approximately 3.2, 2.9, 1.8 and 1.2 Ma (middle Pliocene to early Pleistocene). The z corals (Solenastrea, Orbicella, Porites) derive from unlithified shallow marine carbonates and were carefully screened for primary preservation suited for proxy analysis. We show that skeletal accretion responded with decreasing overall calcification rates (decreasing extension rate but increasing density) to warmer water temperatures. Under high annual water temperatures, inferred from sub-annually resolved δ18O data, skeletal bulk density was high, but extension rates and overall calcification rates were at a minimum (endmember scenario 1). Maximum skeletal density was reached during the summer season giving rise to a growth band of high density within the annually banded skeletons (“high density band”, HDB). With low mean annual water temperatures (endmember scenario 2), bulk skeletal density was low but extension rates and calcification rates reached a maximum, and under these conditions the HDB formed during winter. Although surface water temperatures in the Western Atlantic warm pool during the interglacials of the late Neogene were ∼ 2 °C higher than they are in the present day, intermittent upwelling of cool, nutrient-rich water mitigated water temperatures off south-western Florida and created temporary refuges for z coral growth. Based on the sub-annually resolved δ18O and δ13C records, the duration of the upwelling episodes causing the endmember 2 conditions was variable and lasted from a few years to a number of decades. The episodes of upwelling were interrupted by phases without upwelling (endmember 1) which lasted for at least a few years and led to high surface water temperatures. This variable environment is likely one of the reasons why the coral fauna is dominated by the eurytopic genus Solenastrea, also a genus resistant to high turbidity. Over a period of ∼ 50 years, the oldest sub annually resolved proxy record available (3.2 Ma) documents a persistent occurrence of the HDB during winter. In contrast, the HDB forms in summer in modern z corals from the Florida reef tract. We suggest this difference should be tested as being the expression of a tendency towards decreasing interglacial upwelling since the middle Pliocene. The number of z coral sclerochronological records for the Plio-Pleistocene is still rather low, however, and requires more data and an improved resolution, through records from additional time slices. Nonetheless, our calcification data from the warm periods of past interglacials may contribute to predicting the effects of future ocean warming on z coral health along the Florida reef tract. The inconsistent timing of the HDB within single coral records or among specimens and time slices is unexpected and contrasts the common practice of establishing chronologies on the basis of the density banding.

This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively) and an improved estimated precision of 1 ppm compared to the GOSAT BESD data (3.3 ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00 UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000 km) even up to day 5 compared to its own analysis.

Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (XCO2) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 ppm vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to error2 = a2 + b2/n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of ∼ 0.3 ppm, with biases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between 26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b.

We explore Euclidean quantum gravity using the tetrad field together with a selfdual or anti-selfdual spin-connection as the basic field variables. Setting up a functional renormalization group (RG) equation of a new type which is particularly suitable for the corresponding theory space we determine the non-perturbative RG flow within a two-parameter truncation suggested by the Holst action. We find that the (anti-)selfdual theory is likely to be asymptotically safe. The existing evidence for its non-perturbative renormalizability is comparable to that of Einstein–Cartan gravity without the selfduality condition.

Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

The GHG-CCI project (http://www.esa-ghg-cci.org/) is one of several projects of the European Space Agency’s (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The “ECV Greenhouse Gases” (ECV GHG) is the global distribution of important climate relevant gases – namely atmospheric CO2 and CH4 - with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. The main goal of GHG-CCI is to generate long-term highly accurate and precise time series of global near-surface-sensitive satellite observations of CO2 and CH4, i.e., XCO2 and XCH4, starting with the launch of ESA’s ENVISAT satellite. These products are currently retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode observations in the near-infrared/shortwave-infrared spectral region. In addition, other sensors (e.g., IASI and MIPAS) and viewing modes (e.g., SCIAMACHY solar occultation) are also considered and in the future also data from other satellites. The GHG-CCI data products and related documentation are freely available via the GHG-CCI website and yearly updates are foreseen. Here we present an overview about the latest data set (Climate Research Data Package No. 2 (CRDP#2)) and summarize key findings from using satellite CO2 and CH4 retrievals to improve our understanding of the natural and anthropogenic sources and sinks of these important atmospheric greenhouse gases. We also shortly mention ongoing activities related to validation and initial user assessment of CRDP#2 and future plans.

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