Explore the vast range of titles available.

Climate stabilization remains elusive, with increased greenhouse gas concentrations already increasing global average surface temperatures 1.1°C above pre-industrial levels (World Meteorological Organization 2019). Carbon dioxide (CO2) emissions from fossil fuel use, deforestation, and other anthropogenic sources reached ~ 43 billion metric tonnes in 2019 (Friedlingstein et al 2019, Jackson et al 2019). Storms, floods, and other extreme weather events displaced a record 7 million people in the first half of 2019 (IDMC 2019). When global mean surface temperature four million years ago was 2°C–3°C warmer than today (a likely temperature increase before the end of the century), ice sheets in Greenland and West Antarctica melted and parts of East Antarctica’s ice retreated, causing sea levels to rise 10–20 m (World Meteorological Organization 2019). Methane (CH4) emissions have contributed almost one quarter of the cumulative radiative forcings for CO2, CH4, and N2O (nitrous oxide) combined since 1750 (Etminan et al 2016). Although methane is far less abundant in the atmosphere than CO2, it absorbs thermal infrared radiation much more efficiently and, in consequence, has a global warming potential (GWP) ~86 times stronger per unit mass than CO2 on a 20-year timescale and 28- times more powerful on a 100-year time scale (IPCC 2014). Global average methane concentrations in the atmosphere reached ~1875 parts per billion (ppb) at the end of 2019, more than two-and-a-half times preindustrial levels (Dlugokencky 2020). The largest methane sources include anthropogenic emissions from agriculture, waste, and the extraction and use of fossil fuels as well as natural emissions from wetlands, freshwater systems, and geological sources (Kirschke et al 2013, Saunois et al 2016a, Ganesan et al 2019). Here, we summarize new estimates of the global methane budget based on the analysis of Saunois et al (2020) for the year 2017, the last year of the new Global Methane Budget and the most recent year data are fully available. We compare these estimates to mean values for the reference ‘stabilization’ period of 2000–2006 when atmospheric CH4 concentrations were relatively stable. We present data for sources and sinks and provide insights for the geographical regions and economic sectors where emissions have changed the most over recent decades.

After a decade of stable or slightly decreasing global methane concentrations, ground-based in situ data show that CH4 began increasing again in 2007 and that this increase continued through 2009. So far, space-based retrievals sensitive to the lower troposphere in the time period under consideration have not been available. Here we report a long-term data set of column-averaged methane mixing ratios retrieved from spectra of the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument onboard Envisat. The retrieval quality after 2005 was severely affected by degrading detector pixels within the methane 2v3 absorption band. We identified the most crucial problems in SCIAMACHY detector degradation and overcame the problem by applying a strict pixel mask as well as a new dark current characterization. Even though retrieval precision after the end of 2005 is invariably degraded, consistent methane retrievals from 2003 through 2009 are now possible. Regional time series in the Sahara, Australia, tropical Africa, South America, and Asia show the methane increase in 2007 2009, but we cannot yet draw a firm conclusion concerning the origin of the increase. Tropical Africa even seems to exhibit a negative anomaly in 2006, but an impact from changes in SCIAMACHY detector degradation cannot be excluded yet. Over Assakrem, Algeria, we observed strong similarities between SCIAMACHY measurements and ground-based data in deseasonalized time series. We further show long-term SCIAMACHY xCH4 averages at high spatial resolution that provide further insight into methane variations on regional scales. The Red Basin in China exhibits, on average, the highest methane abundance worldwide, while other localized features such as the Sudd wetlands in southern Sudan can also be identified in SCIAMACHY xCH4 averages.

At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH4) became available from the Thermal And Near infrared Sensor for carbon Observations-Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent {methane (CH4) retrievals} were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH4 retrievals can be compared during the period of overlap. We estimate monthly average CH4 emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH4 retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC \"Full-Physics\" (FP) XCH4 retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH4, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH4. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellite-based inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH4 emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH4 emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH4 fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH4 retrievals available from the Total Carbon Column Observing Network (TCCON).

We present a comprehensive description and benchmark evaluation of the tropospheric chemistry version of the global chemistry transport model TM5 (Tracer Model 5, version TM5-chem-v3.0). A full description is given concerning the photochemical mechanism, the interaction with aerosol, the treatment of the stratosphere, the wet and dry deposition parameterizations, and the applied emissions. We evaluate the model against a suite of ground-based, satellite, and aircraft measurements of components critical for understanding global photochemistry for the year 2006. The model exhibits a realistic oxidative capacity at a global scale. The methane lifetime is ~8.9 years with an associated lifetime of methyl chloroform of 5.86 years, which is similar to that derived using an optimized hydroxyl radical field. The seasonal cycle in observed carbon monoxide (CO) is well simulated at different regions across the globe. In the Northern Hemisphere CO concentrations are underestimated by about 20 ppbv in spring and 10 ppbv in summer, which is related to missing chemistry and underestimated emissions from higher hydrocarbons, as well as to uncertainties in the seasonal variation of CO emissions. The model also captures the spatial and seasonal variation in formaldehyde tropospheric columns as observed by SCIAMACHY. Positive model biases over the Amazon and eastern United States point to uncertainties in the isoprene emissions as well as its chemical breakdown. Simulated tropospheric nitrogen dioxide columns correspond well to observations from the Ozone Monitoring Instrument in terms of its seasonal and spatial variability (with a global spatial correlation coefficient of 0.89), but TM5 fields are lower by 25-40%. This is consistent with earlier studies pointing to a high bias of 0-30% in the OMI retrievals, but uncertainties in the emission inventories have probably also contributed to the discrepancy. TM5 tropospheric nitrogen dioxide profiles are in good agreement (within ~0.1 ppbv) with in situ aircraft observations from the INTEX-B campaign over (the Gulf of) Mexico. The model reproduces the spatial and seasonal variation in background surface ozone concentrations and tropospheric ozone profiles from the World Ozone and Ultraviolet Radiation Data Centre to within 10 ppbv, but at several tropical stations the model tends to underestimate ozone in the free troposphere. The presented model results benchmark the TM5 tropospheric chemistry version, which is currently in use in several international cooperation activities, and upon which future model improvements will take place.

This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003–2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27–35 Tg yr−1. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr−1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy. It may predispose offspring to increased fat mass (FM) and the development of obesity, however few data from Latin America exist. To investigate the influence of GDM on newborn FM in mother-newborn pairs recruited from a public maternity care center in São Paulo, Brazil. Data were collected cross-sectionally in 2013-2014 from 72 mothers diagnosed with GDM, and 211 mothers with normal glucose tolerance (NGT). Newborn FM was evaluated by air-displacement plethysmography (PEA POD), and relevant demographic and obstetric data were collected from hospital records. Associations between maternal GDM status and newborn FM were investigated by multiple linear regression analysis, with adjustment for maternal age, pre-pregnancy BMI, gestational weight gain, type of delivery, sex of the child, and gestational age. FM was greater in GDM versus NGT newborns in a bivariable model (Median (IQR), GDM: 0.35 (0.3) kg vs. NGT: 0.27 (0.2) kg, p = 0.02), however GDM status was not a significant predictor of FM with adjustment for other variables. Rather, pre-pregnancy BMI (coefficient (β) 1.46; 95% confidence interval (CI) 0.66, 2.27), gestational weight gain (β 1.32; 95% CI 0.49, 2.15), and male sex (β -17.8; 95% CI -27.2, -8.29) predicted newborn FM. Analyzing GDM and NGT groups separately, pre-pregnancy BMI (β 6.75; 95% CI 2.36, 11.1) and gestational weight gain (β 5.64; 95% CI 1.16, 10.1) predicted FM in the GDM group, while male sex alone predicted FM in the NGT group (β -12.3; 95% CI -18.3, -6.34). Combined model results suggest that in our cohort, pre-pregnancy BMI and gestational weight gain are more important risk factors for increased neonatal FM than GDM. However, group-specific model results suggest that GDM status may contribute to variation in the relationship between maternal/offspring factors and FM. Our use of a binary GDM variable in the combined model may have precluded clearer results on this point. Prospective cohort studies including data on maternal pre-pregnancy BMI, GWG, and glycemic profile are needed to better understand associations among these variables and their relative influence on offspring FM.

The fires around Moscow in July and August 2010 emitted a large amount of pollutants to the atmosphere. Here we estimate the carbon monoxide (CO) source strength of the Moscow fires in July and August by using the TM5-4DVAR system in combination with CO column observations of the Infrared Atmospheric Sounding Interferometer (IASI). It is shown that the IASI observations provide a strong constraint on the total emissions needed in the model. Irrespective of the prior emissions used, the optimised CO fire emission estimates from mid-July to mid-August 2010 amount to approximately 24 Tg CO. This estimate depends only weakly (< 15%) on the assumed diurnal variations and injection height of the emissions. However, the estimated emissions might depend on unaccounted model uncertainties such as vertical transport. Our emission estimate of 22–27 Tg CO during roughly one month of intense burning is less than suggested by another recent study, but substantially larger than predicted by the bottom-up inventories. This latter discrepancy suggests that bottom-up emission estimates for extreme peat burning events require improvements.

European CH 4 and N 2 O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and La-grangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived emissions, which is particularly important for verifying bottom-up emission inventories. We use continuous observations from 10 European stations (including 5 tall towers) for CH 4 and 9 continuous stations for N 2 O, complemented by additional European and global discrete air sampling sites. The available observations mainly constrain CH 4 and N 2 O emissions from northwestern and eastern Europe. The inversions are strongly driven by the observations and the derived total emissions of larger countries show little dependence on the emission inventories used a priori. Three inverse models yield 26-56 % higher total CH 4 emissions from northwestern and eastern Europe compared Published by Copernicus Publications on behalf of the European Geosciences Union. 716 P. Bergamaschi et al.: Top-down estimates of European CH 4 and N 2 O emissions to bottom-up emissions reported to the UNFCCC, while one model is close to the UNFCCC values. In contrast, the inverse modelling estimates of European N 2 O emissions are in general close to the UNFCCC values, with the overall range from all models being much smaller than the UNFCCC uncertainty range for most countries. Our analysis suggests that the reported uncertainties for CH 4 emissions might be underestimated , while those for N 2 O emissions are likely overestimated .

European CH4 emissions are estimated for the period 2001–2006 using a four‐dimensional variational (4DVAR) inverse modeling system, based on the atmospheric zoom model TM5. Continuous observations are used from various European monitoring stations, complemented by European and global flask samples from the NOAA/ESRL network. The available observations mainly provide information on the emissions from northwest Europe (NWE), including the UK, Ireland, the BENELUX countries, France and Germany. The inverse modeling estimates for the total anthropogenic emissions from NWE are 21% higher compared to the EDGARv4.0 emission inventory and 40% higher than values reported to U.N. Framework Convention on Climate Change. Assuming overall uncertainties on the order of 30% for both bottom‐up and top‐down estimates, all three estimates can be still considered to be consistent with each other. However, the uncertainties in the uncertainty estimates prevent us from verifying (or falsifying) the bottom‐up inventories in a strict sense. Sensitivity studies show some dependence of the derived spatial emission patterns on the set of atmospheric monitoring stations used, but the total emissions for the NWE countries appear to be relatively robust. While the standard inversions include a priori information on the spatial and temporal emission patterns from bottom‐up inventories, a further sensitivity inversion without this a priori information results in very similar NWE country totals, demonstrating that the available observations provide significant constraints on the emissions from the NWE countries independent from bottom‐up inventories.

SCIAMACHY onboard ENVISAT (launched in 2002) enables the retrieval of global long-term column-averaged dry air mole fractions of the two most important anthropogenic greenhouse gases carbon dioxide and methane (denoted XCO2 and XCH4). In order to assess the quality of the greenhouse gas data obtained with the recently introduced v2 of the scientific retrieval algorithm WFM-DOAS, we present validations with ground-based Fourier Transform Spectrometer (FTS) measurements and comparisons with model results at eight Total Carbon Column Observing Network (TCCON) sites providing realistic error estimates of the satellite data. Such validation is a prerequisite to assess the suitability of data sets for their use in inverse modelling. It is shown that there are generally no significant differences between the carbon dioxide annual increases of SCIAMACHY and the assimilation system CarbonTracker (2.00 0.16 ppm yr−1 compared to 1.94 0.03 ppm yr−1 on global average). The XCO2 seasonal cycle amplitudes derived from SCIAMACHY are typically larger than those from TCCON which are in turn larger than those from CarbonTracker. The absolute values of the northern hemispheric TCCON seasonal cycle amplitudes are closer to SCIAMACHY than to CarbonTracker and the corresponding differences are not significant when compared with SCIAMACHY, whereas they can be significant for a subset of the analysed TCCON sites when compared with CarbonTracker. At Darwin we find discrepancies of the seasonal cycle derived from SCIAMACHY compared to the other data sets which can probably be ascribed to occurrences of undetected thin clouds. Based on the comparison with the reference data, we conclude that the carbon dioxide data set can be characterised by a regional relative precision (mean standard deviation of the differences) of about 2.2 ppm and a relative accuracy (standard deviation of the mean differences) of 1.1–1.2 ppm for monthly average composites within a radius of 500 km. For methane, prior to November 2005, the regional relative precision amounts to 12 ppb and the relative accuracy is about 3 ppb for monthly composite averages within the same radius. The loss of some spectral detector pixels results in a degradation of performance thereafter in the spectral range currently used for the methane column retrieval. This leads to larger scatter and lower XCH4 values are retrieved in the tropics for the subsequent time period degrading the relative accuracy. As a result, the overall relative precision is estimated to be 17 ppb and the relative accuracy is in the range of about 10–20 ppb for monthly averages within a radius of 500 km. The derived estimates show that the SCIAMACHY XCH4 data set before November 2005 is suitable for regional source/sink determination and regional-scale flux uncertainty reduction via inverse modelling worldwide. In addition, the XCO2 monthly data potentially provide valuable information in continental regions, where there is sparse sampling by surface flask measurements.