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 2ν3 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).
Recent studies have proposed significant increases in CH4 emissions possibly from oil and gas (O&G) production, especially for the U.S. where O&G production has reached historically high levels over ...the past decade. In this study, we show that an ensemble of time‐dependent atmospheric inversions constrained by calibrated atmospheric observations of surface CH4 mole fraction, with some including space‐based retrievals of column average CH4 mole fractions, suggests that North American CH4 emissions have been flat over years spanning 2000 through 2012. Estimates of emission trends using zonal gradients of column average CH4 calculated relative to an upstream background are not easy to make due to atmospheric variability, relative insensitivity of column average CH4 to surface emissions at regional scales, and fast zonal synoptic transport. In addition, any trends in continental enhancements of column average CH4 are sensitive to how the upstream background is chosen, and model simulations imply that short‐term (4 years or less) trends in column average CH4 horizontal gradients of up to 1.5 ppb/yr can occur just from interannual transport variability acting on a strong latitudinal CH4 gradient. Finally, trends in spatial gradients calculated from space‐based column average CH4 can be significantly biased (>2–3 ppb/yr) due to the nonuniform and seasonally varying temporal coverage of satellite retrievals.
Key Points
Atmospheric inversions using in situ observations do not support large increases in CH4 emissions from U.S. oil and gas production
Short‐term trends in spatial gradients of CH4 column abundance are not sensitive to changes in emissions due to atmospheric variability
Temporal sampling gaps in satellite retrievals and choices of background can give spurious trends in column average CH4 gradients
Plain Language Summary
In this paper we address recent claims of significant increases in methane emissions from U.S. oil and gas production. We find that such claims are inconsistent with observations by examining atmospheric inversions and observations from the NOAA aircraft monitoring program. Furthermore, we show how atmospheric variability, sampling biases, and choice of upwind background can lead to spurious trends in atmospheric column average methane when using both in situ and space‐based retrievals.
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.
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.
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 causes of renewed growth in the atmospheric CH4 burden since 2007 are still poorly understood and subject of intensive scientific discussion. We present a reanalysis of global CH4 emissions ...during the 2000s, based on the TM5‐4DVAR inverse modeling system. The model is optimized using high‐accuracy surface observations from NOAA ESRL's global air sampling network for 2000–2010 combined with retrievals of column‐averaged CH4 mole fractions from SCIAMACHY onboard ENVISAT (starting 2003). Using climatological OH fields, derived global total emissions for 2007–2010 are 16–20 Tg CH4/yr higher compared to 2003–2005. Most of the inferred emission increase was located in the tropics (9–14 Tg CH4/yr) and mid‐latitudes of the northern hemisphere (6–8 Tg CH4/yr), while no significant trend was derived for Arctic latitudes. The atmospheric increase can be attributed mainly to increased anthropogenic emissions, but the derived trend is significantly smaller than estimated in the EDGARv4.2 emission inventory. Superimposed on the increasing trend in anthropogenic CH4 emissions are significant inter‐annual variations (IAV) of emissions from wetlands (up to ±10 Tg CH4/yr), and biomass burning (up to ±7 Tg CH4/yr). Sensitivity experiments, which investigated the impact of the SCIAMACHY observations (versus inversions using only surface observations), of the OH fields used, and of a priori emission inventories, resulted in differences in the detailed latitudinal attribution of CH4 emissions, but the IAV and trends aggregated over larger latitude bands were reasonably robust. All sensitivity experiments show similar performance against independent shipboard and airborne observations used for validation, except over Amazonia where satellite retrievals improved agreement with observations in the free troposphere.
Key Points
A reanalysis of global CH4 emissions during the 2000s is presented
derived global total emissions 2007‐2010 16‐20 Tg CH4/yr higher than 2003‐2005
increase mainly in the tropics and NH mid‐latitudes
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