ALBERT

Description: The GEOS-Chem simulation of atmospheric CH4 was evaluated against observations from the Thermal and Near Infrared Sensor for Carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse Gases Observing Satellite (GOSAT), the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and the Total Carbon Column Observing Network (TCCON). We focused on the model simulations at the 4∘×5∘ and 2∘×2.5∘ horizontal resolutions for the period of February–May 2010. Compared to the GOSAT, TCCON, and ACE-FTS data, we found that the 2∘×2.5∘ model produced a better simulation of CH4, with smaller biases and a higher correlation to the independent data. We found large resolution-dependent differences such as a latitude-dependent XCH4 bias, with higher column abundances of CH4 at high latitudes and lower abundances at low latitudes at the 4∘×5∘ resolution than at 2∘×2.5∘. We also found large differences in CH4 column abundances between the two resolutions over major source regions such as China. These differences resulted in up to 30 % differences in inferred regional CH4 emission estimates from the two model resolutions. We performed several experiments using 222Rn, 7Be, and CH4 to determine the origins of the resolution-dependent errors. The results suggested that the major source of the latitude-dependent errors is excessive mixing in the upper troposphere and lower stratosphere, including mixing at the edge of the polar vortex, which is pronounced at the 4∘×5∘ resolution. At the coarser resolution, there is weakened vertical transport in the troposphere at midlatitudes to high latitudes due to the loss of sub-grid tracer eddy mass flux in the storm track regions. The vertical air mass fluxes are calculated in the model from the degraded coarse-resolution wind fields and the model does not conserve the air mass flux between model resolutions; as a result, the low resolution does not fully capture the vertical transport. This produces significant localized discrepancies, such as much greater CH4 abundances in the lower troposphere over China at 4∘×5∘ than at 2∘×2.5∘. Although we found that the CH4 simulation is significantly better at 2∘×2.5∘ than at 4∘×5∘, biases may still be present at 2∘×2.5∘ resolution. Their importance, particularly in regards to inverse modeling of CH4 emissions, should be evaluated in future studies using online transport in the native general circulation model as a benchmark simulation.

Description: Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget,

Description: Scientific data are almost always represented graphically in figures or in videos. With the ever-growing interest from the general public in understanding climate sciences, it is becoming increasingly important that scientists present this information in ways that are both accessible and engaging to non-experts. In this pilot study, we use time series data from the first United Kingdom Earth System Model (UKESM1) to create six procedurally generated musical pieces. Each of these pieces presents a unique aspect of the ocean component of the UKESM1, either in terms of a scientific principle or a practical aspect of modelling. In addition, each piece is arranged using a different musical progression, style and tempo. These pieces were created in the Musical Instrument Digital Interface (MIDI) format and then performed by a digital piano synthesiser. An associated video showing the time development of the data in time with the music was also created. The music and video were published on the lead author's YouTube channel. A brief description of the methodology was also posted alongside the video. We also discuss the limitations of this pilot study and describe several approaches to extend and expand upon this work.

Description: 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).

Description: Brazil's CH4 emissions over the period 2010–2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT (Greenhouse gases Observing SATellite) satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados, and the high-resolution regional atmospheric transport model NAME (Numerical Atmospheric-dispersion Modelling Environment). We find that Brazil's mean emissions over 2010–2018 are 33.6±3.6Tgyr-1, which are comprised of 19.0±2.6Tgyr-1 from anthropogenic (primarily related to agriculture and waste), 13.0±1.9Tgyr-1 from wetlands and 1.7±0.3Tgyr-1 from biomass burning sources. In addition, between the 2011–2013 and 2014–2018 periods, Brazil's mean emissions rose by 6.9±5.3Tgyr-1 and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the western Amazon increased during the start of the 2015–2016 El Niño by 3.7±2.7Tgyr-1 and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data are beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that (a) calibrated ground-based data are important to include alongside satellite data in a regional inversion and that (b) inversions must account for any offsets between the two data streams and their representations by models.

Description: Wetland emissions contribute the largest uncertainties to the current global atmospheric CH4 budget, and how these emissions will change under future climate scenarios is also still poorly understood. Bloom et al. (2017b) developed WetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH4 wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH4 emission model ensemble against 9 years of high-quality, validated atmospheric CH4 observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH4 mixing ratios based on the WetCHARTs emissions and other sources. Across all years and all ensemble members, the observed global seasonal-cycle amplitude is typically underestimated by WetCHARTs by −7.4 ppb, but the correlation coefficient of 0.83 shows that the seasonality is well-produced at a global scale. The Southern Hemisphere has less of a bias (−1.9 ppb) than the Northern Hemisphere (−9.3 ppb), and our findings show that it is typically the North Tropics where this bias is the worst (−11.9 ppb). We find that WetCHARTs generally performs well in reproducing the observed wetland CH4 seasonal cycle for the majority of wetland regions although, for some regions, regardless of the ensemble configuration, WetCHARTs does not reproduce the observed seasonal cycle well. In order to investigate this, we performed detailed analysis of some of the more challenging exemplar regions (Paraná River, Congo, Sudd and Yucatán). Our results show that certain ensemble members are more suited to specific regions, due to either deficiencies in the underlying data driving the model or complexities in representing the processes involved. In particular, incorrect definition of the wetland extent is found to be the most common reason for the discrepancy between the modelled and observed CH4 concentrations. The remaining driving data (i.e. heterotrophic respiration and temperature) are shown to also contribute to the mismatch with observations, with the details differing on a region-by-region basis but generally showing that some degree of temperature dependency is better than none. We conclude that the data-driven approach used by WetCHARTs is well-suited to producing a benchmark ensemble dataset against which to evaluate more complex process-based land surface models that explicitly model the hydrological behaviour of these complex wetland regions.

Description: We conduct a global inverse analysis of 2010–2018 GOSAT observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4∘×5∘ grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high for China (coal) and Russia (oil and gas) and too low for Venezuela (oil and gas) and the US (oil and gas). We show large 2010–2018 increases in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with high rates of production or use of fossil methane, including the US, Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

Description: We use satellite (GOSAT) and in situ (GLOBALVIEWplus CH4 ObsPack) observations of atmospheric methane in a joint global inversion of methane sources, sinks, and trends for the 2010–2017 period. The inversion is done by analytical solution to the Bayesian optimization problem, yielding closed-form estimates of information content to assess the consistency and complementarity (or redundancy) of the satellite and in situ data sets. We find that GOSAT and in situ observations are to a large extent complementary, with GOSAT providing a stronger overall constraint on the global methane distributions, but in situ observations being more important for northern midlatitudes and for relaxing global error correlations between methane emissions and the main methane sink (oxidation by OH radicals). The in-situ-only and the GOSAT-only inversions alone achieve 113 and 212 respective independent pieces of information (DOFS) for quantifying mean 2010–2017 anthropogenic emissions on 1009 global model grid elements, and respective DOFS of 67 and 122 for 2010–2017 emission trends. The joint GOSAT+ in situ inversion achieves DOFS of 262 and 161 for mean emissions and trends, respectively. Thus, the in situ data increase the global information content from the GOSAT-only inversion by 20 %–30 %. The in-situ-only and GOSAT-only inversions show consistent corrections to regional methane emissions but are less consistent in optimizing the global methane budget. The joint inversion finds that oil and gas emissions in the US and Canada are underestimated relative to the values reported by these countries to the United Nations Framework Convention on Climate Change (UNFCCC) and used here as prior estimates, whereas coal emissions in China are overestimated. Wetland emissions in North America are much lower than in the mean WetCHARTs inventory used as a prior estimate. Oil and gas emissions in the US increase over the 2010–2017 period but decrease in Canada and Europe. The joint inversion yields a global methane emission of 551 Tg a−1 averaged over 2010–2017 and a methane lifetime of 11.2 years against oxidation by tropospheric OH (86 % of the methane sink).

Description: We use 2010–2015 Greenhouse Gases Observing Satellite (GOSAT) observations of atmospheric methane columns over North America in a high-resolution inversion of methane emissions, including contributions from different sectors and their trends over the period. The inversion involves an analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5∘×0.625∘ resolution in concentrated source regions. The analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions in the inverse analysis. Prior estimates for the inversion include a gridded version of the Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTs model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a−1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a−1). The main discrepancy is for the oil and gas production sectors, where we find higher emissions than the GHGI by 35 % and 22 %, respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production, and we find that these are lower than our estimate by a factor of 2. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a−1, on the low end of the prior WetCHARTs inventory uncertainty range (14.2 (3.3–32.4) Tg a−1), which calls for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a−1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil and gas production in the eastern US. We also find that oil and gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.

Description: This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).