We present a gridded inventory of US anthropogenic methane emissions with 0.1° × 0.1° spatial resolution, monthly temporal resolution, and detailed scale-dependent error characterization. The inventory is designed to be consistent with the 2016 US Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) for 2012. The EPA inventory is available only as national totals for different source types. We use a wide range of databases at the state, county, local, and point source level to disaggregate the inventory and allocate the spatial and temporal distribution of emissions for individual source types. Results show large differences with the EDGAR v4.2 global gridded inventory commonly used as a priori estimate in inversions of atmospheric methane observations. We derive grid-dependent error statistics for individual source types from comparison with the Environmental Defense Fund (EDF) regional inventory for Northeast Texas. These error statistics are independently verified by comparison with the California Greenhouse Gas Emissions Measurement (CALGEM) grid-resolved emission inventory. Our gridded, time-resolved inventory provides an improved basis for inversion of atmospheric methane observations to estimate US methane emissions and interpret the results in terms of the underlying processes.

An algorithm for linear scaling geometry optimisation and transition state search using hybrid delocalised internal coordinates (HDLC) has been developed and implemented in the context of a semiempirical quantum-chemistry program (MNDO) and a modular quantum-mechanical/molecular-mechanical (QM/MM) package (ChemShell). Linear scaling is achieved by a divide-and-conquer approach: the system is partitioned into user-defined fragments, and all coordinate manipulations are performed exclusively within these fragments. The optimiser employs a limited-memory quasi-Newton algorithm (L-BFGS) for energy minimisation, and a microiterative scheme for transition state search using a Hessian eigenmode-following algorithm (P-RFO) for the reaction core and the L-BFGS algorithm for the environment. There are automatic procedures for generating redundant sets of internal coordinates and non-redundant sets of HDLC from Cartesian coordinates. The input to the optimiser consists of the initial Cartesian geometry, the fragmentation of the system, the choice of the working coordinate system, and any constraints to be imposed in Cartesian and/or internal coordinates. The optimiser requires an external function that provides the energy and gradient at a given Cartesian geometry. Systems with thousands of atoms have been optimised, and transition states of a model enzymatic reaction have been determined

Methane is the second strongest anthropogenic greenhouse gas and its atmospheric burden has more than doubled since 1850. Methane concentrations stabilized in the early 2000s and began increasing again in 2007. Neither the stabilization nor the recent growth are well understood, as evidenced by multiple competing hypotheses in recent literature. Here we use a multispecies twobox model inversion to jointly constrain 36 y of methane sources and sinks, using ground-based measurements of methane, methyl chloroform, and the C 13 /C 12 ratio in atmospheric methane (δ 13 CH 4 ) from 1983 through 2015. We find that the problem, as currently formulated, is underdetermined and solutions obtained in previous work are strongly dependent on prior assumptions. Based on our analysis, the mathematically most likely explanation for the renewed growth in atmospheric methane, counterintuitively, involves a 25-Tg/y decrease in methane emissions from 2003 to 2016 that is offset by a 7% decrease in global mean hydroxyl (OH) concentrations, the primary sink for atmospheric methane, over the same period. However, we are still able to fit the observations if we assume that OH concentrations are time invariant (as much of the previous work has assumed) and we then find solutions that are largely consistent with other proposed hypotheses for the renewed growth of atmospheric methane since 2007. We conclude that the current surface observing system does not allow unambiguous attribution of the decadal trends in methane without robust constraints on OH variability, which currently rely purely on methyl chloroform data and its uncertain emissions estimates.methane | renewed growth | hydroxyl | oxidative capacity | troposphere A tmospheric methane (CH4) is the second strongest anthropogenic greenhouse gas (1) and concentrations have been increasing for much of the past century (2) due, primarily, to increasing anthropogenic emissions. Atmospheric concentrations stabilized in the early 2000s (3) (hereafter referred to as the "methane stabilization") and began increasing again in 2007 (4, 5) (hereafter referred to as the "renewed growth"). There has been much speculation about the cause of these trends (3-24). Attribution has proved to be a difficult task in part because this period of renewed growth is characterized by a methane growth rate of ∼6 ppb/y, which represents a source-sink imbalance of only 3% [or an increase of 20 Tg/y compared with an estimated annual source of 550 Tg/y (13)].Previous work investigating the trends in atmospheric methane has generally used observations of either atmospheric ethane or bulk carbon isotope ratios in atmospheric methane (δ 13 CH4), in conjunction with methane observations, to provide additional constraints on the sources of methane. This is because ethane is coemitted with methane from fossil-fuel sources, which represent ∼62% of the ethane budget (25), and has been used to infer changes in methane emissions from fossil-fuel sources. Similarly, δ 13 CH4 has been used to determine the sources governing ...

Abstract. Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH 4 ) budget, largely due to poorly constrained process controls on CH 4 production in waterlogged soils. Process-based estimates of global wetland CH 4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH 4 emission estimates. Here we construct a global wetland CH 4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5 • × 0.5 • resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009-2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001-2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH 4 ; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH 4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH 4 emissions, although uncertainty in the temperature CH 4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH 4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for topdown estimates of wetland CH 4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH 4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH 4 emissions.