Abstract.We developed an iterative inverse method to infer inter-annual sources and sinks of methyl chloroform (MCF) from atmospheric measurements, on a monthly basis. The methodology is presented and used to estimate two decades of OH variability between 1980 and 2000, using varying meteorology. When OH concentrations are adjusted with loose prior errors and MCF emissions are adjusted within inventory bounds, we show that substantial OH inter-annual variability (8.5±1.0% of the mean) and trend (−0.7%.yr −1 ) are necessary to match MCF observations. This result is confirmed by a series of sensitivity tests addressing main limitations of previous studies. However, we show that it is also possible to match MCF observations with a 65% reduction of OH year-to-year variations and a 60% reduction of absolute OH trend, but still a consistency of inferred emissions with inventory values at a ±2σ level. On the other hand, the phase of inferred OH variations is a more robust feature of our set of inversions. Overall, MCF inversions can only provide a range of OH variations unless inventory uncertainties are further reduced.

Abstract. The recent increase of atmospheric methane is investigated by using two atmospheric inversions to quantify the global distribution of sources and sinks for the 2006–2008 period, and a process-based model of CH4 emissions by natural wetland ecosystems. Global emissions derived from inversions are found to have increased by 19 Tg on average in 2007 (16 to 21 Tg) and by 13 Tg in 2008 (6 to 20 Tg), as compared to the 1999–2006 period. A positive anomaly of tropical emissions is found to be the main contributor to the global emission anomaly of 2007 (~60–75%), with a dominant share attributed to natural wetlands (~66%). Abnormally high wetlands emissions from high latitudes are also detected by both inversions in 2007, contributing 15–30% of the global anomaly. Good agreement is found between the results of the wetland ecosystem model and the inversions for 2007. The inferred distribution of the source anomaly in 2007 is shown to be consistent with the observation of a more pronounced increase in near surface methane atmospheric growth rate at high latitudes, because the dilution of surface fluxes by convection is strong in the tropics and weak at high latitudes. The source anomaly in 2008 is found to be much larger in the wetland ecosystem model than in the inversions, suggesting a too strong sensitivity of bottom-up modeled emissions to precipitation. Changes in OH radicals during 2006–2008 are found to be less than 1% in inversions, with only a small impact on the inferred methane emissions.

Natural methane (CH₄) emissions from wet ecosystems are an important part of today's global CH₄ budget. Climate affects the exchange of CH₄ between ecosystems and the atmosphere by influencing CH₄ production, oxidation, and transport in the soil. The net CH₄ exchange depends on ecosystem hydrology, soil and vegetation characteristics. Here, the LPJ-WHyMe global dynamical vegetation model is used to simulate global net CH₄ emissions for different ecosystems: northern peatlands (45°–90° N), naturally inundated wetlands (60° S–45° N), rice agriculture and wet mineral soils. Mineral soils are a potential CH₄ sink, but can also be a source with the direction of the net exchange depending on soil moisture content. The geographical and seasonal distributions are evaluated against multi-dimensional atmospheric inversions for 2003–2005, using two independent four-dimensional variational assimilation systems. The atmospheric inversions are constrained by the atmospheric CH₄ observations of the SCIAMACHY satellite instrument and global surface networks. Compared to LPJ-WHyMe the inversions result in a significant reduction in the emissions from northern peatlands and suggest that LPJ-WHyMe maximum annual emissions peak about one month late. The inversions do not put strong constraints on the division of sources between inundated wetlands and wet mineral soils in the tropics. Based on the inversion results we adapt model parameters in LPJ-WHyMe and simulate the surface exchange of CH₄ over the period 1990–2008. Over the whole period we infer an increase of global ecosystem CH₄ emissions of +1.11 Tg CH₄ yr⁻¹, not considering potential additional changes in wetland extent. The increase in simulated CH₄ emissions is attributed to enhanced soil respiration resulting from the observed rise in land temperature and in atmospheric carbon dioxide that were used as input. The long-term decline of the atmospheric CH₄ growth rate from 1990 to 2006 cannot be fully explained with the simulated ecosystem emissions. However, these emissions show an increasing trend of +3.62 Tg CH₄ yr⁻¹ over 2005–2008 which can partly explain the renewed increase in atmospheric CH₄ concentration during recent years

Our understanding of the global budget of atmospheric hydrogen (H2) contains large uncertainties. An atmospheric Bayesian inversion of H2 sources and sinks is presented for the period 1991–2004, based on a two networks of flask measurement stations. The types of fluxes and the spatial scales potentially resolvable by the inversion are first estimated from an analysis of the correlations of errors between the different processes and regions emitting or absorbing H2. Then, the estimated budget of H2 and its uncertainties is presented and discussed, for five groups of fluxes and three groups of large regions, in terms of mean fluxes, seasonal and interannual variations, and long‐term trends. One main focus of the study is the improvement of the estimate of H2 soil uptake, which is the largest sink of H2. Various sensitivity tests are performed defining an ensemble of more than 20 inversions. We show that inferring a robust estimate of the H2 soil uptake requires to prescribe the prior magnitude of some other sources and sinks with a small uncertainty. Doing so an estimate of the H2 soil uptake of −62 ± 3 Tg y−1 is inferred for the period 1991–2004 (the uncertainty is the residual error after inversion). The inferred soil H2 sink presents a negative long‐term trend that is qualitatively consistent with a bottom‐up process‐based model.