Abstract. We present inverse modelling (top down) estimates of European methane (CH 4 The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH 4 emissions with maxima in summer, while anthropogenic CH 4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain.Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH 4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between −40 and 20 % at the aircraft profile sites in France, Hungary and Poland.

Bochud, M. (2019). Effects of short-and long-term exposures to particulate matter on inflammatory marker levels in the general population. Environmental Science and

We adapt general statistical methods to estimate the optimal error covariance matrices in a regional inversion system inferring methane surface emissions from atmospheric concentrations. Using a minimal set of physical hypotheses on the patterns of errors, we compute a guess of the error statistics that is optimal in regard to objective statistical criteria for the specific inversion system. With this very general approach applied to a real-data case, we recover sources of errors in the observations and in the prior state of the system that are consistent with expert knowledge while inferred from objective criteria and with affordable computation costs. By not assuming any specific error patterns, our results depict the variability and the inter-dependency of errors induced by complex factors such as the misrepresentation of the observations in the transport model or the inability of the model to reproduce well the situations of steep gradients of concentrations. Situations with probable significant biases (e.g., during the night when vertical mixing is ill-represented by the transport model) can also be diagnosed by our methods in order to point at necessary improvement in a model. By additionally analysing the sensitivity of the inversion to each observation, guidelines to enhance data selection in regional inversions are also proposed. We applied our method to a recent significant accidental methane release from an offshore platform in the North Sea and found methane fluxes of the same magnitude than what was officially declared

The disintegration of the ice shelves along the Antarctic Peninsula have spurred much discussion on the various processes leading to their eventual dramatic collapse, but without a consensus on an atmospheric forcing that could connect these processes. Here, using an atmospheric river detection algorithm along with a regional climate model and satellite observations, we show that the most intense atmospheric rivers induce extremes in temperature, surface melt, sea-ice disintegration, or large swells that destabilize the ice shelves with 40% probability. This was observed during the collapses of the Larsen A and B ice shelves during the summers of 1995 and 2002 respectively. Overall, 60% of calving events from 2000–2020 were triggered by atmospheric rivers. The loss of the buttressing effect from these ice shelves leads to further continental ice loss and subsequent sea-level rise. Under future warming projections, the Larsen C ice shelf will be at-risk from the same processes.

Abstract. Subsea permafrost and hydrates in the East Siberian Arctic Shelf (ESAS) constitute a substantial carbon pool, and a potentially large source of methane to the atmosphere. Previous studies based on interpolated oceanographic campaigns estimated atmospheric emissions from this area at 8–17 TgCH4 yr−1. Here, we propose insights based on atmospheric observations to evaluate these estimates. The comparison of high-resolution simulations of atmospheric methane mole fractions to continuous methane observations during the whole year 2012 confirms the high variability and heterogeneity of the methane releases from ESAS. A reference scenario with ESAS emissions of 8 TgCH4 yr−1, in the lower part of previously estimated emissions, is found to largely overestimate atmospheric observations in winter, likely related to overestimated methane leakage through sea ice. In contrast, in summer, simulations are more consistent with observations. Based on a comprehensive statistical analysis of the observations and of the simulations, annual methane emissions from ESAS are estimated to range from 0.0 to 4.5 TgCH4 yr−1. Isotopic observations suggest a biogenic origin (either terrestrial or marine) of the methane in air masses originating from ESAS during late summer 2008 and 2009.

Abstract. Eight surface observation sites providing quasicontinuous measurements of atmospheric methane mixing ratios have been operated since the mid-2000's in Siberia. For the first time in a single work, we assimilate 1 year of these in situ observations in an atmospheric inversion. Our objective is to quantify methane surface fluxes from anthropogenic and wetland sources at the mesoscale in the Siberian lowlands for the year 2010. To do so, we first inquire about the way the inversion uses the observations and the way the fluxes are constrained by the observation sites. As atmospheric inversions at the mesoscale suffer from mis-quantified sources of uncertainties, we follow recent innovations in inversion techniques and use a new inversion approach which quantifies the uncertainties more objectively than the previous inversion systems. We find that, due to errors in the representation of the atmospheric transport and redundant pieces of information, only one observation every few days is found valuable by the inversion. The remaining high-resolution quasicontinuous signal is representative of very local emission patterns difficult to analyse with a mesoscale system. An analysis of the use of information by the inversion also reveals that the observation sites constrain methane emissions within a radius of 500 km. More observation sites than the ones currently in operation are then necessary to constrain the whole Siberian lowlands. Still, the fluxes within the constrained areas are quantified with objectified uncertainties. Finally, the tolerance intervals for posterior methane fluxes are of roughly 20 % (resp. 50 %) of the fluxes for anthropogenic (resp. wetland) sources. About 50-70 % of Siberian lowlands emissions are constrained by the inversion on average on an annual basis. Extrapolating the figures on the constrained areas to the whole Siberian lowlands, we find a regional methane budget of 5-28 TgCH 4 for the year 2010, i.e. 1-5 % of the global methane emissions. As very few in situ observations are available in the region of interest, observations of methane total columns from the Greenhouse Gas Observing SATellite (GOSAT) are tentatively used for the evaluation of the inversion results, but they exhibit only a marginal signal from the fluxes within the region of interest.

Abstract. The hydroxyl radical (OH), which is the dominant sink of methane (CH4), plays a key role in closing the global methane budget. Current top-down estimates of the global and regional CH4 budget using 3D models usually apply prescribed OH fields and attribute model–observation mismatches almost exclusively to CH4 emissions, leaving the uncertainties due to prescribed OH fields less quantified. Here, using a variational Bayesian inversion framework and the 3D chemical transport model LMDz, combined with 10 different OH fields derived from chemistry–climate models (Chemistry–Climate Model Initiative, or CCMI, experiment), we evaluate the influence of OH burden, spatial distribution, and temporal variations on the global and regional CH4 budget. The global tropospheric mean CH4-reaction-weighted [OH] ([OH]GM-CH4) ranges 10.3–16.3×105 molec cm−3 across 10 OH fields during the early 2000s, resulting in inversion-based global CH4 emissions between 518 and 757  Tg yr−1. The uncertainties in CH4 inversions induced by the different OH fields are similar to the CH4 emission range estimated by previous bottom-up syntheses and larger than the range reported by the top-down studies. The uncertainties in emissions induced by OH are largest over South America, corresponding to large inter-model differences of [OH] in this region. From the early to the late 2000s, the optimized CH4 emissions increased by 22±6  Tg yr−1 (17–30  Tg yr−1), of which ∼25  % (on average) offsets the 0.7  % (on average) increase in OH burden. If the CCMI models represent the OH trend properly over the 2000s, our results show that a higher increasing trend of CH4 emissions is needed to match the CH4 observations compared to the CH4 emission trend derived using constant OH. This study strengthens the importance of reaching a better representation of OH burden and of OH spatial and temporal distributions to reduce the uncertainties in the global and regional CH4 budgets.

Abstract. The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolving discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields was analyzed and aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz (Laboratoire de Meteorologie Dynamique) with a standard CH4 emission scenario over the period 2000–2016. The multi-model simulated global volume-weighted tropospheric mean OH concentration ([OH]) averaged over 2000–2010 ranges between 8.7×105 and 12.8×105 molec cm−3. The inter-model differences in tropospheric OH burden and vertical distributions are mainly determined by the differences in the nitrogen oxide (NO) distributions, while the spatial discrepancies between OH fields are mostly due to differences in natural emissions and volatile organic compound (VOC) chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0.3×105 molec cm−3 in the tropospheric mean [OH], with year-to-year variations much smaller than during the historical period 1960–2000. Once ingested into the LMDz model, these OH changes translated into a 5 to 15 ppbv reduction in the CH4 mixing ratio in 2010, which represents 7 %–20 % of the model-simulated CH4 increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a CH4 mixing ratio uncertainty of >±30 ppbv. Over the full 2000–2016 time period, using a common state-of-the-art but nonoptimized emission scenario, the impact of [OH] changes tested here can explain up to 54 % of the gap between model simulations and observations. This result emphasizes the importance of better representing OH abundance and variations in CH4 forward simulations and emission optimizations performed by atmospheric inversions.