Methane is an important greenhouse gas, responsible for about 20% of the warming induced by long-lived greenhouse gases since pre-industrial times. By reacting with hydroxyl radicals, methane reduces the oxidizing capacity of the atmosphere and generates ozone in the troposphere. Although most sources and sinks of methane have been identified, their relative contributions to atmospheric methane levels are highly uncertain. As such, the factors responsible for the observed stabilization of atmospheric methane levels in the early 2000s, and the renewed rise after 2006, remain unclear. Here, we construct decadal budgets for methane sources and sinks between 1980 and 2010, using a combination of atmospheric measurements and results from chemical transport models, ecosystem models, climate chemistry models and inventories of anthropogenic emissions. The resultant budgets suggest that data-driven approaches and ecosystem models overestimate total natural emissions. We build three contrasting emission scenarios — which differ in fossil fuel and microbial emissions — to explain the decadal variability in atmospheric methane levels detected, here and in previous studies, since 1985. Although uncertainties in emission trends do not allow definitive conclusions to be drawn, we show that the observed stabilization of methane levels between 1999 and 2006 can potentially be explained by decreasing-to-stable fossil fuel emissions, combined with stable-to-increasing microbial emissions. We show that a rise in natural wetland emissions and fossil fuel emissions probably accounts for the renewed increase in global methane levels after 2006, although the relative contribution of these two sources remains uncertain

Wetland methane (CH 4 ) emissions are the largest natural source in the global CH 4 budget, contributing to roughly one third of total natural and anthropogenic emissions. As the second most important anthropogenic greenhouse gas in the atmosphere after CO 2 , CH 4 is strongly associated with climate feedbacks. However, due to the paucity of data, wetland CH 4 feedbacks were not fully assessed in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The degree to which future expansion of wetlands and CH 4 emissions will evolve and consequently drive climate feedbacks is thus a question of major concern. Here we present an ensemble estimate of wetland CH 4 emissions driven by 38 general circulation models for the 21st century. We find that climate changeinduced increases in boreal wetland extent and temperature-driven increases in tropical CH 4 T errestrial wetlands are among the largest biogenic sources of methane contributing to growing atmospheric CH 4 concentrations (1) and are, in turn, highly sensitive to climate change (2). However, radiative feedbacks from wetland CH 4 emissions were not considered in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and Integrated Assessment Models (IAM) assumed anthropogenic sources to be the only driver responsible for the increase of atmospheric CH 4 burden since the 1750s (3). The role of wetland CH 4 emissions, however, may play an increasingly larger role in future atmospheric growth of methane because of the large stocks of mineral and organic carbon stored under anaerobic conditions in both boreal and tropical regions. Paleoclimatological and contemporary observations of the climate sensitivity of wetland methane emissions suggest the potential for a large feedback (4), but there remains large uncertainty in quantifying the actual range of the response (5, 6).Increasing air temperature is linked to the thawing of permafrost and to increased rates of soil microbial activity (7), which directly lead to greater CH 4 production in soils due to thaw-induced change in surface wetland areas (8). In the tropics, wetland areal extent is also influenced by precipitation, which affects the area of surface inundation, water table depth, and soil moisture that, in turn, promote methanogenesis. Elevated CO 2 concentrations can increase ecosystem water use efficiency and thus soil moisture, and also increase soil carbon substrate availability for microbial activities (9). Tropical wetlands, for which a decline in inundation was observed in recent decades (10), are already exposed to increasing frequencies in extreme climate events, e.g., heat waves, floods, and droughts, and changes in rainfall distribution (11) and variability in methane emissions (12). Meanwhile, northern highlatitude ecosystems are experiencing a more rapid temperature increase than elsewhere globally and with increased rates of soil respiration (13), yet, locally at least, no response in methane emissions (14). Despite the importance of these feedbacks noted by the Intergovernmen...

Abstract. The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistryclimate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10-20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (ß) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeographybiogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30 % (20 %) uncertainty in the sensitivity of GPP (transpiration) to precipitation. TheCorrespondence to: B. Poulter (benjamin.poulter@lsce.ipsl.fr) availability of PFT datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.

Development of reliable source emission inventories is particularly needed to advance the understanding of the origin of Arctic haze using chemical transport modeling. This study develops a regional anthropogenic black carbon (BC) emission inventory for the Russian Federation, the largest country by land area in the Arctic Council. Activity data from combination of local Russia information and international resources, emission factors based on either Russian documents or adjusted values for local conditions, and other emission source data are used to approximate the BC emissions. Emissions are gridded at a resolution of 0.1° × 0.1° and developed into a monthly temporal profile. Total anthropogenic BC emission of Russia in 2010 is estimated to be around 224 Gg. Gas flaring, a commonly ignored black carbon source, contributes a significant fraction of 36.2% to Russia's total anthropogenic BC emissions. Other sectors, i.e., residential, transportation, industry, and power plants, contribute 25.0%, 20.3%, 13.1%, and 5.4%, respectively. Three major BC hot spot regions are identified: the European part of Russia, the southern central part of Russia where human population densities are relatively high, and the Urals Federal District where Russia's major oil and gas fields are located but with sparse human population. BC simulations are conducted using the hemispheric version of Community Multi‐scale Air Quality Model with emission inputs from a global emission database EDGAR (Emissions Database for Global Atmospheric Research)‐HTAPv2 (Hemispheric Transport of Air Pollution) and EDGAR‐HTAPv2 with its Russian part replaced by the newly developed Russian BC emissions, respectively. The simulation using the new Russian BC emission inventory could improve 30–65% of absorption aerosol optical depth measured at the AERONET sites in Russia throughout the whole year as compared to that using the default HTAPv2 emissions. At the four ground monitoring sites (Zeppelin, Barrow, Alert, and Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out‐of‐date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. This study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.

[1] Global measurements of atmospheric methane (CH 4 ) concentrations continue to show large interannual variability whose origin is only partly understood. Here we quantify the influence of the El Niño-Southern Oscillation (ENSO) on wetland CH 4 emissions, which are thought to be the dominant contributor to interannual variability of the CH 4 sources. We use a simple wetland CH 4 model that captures variability in wetland extent and soil carbon to model the spatial and temporal dynamics of wetland CH 4 emissions from 1950-2005 and compare these results to an ENSO index. We are able to explain a large fraction of the global and tropical variability in wetland CH 4 emissions through correlation with the ENSO index. We find that repeated El Niño events throughout the 1980s and 1990s were a contributing factor towards reducing CH 4 emissions and stabilizing atmospheric CH 4 concentrations. An increase in emissions from the boreal region would likely strengthen the feedback between ENSO and interannual variability in global wetland CH 4 emissions. Our analysis emphasizes that climate variability has a significant impact on wetland CH 4 emissions, which should be taken into account when considering future trends in CH 4 sources.