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We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006-2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2-29.7) TgCH(4) yr(-1) (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006-2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 TgCH(4) yr(-1) (2006) to 18.8 TgCH(4) yr(-1) (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3-8.2) TgCH(4) yr(-1) from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 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 CH4 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.

Natural wetlands are among the most important sources of atmospheric methane and thus important for better understanding the long-term temporal variations in the atmospheric methane concentration. During the last 60 years, wetlands have experienced extensive conversion and impacts from climate warming which might result in complicated temporal and spatial variations in the changes of the wetland methane emissions. In this paper, we present a modeling framework, integrating CH4MODwetland, TOPMODEL, and TEM models, to analyze the temporal and spatial variations in CH4 emissions from natural wetlands (including inland marshes/swamps, coastal wetlands, lakes, and rivers) in China. Our analysis revealed a total increase of 25.5 %, averaging 0.52 g m−2 per decade, in the national CH4 fluxes from 1950 to 2010, which was mainly induced by climate warming. Larger CH4 flux increases occurred in northeastern, northern, and northwestern China, where there have been higher temperature rises. However, decreases in precipitation due to climate warming offset the increment of CH4 fluxes in these regions. The CH4 fluxes from the wetland on the Qinghai–Tibet Plateau exhibited the lowest CH4 increase (0.17 g m−2 per decade). Although climate warming has accelerated CH4 fluxes, the total amount of national CH4 emissions decreased by approximately 2.35 Tg (1.91–2.81 Tg), i.e., from 4.50 Tg in the early 1950s to 2.15 Tg in the late 2000s, due to the wetland loss totalling 17.0 million ha. Of this reduction, 0.26 Tg (0.24–0.28 Tg) was derived from lakes and rivers, 0.16 Tg (0.13–0.20 Tg) from coastal wetlands, and 1.92 Tg (1.54–2.33 Tg) from inland wetlands. Spatially, northeastern China contributed the most to the total reduction, with a loss of 1.68 Tg. The wetland CH4 emissions reduced by more than half in most regions in China except for the Qinghai–Tibet Plateau, where the CH4 decrease was only 23.3 %.

We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006-2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2-29.7) TgCH(4) yr(-1) (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006-2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 TgCH(4) yr(-1) (2006) to 18.8 TgCH(4) yr(-1) (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3-8.2) TgCH(4) yr(-1) from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 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 CH4 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.

International audience; Tropical wetlands are estimated to represent about 50 % of the natural wetland methane (CH 4) emissions and explain a large fraction of the observed CH 4 variability on timescales ranging from glacial–interglacial cycles to the currently observed year-to-year variability. Despite their importance , however, tropical wetlands are poorly represented in global models aiming to predict global CH 4 emissions. This publication documents a first step in the development of a process-based model of CH 4 emissions from tropical flood-plains for global applications. For this purpose, the LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was slightly modified to represent floodplain hydrology, vegetation and associated CH 4 emissions. The extent of tropical floodplains was prescribed using output from the spatially explicit hydrology model PCR-GLOBWB. We introduced new plant functional types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote-sensing data sets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH 4 flux densities were evaluated against field observations and regional flux inventories. Simulated CH 4 emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX reproduces the average magnitude of observed net CH 4 flux densities for the Amazon Basin. However , the model does not reproduce the variability between sites or between years within a site. Unfortunately, site information is too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH 4 emissions. Sensitivity analyses gave insights into the main drivers of floodplain CH 4 emission and their associated uncertainties. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr −1. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin, modulated emissions by about 20 %. Correcting the LPX-simulated NPP using MODIS reduces the Amazon emissions by 11.3 %. Finally, due to an intrinsic limitation of LPX to account for season-ality in floodplain extent, the model failed to reproduce the full dynamics in CH 4 emissions but we proposed solutions to this issue. The interannual variability (IAV) of the emissions increases by 90 % if the IAV in floodplain extent is accounted Published by Copernicus Publications on behalf of the European Geosciences Union. 1520 B. Ringeval et al.: Methane emissions from floodplains in the Amazon Basin for, but still remains lower than in most of the WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH 4 emissions of the Amazon Basin. Our results helped identify and prioritize directions towards more accurate estimates of tropical CH 4 emissions, and they stress the need for more research to constrain floodplain CH 4 emissions and their temporal variability , even before including other fundamental mechanisms such as floating macrophytes or lateral water fluxes.

Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m−2), then gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), net ecosystem exchange (NEE) (−0.01 ± 0.19 kg C m−2 yr−1), and CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.

ISI Document Delivery No.: 274OV Times Cited: 1 Cited Reference Count: 85 Cited References: Artuso F, 2007, ATMOS ENVIRON, V41, P3877, DOI 10.1016/j.atmosenv.2007.01.024 Aydin M, 2011, NATURE, V476, P198, DOI 10.1038/nature10352 Banda N, 2013, ATMOS CHEM PHYS, V13, P2267, DOI 10.5194/acp-13-2267-2013 Bloom AA, 2010, SCIENCE, V327, P322, DOI 10.1126/science.1175176 Bousquet P, 2005, ATMOS CHEM PHYS, V5, P2635 Bousquet P, 2006, NATURE, V443, P439, DOI 10.1038/nature05132 Bousquet P, 2011, ATMOS CHEM PHYS, V11, P3689, DOI 10.5194/acp-11-3689-2011 Brunke EG, 2001, ATMOS ENVIRON, V35, P777, DOI 10.1016/S1352-2310(00)00260-0 Chen YH, 2006, J GEOPHYS RES, V111, DOI DOI 10.1029/2005JD006058 Chevallier F, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006390 Crevoisier C, 2009, ATMOS CHEM PHYS, V9, P6337 Cunnold DM, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001226 Decharme B, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009376 Decharme B, 2012, CLIM DYNAM, V38, P1389, DOI 10.1007/s00382-011-1054-9 Dlugokencky E. J., 2011, AGU FALL M, pD6 Dlugokencky EJ, 1998, NATURE, V393, P447, DOI 10.1038/30934 Dlugokencky EJ, 1996, GEOPHYS RES LETT, V23, P2761, DOI 10.1029/96GL02638 DLUGOKENCKY EJ, 1994, J GEOPHYS RES-ATMOS, V99, P17021, DOI 10.1029/94JD01245 Dlugokencky EJ, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006035 Dlugokencky EJ, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL018126 Dlugokencky EJ, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039780 EDGAR 4, 2009, EDGAR 4 EM DAT GLOB EPA, 2011, 430D11003 US EPA Folberth G, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021812 Francey RJ, 1999, J ATMOS SCI, V56, P279, DOI 10.1175/1520-0469(1999)0562.0.CO;2 Frankenberg C, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034300 Frappart F, 2012, ENVIRON RES LETT, V7, DOI 10.1088/1748-9326/7/4/044010 FUNG I, 1991, J GEOPHYS RES-ATMOS, V96, P13033, DOI 10.1029/91JD01247 Gauci V, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000499 Gomez-Pelaez A., 2010, P S ATM CHEM PHYS MO, P157 Guimberteau M, 2012, HYDROL EARTH SYST SC, V16, P911, DOI 10.5194/hess-16-911-2012 Hauglustaine DA, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003957 Heimann M., 2011, AGU FALL M, pC6 Hess LL, 2003, REMOTE SENS ENVIRON, V87, P404, DOI 10.1016/j.rse.2003.04.001 Hodson EL, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL046861 HOGAN KB, 1994, GEOPHYS RES LETT, V21, P2445, DOI 10.1029/94GL02601 Hourdin F, 2006, CLIM DYNAM, V27, P787, DOI 10.1007/s00382-006-0158-0 Kai FM, 2011, NATURE, V476, P194, DOI 10.1038/nature10259 Kaminski T, 2001, J GEOPHYS RES-ATMOS, V106, P4703, DOI 10.1029/2000JD900581 Kaplan J. O., 2002, GEOPHYS RES LETT, V29 Kirschke S, 2013, NAT GEOSCI, V6, P813, DOI 10.1038/NGEO1955 Koven CD, 2011, P NATL ACAD SCI USA, V108, P14769, DOI 10.1073/pnas.1103910108 Krinner G, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2003GB002199 Lamarque JF, 2010, ATMOS CHEM PHYS, V10, P7017, DOI 10.5194/acp-10-7017-2010 Langenfelds RL, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/2001GB001466 Lawrence D. M., 2007, CLIM DYNAM, V30, P145, DOI [10.1007/s00382-007-0278-1, DOI 10.1007/S00382-007-0278-1] Levin I, 2012, NATURE, V486, pE3, DOI 10.1038/nature11175 LOWE DC, 1991, J GEOPHYS RES-ATMOS, V96, P15455, DOI 10.1029/91JD01119 Matsueda H., 2004, PAP METEOROL GEOPHYS, V54, P91, DOI [10.2467/mripapers.54.91, DOI 10.2467/MRIPAPERS.54.91] Matthews E., 1987, GLOBAL BIOGEOCHEMICA, V1, P61, DOI DOI 10.1029/GB001I001P00061 Melton JR, 2013, BIOGEOSCIENCES, V10, P753, DOI 10.5194/bg-10-753-2013 Miguez-Macho G, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017539 Miller JB, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL029213 Monteil G, 2011, ATMOS CHEM PHYS, V11, P9141, DOI 10.5194/acp-11-9141-2011 Montzka SA, 2011, SCIENCE, V331, P67, DOI 10.1126/science.1197640 Montzka SA, 2000, SCIENCE, V288, P500, DOI 10.1126/science.288.5465.500 Morino I, 2011, ATMOS MEAS TECH, V4, P1061, DOI 10.5194/amt-4-1061-2011 Olivier J. G. J., 2001, CLIMATE SYSTEM, V33-77 Papa F, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012674 Peylin P, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000857 Peylin P, 2000, GEOPH MONOG SERIES, V114, P295 Pison I, 2009, ATMOS CHEM PHYS, V9, P5281 Prigent C, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051276 Prinn R. G., 2012, AGAGE DATA BASE Prinn RG, 2000, J GEOPHYS RES-ATMOS, V105, P17751, DOI 10.1029/2000JD900141 Prinn RG, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL022228 Rigby M, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL036037 Ringeval B, 2011, BIOGEOSCIENCES, V8, P2137, DOI 10.5194/bg-8-2137-2011 Ringeval B, 2010, GLOBAL BIOGEOCHEM CY, V24, DOI 10.1029/2008GB003354 Ringeval B, 2013, CLIM PAST, V9, P149, DOI 10.5194/cp-9-149-2013 Ringeval B, 2012, GEOSCI MODEL DEV, V5, P941, DOI 10.5194/gmd-5-941-2012 Schmidt M., 2006, 168 WMO, P165 Simpson IJ, 2012, NATURE, V488, P490, DOI 10.1038/nature11342 Sussmann R, 2012, ATMOS CHEM PHYS, V12, P4885, DOI 10.5194/acp-12-4885-2012 Uppala SM, 2005, Q J ROY METEOR SOC, V131, P2961, DOI 10.1256/qj.04.176 van der Werf GR, 2010, ATMOS CHEM PHYS, V10, P11707, DOI 10.5194/acp-10-11707-2010 van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423 van der Werf GR, 2004, SCIENCE, V303, P73, DOI 10.1126/science.1090753 Viovy N., 2009, COMBINED DATASET ECO Walter BP, 2001, J GEOPHYS RES-ATMOS, V106, P34189, DOI 10.1029/2001JD900165 Walter BP, 2001, J GEOPHYS RES-ATMOS, V106, P34207, DOI 10.1029/2001JD900164 Wang JS, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002180 Wania R, 2013, GEOSCI MODEL DEV, V6, P617, DOI 10.5194/gmd-6-617-2013 White JR, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000609 Worthy DEJ, 1998, J GEOPHYS RES-ATMOS, V103, P15995, DOI 10.1029/98JD00925 Pison, I. Ringeval, B. Bousquet, P. Prigent, C. Papa, F. Papa, Fabrice/D-3695-2009 Papa, Fabrice/0000-0001-6305-6253 1 COPERNICUS GESELLSCHAFT MBH GOTTINGEN ATMOS CHEM PHYS; Two atmospheric inversions (one fine-resolved and one process-discriminating) and a process-based model for land surface exchanges are brought together to analyse the variations of methane emissions from 1990 to 2009. A focus is put on the role of natural wetlands and on the years 2000-2006, a period of stable atmospheric concentrations. From 1990 to 2000, the top-down and bottom-up visions agree on the time-phasing of global total and wetland emission anomalies. The process-discriminating inversion indicates that wetlands dominate the time-variability of methane emissions (90% of the total variability). The contribution of tropical wetlands to the anomalies is found to be large, especially during the post-Pinatubo years (global negative anomalies with minima between -41 and -19 Tg yr(-1) in 1992) and during the alternate 1997-1998 El-Nino/1998-1999 La-Nina (maximal anomalies in tropical regions between +16 and +22 Tg yr(-1) for the inversions and anomalies due to tropical wetlands between +12 and +17 Tg yr(-1) for the process-based model). Between 2000 and 2006, during the stagnation of methane concentrations in the atmosphere, the top-down and bottom-up approaches agree on the fact that South America is the main region contributing to anomalies in natural wetland emissions, but they disagree on the sign and magnitude of the flux trend in the Amazon basin. A negative trend (-3.9 +/- 1.3 Tg yr(-1)) is inferred by the process-discriminating inversion whereas a positive trend (+1.3 +/- 0.3 Tg yr(-1)) is found by the process model. Although processed-based models have their own caveats and may not take into account all processes, the positive trend found by the B-U approach is considered more likely because it is a robust feature of the process-based model, consistent with analysed precipitations and the satellite-derived extent of inundated areas. On the contrary, the surface-data based inversions lack constraints for South America. This result suggests the need for a re-interpretation of the large increase found in anthropogenic methane inventories after 2000.