dataesr/scientific-paragraphs-categorization huggingface dataset:
(2013). They are consistent with the FAO-CH 4 estimate of 102 Tg CH 4 yr -1 for 2005  (Tubiello et al. , 2013). Here, for the 2003-2012 decade, based on all the databases aforementioned, we infer a range of 97-111 Tg CH 4 yr -1 for the combination of enteric fermentation and manure with a mean value of 106 Tg CH 4 yr -1 (Table  2 ), about one-third of total global anthropogenic emissions.
, 2013; Yin et al. , 2015). Usually the biomass burning emissions are estimated using following Eq. (  2 ) (or similar): E(xt) = A(x, t) × B(x) × FB × EF, (2) where A(x, t) is the area burned, B(x) the biomass loading (depending on the biomes) at the location, FB the fraction of the area burned (or the efficiency of the fire depending of the vegetation type and the fire type) and EF the emission factor (mass of the considered species/mass of biomass burned). Depending on the approach, these parameters are derived using satellite data and/or biogeochemical model, or more simple equations. The Global Fire Emission Database (GFED) is the most widely used global biomass burning emission dataset and provides estimates from 1997. In this review, we use both GFED3 (van der Werf et al. , 2010) and GFED4s  (Giglio et al.
(2004) , and the fraction of biomass burned is assigned as a function of tree cover  (Wiedinmyer et al. , 2006). The Global Fire Assimilation System (GFAS,  Kaiser et al. , 2012)  calculates biomass burning emissions by assimilating Fire Radiative Power (FRP) observations from MODIS at a daily frequency and 0. 5 • resolution and is available for the time period 2000-2013. After correcting the FRP observations for diurnal cycle, gaps etc. , it is linked to dry matter combustion rate using  Wooster et al. (2005)  and CH 4 emission factors from  Andreae and Merlet (2001). For FAO-CH 4 , yearly biomass burning emissions are based on burned area data from the Global Fire Emission Database v. 4 (GFED4;  Giglio et al. , 2013). For forest, the GFED4 burned forest area is an aggregate of burned area in the following MODIS land cover classes (MCD12Q1,  Hansen et al.
, 2010). These remote-sensed data do not exactly correspond to wetlands, as all flooded areas are not wetlands (in methane emission sense) and some wetlands (e. g. northern bogs) are not always flooded. Inundated areas also include inland water bodies (lakes, ponds, estuaries) and rice paddies, which have to be filtered out to compute wetland emissions. Overall, current remote sensing of wetlands tends to underestimate wetland extent partly because of signal deterioration over dense vegetation and partly because microwave signals only detect water above or at the soil surface and therefore do not detect emitting peatlands that are not inundated  (Prigent et al. , 2007). For example, the Global Lakes and Wetlands Dataset (GLWD)  (Lehner and Döll, 2004)  estimates between 8. 2 and 10. 1 Mkm 2 of wetlands globally, while remote-sensing inundation area is smaller, i.
e. ∼ 6 Mkm 2  (Prigent et al. , 2007). Some ancillary data used in the GIEMS processing are not available after 2007 and have prevented so far the extension of the dataset after 2007. Integrated at the global scale, wetlands are the largest and most uncertain source of methane to the atmosphere  (Kirschke et al. , 2013). An ensemble of land surface models estimated the range of methane emissions of natural wetlands at 141-264 Tg CH 4 yr -1 for the 1993-2004 period, with a mean and 1σ value of 190 ± 39 Tg CH 4 yr -1  (Melton et al. , 2013). Kirschke et al. (  2013 ) assessed a consistently large emission range of 142-287 Tg CH 4 yr -1 , using the  Melton et al. (2013)  land surface models and atmospheric inversions. These emissions represent about 30 % of the total methane source. The large range in the estimates of wetland emissions results from difficulties in defining wetland CH 4producing areas as well as in parameterizing terrestrial anaerobic sources and oxidative sinks  (Melton et al.
, 2013; Wania et al. , 2013). In this work, following  Melton et al. (2013) , 11 land surface models (Table  1 ) computing net CH 4 emissions have been run under a common protocol with a 30-year spin-up  followed by a simulation until the end of 2012 forced by CRU-NCEP v4. 0 reconstructed climate fields. Atmospheric CO 2 influencing NPP was also prescribed in the models, allowing the models to separately estimate carbon availability for methanogenesis. In all models, the same wetland extent (SWAMPS-GLWD) has been prescribed. The SWAMPS-GLWD is a monthly global wetland area dataset, which has been developed to overcome the aforementioned issues and combines remote-sensing data from  Schroeder et al. (2015)  and GLWD inventory in order to develop a monthly global wetland area dataset  (Poulter et al.
Despite the lower area extent, the higher per-unit area methane emissions of tropical wetlands results in a larger wetland source from the tropics than from the boreal zone  (Melton et al. , 2013). The average emission map from wetlands for 2003-2012 built from the 11 models is plotted in Fig. 3. The zones with the largest emissions reflect the GLWD database: the Amazon basin, equatorial Africa and Asia, Canada, western Siberia, eastern India, and Bangladesh. Regions where methane emissions are robustly inferred (i. e. regions where mean flux is larger than the standard deviation of the models) represent 80 % of the total methane flux due to natural wetlands. Main primary emission zones are consistent between models, which is clearly favoured by the common wetland extend prescribed. But still, the different sensitivity of the models to temperature can generate substantial different patterns, such as in India.
Some secondary (in magnitude) emission zones are also consistently inferred between models: Scandinavia, continental Europe, eastern Siberia, central USA, and tropical Africa. Using improved regional methane emission datasets (such as studies over North America, Africa, China, and Amazon) can enhance the accuracy of the global budget assessment  (Tian et al. , 2011; Xu and Tian, 2012; Ringeval et al. , 2014; Valentini et al. , 2014). The resulting global flux range for natural wetland emissions is 153-227 Tg CH 4 yr -1 for the 2003-2012 decade, with an average of 185 Tg CH 4 yr -1 with a 1σ standard deviation of 21 Tg CH 4 yr -1 (Table  2 ).
The average total estimated open water emission including the recent estimates from smaller streams is 122 Tg CH 4 yr -1. The uncertainty is high with a coefficient of variation ranging from 50 to > 100 % for various flux components and biomes  (Bastviken et al. , 2011)  resulting in a minimum uncertainty range of 60-180 Tg CH 4 yr -1. The present data indicate that lakes or natural ponds, reservoirs, and streams/rivers account for 62, 16 and 22 % of the average fluxes, respectively (given the large uncertainty the percentages should be seen as approximate relative magnitudes only). Potentially, the emissions from reservoirs should be allocated to anthropogenic emissions (not done here). Regarding lakes and reservoirs, tropical (< 30 • latitude) and temperate (30-50 • latitude) emissions represent 49 and 33 % of the flux, respectively, with 18 % left for regions above 50 • latitude.
For comparison, approximately 40 % of the inland water surface area is found above 50 • latitude in the Northern Hemisphere and 34 % of the area is situated between 20 • S and 20 • N  (Verpoorter et al. , 2014). Ebullition typically accounted for 50 to more than 90 % of the flux from the water bodies, while contributions from ebullition appear lower from rivers, although this is currently debated (e. g. Crawford et al. , 2014). Several aspects will need consideration to reduce the remaining uncertainty in the freshwater fluxes, including the generation of flux measurement that is more representative in time and space and an update of global lake area databases (e. g. GLOWAB,  Verpoorter et al. , 2014).
In Kirschke et al. (2013)  (see their Supplement), a reanalysis of CH 4 emissions from termites at the global scale was proposed and CH 4 emissions per unit of surface were estimated as the product of termite biomass, termite CH 4 emissions per unit of termite mass and a scalar factor expressing the effect of land-use/land-cover change. The latter two terms were estimated from published literature reanalysis  (Kirschke et al. , 2013, Supplement). A climate zoning (following the Köppen-Geiger classification) was applied to updated climate datasets by Santini and Di Paola (2015) and was adopted to take into account different combinations of termite biomass per unit area and CH 4 emission factor per unit of termite biomass. In the case of tropical climate, first termites' biomass was estimated by a simple regression model representing its dependence on gross primary productivity (Kirschke et al.
However, indirect methane emissions from permafrost thawing are difficult to estimate at present, with no data yet to refer to, and in any case they largely overlap with wetland and freshwater emissions occurring above or around thawing areas. Here, we choose to report here only the direct emission range of 0-1 Tg CH 4 yr -1 , keeping in mind that current wetland, thermokarst lakes and other freshwater methane emissions already likely include a significant indirect contribution originating from thawing permafrost. For the next century, it has been recently estimated that 5-15 % of the terrestrial permafrost carbon pool is vulnerable to release in the form of greenhouse gases, corresponding to 130-160 Pg C. The likely progressive release in the atmosphere of such an amount of carbon as carbon dioxide and methane will have a significant impact on climate change trajectory  (Schuur et al.
eu/). The combined datasets provide the longest time series of globally averaged CH 4 abundance. Since the early 2000s, remotely sensed retrievals of CH 4 have provided CH 4 atmospheric columnaveraged mole fractions  (Buchwitz et al. , 2005a; Frankenberg et al. , 2005; Butz et al. , 2011; Crevoisier et al. , 2009; Wunch et al. , 2011). Fourier transform infrared (FTIR) measurements at fixed locations also provide methane column observations  (Wunch et al. , 2011). Prinn et al. , 2000; Cunnold et al. , 2002; Rigby et al. , 2008) , the Commonwealth Scientific and Industrial Research Organisation (CSIRO,  Francey et al. , 1999)  and the University of California Irvine (UCI,  Simpson et al. , 2012). 4. The data are archived at the World Data Centre for Greenhouse Gases (WDCGG) of the WMO Global Atmospheric Watch (WMO-GAW) programme, including measurements from other sites that are not operated as part of the four networks.
Global methane emissions per source category The global methane budget for five source categories (see Sect. 2. 3) for 2003-2012 is presented in Fig. 5  and Table 2. Top-down estimates attribute about 60 % of the total emissions to anthropogenic activities (range of 50-70 %) and 40 % to natural emissions. As natural emissions from bottom-up models are much larger, the anthropogenic vs. natural emission ratio is more balanced for bottom-up (∼ 50 % each). A predominant role of anthropogenic sources of methane emissions is strongly supported by the ice core and atmospheric methane records. The data indicate that atmospheric methane varied around 700 ppb during the last millennium before increasing by a factor of 2. 6 to ∼ 1800 ppb. Accounting for the decrease in mean lifetime over the industrial period,  Prather et al.
Regional methane emissions per source category The analysis of the regional methane budget per source category (Fig. 7 ) can be performed both for bottom-up and top-down approaches but with limitations. A complementary view of the methane budget is also available as an interactive graphic produced using data visualization techniques (http: //lsce-datavisgroup. github. io/MethaneBudget/). Moving the mouse over regions, processes or fluxes reveals their relative weights in the global methane budget and provides the mean values and the minimum-maximum ranges of their contributions (mean  [min, max] ). The total source estimates from the bottom-up approaches are further classed into finer subcategories. This graphic shows that there is good consistency between top-down and bottom-up approaches in the partition of anthropogenic emissions between agriculture and waste, fossil fuel extraction and use, and biomass and biofuel burning, and it also highlights the disequilibrium between top-down (left) and bottom-up (right) budgets, mainly due to natural sources.
Future developments, missing elements and remaining uncertainties Kirschke et al. (  2013 ) identified four main shortcomings in the assessment of regional to global CH 4 budgets, which we revisit now. Annual to decadal CH 4 emissions from natural sources (wetlands, fresh water, geological) are highly uncertain. The work by  Poulter et al. (2016) , following  Melton et al. (2013)  allows partitioning the uncertainty (expressed as the range in the estimates) of methane emissions from natural wetlands between wetland extent and other components, based on the use of a common and newly developed database for wetland extent. This approach confirms that wetland extent dominates the uncertainty of modelled methane emissions from wetlands (30-40 % of the uncertainty). The rest of the uncer-tainty lies in the model parameterizations of the flux density, which remains poorly constrained due to very few methane flux measurements available for different ecosystems over time.
, 2016) , which could be consistent when assuming a concomitant decrease in biomass burning emissions (heavy source for 13 C), as suggested by the GFED database  (Giglio et al. , 2013). Yet accounting for the uncertainties in the isotopic signatures of the sources and their trends may suggest different portionings of the global methane sources between fossil fuel and biogenic methane emissions  (Schwietzke et al. , 2016). A possible positive OH trend has occurred since the 1970s followed by stagnation to decreasing OH in the 2000s, possibly contributing significantly to recent observed atmospheric methane changes  (Dalsøren et al. , 2016; Rigby et al. , 2008; McNorton et al. , 2016). The challenging increase of atmospheric methane during the past decade needs more efforts to be fully understood.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights on the methane budget and its changes and help to reduce the uncertainties in the methane budget. The free availability of the data does not constitute permission for publication of the data. For research projects, if the data used are essential to the work, or if the conclusion or results depend on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data are given in the accompanying database. The accompanying database includes one Excel file organized in the following spreadsheets and two netcdf files defining the regions used to produce the regional budget. The file Global_Methane_Budget_2000-2012_v1.
This database is available from the Carbon Dioxide Information Analysis Center  (Saunois et al. , 2016)  and the Global Carbon Project (http://www. globalcarbonproject. org). The Supplement related to this article is available online at doi:10. 5194/essd-8-697-2016-supplement. Acknowledgements. This collaborative international effort is part of the Global Carbon Project activity to establish and track greenhouse gas budgets and their trends.
Fortunat Joos and Renato Spahni acknowledge support by the Swiss National Science Foundation. Heon-Sook Kim and Shamil Maksyutov acknowledge use of the GOSAT Research Computation Facility. Donald R. Blake and Isobel J. Simpson (UCI) acknowledge funding support from NASA. Josep G. Canadell thanks the support from the National Environmental Science Program -Earth Systems and Climate Change Hub. Marielle Saunois and Philippe Bousquet acknowledge the Global Carbon Project for the scientific advice and the computing power of LSCE for data analyses. Peter Bergamaschi and Mihai Alexe acknowledge the support by the European Commission Seventh Framework Programme (FP7/2007-2013) project MACC-II under grant agreement 283576, by the European Commission Horizon2020 Programme project MACC-III under grant agreement 633080, and by the ESA Climate Change Initiative Greenhouse Gases Phase 2 project.
William J. Riley and Xiyan Xu acknowledge support by the US Department of Energy, BER, under contract no. DE-AC02-05CH11231. The FAOSTAT database is supported by regular programme funding from all FAO member countries. Prabir K. Patra is supported by the Environment Research and Technology Development Fund (2-1502) of the Ministry of the Environment, Japan. David J. Beerling acknowledges support from an ERC Advanced grant (CDREG, 322998) and NERC (NE/J00748X/1). David Bastviken and Patrick Crill acknowledge support from the Swedish Research Council VR. Glen P. Peters acknowledges the support of the Research Council of Norway project 244074. Hanqin Tian and Bowen Zhang acknowledge funding support from NASA (NNX14AF93G; NNX14AO73G) and NSF (1243232; 1243220). Changhui Peng acknowledges the support by National Science and Engineering Research Council of Canada (NSERC) discovery grant and China's QianRen Program.
(2011) SDGVM Natural wetlands 2000-2012 X Woodward and Lomas (2004), Cao et al. (1996) TRIPLEX-GHG Natural wetlands 2000-2012 X Zhu et al. (2014, 2015) VISIT Natural wetlands 2000-2012 X Ito and Inatomi (2012) nally the FAO emission database accessed in April 2016. Further details of the inventories used in this study are provided in Table.
(2013)  presented CIRS observations obtained in limbviewing geometry, hence sounding up to the 0. 01-hPa pressure level. Here, we present high spectral resolution observations of Saturn obtained with the Texas Echelon Cross Echelle Spectrograph (TEXES) mounted on the NASA InfraRed Telescope Facility (IRTF). Greathouse et al. (2005)  demonstrated that TEXES observations of hydrocarbon thermal emissions could be used to probe the temperature and the chemical composition up to the 0. 01-hPa pressure level. Section 2 presents the instrument and the observations. Sec-tion 3 presents the radiative transfer code and the retrieval methods leading to the results presented in Sec. 4 and discussed in Sec. 5.
, 2002). TEXES absolute calibration is estimated to lie within ±20% from comparisons of observed fluxes of standard stars measured over many observing runs. The scan maps were processed with the TEXES pipeline reduction software and then post-processed through special mapping software designed to solve for the latitude and longitude on Saturn of each pixel of the scan. Two examples of these scan maps are displayed in Fig. 1  for the 1245-cm -1 and 587-cm -1 settings on July 15th. The scan maps could then be co-added into cylindrical-map projections allowing us to average much data from scan to scan to improve the signal-to-noise ratio for a given latitude and longitude on Saturn. We chose to average the data in bins of 5°×5°in latitude×longitude of the System III planetocentric reference system (all latitudes hereafter are given as planetocentric).
84, 1277. 47, 1280. 09, 1281. 61, 1282. 62, and 1282. 98 cm -1. These lines exhibit the same spectral shape as the strong lines in the 1245-1250 cm -1 wavenumber range: within the beacon, broad emission wings and a narrow absorption core, and outside of the beacon, a relatively narrow emission. This confirms the qualitative thermal vertical structure inferred from the 1245-cm -1 setting. The beacon maximum radiance displayed in Fig. 4  also documents the lowest quality of the dataset taken on July 19th. If the spatial blurring induced by the poor observing conditions and seeing did not affect the line shape, it did affect the absolute intensities of the observed emissions. However, as for the 1245cm -1 setting, several weak methane lines with lower energy levels higher than 1000 cm -1 at 1278.
In the longitude-pressure cross sections presented by  Fletcher et al. (2012)  for July 8th and July 26th, the tropopause temperature oscillated between 80K and 85K away from the beacon, a value very similar to our retrieved temperature, but presented a warm anomaly of about 10K just underneath the beacon at 100 hPa, with a longitudinal FWHM of 40°. We do not detect this warm 10-K anomaly below the stratospheric beacon using the TEXES dataset. In fact, we rather measure a temperature about 3K colder underneath the beacon than elsewhere in the same latitudinal band.
TEXES spatial resolution: comparing the CIRS and TEXES datasets To further investigate the differences in the temperatures inverted from the TEXES and CIRS datasets, we directly compared the two datasets. The TEXES dataset provides simultaneously a high spectral resolution and a broad spatial coverage, two assets that the CIRS dataset cannot match simultaneously. On its side, the CIRS dataset provides a much finer spatial resolution than TEXES, and is not affected by the terrestrial transmission. To address the differences between the two datasets in the spectral and spatial domains, we chose to compare the TEXES spectra obtained on July 15th using the 1245-cm -1 setting with two different CIRS observations. The first observation, identified by 150SA_COMPSIT001, was obtained on July 7th & 8th at the highest CIRS spectral resolution, 0.
It is however difficult to attribute such a large difference to the absolute calibration of both instruments, reliable within ±20%. To investigate whether such a large factor could be explained by a difference in spatial resolution between the CIRS and TEXES datasets, we spatially convolved the CIRS sequence 152SA_FIRMAP001 to mimic the spatial blurring affecting the TEXES dataset due to atmospheric transmission, and telescope and instrument diffraction. Since this CIRS sequence was obtained at a spectral resolution of 15. 5 cm -1 and a sampling step of 5 cm -1 , it is irrelevant to directly compare the CIRS spectra and TEXES spectra. To perform the comparison, we mapped the mean CIRS radiance at 1245-1250 cm -1 on Saturn's sphere. Then, we projected the Saturn's sphere onto the sky. In this process, we accounted for the westward drift of the beacon, 100°of longitude between July 15th and August 22nd, by projecting the CIRS data onto the sky with the same longitude offset between the beacon center and the central meridian as that of our TEXES observations.
In contrast, TEXES observed a smaller contrast, a factor of about 1. 5, between the measured radiances at beacon's position and in the surroundings. The CIRS and TEXES radiance contrasts can be reconciled by convolving the CIRS dataset with a seeing of ∼ 2 arcseconds. This larger seeing for the H 2 setting than for the CH 4 setting is expected as we used a wider slit for the H 2 setting than for the CH 4 setting. Since the thermal anomaly at the tropopause had a larger FWHM than the thermal anomaly in the stratosphere, the effective contrast in the TEXES dataset of the former was more reduced than that of the latter. In the inversion process, the warm stratosphere needed to fit the strong H 2 quadrupolar line within the beacon raised the continuum level more than was observed by TEXES, a defect that the algorithm compensated by slightly decreasing the tropopause temperature.
We thus conclude that the CIRS and TEXES datasets are in agreement with each other assuming seeings of 1. 5 arcseconds for the CH4 setting and 2 arcseconds for the H 2 setting at the time of our TEXES observations on July 15th. Such values seem sensible for late afternoon observations at the Mauna Kea. Nevertheless, as the exact seeing was not monitored independently at the IRTF, we cannot exclude that offsets in radiometric calibration could also be contributing.