dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1998 (Fujiwara et al. , , 2001;; Plumb, 2002; Fueglistaler et al. , 2009). It has been observed that mixed Rossby-gravity (MRG) wave circulations in the lower troposphere are related to tropical cyclogenesis  (Dickinson and Molinari, 2002; Zhou and Wang, 2007). In the stratosphere, the Kelvin and MRG waves, along with smaller-scale waves, are known as sources of momentum needed to drive the easterly-towesterly and westerly-to-easterly phase transitions of the quasi-biennial oscillation (QBO), respectively  (Holton and Lindzen, 1972; Dunkerton, 1997; Baldwin et al. , 2001) , although the momentum transported by the MRG waves needs to be further quantified  (Randel et al. , 1990; Kim and Chun, 2015a). Investigation of the distribution and variability of largescale equatorial waves requires datasets with global coverage, and global (re)analyses are extremely useful datasets for this purpose.
To the best of our knowledge, impacts of such satellite transitions in reanalyses have so far not been studied in the context of equatorial waves. It is likely more difficult to identify discontinuities or inhomogeneities in wave fields which, by definition, contain smaller-scale variations in space and time than mean fields. Recently, a pair of reanalyses have been identically produced using a single assimilation system, with the exception that the satellite data are assimilated in one (JRA-55) but not in the other (JRA-55C; see Table  1  for the abbreviations and references). These datasets can allow us to identify the impact of satellite data in that assimilation system and, in particular, to help further distinguish the impact of the TOVS-ATOVS transition. In this study, we investigate the characteristics of equatorial Kelvin and MRG waves in the TTL and stratosphere, and how they differ between recent reanalyses for the period of  1981 -2010 (Sect.
3. 1) (Sect. 3. 1). Spatial distributions and patterns of the waves in the reanalyses are presented in Sect. 3. 2. In Sect. 3. 3, long-term changes in equatorial wave amplitudes are compared among the reanalyses and, based on the comparison between JRA-55 and JRA-55C, the effects of the satellite data on the assimilated waves are discussed. In addition, spectra of the Eliassen-Palm (EP) flux and its divergence, a measure of wave-mean flow interaction, are presented to compare the equatorial wave forcing of the QBO estimated from different reanalyses (Sect. 3. 4). Among the results presented in Sect. 3. 1, we identify a wave signal that has not been dealt with in the literature before, found at 20 hPa along with the well-documented equatorial waves in all the reanalyses studied. This wave spectrum is further discussed in Sect.
4. A summary of the results is included in Sect. 5. at 3 h intervals for MERRA and MERRA-2 and 6 h intervals for the others. The results do not significantly change if we use 6 h subsampled time series for MERRA and MERRA-2 (not shown) as we analyze equatorial waves with periods longer than 2 d. Both pressure-level datasets, which are interpolated to standard levels (SLs, e. g. , 100 hPa), and modellevel (ML) datasets are used for each reanalysis, except for MERRA of which ML data are not available. At 100 hPa and above in the tropics, pressure variations on a model level are negligibly small or absent in all of the reanalyses used in the present study (see the Supplement to  Fujiwara et al. , 2017). Therefore, the model levels can be regarded as being at nearly constant pressure levels, which allows us to simply perform spectral calculations on horizontal planes without the need to introduce vertical interpolation.
For spectral shapes and spatial distributions of Kelvin and MRG waves, the results were very similar between SL and ML datasets (not shown), whereas wave amplitudes differ significantly, as will be seen in Fig. 8. Therefore, only the SL results are presented in Sect. 3. 1 and 3. 2, and the ML results (or both) are presented for quantitative analysis of wave variances and EP flux profiles in Sect. 3. 3 and 3. 4. Zonal wavenumber-frequency (k-ω) spectra are calculated monthly at each latitude and height for each of the symmetric and antisymmetric components of variables with respect to the Equator. To obtain the monthly spectra, we use a 90 d time window centered on the target month. The window function is defined as C for the central 30 d and C sin(π t/60) and C cos(π t/60) for the first and last 30 d, respectively, where t is the time (in days) relative to the first day of each 30 d segment and the normalization constant C = √ 3/2.
Spectral characteristics Figure  1  shows k-ω power spectra for the eastwardpropagating symmetric component of temperature (T S ) at 100, 70, 50, and 20 hPa from the SL datasets of the six reanalyses, averaged over the 30-year period. The major portion of the spectral power appears along the Kelvin wave dispersion curves (black diagonal lines) at all altitudes. The spectral characteristics are broadly similar among the reanalyses with peaks at k = 2-3. The reanalyses commonly exhibit a gradual shift of the Kelvin wave spectrum to larger equivalent depths (h) with respect to the altitude: the majority of the spectral power appears at h < 60 m at 100 hPa, at h ∼ 60 m at 70 hPa, and at h = 60-240 m above 70 hPa. There exist variations in the spectral width in h: for example, compared to the other reanalyses, CFSR shows broader spectra at small equivalent depths (h < 60 m) consistently at 70-20 hPa, with peaks at slightly smaller equivalent depths (70-50 hPa).
In addition to the Kelvin wave signal, at 100 hPa, another peak exists at k = 2 in the low-frequency range (ω < 0. 04 cyc d -1 ). As mentioned in Sect. 2, this is likely related to the intraseasonal MJO  (Hendon and Wheeler, 2008). The relative magnitude of Kelvin wave spectral power between ERA-I, MERRA, MERRA-2, and CFSR varies with height (Fig. 1 ), while JRA-55 and JRA-55C show generally less power below 20 hPa compared to the other four datasets. Note that relatively small variances in JRA-55 and JRA-55C are found in the temperature field but not in the wind fields (see Fig. S1  in the Supplement). In Fig. 1 , the ratio of the spectral power to the background spectrum is also shown (thin purple contours), which indicates statistical significance of the spectral signals. A ratio of 1. 2 was deemed to be statistically significant at the 95 % level by  Wheeler and Kiladis (1999) , and for the majority portions of the Kelvin wave spectra in Fig.
4. As will be seen therein, the waves with this spectrum do not originate from below, and they appear with different timing and different characteristics from the lower-frequency upward-propagating MRG waves which dominate the v S spectra in the lower altitudes. In Sect. 3. 2-3. 4, we focus on the lower-frequency MRG waves filtered with a cut-off frequency of 0. 33 cyc d -1 (period of 3 d). The spectral shapes of Kelvin and MRG waves obtained from the ML datasets (not shown) are very similar to those from the SL datasets (Figs. 1  and 2 ) for each reanalysis, whereas their spectral power is larger by up to about 35 %, depending on the altitude and reanalysis. This will be further discussed in Sect. 3. 3. In the following sections, we define Kelvin waves as the symmetric mode with h = 8-240 m, k = 1-10, and periods of 2-20 d following  Fujiwara et al.
The filter band was based on the well-documented strong spectral peak in the equatorial meridional wind centered at around the 4. 5 d period (Fig. 2 ). Dynamical fields associated with each EOF were obtained by projecting unfiltered ERA-I data at each grid point onto the associated principal component (PC) time series. Here, we use data interpolated to 2. 5 • resolution for the six reanalyses considered. A similar technique as in  Kiladis et al. (2016)  is used to obtain the statistical structures of the equatorial Kelvin waves, except that a 2-25 d eastward-only filter band is applied to the equatorial zonal wind based on the spectral peaks in Fig. 1. The results are very robust to changes in the filtering, as long as the filter band contains the spectral peaks shown in Figs. 1  and 2 , and to changes in latitudinal extent of the EOF basis.
Long-term change and satellite effects In this section, we analyze long-term variations in the Kelvin and MRG wave activity and discuss the impact of satellite data upon the assimilated wave activity based on the comparison of JRA-55 and JRA-55C. Figure  6  shows annual-mean time series of 100 hPa T Kelvin and v MRG variances, as defined in Sect. 3. 1. The results from the ML datasets are presented (solid lines), except for MERRA for which the SL datasets are used (dashed lines) due to the lack of ML data. The variance of T Kelvin in ERA-I fluctuates between about 0. 5 and 0. 85 K 2 with an increasing trend, whereas that in JRA-55C fluctuates between ∼ 0. 35 and 0. 58 K 2 without such an obvious trend. Despite this difference, much of the interannual  variability in the Kelvin wave activity is reflected in both time series, with a Pearson linear correlation of 0.
It is evident that the difference (JRA-55 minus JRA-55C) in the T Kelvin variance is generally positive from 100 to 20 hPa during the 30-year period, indicating the enhancement of the Kelvin wave amplitude by assimilation of satellite data in JRA-55. In addition, it is noteworthy that at 100 hPa, the difference is systematically larger from around 2000, compared to that before late 1990s: it is roughly 6 % (up to ∼ 8 %) in the years before 1998, whereas it increases to ∼ 10 % in 1998-2000 and becomes ∼ 20 % afterward. A similar systematic change of the difference in the T Kelvin variance is even more evident in the upper stratosphere: at 10 and 5 hPa (Fig. 7 ) and above (not shown), where the difference is mostly negative until 1998 and becomes positive after 1999. Such changes are not found at 50 hPa.
In the ML results, the T Kelvin variance is maximized at approximately 80 hPa in ERA-I and at 70 hPa in the others for both periods. These peak altitudes are generally consistent with that obtained using GNSS-RO data in 2007-2014 by  Scherllin-Pirscher et al. (2017, Fig. 6 ): 18 km (75-80 hPa), considering vertical grid spacings of the reanalyses. The T Kelvin variance decreases with height from these levels to ∼ 30 hPa and gradually increases from ∼ 20 hPa in both periods in all the reanalyses, except in MERRA-2 during P1. Further examination indicates that sometimes the temperature seems not to be properly assimilated in the upper stratosphere during P1 in MERRA-2 (Fig. S3 ; note the lack of Kelvin wave peaks in  1984, 1989, and 1991) , which may result in the decrease above ∼ 20 hPa in P1 in MERRA-2.
The profiles of the difference between P1 and P2 may imply that above 10 hPa, the large increase in the v MRG variance in JRA-55 (10 %-25 %) and ERA-I (∼ 40 %), compared to the small increase in JRA-55C (∼ 5 %), is probably in large part due to the satellite transition. In each reanalysis, the variances obtained from the SL datasets are smaller than those from the ML datasets in both periods (Fig. 8 ) by 3 %-29 % (0 %-38 %) for Kelvin (MRG) waves depending on the altitudes and reanalyses. The differences in the variances between the ML and SL results (ML minus SL) normalized by the variances in the ML results at each altitude are reported in Table  3. The smaller amplitudes of the waves in the SL datasets compared to those in the ML datasets result from vertical interpolation of reanalysis output variables, which inevitably damps wave perturbations in the standard-level products of reanalyses.
The damping by the interpolation is more significant for waves with smaller vertical wavelengths (e. g. ,  Kim and Alexander, 2013; Kim and Chun, 2015a). In addition, for a given pressure level, the damping of waves in the SL datasets also depends on the distance between the given level and its adjacent model level. That is, the interpolation effect must be less if a model level is very close to the given pressure level. For example, the distance between 100 hPa and its adjacent model level in MERRA-2 is very small (< 50 m), and thus the difference between the SL and ML results of MERRA-2 is only 3 %-4 % at 100 hPa (Table  3 ). The same (opposite) is true for the 10 hPa (50 hPa) in ERA-I, with a small (large) difference at that level. In JRA-55 and JRA-55C, the difference in the v MRG variance between the ML and SL results is less than 5 % at all levels, implying that the ML fields of these two reanalyses contain less of the MRG wave perturbations with small vertical wavelengths (∼ 2 z-4 z, where z is the vertical grid spacings of their model), compared to the other reanalyses.
The composite is made by selecting 1 month for each QBO cycle when the zonal wind tendency is largest at a given altitude and then averaging over the 13 cycles available. The observed monthly mean near-Equator wind profiles compiled by the Freie Universität Berlin (FUB) are used to select the months. The four composites presented in Fig. 11  are for the • S for the Kelvin waves at c > 0 and MRG waves at c < 0, composited for the QBO phases of maximum westerly tendencies at 20 and 50 hPa (the first and second rows, respectively) and easterly tendencies at 20 and 50 hPa (the third and last rows, respectively) within the period of 1981-2010, from the model-level datasets of ERA-I, MERRA-2, JRA-55, and JRA-55C. The zonal-wind profiles for those composites are also indicated using each reanalysis (thin red lines) and radiosonde observations (thick green lines; see the text for the composite method).
The contour intervals of the EP flux are 0. 008 and 0. 0003 mPa / (m s -1 ) for the Kelvin and MRG waves, respectively, and every third contour is distinguished by thicker lines. Note that the contour interval of the EP flux divergence for the Kelvin waves is 4 times larger than that for the MRG waves. months of maximum westerly and easterly tendencies at 20 and 50 hPa. The composite averages of the FUB wind are also plotted in Fig. 11  (thick green lines) as well as those of each reanalysis wind (thin red lines). The results are generated using the ML datasets of the four reanalyses available for EP flux calculation (ERA-I, MERRA-2, JRA-55, and JRA-55C), since the EP flux could be largely underestimated when using the SL datasets. Furthermore, the height dependence of the amplitude damping, as reported in Table  3 , could also affect the estimation of the vertical divergence of EP flux (see Fig.
3  in  Kim and Chun, 2015a , for a large difference in the EP flux divergence between ML and SL datasets of ERA-I). The EP flux divergence of the Kelvin waves is found to be quite different between reanalyses not only in magnitude but also in its peak altitude. At the maximum westerly tendency phase at 20 hPa (Fig. 11 , the first row), the vertical EP flux is notably the largest in ERA-I at ∼ 20 hPa. However, the EP flux divergence at this level is the largest in MERRA-2, implying that the assimilated wave fields exhibit the most severe dissipation in MERRA-2 among the reanalyses around this level. ERA-I and JRA-55 show the peaks of the EP flux divergence at ∼15 and 12 hPa, respectively. It is important to note that the easterly-to-westerly transition (i. e. , zero wind) occurs at different altitudes between the reanalyses (Fig.
11. As mentioned in the introduction, the momentum budget of the QBO is currently not fully constrained by observations, and it is believed to be essential for modeling the QBO. The different altitudes of Kelvin wave forcing around 15 hPa among the reanalyses, as well as the different magnitudes, shown in Fig. 11  may imply a limitation in quantifying wave forcing of the QBO using a reanalysis dataset. Advances in observation and assimilation of stratospheric waves might be needed to reduce the spread of the assimilated waves and to  further improve global models. In addition, given that the representation of equatorial waves in reanalyses is dependent on the assimilated mean winds (Fig. 11 ), the reality of small-scale gravity-wave parameterization in forecast models, which can affect the details of the QBO winds, might also be important for the assessment of the large-scale wave forcing.
Below 10 hPa, the satellite impacts on the waves are identifiable in JRA-55 only by direct comparison with JRA-55C at 100 hPa where the SSU instruments in the TOVS suite do not cover. The time series of the Kelvin wave variances at 100 hPa (Fig. 6 ) exhibit increasing trends in all the reanalyses except JRA-55C. However, the veracity of the trends in Kelvin wave activity is uncertain due to potential satellite effects in the reanalyses. On the other hand, the MRG wave variances show a long-term increase at 100 hPa, common to all of the reanalyses including JRA-55C which is not affected by satellite transitions. It is also noted that variances of equatorial waves can be underestimated by up to around 30 % when standardpressure-level datasets are used. The wave perturbations in these datasets are damped out by vertical interpolation, and the damping effect is large for waves with small vertical wavelengths.
Data availability. The ERA-Interim data can be obtained from https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/ era-interim and the MERRA and MERRA-2 data from https://disc. gsfc. nasa. gov/datasets. The CFSR data can be downloaded from https://www. ncei. noaa. gov/thredds/catalog/ cfs_reanl_ts/catalog. html and the JRA-55 and JRA-55C data can be found in https://jra. kishou. go. jp/JRA-55/index_en. html. The near-Equator monthly zonal wind from the radiosonde observations compiled by FUB can be found in https: //www. geo. fu-berlin. de/en/met/ag/strat/produkte/qbo/index. html. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-19-10027-2019-supplement.
Emission of greenhouse gases (GHG) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, along with estimates of their inherent uncertainties, in order to support credible mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in Europe. The data integrates recent AFOLU emission inventories with ecosystem data and land carbon models, covering the European Union 30 (EU28) and summarizes GHG emissions and removals over the period 1990-2016, of relevance for UNFCCC. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGI) with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals.
Particular effort is devoted to the estimation of uncertainty, its propagation and role in the comparison of different 35 estimates. While NGHGI data for EU28 provides consistent quantification of uncertainty following the established IPCC guidelines, uncertainty in the estimates produced with other methods will need to account for both within model uncertainty and the spread from different model results. At EU28 level, the largest inconsistencies between estimates are mainly due to different sources of data related to human activity which result in emissions or removals taking place during a given period of time (IPCC 2006) referred here as activity data (AD) and methodologies (Tiers) used 40 for calculating emissions/removals from AFOLU sectors.
Compilation of AFOLU emission estimates We collected available data of AFOLU emissions and removals (Table  1 ) between 1990 and 2016 that have been documented in peer reviewed literature. NGHGI and other data sources for AFOLU emissions or component 150 fluxes as well as methodologies are described in Appendix B. For all three GHGs, total emissions from Agriculture and LULUCF for EU28 are presented in Appendix Table  A2. Whenever necessary we provide details on individual countries separating CO2, CH4 and N2O. The units are based on the metric tonne (t) [1kt = 10 9 g; 1Mt = 10 12 g] for individual gases and [Mt =10 12 g; 1Tg=10 12 g] for CO2 and carbon (C) from AFOLU sectors. We rely on observational data-streams to quantify GHG fluxes from bottom-up models together with country specific inventory from NGHGI 155 official statistics (UNFCCC), global inventory datasets (EDGAR), global statistics (FAOSTAT) and global land GHG biogeochemical models used for research assessments (e.
Agriculture CH4 and N2O emissions At EU28 level, GHG emission reporting is mandatory for all countries and is done under the consistent 185 framework of UNFCCC. Every year in May all EU parties report to the convention their National Inventory Report (NIR) and provide data using Common Reporting Format (CRF) tables. The NIRs contain detailed descriptive and numerical information on all emission sources and the CRF tables contain all GHG emissions and removals, implied EFs and AD for the whole time series 1990 to two years before the submission year (https://unfccc. int/process-andmeetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-190 annex-i-parties/national-inventory-submissions-2018). It is important to note that the 2006 IPCC Guidelines used for this process do not provide methodologies for the calculation of CH4 emissions and CH4 and N2O removals from agricultural soils and field burning of agricultural residues.
205 and values among sources. For the agriculture sector totals our results show a relatively good match between UNFCCC and the four other data sources, with the lowest estimate (CAPRI) within 15 % of the UNFCCC value. The differences pertain mostly to Tier use (e. g. CAPRI) and expert judgment on the choice of EFs (e. g. EDGARv4. 3. 2. ). Considering that the 2016 UNFCCC total agriculture reported uncertainty is 10 %, we acknowledge this relative difference of up to 15 % to be important in the emissions reconciliation process. In Table  2  we present the allocation of emissions by  As a consequence of the similar trends and distribution of emissions to sectors presented in Table  2 , we notice 220 a small but consistent variability of total emissions between the five data sources (Fig. 2 ).
N2O According to UNFCCC NGHGI data, in 2016 agricultural activities accounted for 78 % of the total N2O emissions in EU28. For the agriculture sector, key categories on the EU28 level are N2O emissions from manure management, direct N2O emissions from agricultural soils and indirect N2O emissions from agricultural soils. In Table  3  we present the allocation of emissions by subsector following the IPCC classification and we notice that each data 305 source has its own particular way of grouping emissions.
310 EU28 emissions (Fig. 6 ). We note as well that uncertainties of UNFCCC and EDGAR are large but have similar magnitudes. Similar to CH4, CAPRI has the lowest estimate but well within the uncertainty interval. consequent impacts on the agriculture sector. The poorest consistency between data sources in EU28 is seen for Belgium, Estonia, Lithuania, Latvia and Luxembourg (http://doi. org/doi:10. 5281/zenodo. 3460311) and count for as 345 much as 4. 5 % of total EU28 N2O emissions. In general, the uncertainties reported by UNFCCC for total N2O emissions from the agriculture sector are very high and have a range between 22 % (Malta) and 207 % (Romania). For sub-activities extreme uncertainties are reported by Denmark and Bulgaria as 300 % for N2O emissions from manure management, while Greece reports a very small uncertainty of less than 2 % for N2O emissions from agricultural soils.