dataesr/scientific-paragraphs-categorization huggingface dataset:
(2000)  were later able to identify a potential role of halocarbon species in the mid latitude marine boundary layer using multi-year Cape Grim surface ozone data. Boylan et al. (2015)  compared shipborne marine boundary layer ozone data with surface ozone measurements from four coastal and island GAW stations. Their analyses of diurnal ozone cycles suggest an even more enhanced influence of halogen chemistry over the ocean in comparison to the island/coastal-based observations. There are fewer studies on trends in other reactive gases. Bottenheim et al. (1994)  analyzed surface ozone and peroxyacetyl nitrate trends at Egbert, Canada, and found significant differences between the two which they explained with possible changes in the VOC emissions in North America. Numerical model evaluation with GAW VOC data was conducted by  Pozzer et al.
(2010). A review of VOC trend analyses was reported by Helmig et al. (manuscript in preparation for this special issue). The distribution and trends of near-surface carbon monoxide mole fractions were analyzed by  Novelli et al. (1994 Novelli et al. ( , 1998 Novelli et al. ( , 2003)). They identified the impact of anthropogenic activities in the northern mid-latitudes on the atmospheric composition in the Arctic region. The strong inter-hemispheric gradient and its seasonal cycle were also documented. The observed downward trends in the Northern Hemisphere were attributed to emission controls in North America and Europe. No significant trend was found in the Southern Hemisphere. Examples for the use of nitrogen oxide observations are largely limited to Europe and the US. Kaiser et al. (2007)  used the NO x , CO, and ozone data of the four GAW "DACH" sites (Sonnblick, Zugspitze, Hohenpeissenberg, and Jungfraujoch) and from Mt.
The latter were smaller than the expectation based on European emission reductions. Generally, in Europe and North America measurements from air quality monitoring networks show significant reductions of NO x mole fractions in urban and suburban environments in response to emission reductions  (Pandey-Deloal et al. , 2012; Tong et al. , 2015). In contrast, satellite observations indicate that the growing economies in East Asia and the Middle East have led to increasing NO x burdens in those parts of the world (e. g. Hilboll et al. , 2013; Mijling et al. , 2013). The few GAW stations measuring NO x outside Europe don't exhibit any clear trend signal  (WMO, 2015). A combined dataset covering several ground-based networks and other platforms could be very valuable in order to achieve a better understanding of the distribution and trends of this important ozone precursor.
Concluding remarks Since its inauguration in 1989, GAW has coordinated an extensive global network of high-quality reactive gases measurements. The emphasis on network-wide standards for data quality has led to better comparability of measurements among GAW partners. GAW has provided opportunities for the research community to develop a global perspective on the distribution and trends of reactive gases in the lower atmosphere through its growing archive of measurements. In spite of the great success of GAW, large gaps remain in the global coverage of surface observations. It remains a continuing challenge to maintain the stations and the network-wide quality control. The current reactive gases program within GAW focuses on ozone, CO, NO x , SO 2 , and selected VOCs. In its next phase, the GAW reactive gases program is strengthening its efforts to connect to other existing in-situ measurement networks, both ground-based and airborne, and in the future the link with remote sensing data Elementa: Science of the Anthropocene • 3: 000067 • doi: 10.
12952/journal. elementa. 000067 shall also be strengthened. Additional substances have been identified for which monitoring could provide new insights into the chemistry of the atmosphere and the reasons for observed changes. Numerical modeling will become an integral part of GAW activities as the new GAW implementation plan targets applications instead of focusing on the observation of individual species. GAW reactive gases data have been used extensively in previous analyses of global and regional atmospheric composition. The surface ozone data set from the WDCGG will form an important component of the Tropospheric Ozone Assessment Report (TOAR; http://www. igacproject. org/TOAR) which has recently been endorsed as an activity under the International Global Atmospheric Chemistry (IGAC; http://www.
igacproject. org/) initiative. The reactive gas measurement network provides unique and essential data for model evaluation. There are clear opportunities for GAW to further contribute to the ongoing Stratospheric Processes and their Role in Climate (SPARC; http://www. sparc-climate. org/)/IGAC Chemistry-Climate Model Initiative (CCMI; http:// www. met. reading. ac. uk/ccmi/), especially for evaluating the 1960-2010 hindcast runs that are the focus of this year's CCMI activities. Active engagement by GAW representatives in the ongoing CCMI exercise will benefit the GAW and CCMI communities by strengthening the use of observations for model evaluation. Recently, there has been increasing demand for reactive gases data that are delivered with fast turnaround times to be used for the validation of global atmospheric composition forecasting systems.
Acknowledgments The authors, who are largely identical to the present members of the  Science Advisory Group reactive gases (SAG RG ), are very grateful to the many hundreds of scientists and technical staff who have acquired the valuable measurements at GAW stations or in contributing networks. We also want to thank the staff at the GAW central facilities, who prepare and distribute measurement standards, perform inter-calibrations, inter-comparisons, and station audits, and the staff at the  WDCGG  who support the submission and archiving of GAW reactive gases data and disseminate them to the scientific community. Without the continuous support of the GAW secretariat, coordination and planning of the network would be impossible. Last not least we want to express our gratitude to all former members of the SAG RG, and in particular its former chair  S.
Spectral signatures of ethylene have been observed in the outer planets Jupiter, Saturn, Neptune, and satellites  [6]. Accurate knowledge of line and band intensities as well as of their temperature dependence is essential for reliable remote sensing diagnostics. Rotationally resolved ethylene spectra are known to be quite complex due to irregular couplings of twelve vibrational modes. Their analyzes represent difficult tasks because of accidental resonance perturbations in congested patterns of overlapping bands including hot ones. For this reason the ethylene line-by-line information in available spectroscopic databases  [8, 9]  is far from being complete. Ab initio theoretical studies of triatomics and small polyatomic molecules of planetological and astrophysical interest such as ammonia NH 3 , phosphine PH 3 , methane CH 4 grew up during last decades along with the improvement of ab initio surfaces and computational codes solving the rovibrational Schrödinger equation (see for example Refs.
10, 11, 12, 13, 14, 15, 16, 17, 18, 19 and references therein). This has helped resolving many issues related to spectra analyzes at high energy ranges  [20, 21, 14, 22, 23]  (the list being not exhaustive). Quantitatively accurate ab initio rovibrational spectra of five-atomic hydrocarbon isotopologues have been recently reported  [16, 24, 25]  together with detailed comparison against experimental data. Examples of global hightemperature methane spectra predictions for astrophysical applications can be found in Refs. 26, 27. Concerning the ethylene molecule, data assignments and analyzes present in databases remain much more sparse, although many recent studies  [28, 29, 30, 31, 32, 33]  tend to extend the informations. For these reasons it is a perfect candidate to take the next step in accurate ab initio spectra calculations.
This works aims at filling this gap. We provide analytical symmetry-adapted representation of full 12-dimensional (12D) ethylene DMSs computed at large extent of nuclear configurations (82 542 points) using the coupled-cluster approach CCSD(T) and its explicitly correlated counterpart CCSD(T)-F12 combined with cc-pVQZ and cc-pVTZ-F12 basis sets. The comparison with experimental spectra suggests that the corresponding first principle intensity calculations are currently the most accurate ones. This Letter is structured as follows. Section 2 describes computational approach to the construction of ab initio DMSs together with a brief review of the previous works. Sections 3 and 4 are devoted to the variational method of intensity calculations in the normal mode representation and to the comparison of intensities in the 0-3200 cm -1 range with recent calculations, experimental spectra and databases.
A second set of DMS surfaces was calculated at the CCSD(T)-F12 level of the theory combined with specially optimized correlation consistent F12 basis set cc-pVTZ-F12  [42]. As generally advised, CCSD(T)-F12a method was employed with this VTZ-F12 basis set. The Molpro program package version 2010. 1  [43]  was used to carry out all the ab initio calculations of electronic ground state energies. Dipole moments were computed as the derivative of the energy with respect to the weak external uniform electric field using the finite difference scheme with the field variation of ±0. 001 a. u. around at the zero field strength. The dependence of the dipole moments on the size of finite steps external field derivatives was not significant in the range of 0. 002 -0. 0001 a. u. We have checked this for various nuclear configurations with three field variation steps: 0.
Comparisons of ab initio intensities with empirical values and with spectroscopic databases An example of the comparison of predicted spectra using DNRST PES  [38]  and our ab initio DMSs with empirically based HITRAN line list of 12  Figure  2 : Comparison of spectra calculated from ab initio DMS using VQZ and VTZ-F12 basis sets with HITRAN 2012 and with latest experimental spectra analyzes  [33]  in 650 -800 cm -1 region. In all calculations the DNRST PES  [38]  was used for line positions and vibration-rotation basis functions. Figure  3 : Comparison of spectra calculated from ab initio DMS using VQZ and VTZ-F12 basis sets with HITRAN 2012 and with latest experimental spectra analyzes  [33]  in 850 -1100 cm -1 region. In all calculations the DNRST PES  [38]  was used for line positions and vibration-rotation basis functions.
Indeed the HITRAN intensities were too low and many of high-J transitions were missing in the center of the band and in the band edges that is clearly seen in Figures  2  and 5. However irregular strong lines (example at 951 cm -1 ) appear here not because of misassignments but due to resonance intensity borrowing as further discussed in Section 5. This conclusion is supported by recent results of new experimental spectra analyzes  [33]  which are in excellent agreement with our calculations (Figures  2  and 5 ). Table  2  summarizes integrated intensities for six fundamental C 2 H 4 bands for which the results of line-by-line high-resolutions spectra analyzes have been included in HITRAN database  [8]. Integrated intensity in a range R is the sum S = Si(min) i in R S i of line intensities S for all calculated or observed transitions in this range.
Second type of tests concerns interval-by-interval comparison of integrated intensities with low-resolution measurements (Table  3 ). Lebron and Tan  [46]  have reported Fourier transform experimental determination of the absorbance area in ethylene infrared spectra at a resolution of 0. 5 cm -1 with various gas pressures. They have distinguished five spectral intervals given in Table  3  where the ethylene infrared opacity was the most significant. The advantage of such comparison is that this implicitly involves contributions from line of all bands in the considered range including weak overtone, combination and hot bands which are not yet analyzed in high-resolution ex- , line positions RMS errors (last column) are in cm -1. All "empirical" rovibrational lines assigned to known fundamental bands are taken from HITRAN-2012 database  [8]  except for ν 10.
a For ν 10 empirical data are replaced by recent more accurate analysis  [33]. b Number of lines used for the calculation of integrated band intensities. c Relative deviations [(S emp -S calc )/S emp ] in % between empirical line lists and variational calculations using ab initio PES and DMSs. c Deviations between empirical and calculated line positions up to J = 30. a In wavenumbers cm -1. b Bands providing main contributions to considered interval. c Measured by the total absorbance area in each interval  [46]. d Obtained as a sum of VQZ ab initio line intensities for all bands contributing to each interval up to J max = 30. e Relative deviations [(S obs -S calc )/S obs ] in % between Ref. [46]  and calculations for integrated line intensities. periments. Also, this permits getting round very tedious line-by-line analyzes of rotationally resolved spectra and in a sense compensate incompleteness of HITRAN-like databases.
For the comparison of Table  3  we have included between 2 and 10 times more theoretical lines than those presently available in HITRAN depending on the range. This gives an idea of a lack of information for ethylene data in existing databases. A shortcoming of the low resolution method is that it is locally less accurate and that contributions of impurities could hardly be totally excluded. Bearing in mind above considerations, the agreement of our ab initio results with low-resolution Lebron and Tan measurements is also very good, particularly for the most absorbing regions 640 -1200 cm -1 and 2920 -3260 cm -1 were the deviations are of 0. 75% and of 4%, respectively. In the middle ranges 1340 -1540 cm -1 and 1820 -1950 cm -1 the low resolution measurements show slightly larger absorption but could possibly be biased by a contribution of water vapor due to very large and strong ν 2 band centered at about 1600 cm -1.
for fundamental bands in the 600 -3200 cm -1 range is given in Figures2, 3 and 4. Globally, all calculations with VQZ and VTZ-F12 basis sets are in a very good agreement with experimental intensities. In order to compare integrated intensities we extended line calculations up to rotational quantum number J = 30. In Tables 2, 3 and 4 integrated intensities in the ranges of fundamental bands are collected at 296 K and compared with various empirical data including HITRAN-2012 database, Lebron et al. [46] , Bourgeois et al. [33]  and with ab initio results of Carter et al. [34]. In all cases we use the HITRAN intensity units cm/molecule. Natural abundance factor 0. 97729 of 12 C 2 H 4 was taken into account in all our calculated intensities. First type of comparisons concerns rotationally resolved spectra.
Summed up intensities of J = 0, 1, 2 ethylene transitions: comparison between ab initio predictions and HITRAN empirical data in the range of fundamental bands. is per band or per interval of all line intensities from TableVof Ref. 34. b The same sample computed with our VQZ DMS. c The same sample with empirical data from HITRAN-2012 database. d Relative deviations [(S emp -S calc )/S emp ] in % between HITRAN and calculations. e Wavenumber range in cm -1 including ν 2 + ν 12 and 2ν 10 + ν 12 combination bands.
C 2 H 4 4 for fundamental bands in the 600 -3200 cm -1 range is given in Figures2, 3 and 4. Globally, all calculations with VQZ and VTZ-F12 basis sets are in a very good agreement with experimental intensities. In order to compare integrated intensities we extended line calculations up to rotational quantum number J = 30. In Tables 2, 3 and 4 integrated intensities in the ranges of fundamental bands are collected at 296 K and compared with various empirical data including HITRAN-2012 database, Lebron et al. [46] , Bourgeois et al. [33]  and with ab initio results of Carter et al. [34]. In all cases we use the HITRAN intensity units cm/molecule. Natural abundance factor 0. 97729 of 12 C 2 H 4 was taken into account in all our calculated intensities. First type of comparisons concerns rotationally resolved spectra.
A reduction of the global annual biogenic emissions of isoprene is derived, of 9 and 13 % according to GOME-2 and OMI, respectively, compared to the a priori estimate of 363 Tg in 2010. The reduction is largest (up to 25-40 %) in the Southeastern US, in accordance with earlier studies. The GOME-2 and OMI satellite columns suggest a global pyrogenic flux decrease by 36 and 33 %, respectively, compared to the GFEDv3 (Global Fire Emissions Database) inventory. This decrease is especially pronounced over tropical forests, such as in Amazonia, Thailand and Myanmar, and is supported by comparisons with CO observations from IASI (Infrared Atmospheric Sounding Interferometer). In contrast Published by Copernicus Publications on behalf of the European Geosciences Union. to these flux reductions, the emissions due to harvest waste burning are strongly enhanced over the northeastern China plain in June (by ca.
Rain and cloud fields (and therefore also the photolysis and wet scavenging rates) are also based on daily ERA-Interim fields. The effect of diurnal variations are considered through correction factors on the photolysis and kinetic rates obtained from model simulations accounting for the diurnal cycle of photo rates, emissions, convection and boundary layer mixing  (Stavrakou et al. , 2009a). A thorough model description is given in  Stavrakou et al. (2013)  and references therein. The target year of this study is 2010. Anthropogenic emissions are obtained from the RETRO 2000 database (http://eccad. sedoo. fr; Schultz et al. , 2008) , except over Asia where the REASv2 (Regional Emission inventory in ASia) inventory for year 2008 is used  (Kurokawa et al. , 2013). The diurnal profile of anthropogenic emissions follows  Jenkin et al.
Note that the non-isoprene biogenic VOC emissions are not varied in the source inversions. Open vegetation fire emissions are taken from GFEDv3 (Global Fire Emissions Database; van der  Werf et al. , 2010) , with emission factors for tropical, extratropical, savanna and peat fire burning provided from the 2011 update of the recommendations by  Andreae and Merlet (2001). The GFEDv3 emission is estimated at 105. 4 TgVOC in 2010, equivalent to 2. 26 Tmol (average molecular weight of 46. 5 kg kmol -1 ) (Fig. 1 ). The diurnal profile of biomass burning emissions was derived based on a complete year of geostationary active fires and fire radiative power observations from the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) imager over Africa  (Roberts et al. , 2009). The analysis of the fire A priori VOC emissions Biogenic isoprene Anthropogenic Biomass burning Figure  1.
Satellite observations The current version (v14) of the HCHO retrievals applied to GOME-2/Metop-A and OMI/Aura measurements is based on the algorithm developed for  GOME-2 (version 12, De Smedt et al. , 2012) , with significant adaptations, as detailed below. A classical DOAS algorithm is used, including three main steps: (1) the fit of absorption cross-section databases to the measured Earth reflectance in order to retrieve HCHO slant columns, (2) a background normalization procedure to eliminate remaining unphysical dependencies, and (3) the calculation of tropospheric air mass factors using radiative transfer calculations and modeled a priori profiles. In GOME-2 v12, two fitting intervals were introduced to improve the treatment of BrO absorption features, and to reduce the noise on the HCHO columns (328.
, 2011). In addition, this version of the algorithm makes use of radiance spectra, daily averaged in the equatorial Pacific, which serve as reference spectra. The background normalization now depends on the day, the latitude, and on the viewing zenith angle of the observation. This also serves as a destriping procedure, needed for an imager instrument such as OMI  (Boersma et al. , 2011). The air mass factor calculation is based on  Palmer et al. (2001). Scattering weighting functions are calculated with the LIDORT v3. 3(linearized discrete ordinate radiative transfer) radiative transfer model  (Spurr, 2008). The a priori profile shapes are provided by the IMAGES model, at 09:30 LT for GOME-2 and 13:30 LT for OMI (cf. Sect. 2). The OMI-based surface reflection database from  Kleipool et al.
The mean absolute deviation between the a priori (left) and optimized (right) modeled columns and the observed columns is given inset in each panel (in blue for GOME-2, in red for OMI). Error bars (blue for GOME-2, red for OMI) represent the retrieval error provided for each data set. constraints over highly polluted regions, notably in eastern China where the discrepancy between the two sensors is most evident (see Sect. 7. 3). Annual emission updates for the different source categories and the monthly variation of the a priori and optimized flux estimates are illustrated in Figs. 9-13. Modifying the errors on the flux parameters infers global isoprene emission decreases of 8. 5 % (OMI-HE) and 16 % (OMI-DE) with regard to the initial isoprene inventory and within 7 % of the standard OMI inversion; cf.
Accordingly, the isoprene fluxes inferred from the OMI inversion in August are also larger, about 40 % higher than the a priori estimate in the Moscow area, whereas the increase derived by GOME-2 does not exceed 25 %. Overall, the OMI data suggest annual isoprene fluxes in Europe are 11 % higher than the a priori inventory (Table  3 ). Note that, although the isoprene enhancement over Russia peaks earlier (July) and at slightly higher latitudes (ca. 61 • N) than the biomass burning emission enhancement (55-57 • N in August), the significant overlap of the two distributions makes it impossible to rule out that pyrogenic emissions are the only cause for the observed strong formaldehyde columns. The very widespread extent of the observed formaldehyde plume cannot be easily explained by the comparatively much more localized emissions of the GFEDv3 inventory, and an additional, more widespread formaldehyde source (such as isoprene) could help to explain the observations.
e. , about 7-10 times higher than in the bottom-up inventory (GFEDv3). These values are comparable with the ranges of 19-33 and 34-40 Tg CO suggested by R'honi et al. (  2013 ) and  Yurganov et al. (2011) , respectively, but are much higher than reported values of ca. 10 Tg CO derived using surface CO measurements in the Moscow area  (Konovalov et al. , 2011). The latter study identifies the contribution of peat burning to the total CO fire emission in this region to be as high as 30 %. The IMAGESv2 a priori CO simulation (using GFEDv3 inventory) underestimates substantially the IASI CO observations. Scaling the CO emissions in IMAGESv2 to the fire VOC increase suggested by the OMI HCHO optimization, i. e. , ca. 60 % in July and August 2010, barely improves the model agreement with the satellite, indicating that, in accordance with earlier studies, more drastic fire flux enhancements (factor of 5-10) are required to reconcile CO modeldata mismatches.
The total GOME-2 emission, estimated at 20. 6 TgVOC, is lower than the OMI result but in good agreement with the estimate (20. 2 Tg in 2008) of the Multi-resolution Emission Inventory for China (MEIC, http://www. meicmodel. org). The flux distributions from both inversions have common features, e. g. , decreased fluxes in Shanghai and Guangzhou regions, but contradicting esti-mates in the northeast where GOME-2 observations do not support the emission enhancements suggested by OMI. This discrepancy is primarily due to the lower modeled ratios of 13:30 to 09:30 LT columns (average ratio of 1. 0 in the model in northern China between March and November) compared to the satellite data sets (average ratio of 1. 16). Note that, however, the model was found to overestimate this ratio against MAX-DOAS data at Beijing and Xianghe (Fig.