dataesr/scientific-paragraphs-categorization huggingface dataset:
Input data Both horizons and faults are interpreted on five fictive seismic lines orthogonal to the regional horst direction. These five lines and the associated 2D structural interpretations are generated from a 3D seismic dataset and the corresponding reference interpretation (Fig. 13 ). For each fault trace S, the maximum vertical offset of horizons on both sides of the fault is measured and associated to S in the data base. Interpreted data are composed of twentyfive fault traces and data points corresponding to stratigraphic horizons. Uncertainty about fault location is set to 10m for each fault trace to reflect picking and migration uncertainty, with a triangular radial probability function centred on the reference location. In this scenario, uncertainty in time-to-depth conversion is assumed negligible as compared to the uncertainty due to the lack of data between the sections, so horizon picks are considered deterministic.
Gross Rock Volumes (GRV) For each stochastic fault network, five stratigraphic fields have been computed by adding the stratigraphic field obtained by interpolating horizon data through the fault blocks with a random noise composed of the first and second order perturbation R1 and R2. Consequently, 950 structural models have been generated using the method presented above. These models, referred to as case A, were then used to study the uncertainty on the gross rock volumes above a deterministic planar water-oil contact with no transition zone located at 2800m depth in the three distinct stratigraphic units. To compare the GRV uncertainty accounting for topological fault uncertainty method with classical uncertainty models which consider fixed fault networks, we also generated a case B comprised of 950 models obtained by perturbing only the stratigraphy of the reference fault model.
This set of models can then be used for making predictions such as gross rock volumes as presented on a Middle East field case study, which enables to better evaluate risks and be more confident for decision-making in exploration and development settings. Such a stochastic approach is most relevant in sparse data situations, because of the large uncertainties and broad range of possible models that cannot be sampled by geometrical perturbations of a single deterministic model. In the presented workflow, the algorithm first generates fault surfaces, using fault displacement information at the data clustering step. Then, the fault displacement is computed using horizon data. Whereas faults can be seeded so as to reproduce interactions, some of the generated fault networks are not fully consistent.
Acknowledgements The authors would like to acknowledge the  RING-GOCAD Consortium  managed by ASGA (http://ring. georessources. univ-lorraine. fr)for funding this research, as well as  Paradigm  for providing the SKUA-Gocad software and API. We also acknowledge the petroleum company which provided us with the Middle East field dataset. We also appreciate the constructive comments made by two anonymous reviewers which helped us improve this paper.
5-2 mm/yr global sea-level rise; a substantial increase in the estimates of 20th century ocean heat storage; and a substantial change in the interpretation of the astronomic record" (p. 6550). As we demonstrate here, the first and third of these possibilities play a role in resolving the enigma, and an additional contribution comes from an improvement in the GIA model that Munk (1) applied. A recent probabilistic analysis of a global database of tide gauge records  (22)  has estimated a GMSL rise of 1. 2 ± 0. 2 mm/year in 1900-1990. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change estimated three contributions to GMSL rise over this period: melting of glaciers (0. 7 ± 0. 1 mm/year, as noted previously)  (18) , thermal expansion (0. 4 ± 0. 1 mm/year)  (10) , and net anthropogenic storage of water on land (-0.
The nesting scheme is presented in Fig. 1. CHIMERE is a cartesian mesh-grid model including gas-phase, solidphase and aqueous chemistry, biogenic emissions modeling with the MEGAN model  (Guenther et al. , 2006) , dust emissions  (Menut et al. , 2005)  and resuspension  (Vautard et al. , 2005). Gas-phase chemistry is based on the MELCHIOR mechanism  (Lattuati, 1997)  which includes more than 300 reactions of 80 gaseous species. The aerosols model species are sulfates, nitrates, ammonium, organic and black carbon and sea-salt  (Bessagnet et al. , 2010) , and the gas-particle partitioning of the ensemble Sulfate/Nitrate/Ammonium is treated by the ISORROPIA code  (Nenes et al. , 1998)  implemented on-line in CHIMERE. CHIMERE has been benchmarked in the past in a number of model inter-comparison experiments (see  Menut et al.
Climate and emissions The RCP-4. 5 long-term scenario of greenhouse gases, used as global-scale predictor of present-time climate, displays a 20 % GHG emission reduction for Europe, constant population at about 575 million inhabitants and mid-21st century change in global radiative forcing by 4 W m -2 , increasing to 4. 5 W m -2 by 2065 and stabilizing thereafter. The RCP-4. 5 also includes century-long estimates of air-pollutant emissions and aerosols and was used to drive the LMDz-INCA simulations at the global scale. The choice of the RCP-4. 5 was dictated by the availability of chemical simulations on the regional scale. The regional-scale simulations for the present-time (2010) employ an emission database developed in the framework of the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project  (Klimont et al.
VERT) is not the number of layers but the depth of the first model layer, which is reduced from 20 to 8 m in VERT. We note that because the WRF meteorology (resolved in 31 layers) is interpolated to the CTM's vertical grid, technically, increasing the number of vertical layers in CHIMERE from 8 to 12 will result in a refinement of the meteorological input used for the chemical simulations as well. The fourth sensitivity case estimates the discrepancy in modeled ozone and PM 2. 5 concentrations between two runs where emission totals stem from different inventories, namely the local AIRPARIF inventory and the ECLIPSE regional-scale data set. In  Menut et al. (2003)  it was shown that the sensitivity of ozone concentrations in the afternoon peak hour due to surface emissions was the second largest after the sensitivity associated with meteorology.
Therefore the only difference amongst the two runs stem from the use of different annual quantified emission fluxes for the region (Table  1 ). To give a sense of the discrepancies between the two inventories over IdF we compare the annual domain-wide fluxes of NO x , NMVOCs and PM 2. 5 (Fig. 2 ). NMVOCs emissions are considerably higher in the ECLIPSE inventory while NO x emissions are lower than AIRPARIF. In terms of photochemical ozone production, this makes ECLIPSE more favorable of NO x -limited conditions than the bottom-up AIRPARIF inventory, which is consistent with the findings of  Markakis et al. (2014). Fine particle emissions are 2. 4 times more in ECLIPSE, which probably stems from the use of a population proxy to spatially allo- cate wintertime emissions from wood-burning. We note here, that the interest of comparing the two emission inventories is strictly to quantify the added value of implementing localscale information in city-scale climate studies and not by any means to compare qualitatively the two data sets.
It should be made clear that the ECLIPSE data set is not meant to accurately represent emissions at such fine scales. In the fifth sensitivity case we study the impact of the post-processing methodology e. g. , the process followed in order to split the annual emission totals into hourly emission fluxes for all the species and vertical layers required by the air-quality model. Menut et al. (2012a)  showed that model performance improves when time-variation profiles developed on the basis of observations are applied for the temporal allocation of emissions instead of the EMEP coefficients. Mailler et al. (2013)  found that model results are highly sensitive to the coefficients used for the vertical distribution of emissions. Makar et al. (2014)  investigated the response of modeled concentrations to the refinement of the spatial and temporal allocation of input emissions and found that the model was as sensitive to these improvements as to the vertical mixing parameterization.
Also they conclude that the temporal distribution of emissions in particular, could be very important in stable urban atmospheres and that this sensitivity is reduced with increased mixing conditions. For our test emission totals must match between the two emission data sets. We compile a new emission data set (POST) where the ECLIPSE annual totals are spatially (both horizontally and vertically) and temporally downscaled on the 4 km-resolution IdF grid. This procedure is based on coefficients extracted from the ECLIPSE post-processed inventory which, in turn, derive from the EMEP model. Comparing between the POST and ANN runs (Table  1 ) we can model the impact on pollutant concentrations of integrating a bottomup approach in regional emission modeling. Finally the impact of model horizontal resolution is a crucial issue for air-quality modeling.
Valari and Menut (2008)  showed that the impact of the resolution of emissions on modeled concentrations of ozone may be higher than the model resolution itself. This question has not yet been raised in the framework of climate-driven atmospheric composition modeling at the local scale. In our study we disentangle the impact of the resolution of the emission data set from the effect of model resolution itself by conducting two more tests. In the first test we employ the 0. 5 • resolution simulation (REG hereafter) from which all aforementioned simulations take their boundary conditions. We also compile the AVER database which uses as a starting point the modeled concentrations at 4 km resolution from the POST run spatially averaged over the 0. 5 • grid-cells of the REG resolution mesh. REG vs.
A strong positive bias is observed in modeled wind speed for both MET_CLIM and   (Sillman and Samson, 1995). RH is generally well represented in both cases. Finally we notice that the finer resolution reanalysis data set (MET_ERA01) is not able to reduce the observed domain-wide biases of the coarse meteorological run with the exception of specific locations such as the Montsouris station in Paris where the bias in wintertime precipitation and wind speed bias is reduced by 22 and 40 %, respectively.
Sensitivity to climate-model-driven meteorology (REF vs. ERA05) This case study estimates the discrepancy between an airquality model run where regional meteorology is downscaled with WRF from reanalysis data (ERA05) and a simulation where meteorology is downscaled from a global-scale climate model (REF). The wet bias in MET_CLIM meteorol-ogy is significantly reduced with meteorology from reanalysis data (Sect. 3. 1). This is expected to have a significant role in the modeled PM concentrations. Another influential factor is the colder bias found in summertime temperature in the MET_ERA05 data set. This could lead to decreased reaction rates, less biogenic emissions and consequently to less ozone. The lower bias in 10 m wind speed under MET_ ERA05 is bound to increase surface concentrations through reduced dispersion.
We also compare the average modeled boundary layer height (PBL) for the summer and winter periods between the two data sets: PBL is reduced by 5 and 12 %, respectively, in summer and winter (not shown) when reanalysis data are used instead of climate model output. This may result in less dilution of emissions, and therefore higher surface concentrations for primary emitted species, such as PM and NO x. Comparing the results of the two air-quality model runs for ozone (Fig. 4a  and Table  3 ) we find only a small sensitivity to using meteorology from a climate model or reanalysis data over all three types of monitor sites (| c| ∼ 1 ppb or 3. 4 %). The small improvement of model performance with the reanalysis data set (ozone decreases through higher NO x emissions following the PBL scheme described above) is due to the fact that titration is more realistically represented in ERA05 (the difference is O x between the two runs is negligible).
Sensitivity to the annual emission totals (REF vs. ANN) This case study compares modeled concentrations between two runs where annual emission totals stem from either the AIRPARIF inventory (REF) or the ECLIPSE data set (ANN). Changes in modeled urban daily average ozone concentrations are small (| c| = 0. 8 ppb or 2. 5 %), with the regional inventory (ECLIPSE) tending to increase the bias of the REF run (Fig. 8a  and Table  3 ). This is due to the fact that when passing from the AIRPARIF to the ECLIPSE inventory (see also Fig. 2 ), NO x emissions decrease (weakening titration) and NMVOCs increase (intensifying production). This is also seen in the weaker sensitivity of O x (0. 4 ppb or 1 %), suggesting that the main reason for the improvement brought about by the use of the local inventory (REF run) is due to a better representation of the ozone titration process.
Sensitivity to emission post-processing (ANN vs. POST) Here we use identical annual totals but two different methods for their vertical and temporal allocation to obtain hourly fluxes over the 4 km-resolution domain as well as different matrices for their chemical speciation. The ANN data set uses the AIRPARIF bottom-up approach, whereas the EMEP methodology is applied to the POST data set. To compile the ANN inventory we had to extract the post-processing coefficients of the bottom-up inventory and apply them on the ECLIPSE annual totals. This procedure, however, was not emission-source-sector-oriented, and this inconsistency definitely affects model results. On the other hand, the posttreatment of the (sectoral) raw emissions in large-scale applications are typically based on sectoral coefficients that do not link back to the same quantified emissions either.
The ANN simulation is able to increase the skill score by 14 % and reduce MNB by 26 %. The low O x sensitivity suggests that discrepancies are mainly due to a better representation of ozone titration. Suburban and rural ozone is practically insensitive to the post-processing technique. Even if emission totals are identical between the two configurations, ozone concentrations over the urban area are lower in the ANN run than in the POST run because ANN has more ground-layer NO x emissions than POST enhancing ozone titration. This stems from the fact that the annual emission totals are allocated in the CTM's vertical layers very differently. Following the AIRPARIF post-processing (ANN), all urban emissions are released in the surface layer because according to the local point source emission database no major industrial units are found within the urban area.
Considering the discrepancies in the inventorying methodologies used to compile the ECLIPSE and the AIRPARIF data sets (top-down vs. bottom-up), it is very interesting that the least influential factor to the urban ozone response is the annual emissions totals. It seems that the regional simulation would not benefit much from the integration of the local an-  nual totals alone but a more important gain would stem from the application of the AIRPARIF post-processing methodology. The added value from both of these factors would reduce the positive bias of REG by 2. 7 ppb. Even largest improvement comes through the better spatial representation of ozone precursors emissions in the local emission inventory (| c| = 2. 8 ppb) leading to more faithful titration process; O x levels are very close in REF and REG (Fig.
Acknowledgements. This work is supported by the  ERA-ENVHEALTH  project (grand agreement no  219337 ), co-funded by the  European Commission  under the 7th  Framework Programme. The authors would also like to acknowledge  Laurent Menut  ( LMD/Institute Pierre-Simon Laplace ) and  Augustin Colette (INERIS)  for their insightful comments that helped improve this manuscript. Edited by:  K. Tsigaridis.
We present the features seen in the first 2 months (July and August 2012) of data collected over the Pacific by IAGOS (In-service Aircraft for a Global Observing System)-equipped aircraft. IAGOS is the continuation and development of the well-known MOZAIC (Measurement of Ozone and Water Vapour on Airbus in-service Aircraft) project where scientific instruments were carried on commercially operated A340 aircraft to make measurements of chemical species in the atmosphere. Here, we show data from an aircraft operated by China Airlines on routes from Taipei to Vancouver, which provided the first trans-Pacific measurements by an IAGOS-equipped aircraft. We describe the chemical composition of the extratropical upper troposphere/lower stratosphere (Ex-UTLS) across the Pacific basin in the Northern Hemisphere.
Within the successor programme IAGOS, the first Asianbased aircraft has been equipped. The aircraft is operated by China Airlines from Taipei and provides data on many routes in the Asia-Pacific region, and particularly across the northern Pacific where hitherto there have been very little in-situ data collected on a regular basis. In this paper, we present the data obtained during the first 2 months of trans-Pacific flights between Taipei and Vancouver. Ninety percent of the data are acquired in the upper troposphere/ lower stratosphere (UTLS) when the aircraft attains its cruise altitude somewhere between 300 and 180 hPa. One of the important features of MOZAIC-IAGOS is the simultaneous measurements of O 3 , CO, water vapour and clouds. In the extratropical UTLS (Ex-UTLS; e. g. Gettelman et al.
, 2010). Until now, most in-situ data over the Pacific have been obtained as part of research campaigns such as Transport and Chemical Evolution over the Pacific (TRACE-P) in 2001  (Jacob et al. , 2003)  or Intercontinental Chemical Transport Experiment, Phase B (INTEX-B) in 2006  (Singh et al. , 2009). Such research campaigns are targeted at particular scientific questions, and thus are of limited duration and have more focused spatial extent. For example, INTEX-B was aimed at understanding transport and evolution of Asian pollution off the Pacific West Coast, and it was conducted over a 2-month period in boreal spring 2006. In contrast, this IAGOS-equipped aircraft assures regular measurements across the Northern Pacific basin. Based on these first 2 months of operation, we can expect about 10 crossings per month.
The data are capable of capturing individual events such as episodes of pollution and are also suitable for statistical or climatological analysis. In addition, the continuity of the measurements will allow seasonal and eventually interannual variability to be studied as well as providing a platform for long-term monitoring. In this article, we describe these first measurements across the Pacific, focusing on the period JulyÁAugust 2012, the only period so far when the complete validated dataset is available. We present case studies which highlight the seasonal monitoring potential of IAGOS, which is ongoing. We highlight the differences between the Pacific and 8 yr (2002Á2009) of CO and O 3 measurements over the Atlantic obtained by MOZAIC aircraft, and show that these new data, in combination with water vapour and cloud observations, offer opportunities for new research interests.
IAGOS data The instruments used for IAGOS are almost the same as those used for MOZAIC. They have been miniaturised and IAGOS includes a data transmission system which allows the data to be transmitted as soon as the aircraft arrives at its parking gate and to be available in quasi near-real time. The availability of data in quasi near-real time is useful for operational purposes, but for scientific studies, the data are subject to further calibration and quality control procedures after a 6-month period of operation. Full details are provided by  Ne´de´lec et al. (2015). Confidence in the IAGOS data and in the operation of the programme is derived from its history as MOZAIC. The ozone and CO instruments used by MOZAIC are described in more detail by  Thouret et al. (1998)  and  Ne´de´lec et al.
(2003)  respectively. Ozone is measured by the dual-beam UV absorption principle and has an estimated accuracy of 92 ppbv or 92% and a response time of 4 s. The CO infrared analyser gives an accuracy of 95 ppbv 95% with a response time of 30 s (7. 5 km at cruise altitude). Test-flights showed the stability of the measurements at concentrations above 40 ppbv and a minimum detectable concentration of 10 ppbv. Water vapour is measured by a capacitive hygrometer  (Helten et al. , 1998) , which was improved for operation aboard IAGOS aircraft  (Neis et al. , 2015). The accuracy of the hygrometer is 95%RH, whereas the response time is of the order of 5 minutes under cruise altitude conditions. Cloud elements are detected by a backscatter cloud probe  (Beswick et al. , 2014). During MOZAIC, a consistent and reliable set of observations of ozone has been obtained since 1994 and of CO since 2002, and data have been used in numerous wide ranging studies  (Petzold et al.
, 2015). The quality control procedures which were established for MOZAIC are maintained for IAGOS to ensure that the dataset remains free of instrumental artefacts. The instruments are calibrated in the laboratory against a National Institute of Standards and Technology (NIST) reference before and after an operational period of about 6 months and in addition, each instrument's zero and calibration factor are regularly checked in-flight, using a built-in, ozone generator. The responses of different sets of MOZAIC instruments are compared whenever they fly in the same route within a delay of less than 2 hours. This issue contains more details of the instrumentation and the operation of the programme. More information about MOZAIC and IAGOS, along with access to the database, is available at www.
, 1995). The association of the observations with PV has recently been completed for the MOZAIC database and for the IAGOS flights studied in this article. In 2003, the ECMWF operational analysis used had a resolution of TL511 (0. 38) with 60 levels in the vertical. The horizontal resolution was increased to TL799 (0. 28) in 2006 and the number of vertical levels was increased to 91.
Chemical composition of the Pacific Ex-UTLS In this section, we describe the chemical composition of the extratropical UTLS (Ex-UTLS) over the northern Pacific as sampled by the flights from Taipei to Vancouver during July and August 2012. The trajectories for these flights are shown in Fig. 1. We show in Fig. 2 , a time series of the complete dataset provided on 25 July 2012 (referred to as Flight-20120725) from the IAGOS aircraft. The measurements were made at cruise altitude between 274 and 196 hPa, and span latitudes ranging from 24 to 648N. The abundances of O 3 and CO measured on these flights are presented as a tracerÁtracer plot in Fig. 3. Plotting O 3 against CO forms a well-known, 'L'-shaped distribution (e. g. Pan et al. , 2004) , whereby the stratospheric branch is characterised by high mixing ratios of ozone with low mixing ratios of CO, and the tropospheric branch by high CO mixing ratios and low O 3 mixing ratios.
8 ). These gradients are less pronounced over the Pacific, suggesting that we are further from the tropopause, or that the tropopause layer is more mixed. Further evidence for this is given by the PV of each observation, calculated by associating each measurement with the PV from the ECMWF's operational analysis via FLEXPART. Figure  13c  shows that, as with ozone (Fig. 12b ), the median values of PV are higher over the Atlantic than over the Pacific on all flight-levels (except for 250 and 275 hPa), and on average over the five flightlevels, the PV over the Atlantic is 4. 4 PVU compared with 2. 6 PVU over the Pacific. This offers further indication that the Atlantic observations have an origin deeper in the stratosphere. The Pacific dataset is small for drawing firm conclusions. An investigation using PV equivalent latitudes (e.