dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2005). Gao et al. (2006)  report measurements of water vapor and nitric oxide (NO) molar mixing ratio, p, T, RHi, ice and particle number, size, surface area, and volume as derived from measured 1-s size distributions. The contrail was formed at low temperatures (-77°C at 120 hPa, ~15 km altitude). The temperature data agree well with the nearby 30 Miami radiosonde data. From the plotted results we read mean values for 780 s and 1560 s contrail ages with n ice ~50 to 160  Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2016 -773, 2016  Manuscript under review for journal Atmos. Chem. Phys. Published: 15 September 2016 c Author(s) 2016. CC-BY 3. 0 License. cm -3 and low IWC of 0. 18 to 0. 23 mg m -3 , implying low r vol values of 0. 7 to 1 m, and r eff of 1 to 2 m. Large, steady-state RHi values were measured inside the contrails.
The analysis suggests that nitric acid increases relative humidity in lowtemperature cirrus clouds  (Gao et al. , 2004; Gao et al. , 2016). The same measurements were analyzed independently with respect to microphysical properties in a Master's thesis  (Mullins, 2006)   (Timothy Garrett, advisor). The thesis presents reevaluated CAPS  (Baumgardner et al. , 2001)  data implying 5 smaller extinction ( ext ) values. Together with a larger IWC estimate, including a few larger ice particles possibly from ambient subvisible cirrus, nearly a factor 2 smaller effective radius r eff is derived from the IWC and  ext values. The contrail W is estimated (about 200 to 1500 m from the first penetration to 1800 s age) from a sequence of photos at various times of contrail development. Using a wake vortex descent model, the total depth D of the contrail was estimated.
The reported W is consistent with age, ambient wind shear, and the estimated D. The effective depth D eff is, however, likely far smaller than D 10 because of shear induced cross-section deformation, giving smaller cross-section A c = D eff W < D W and, hence, about constant total number of ice particles N ice. The data suggest quite high ice particle numbers per fuel consumption. At ~10 K lower temperatures, a contrail was observed during the NASA Costa Rica Aura Validation Experiment (CR-AVE) over the Pacific south of Costa Rica on 1 February 2006. During validation flights for satellite retrievals, airborne measurements were performed again with the NASA WB-57F, sampling its own contrail in the uppermost tropical 15 troposphere. The contrail observations are described briefly in two informal conference papers  (Flores et al.
, 2006; Lawson et al. , 2006). The cirrus data are discussed in  Lawson et al. (2008). Here we compile the essential data. Raw data are available from https://espoarchive. nasa. gov/archive/browse/cr_ave/WB57/20060201. Only 30-s averaged CAPS data and no CO and HNO 3 trace gas data are available for the contrail period. At the time of contrail formation (about 64700 s UTC), the aircraft was cruising at a constant altitude of 18 km (79 hPa) near the equator at 83°W. It measured its 20 own contrail between 65350 and 65700 s UTC. Fuel consumption and aircraft mass were estimated by the flight operators as 1270 kg h -1 and (24800±450) kg. The contrail formed at a low ambient temperature, near -88°C, consistent with radiosonde data of San Andres Island (12. 58°N, 81. 71°W) from 12 UTC the same day.
, 2008). The water data from WB-57's NOAA frost point measurements show the lowest values with subsaturation outside the contrail and high supersaturation inside the contrail, but apparently with some timing uncertainty. The H 2 O peaks fit the other peaks better 5 when shifted by 60 s forward in time. The peaks agree with expected CO 2 and H 2 O molar mixing ratio increases c = (29/44) EI CO2 /Ndil and (29/18) EI H2O /Ndil of order 1 ppm for the given plume ages and dilution as in Eq. (  1 ). For a fuel flow per flight distance of m F = 2 g m -1 , this implies a cross-sectional area A c = m F /( N dil ) of about 0. 025 km 2 (e. g. , W = 500 m and D eff = 50 m). The CAPS instrument shows low concentrations of rather large ice particles in the ambient air and, as the contrail was 10 penetrated, clear concentration peaks, mainly of small ice particles (90% of the particles were smaller than 2 m diameter, maximum diameter 25 m).
Concentrations at 1-s time resolution  (Flores et al. , 2006)  reach maximum values of 250 cm -3. As derived from the forward to backscatter ratio from the CAS (one component of the CAPS), approximately 40% of the particles have a refractive index between 1. 35-1. 40, suggesting the presence of sulfuric acid. The habits were 15% spherical and 50% with aspect ratios between 1. 3 and 1. 5 (also derived from the CAS forward to backscatter ratios). The spatial 15 distributions of particles in the contrail are non-Poissonian and suggest smaller-scale contrail cloud structures with unknown origin. From the 30-s mean values available in the database (65679 to 65709 s), the contrail contributes n ice of 0. 51 cm -3 in the first channel, a total of 0. 78 (<2. 5) cm -3 in all channels, IWC of 0. 20 mg m -3 (r vol = 4 m), and  ext of 0.
Measurement periods with Geophysica exhaust and contrail contributions were found 15 near and above deep tropical convection in the tropics, during the project Stratospheric-Climate Links with Emphasis on the Upper Troposphere and Lower Stratosphere (SCOUT-O3) flights over the Tiwi Islands (131°E, 11. 5°S), near Darwin, Australia in November 2005. The measurements were performed with the M-55 in coordinated flights with other research aircraft, e. g. , the DLR Falcon, carrying an upward-looking lidar  (Kiemle et al. , 2008; Wirth et al. , 2009). The analysis provides remote sensing results based on photography, lidar, and NOAA Advanced Very High Resolution Radiometer 20 (AVHRR) satellite data as explained in Section 3. 2. 11. From in-situ measurements during three flights of SCOUT-O3 near Darwin (November 29, and 30 morning and afternoon, 2005), several potential contrail self-encounters in cirrus were identified from trajectory analyses.
Potential encounters are those for which the flight path crosses computed plume positions within ±100 m vertical distance. The mean measured cirrus properties at these times were taken as being representative for contrails at those positions. Since there is no 25 proof that these cirrus clouds were actual contrails, these results should be interpreted with caution. We include these data in COLI with this annotation. During the "Golden day" of the SCOUT-O3 project, 30 November 2005, six ice events longer than 30-s measurement duration were found in the morning flight above a deep convective cloud, "Hector", over the Tiwi Islands near Darwin, at altitudes up to 18. 7 km, in the lower stratosphere, as documented in detail by de  Reus et al. (2009). Several indications 30 suggest that these ice events were influenced by nonvolatile aircraft aerosol and contrails  (Schumann and co-authors, 2016).
CIAP: Contrails in the jet and wake vortex phases During the Climatic Impact Assessment Program (CIAP), considering the impact of supersonic transport on the ozone layer and on climate, the vertical and lateral dimensions of a B-59 jet aircraft contrail were determined from ground-based camera 25 observations near San Francisco, likely in fall 1972. The contrail formed in the upper troposphere at 12. 77 km and lasted for about 1800 s  (Conti et al. , 1973). The contrail scales were derived during the wake phase and until the early dispersion phase (about 600 s), with maximum W and D of 500/400 m  (Hoshizaki et al. , 1975). Sounding data and the day of measurements were not reported.
The optically effective depth measured from lidar backscatter plots was D eff 35±3 m, slightly decreasing with age because of sublimation in subsaturated air  (Busen et al. , 1994). The mean descent speed was 2. 5 m s -1 causing a total wake depth D of 125 m after 50 s. For the given flight level (350, p = 238 hPa), T = -63. 3°C, ambient 15 humidity (RHi = 39%), wind shear (0. 002 s-1), and stratification (N BV = 0. 027 s -1 ) were derived from the nearby Munich radiosonde at that time. The SAC threshold is T LC = -50. 4 °C for these values. Data on the Falcon flight path are available from the COLI data bank. Figure  2  shows that the width (~80 to 150 m) increases slowly for low ages < 200 s and then linearly with longer age.
IFU-Lidar: Ground-based lidar observations of contrail geometry and optics The vertical growth was often limited by the depth of the humid layers within the flight levels, as later found also in large eddy simulations  (Unterstrasser and Gierens, 2010a, b; Lewellen, 2014). 5 Freudenthaler et al. (1996)  derived  and depolarization values from the lidar observations. Data from this work in 1994 to 1996 (69 individual observations with contrail D, W, A c , p, T and RHi, including  for 48 observations) were made available for this paper. Those for 1995 and 1996 were partially reanalyzed for this purpose. Contrail age is provided for all of these data, but the aircraft types were not recorded. The data for 1994-1995 were obtained within the project SiL and a German Science Foundation (DFG) funded project  (Schumann, 1998) , while those for 1996 are from the project 10 AEROCONTRAIL  (Ström and Ohlsson, 1998)  and include observations reported by  Sussmann (1999).
SUCCESS: Long-lived racetrack contrail in satellite images The contrail observed during SUCCESS on 12 May 1996 was generated while the DC-8 was flying a racetrack causing an oval contrail pattern. The oval was visible in geostationary satellite imagery (4-km resolution GOES-9) for seven hours  (Jensen et al. , 1998a; Lawson et al. , 1998; Minnis et al. , 1998). Additional data were derived from 1-km resolution polar orbiting NOAA-12 AVHRR data. The contrail visible τ, the effective radius r eff , and the temperature T of the contrails were 5 derived from multispectral satellite data  (Minnis et al. , 1998). Data derived from these studies for contrail ages of 14400 (3600-25200) s in Figure  2  are W = 5 -10 km,  = 0. 5±0. 25, and r eff = 30  m. The study showed high variability of the results with time.
0012 m s - 10 1 ), and can explain the large observed W only for large D, again suggesting possible enhancement by sedimentation. Atlas et al. (2006)  observed individual contrails for ages up to 2 h. The first set of contrails was observed between 15:30 and 18 UTC 7 September 2003 with a camera from the ground, with a lidar at Goddard Space Flight Center (39°N, 76. 9°W), and in MODIS images. The generating airliner flights were identified and used to estimate the contrail ages (1000 to 7100 s). The 15 corresponding aircraft types are listed in the Supplement. Flight information is available from a flight track database  (Garber et al. , 2005) , ready for a simulation of this case with proper NWP data. The observations showed contrails producing cirrus uncinus and fallstreaks. The  was estimated as 0.
Acknowledgments. We are grateful to input and comments from  William A. Cooper ,  Ru-Shan Gao ,  Timothy Garrett ,  Kaspar 25 Graf ,  Christian Kiemle ,  Andreas Minikin ,  Andrew Roberts ,  Daniel Sauer , and  Simon Unterstraßer. S. Bedka ,  D. Duda , and  P. Minnis  were supported by the  FAA ACCRI Program. We gratefully acknowledge the wide set of data sources as explained in the text. The first author dedicates this work to his former colleague,  Dr. Manfred E. Reinhardt  ( 26 January 1927  to 1 October 2015), a reliable partner, for fruitful cooperation 1982-1991, and for early contrail research with research aircraft.
4. Industrial and natural contributions to the sediment deposits 4. 1. From historical data and 137 Cs measurements, we assumed that the 120 cm black materials (11 -131 cm) had settled during the industrial activity period, i. e. from 1960 to 1988. We assumed that the bottom of the deposits was reached at 131 cm, and that no material was eroded during that period. The estimated sedimentation rate is about 4. 7 cm per year, which is more than ten times the sedimentation rate measured in fish ponds located in the Moselle watershed  (Bertrand et al. , 2012). This estimated sedimentation rate should be put against the activity of the steelmaking site. Indeed, the blast furnaces and the sintering plants were reported to produce 1. 3 Mt/yr and 2. 4 Mt/yr, respectively  (Freyssenet, 1979). The top of the core might represent the materials that had deposited between 1988 and 2014; the estimated sedimentation rate (0.
Acknowledgements The authors would like to thank  Long-Term Ecosystem Research (LTER) France ,  Agence Nationale de la Recherche  ( ANR)  project number  ANR-14-CE01-0019 ,  RésEAU LorLux  and  Region Lorraine through the research network of Zone Atelier Moselle (ZAM)  for partially funding the work. We would also like to thank  Ms. Marie-Françoise Gerard  and  Ms. Manel Khedri  who worked on several aspects of the sediments of the Orne,  Mr. Marc Desmet  ( GEHCO, University of Tours ) for providing the aquaraft that was used for the coring,  Mr. Patrick Billard  ( LIEC, Lorraine University ) for helping during the sampling, Mr. Jean-Marie Fischbach  and  Mr. Frederic Diot  ( GeoRessources, Lorraine University ) for the access to the  XRF Niton gun ,  Ms. Lise Salsi  ( GeoRessources, Lorraine University ) for SEM access,  Ms.
1 ). EPS-1 was drilled vertically to a depth of 2227 m between 1990 and 1991 and continuous core was retrieved between the measured depths of 930 and 2227 m (all depths reported in this study are measured depths). The cored interval sampled the Triassic Muschelkalk unit (930-1000 m depth) and the Permo-Triassic Buntsandstein unit (1000-1417 m; Fig. 1b ) directly overlying the granite basement (encountered at a depth of 1417 m). The Buntsandstein unit-the focus of this study-extends over large parts of west and central Europe  (McCann 2008) , and the formations specific to those found at Soultz-sous-Forêts can be readily identified in the wells drilled at Rittershoffen  (Aichholzer et al. 2016) , located ~ 6. 5 km from the Soultz-sous-Forêts site (Fig. 1c ). Therefore, although our study uses core material from Soultz-sous-Forêts, the data presented herein are likely relevant for current and future geothermal projects within the Upper Stratigraphic column showing the units within the Buntsandstein from exploration well EPS-1 at Soultz-sous-Forêts (Alsace, France).
Porosity The connected porosity of each 20-mm-diameter sample was determined using a helium pycnometer (Micromeritics AccuPyc II 1340). The total porosity of each sample was calculated using their bulk density and the solid density of the material. The solid densities were determined by measuring the mass and volume (using the pycnometer) of powdered offcuts of each sample (powdered using a pestle-and-mortar). The average connected porosity of each of the sampled intervals is given in Table  1. A plot of the total porosity of these samples as a function of their connected porosity is shown in Fig. 3. The data of Fig. 3  show that the porosity of the EPS-1 samples ranges from ~ 0. 03 to ~ 0. 2, and that the porosity of the quarry samples ranges from ~ 0. 18 to ~ 0. 25. Figure  3  also shows that there is essentially no isolated porosity in the rocks studied, with the exception of the quarry rock from Adamswiller (the samples containing the highest porosity), which contains an isolated porosity of ~ 0.
Mineral content (X-ray powder diffraction) Mineral content of the materials used in this study was quantified using X-ray powder diffraction (XRPD). Powdered sandstone samples were ground for 8 min with 10 ml of isopropyl alcohol in a McCrone Micronising Mill using agate cylinder elements. The XRPD analyses were performed on powder mounts using a PW 1800 X-ray diffractometer (CuKα, graphite monochromator, 10 mm automatic divergence slit, step-scan 0. 02° 2θ increments per second, counting time one second per increment, 40 mA, 40 kV). The phases in the whole rock powders were quantified using the Rietveld program BGMN  (Bergmann et al. 1998). To identify the clay minerals, we also separated < 2 µm fractions by gravitational settling and prepared oriented mounts that were X-rayed in an air-dried and an ethylene-glycolated state.
The mass loss data from these experiments (Fig. 5 ) are in agreement with our XRPD data (Table  2 ). Samples containing a high wt% of illitesmectite and/or dolomite suffer the greatest loss in mass (Fig. 5 ). The temperatures corresponding to the measured mass losses are in accordance with the dehydroxylation of illite-smectite  (Earnest 1991a, b)  and the decarbonation of dolomite  (McIntosh et al. 1990). No mass changes were measured during the cooling cycle and the subsequent heating/cooling cycle, attesting to the completion of the devolatilization reactions.
Grain size and shape analysis We determined grain size and shape descriptors for the sandstones sampled from EPS-1 using images of double-polished thin sections taken using a scanning electron microscope (SEM) (Tescan Vega 2 XMU). We first manually traced around all of the quartz grains in a ~ 20 mm 2 area on a backscattered SEM image (BSE) of each sample. These binary images were then analysed using open source software ImageJ. The number of grains analysed ranged from 200 to 700. The equivalent grain diameter, d, was calcu- lated using d = 3/2(d F ), where d F is the average Feret diameter. Grain aspect ratio was defined as l major /l minor , where l major and l minor are the lengths of the major and minor axes of the best-fit ellipse, respectively. Grain "circularity" was defined as 4π × g a /g 2 p , where g a and g p are the area and perimeter of a grain, respectively (where unity Fig.
Mercury porosimetry Mercury injection porosimetry was performed on five of the borehole samples: 84, 299, 347, 402, and 540. These samples were selected based on their differences in grain size, clay content, and porosity. Mercury injection data permit the calculation of the pore throat size distribution within a particular sample. We performed tests on pieces (2-7 g) of the aforementioned sandstones using the Micromeritics AutoPore IV 9500 at the University of Aberdeen. The evacuation pressure and evacuation time were 50 μmHg and 5 min, respectively, and the mercury filling pressure and equilibration time were 0. 52 lb per square inch absolute (psia) and 10 s, respectively. The pressure range was 0. 1 to 60,000 psia (i. e. up to a pressure of about 400 MPa). Figure  7  shows the pore throat diameter distribution for each of the five sandstones tested (discussed in detail below).
The Forchheimer correction is necessary if the data are well described by a positive linear relationship. The Forchheimer-corrected permeability k forch is taken as the inverse of the y-intercept of the best-fit linear regression of this positive linear relationship. If the Forchheimer correction is required, we then check whether the Klinkenberg correction is required. To do this, k forch is calculated for each P using: where ξ, not strictly needed in this analysis, is the slope of the plot of 1/k D as a function of Q v. k forch is then assessed as a function of 1/P m. The Klinkenberg correction is neces- sary if these data are well described by a positive linear relationship, and the true permeability is taken as the y-intercept of the best-fit linear regression of the data. If the data on the plot of k forch as a function of 1/P m cannot be described by a positive linear slope, then the true permeability is taken as k forch (i.
Funding This study was funded by  LABEX  grant  ANR-11-LABX-0050_G-EAU-THERMIE-PROFONDE  (this research therefore benefited from state funding managed by the  Agence National de la Recherche (ANR)  as part of the  "Investissements d'avenir" program ) and  ANR  Grant  CANTARE  ( ANR-15-CE06-0014-01 ).
Introduction CO 2 emitted from fossil fuels (FFCO 2 ) is the major contributor to the increase of atmospheric CO 2  (Ballantyne et al. , 2015). Knowledge of FFCO 2 emissions and their trends is essential to understand the drivers of their variations and assess the effectiveness of agreed upon emission reduction policies over time  (Pacala et al. , 2010). At national scale, FFCO 2 emission inventories are derived based on energy and fuel use statistics, combustion efficiencies and emission factors. These inventories have low uncertainties in OECD countries and large uncertainties in developing countries due to uncertain energy data and fuel-specific emission factors  (Liu et al. , 2015; Ballantyne et al. , 2015; Andres et al. , 2014; Ciais et al. , 2010). At subnational and intra-annual scales, the uncertainties in the estimates of FFCO 2 emissions are higher than at national and annual scale  (Ciais et al.
The study primarily aims at providing a typical quantification of the inversion performances and at understanding qualitatively how the inversion behaves depending on the level of FFCO 2 emissions, on the knowledge on these emissions and on the network density. Furthermore, we assume here that the uncertainties in the signals from 14 CO 2 fluxes other than the FFCO 2 emissions, such as that from terrestrial biosphere, oceans, nuclear power plants and cosmogenic production, should have a moderate impact on the order of magnitude of the inversion performances that are analyzed in this study. This leads us to ignore these uncertainties and consider that the only uncertainties in the FFCO 2 mixing ratios data are related to the instrumental precision of CO 2 and 14 CO 2 measurements. In practice, in the frame of this study, which focuses on the propagation of uncertainties, this is mathematically equivalent to assuming that 14 CO 2 is a perfect tracer of FFCO 2.
Prior error covariance matrix Emission estimates from inventories are limited to annual and national scales and rarely provide systematic assessments of uncertainties. There are a limited number of datasets providing emission maps at higher spatial-temporal resolutions. Although there have been some efforts to compare such FFCO 2 emission maps  (Macknick et al. , 2009; Ciais et al. , 2010; Andres et al. , 2012 Andres et al. , , 2016)) , the ability to characterize the uncertainties of an emission inventory is limited, especially for subnational and subannual scales. In this study, we use different streams of information to model the prior emission uncertainty covariance matrix B and we use two different configurations of this matrix in the inversions. The first configuration of the B matrix, called here notional or B notion , is related to the notional estimates of (1sigma) uncertainties for national emissions claimed by inventory compilers to range from 1 to 2.
Acknowledgements. The authors acknowledge the support of the  French Commissariat  à l'énergie atomique et aux énergies alternatives (CEA). This study is co-funded by the  European Commission  under the  EU Seventh Research Framework Programme  (grant agreement no. 283080 , geocarbon project). Grégoire Broquet and Frédéric Vogel acknowledge funding from the industrial chair BridGES  (supported by the  Université de Versailles Saint-Quentinen-Yvelines , the  CEA , the  Centre National de la Recherche Scientifique ,  Thales Alenia Space and Veolia). We are also grateful to  Ingeborg Levin  for the useful discussions on this topic. We also would like to thank the partners of the ICOS infrastructure for details of radiocarbon samplings and FFCO 2 monitoring. Edited by: Qiang Zhang Reviewed by:  Peter Rayner  and one anonymous referee.