dataesr/scientific-paragraphs-categorization huggingface dataset:
GPCC and HPS data are not independent, as some stations used in GPCC are also used in HPS. However, the statistical treatment of raw data for the two datasets is largely different. HPS is a priori more adapted to the study of low-frequency variations because of the homogenization procedure applied. The extremely small differences seen between multi-decadal precipitation variations in GPCC and HPS show the robustness of those features to the statistical pre-treatment of precipitation data and to the exact number and location of stations. Moreover, it shows that even if it is not homogenized, GPCC data can be used in the following analyses to estimate averaged precipitation over France, as it has the advantage to be available on a longer period compared to HPS. Spring precipitation over France in 20CR exhibits a temporal pattern that is very similar to the one seen in HPS and GPCC, even if some limited differences exist, especially at the beginning of the record.
This consistency with direct observations (GPCC, HPS) is noteworthy as 20CR provides an indirect and totally independent estimation of precipitation. Indeed, no observed precipitation is used in 20CR. Note that SLP observations, the only variable assimilated in 20CR, are much sparser in the early period of the reanalysis, which has an impact on the quality of 20CR data then  (Compo et al. , 2011). variations in spring precipitation are well captured by 20CR also suggests that they may be largely driven by SLP. The previous analysis shows that the large multi-decadal variations seen in spring precipitation over the 20th century are a robust feature of French climate. Indirectly, it also reinforces the confidence in the general robustness of multi-decadal variations seen in river flows. The multi-decadal variations in spring precipitation noted here are consistent with the results of  Sutton and Dong (2012).
2), multidecadal variability in French river flows is characterized (Sect. 3). The mechanisms behind river flows variations in spring are described in Sect. 4. Hydro-meteorological Discussion Paper | Discussion Paper | Discussion Paper |simulations are analyzed in Sect. 5 in the objective to better understand multi-decadal variations in summer flows. Finally, conclusions and perspectives are given in Sect. 6. 2 Data, models, and methodsDaily river flows at 38 gauging stations over France are analyzed in this study. Data have been extracted from the national HYDRO database (http://www. hydro. eaufrance. fr/). Series at all selected gauging stations start before 1940 and cover at least 70 yr (the median length is 90 yr). Gauging stations with too much missing values have been discarded. When less than 3/4 of daily values are available during a given year (season), the corresponding annual (seasonal) mean is considered as missing.
Acknowledgements. This work was supported by the  French National Research Agency (ANR)  in the framework of its  JCJC program  ( ECHO, decision  no  ANR   2011 JS56 014 01 ). The authors thank  Laurent Terray ,  Christophe Cassou  and  Yohan Ruprich-Robert  for useful discussions about this work. The authors also acknowledge  Météo France  ( Direction de la climatologie ) for providing the homogenized precipitation and temperature data-sets and  Eric Martin  for providing the SAFRAN-ISA-MODCOU data. Support for the Twentieth Century Reanalysis Project dataset is provided by the  US Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment  ( DOE INCITE) program , and  Office of Biological and Environmental Research (BER) , and by the  National Oceanic and Atmospheric Administration Climate Program Office.
We present the scaling relation between Sunyaev-Zeldovich (SZ) signal and stellar mass for almost 260,000 locally brightest galaxies (LBGs) selected from the Sloan Digital Sky Survey (SDSS). These are predominantly the central galaxies of their dark matter halos. We calibrate the stellar-to-halo mass conversion using realistic mock catalogues based on the Millennium Simulation. Applying a multi-frequency matched filter to the Planck data for each LBG, and averaging the results in bins of stellar mass, we measure the mean SZ signal down to M * ∼ 2 × 10 11 M ⊙ , with a clear indication of signal at even lower stellar mass. We derive the scaling relation between SZ signal and halo mass by assigning halo properties from our mock catalogues to the real LBGs and simulating the Planck observation process.
These were selected from the Sloan Digital Sky Survey (SDSS) using criteria designed to maximize the fraction of objects that are the central galaxies of their dark matter halos. We stack the Planck data in order to estimate the mean SZ signal for LBGs in a series of stellar mass bins. We then use mock galaxy catalogues based on the Millennium Simulation and tuned to fit the observed abundance and clustering of SDSS 1 Planck (http://www. esa. int/Planck) is a project of the European Space Agency (ESA) with instruments provided by two scientific consortia funded by ESA member states (in particular the lead countries France and Italy), with contributions from NASA (USA) and telescope reflectors provided by a collaboration between ESA and a scientific consortium led and funded by Denmark. galaxies to establish the relation between stellar and halo mass.
Two data processing centres (DPCs) check and calibrate the data and make maps of the sky (Planck HFI Core Team 2011b;  Zacchei et al. 2011 ). Planck's sensitivity, angular resolution, and frequency coverage make it a powerful instrument for Galactic and extragalactic astrophysics as well as for cosmology. Early astrophysics results are given in Planck Collaboration VIII-XXVI 2011, based on data taken between 13 August 2009 and 7 June 2010. Intermediate astrophysics results are now being presented in a series of papers based on data taken between 13 August 2009 and 27 November 2010.
A Locally Brightest Galaxy Catalogue To select a sample of central galaxies, we first define a parent population with r < 17. 7 (r-band, extinction-corrected, Petrosian magnitude) from the spectroscopic galaxy catalogue of the New York University Value Added Galaxy Catalogue 2. This was built by  Blanton et al. (2005)  based on the seventh data release of the Sloan Digital Sky Survey (SDSS/DR7  Abazajian et al. 2009 ). This parent catalogue contains 602,251 galaxies. We then define "locally brightest galaxies" to be the set of all galaxies with z > 0. 03 that are brighter in r than all other sample galaxies projected within 1. 0 Mpc and with redshift differing by less than 1,000 km s -1. After this cut 347,486 locally brightest galaxies remain. The SDSS spectroscopic sample is incomplete because it proved impossible to place a fibre on every object satisfying the photometric selection criteria, and because some spectra failed to give acceptable redshifts.
This increases the apparent extent of the signal in stacked maps like Fig. 5 , but decreases the contribution to the signal through a matched filter centred on the galaxy (see Appendix C). Using our simulation of the LBG catalogue, we can account fully for these effects and extract the underlying Y 500 -M 500 relation in an unbiased way. Within each stellar mass bin, we identify each observed LBG with a randomly chosen simulated LBG of the same stellar mass, assigning it the halo mass and positional offset from halo centre of its partner, but retaining its observed redshift. We give each halo a SZ signal distributed according to the "universal pressure profile" and normalized using a specific model Y 500 -M 500 scaling relation. Each synthesised object is then observed with the three-band MMF centred on the galaxy's position, and the measurements are binned and weighted in the same way as the real data to obtain Y s.
Any offset between these two halo mass scales will result in offsets in Fig. 9. For example, a number of recent papers have argued that failure of some of the assumptions underlying the standard methods for estimating cluster masses from X-ray data (e. g. , detailed hydrostatic equilibrium or the unimportance of turbulent and nonthermal pressure) could produce a systematic bias in the X-ray cluster mass scale  (Planck Collaboration XII 2011; Rozo et al. 2012; Sehgal et al. 2012). Finally, as for the LBG sample, each luminosity bin of the MCXC contains a distribution of halo properties that are averaged in complicated fashion by our stacked SZ measurement. Understanding the relative importance of these various effects at a precision better than 20% would again require detailed modeling of the heterogeneous MCXC catalogue.
This is the lowest mass scale to which an SZ scaling relation has so far been measured. The fact that the signal is close to the self-similar prediction implies that Planckdetected hot gas represents roughly the mean cosmic fraction of the mass even in such low-mass systems. Consistency with their low observed X-ray luminosities then requires the gas to be less concentrated than in more massive systems. Integration of the halo mass function down to M 500 = 4 × 10 12 M ⊙ shows that Planck has now seen about a quarter of all cosmic baryons in the form of hot gas, about four times as many as are inferred from X-ray data in clusters with M 500 > 10 14 M ⊙. At the high mass end, the scaling relation we derive from our LBG data shows reasonable agreement with X-ray cluster results. The 20% lower normalisation that we find (significant at the 2.
Acknowledgements. The authors from the consortia funded principally by  CNES ,  CNRS ,  ASI ,  NASA , and  Danish Natural Research Council  acknowledge the use of the pipeline-running infrastructures Magique3 at  Institut d'Astrophysique de Paris (France) ,  CPAC at Cambridge (UK) , and  USPDC at IPAC (USA). The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www.
Spectroscopic data processing, redshift measurement, and reliability flag The multi-slit spectroscopy data processing has been performed using the VIPGI data processing environment  (Scodeggio et al. , 2005). It consists of 2D spectra extraction, sky-subtraction, combination of individual 2D spectra, 1D spectra tracing and extraction, wavelength and flux calibration of the 2D and 1D spectra. The redshift measurements have been performed in several steps using the EZ engine developed within the VVDS  (Garilli et al. , 2010) , based on cross-correlation with spectra templates and augmented by knowledge-based software. The redshifts are measured separately by 2 persons in the team, and reconciled from a face to face confrontation. This confrontation is designed to smooth-out the possible biases of individual observers and produce an homogeneous reliability assessment by means of a flag.
5 have been observed with SINFONI in a pilot program for MASSIV (Lemoine-Busserolle et al. 2010), including 2 flag 3 and one flag 4 object, their redshifts were all confirmed from at least [OIII]5007Å detection. From the VVDS objects re-observed by VIPERS we derive the velocity difference distribution. The 1-sigma of the distribution normalized to the expansion factor (1+z) is σ 1+2 = 0. 00095, or σ 1+2 ∼ 285 km/s between two objects, corresponding to a velocity error per object of σ v = σ 1+2 / √ 2 = 202km/s, while for VVDS objects re-observed by MASSIV, we find σ 1+2 = 0. 00072 hence σ v ∼ 153km/s. We find that between VVDS and MASSIV, using fully independent instrument setups, the absolute velocity zero point differs by only ∆z = 0. 0003. This corresponds to ∼ 40 km/s at the mean redshift of MASSIV, well within the expected difference given that the measurements are coming from two different instrument systems with different spectral resolutions.
We contend that they are not of the same nature, and that care must be taken when comparing them. From our VIMOS experience dealing with > 1. 5 × 10 5 spectroscopic redshift measurements from the VVDS, zCOSMOS, and VIPERS surveys, it appears that it is illusory to aim at a classification of galaxy redshifts measurements into a scheme as basic as good and bad, but rather that using a more continuous distribution of redshift reliability as described above is more appropriate to this type of dataset.
In the following we call these samples the 'Ultra-Deep', the 'Deep-re-observed', and the 'colour-selected' samples. The VMMPS mask-design software  (Bottini et al. , 2005)  was first used to force slits on the colour-selected sample (a few per VIMOS quadrant), then to force slits on the Deep-reobserved sample (about 20 per quadrant), and finally to place slits randomly on the Ultra-Deep sample (about 80 per quadrant), for an average total number of about 450 slits observed in one observation. As described in Section 3. 4, the data have been processed using the VIPGI software. The redshift measurements have been performed in several steps using the EZ engine. The redshifts of the blue spectra and the red spectra have been measured separately, each by 2 persons in the team, and two reconciled lists of redshifts derived from the blue and red observations have been separately produced.
VVDS complete data release The data produced by the VVDS are all publicly released on a database with open access. A complete information system has been developed with the data embedded in a reliable database environment, as described in Le  Brun et al. (in preparation). Queries combining the main survey parameters can be defined on the high-level user interface or through SQL commands. Interactive plotting capabilities enable to identify the galaxies with redshifts on the deep images, and to view all the spectroscopic and photometric information of each object with redshift, including spectra and thumbnails image extractions. Spectra and images can be retreived in FITS format. The main parameters available on the database are as follows: -Identification number, following the IAU notation ID-alphadelta α 2000 , δ 2000 coordinates -Broad band photometric magnitudes u'grizJHKs -Spectroscopic redshift -Spectroscopic redshift reliability flag -Photometric redshift -TSR, SSR, PSR weights -Spectroscopic and photometric masks (region files) to each redshift measurement through inter-comparison of measurements performed independently by two team members.
We demonstrate that galaxies with VVDS flags 2, 3, 4 and 9 have a reliability ranging from 83 to 100%, making this the primary sample for science analysis. Galaxies with flag=1 can be used for science analyses after taking into account that they have a ∼50% probability to be correct. We emphasize that this probabilistic flag system enables a robust statistical treatment of the survey selection function, compiling a finer information than a simplistic good/bad redshift scheme. This leads to a well described selection function with the TSR (Target Sampling Rate), PSR (Photometric Sampling Rate), and SSR (Spectroscopic Success Rate) which are magnitude and redshift dependent, as characterized in this paper, and available in our final data release. We have emphasized the dependency of the 'redshift desert' on instrumental setups, and demonstrated that it can be successfully crossed when using a wavelength domain 3650 ≤ λ ≤ 9350Å.
VVDS-Deep sample is based solely on I-band selection 17. 5 ≤ I AB ≤ 24. Total integration times of 4. 5h have been obtained with the LRRED grism. The VVDS-Deep observations of the 0226-04 field have been extensively described in Le Fèvre et al. (2005a) and the ECDFS in Le Fèvre et al. (2004a). The VVDS-Deep dataset now includes a sample of ∼ 4000 additional objects from 'epoch 2' observations of 8 VIMOS pointings. This sample has been observed and the data processed following the exact same procedure as described in Le Fèvre et al. (2005a). The full field observed from the VVDS-Deep 'Epoch 1' (Le Fèvre et al. 2005a) and 'Epoch 2' observations covers 2200 arcmin 2 as shown in Figure 5.
Acknowledgements. We thank the referee,  N. Padilla  for his careful review of the manuscript and suggestions which have significantly improved this paper. We thank  ESO  staff for their continuous support for the VVDS surveys, particularly the Paranal staff conducting the observations and the  ESO  user support group in Garching. This work is supported by funding from the  European Research Council Advanced  Grant  ERC-2010-AdG-268107-EARLY. This survey is dedicated to the memory of  Dr. Alain Mazure , who passed away when this paper was being refereed, and who has supported this work ever since its earliest stages.
Note that any misalignment of those surfaces generates spurious waves in the measured output bar signals. The amplified signal of the strain gages is recorded at a frequency of 1MHz. The recorded strain signals are post-processed with the software package DAVID  (Gary, 2005)  to reconstruct the time histories of the axial strain at the end of the input and output bars, following the procedures described in  Zhao and Gary (1996). Low pass filtering, with a cutoff frequency of 100kHz, is used during post processing to remove electrical noise. Accounting for wave dispersion, the measured incident and reflected waves are "transported" to the input bar/loading device interface. Analogously, the measured transmitted waves in the output bars are transported to the output bar/loading device interfaces.
Local displacement measurements A high speed video system (Phantom v7. 3 with 90mm macro lenses) is employed to record pictures of the specimen surface at a frequency of 111kHz (1 frame every 9µs). A resolution of 448x32 pixels is chosen. The camera is positioned at a distance of about 400mm from the specimen surface, which results in a square pixel edge length of 44µm. A thin layer of white matt paint is applied to the specimen surface along with a black speckle pattern of a speckle size of about 70µm. An exposure time of 1µs is set up with a 150W halogen bulb providing the necessary lighting of the gage section. The video recording is triggered at ‫ݐ‬ ൌ ܶ ௧ when the input bar strain gage detects the rising edge of the loading pulse. The virtual extensometer function of the digital image correlation software VIC2D (Correlated Solutions, SC) is used to determine the relative displacement of two points located on the axis of symmetry of the specimen gage section.
Finite element model The explicit solver of the finite element analysis software Abaqus (  2007 ) is used to perform the analysis. All parts are meshed with reduced integration 3D solid elements (type C3D8R from the Abaqus element library). The elements of the bar meshes have an edge length of 5mm in the axial direction, and 2mm in the radial direction, allowing for an accurate description of axial frequencies of up to 50 kHz and possible bending waves. The different parts of the loading device are meshed with the same type of elements with an edge length of 1mm. The mesh of the 12mm long and 5mm wide specimen features an edge length of 0. 4mm and four elements along the sheet thickness direction. Due to the symmetry of the mechanical system (with respect to the x-y plane defined in Fig. 2 ), only one half of each component is modeled along with the corresponding symmetry boundary conditions.
Acknowledgements The partial financial support of the  French National Center for Scientific Research (CNRS)  and the  MIT/Industry fracture consortium  is gratefully acknowledged. Professors Tomasz Wierzbicki  (MIT) and  Bengt Lundberg  ( Uppsala University ) are thanked for valuable discussion. Mr. Philippe Chevalier  from  Ecole Polytechnique  is thanked for his assistance in carrying out the experimental work.
Suggested volcanoes and associated stratospheric surface area density follow CCMVal (http://www. pa. op. dlr. de/CCMVal/Forcings/ CCMVal Forcings WMO2010. html), with no volcanic eruptions in the future. The proposed simulation length was 4-10 yr (excluding spinup, see Table  2 ) using prescribed monthly sea surface temperature (SST) and sea ice concentration (SIC) distributions, valid for each time slice and averaged over 10 yr. This averaging was designed to reduce the effect of interannual variability and therefore provide optimal conditions from which average composition changes and associated forcings can be more readily computed. Output from transient simulations performed with two coupled chemistryclimate-ocean models (i. e. CMIP5 runs, performed by GISS-E2-R and LMDzORINCA) is also part of the ACCMIP data collection.
Emissions and concentration boundary conditions Consistent gridded emissions data from 1850 to 2100 were created in support of CMIP5 and of this activity; the historical  portion of this dataset is discussed in  Lamarque et al. (2010). The year 2000 dataset was used for harmonization with the future emissions determined by Integrated Assessment Models (IAMs) for the four RCPs described in  van Vuuren et al. (2011)  and references therein. As shown in Fig. 1 , all emissions necessary for the simulation of tropospheric ozone and aerosols between 1850 and 2100 are available for both anthropogenic activities (including biofuel, shipping and aircraft) and biomass burning. Concentrations of long-lived chemical species and greenhouse gases were based on the observed historical record  and on the RCP emissions, converted to concentrations by  Meinshausen et al.
, 2005)  Constant present-day isoprene, Other VOCs, Oceanic CO, Soil NOx NCAR-CAM3. 5 Interactive, based on model's convection  (Price et al. , 1997; Ridley et al. , 2005) , scaled to produce ∼ 3-5 Tg N. Not every model performed all simulations, even the primary ones. The overall availability of results for each model and each simulation is shown in Table  2. Data are currently archived at the British Atmospheric Data Centre (see http://badc. nerc. ac. uk), with a data access policy providing one year of access to participating groups only, followed by general public access to be granted no later than 31 July 2013.
On the other hand, UM-CAM and STOC-HadAM3 share the same dynamical core. A high degree of similarity is also found in GISS-E2-R and GISS-E2-R-TOMAS (different aerosol scheme; we will use the terminology GISS-E2s when common characteristics are discussed) and NCAR-CAM3. 5 and CESM-CAM-Superfast (different chemistry scheme). Also, NCAR-CAM3. 5 and NCAR-CAM5. 1 share the same dynamical core and several physics parameterizations but differ in their representation of clouds, radiation and boundary-layer processes; these models can be considered as distinct. No coupling (CTM) 1-D, two-stream model  (Hough, 1988). Uses climatological ozone above tropopause and modelled ozone be- low UM-CAM Offline O 3 (CMIP5 database), CH 4 , CO 2 , N 2 O, CFCs Look-up table  (Law and Pyle, 1993) ; no correction for modeled fields Different grid structures are used for the horizontal discretization.
Boundary conditions As mentioned in Sect. Model, also based on ECHAM5, but with differences in resolution and shortwave radiation  (Cagnazzo et al. , 2007). Some models used the previous AR4 simulations, applying an approximate correspondence between RCPs and SRES scenarios (GEOSCCM, NCAR-CAM3. 5; see  Lamarque et al. , 2011). Finally, the CICERO-OsloCTM2 model used analysis data for year 2006 from ECMWF IFS analysis for all experiments. Methane concentration (from  Meinshausen et al. , 2011 , see Fig. 2 ) is prescribed at the surface (bottom layer or two layers, with or without a specified latitudinal gradient) in many models. It is prescribed over the whole atmosphere in CESM-CAM-Superfast and NCAR-CAM5. 1 (using the NCAR-CAM3. 5 distribution) and in STOC-HadAM3 and UM-CAM. Only LMDzORINCA uses methane emissions for historical and future simulations, while GISS-E2s use emissions only for future simulations.