dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Sybren Wijmenga  for providing a copy of the software NUCHEMICS. We are grateful to  Kurt W üthrich ,  James Fraser  and  R. Bryn Fenwick  for stimulating discussions and careful reading of the manuscript. J. B. acknowledges access to the  HPC resources of TGCC (Très Grand Centre de calcul du CEA)  under the allocation t2013077065 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). This work is part of the  ITSNAP Associate Team. We thank the  Inria Équipe Associée program  for financial support.
This will require in situ and remotely sensed data at much higher resolution and density than cur-rently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO 2 proxy measurements such as radiocarbon in CO 2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.
Framework for the carbon monitoring system Although research priorities for carbon-cycle science are established, climate policies including detailed provisions of emission reduction treaties, national legislation, and voluntary programs are only partially defined and will continue to evolve over the coming decades. This complicates the formulation of requirements for an integrated carbon monitoring system, and the assessment of relative strengths of different observational approaches and model-data integration schemes. In order to provide a necessary context for the proposed strategy, we establish in this study a framework for a carbon monitoring system based on the following questions: -What are and were the magnitudes, distribution, variability and trends of anthropogenic CO 2 and CH 4 emissions, including their attribution to relevant sectors? -How effective will national, regional, and city-and project-scale policy interventions be in reducing greenhouse gas emissions and/or increasing carbon sequestration? -What are the magnitudes, distribution, variability, and trends of regional natural CO 2 and CH 4 fluxes, and what does this information tell us about the underlying natural and human induced mechanisms in each region? -What and were are the magnitudes, distribution, variability, and trends of regional carbon stocks in natural and managed ecosystems? -How are CO 2 and CH 4 sources and sinks likely to behave in the future under higher atmospheric CO 2 concentrations and altered patterns of atmospheric composition (nitrogen deposition, elevated ozone), climate, land vegetation, and ocean circulation as well as from human appropriation of terrestrial and marine resources impacting the carbon cycle? -How soon might positive/negative feedbacks that may enhance/reduce natural CO 2 and CH 4 emissions, reduce/enhance sinks, possibly associated with thresholds, come into play over different sensitive regions, and how could these feedbacks be detected and quantified by observations? This study builds on the Integrated Global Observing System-Partnership (IGOS-P) Carbon Theme Team report  (Ciais et al.
Fossil fuel emissions Current data sets of fossil fuel CO 2 emissions averaged by country, sector and year are maintained by the International Energy Agency (IEA, 2012) (http://www. iea. org/ co2highlights/co2highlights. pdf), the Carbon Dioxide Information Analysis Center (CDIAC)  (Boden et al. , 2012)  based on the United Nations Statistics Division (UNSO) energy data set, and Emission Database for Global Atmospheric Research -EDGAR4. 2 (a product of the Joint Research Center of the European Commission (JRC) together with the Netherlands Environmental Assessment Agency (PBL) (EDGAR4-database, 2009) based on IEA energy data set. Emission maps exist over the globe from different data products  (Andres et al. , 1996)  with limited temporal information (e. g. monthly in  Andres et al. , 2011).
, 2010 (Wunch et al. , , 2011a)). For middle-tropospheric CO 2 column abundances from infrared sounders, precision estimates of 1 ppm on 2 • spatial and bi-weekly temporal scale for AIRS  (Maddy et al. , 2008) , 2 ppm precision on 5 • spatial and monthly temporal scale for IASI  (Crevoisier et al. , 2009a) , and 10 ppm single sounding precisions for TES  (Kulawik et al. , 2010)  are reported. For XCO 2 from solar backscatter measurements, precision estimates for single soundings of 3 ppm dry air mole fraction for SCIAMACHY  (Reuter et al. , 2011) , and 2 ppm for GOSAT  (Yoshida et al. , 2013)  are reported. Spatially and temporally aggregation of SCIAMACHY and GOSAT data further improve the precision, if errors are mainly random, depending on cloud cover and spatial sampling. Future missions like OCO-2 and Carbon Monitoring Satellite (CarbonSat) target aggregated precisions of 1 ppm and better.
4 ). In addition, the methods are under development to account for large-scale biases using TCCON data  (Wunch et al. , 2011b). For current mid-to-upper tropospheric concentrations of methane (CH 4 ) from thermal infrared sounders like IASI, reported precision estimates are in the range of 17-35 ppb on 5 • spatial and monthly temporal scale  (Crevoisier et al. , 2009b). For XCH 4 , reported precision estimates for individual soundings are about 17-35 ppb for SCIAMACHY  (Frankenberg et al. , 2006)  and 12. 5 ppb for GOSAT  (Yoshida et al. , 2013). Spatially and temporally aggregated precisions of SCIAMACHY and GOSAT can reach the 10 ppb level (and even below for GOSAT) depending on cloud cover and spatial sampling. Future missions like CarbonSat target aggregated measurement precisions of 5 ppb.
Surface network of remote-sensing measurement stations The Total Carbon Column Observing Network (TCCON) ground-based network (www. tccon. caltech. edu/, Wunch et al. , 2011a)  measured at some 18 stations in 2012 (Fig. 3b ) in the near-infrared solar absorption spectrum. The current TC-CON standard product already consisted of XCO 2 , XCH 4 , XCO, XN 2 O, XH 2 O, XHCl, XHF, XHDO, and all routinely submitted to TCCON data archive. More species are possible and could be retrieved from the already existing measured spectra. An extension of the TCCON wavelength range would allow measurements of XOCS, XC 2 H 6 , and many other species, although it will require an upgrade of instrument and lead to costly logistics. The TCCON instruments currently work to retrieve column-average mixing ratios with a precision of ∼ 1 ppm for XCO 2 and 3 ppb for XCH 4.
A key ongoing international effort, the Surface Ocean CO 2 Atlas (SOCAT) aims to synthesize pCO 2 data collected over the last 40 years into a quality controlled data base, along with uniform metadata, that can be used to examine pCO 2 variability over a range of time and space scales  (Pfeil et al. , 2013; Sabine et al. , 2013). The current version of the SOCAT pCO 2 database contains more than 6 million observations collected by research ships, commercial volunteer ships, and moorings (Fig. 5 ). It is expected to reach more than 10 million in the second release. Over the best-sampled ocean regions, such as the North Atlantic and the equatorial Pacific, mean air-sea CO 2 fluxes can be reconstructed to within 20 %, and their interannual variation to within 10 %  (Watson et al. , 2009). However, the majority of the ocean is still under-sampled despite the 40 yr data set (Fig.
, 2011). Systematic and global re-occupation of select hydrographic sections was initiated by the international community in the early 2000s to quantify changes in storage and transport of heat, fresh water, carbon dioxide (CO 2 ), and related parameters (internationally coordinated through the Global Ocean Ship-Based Hydrographic Investigation Pro-gram (GO-SHIP); www. go-ship. org). The current situation is that data from these repeat occupations have already revealed substantial changes in the ocean interior carbon storage in response to the continuing uptake of anthropogenic CO 2 (e. g. ,  Wanninkhof et al. , 2010; Sabine et al. , 2008; Murata et al. , 2007; Feely et al. , 2012)  as well as the presence of a substantial amount of decadal variability in the ocean carbon cycle. In addition to documenting changes that already occurred since the first occupation, these repeat hydrographic measurements continue to serve as a baseline to assess future changes.
Repeat surveys of the ocean with physical parameter measurements are also suited to detect changes in oceanic transport of heat, as well as changes in oxygen  (Keeling et al. , 2010)  and nutrients. Below the level of the ARGO array of automated floats (www. argo. ucsd. edu/) repeat hydrography is the only global method capable of observing long-term trends in ocean carbon. The program also provides data for sensor calibration and to support continuing model development that lead to improved forecasting skills for oceans and global climate. For the global ocean, the Global Ocean Data Analysis Project (GLODAP) data set  (Key et al. , 2004 ) that assembled the data from the first global survey has become a benchmark for testing biogeochemical ocean general circulation models. It also has served as the basis for first data assimilation efforts to estimate global-scale ocean-atmosphere CO 2 fluxes  (Gruber et al.
, 2009; Gloor et al. , 2003). For different oceans or ocean basins, new repeat hydrography data syntheses have been created (e. g. CARbon dyoxide IN the Atlantic Ocean -CARINA,  Key et al. , 2010)  or are emerging (e. g. PA-CIFic ocean Interior CArbon -PACIFICA data set through the North Pacific Marine Science Organization (PICES) and other partners). The data collected so far through the repeat hydrography program are too sparse to unambiguously document the global-scale accumulation of anthropogenic CO 2 since the 1990s, although the data collection PACIFICA is now finalized and published at http://cdiac. ornl. gov/oceans/ PACIFICA/ and ongoing synthesis work will likely resolve this challenge soon (e. g. Sabine and Tanhua, 2010). Currently, monitoring programs do not exist for oceanic pCH 4 , as the ocean is considered to be only a minor source of this greenhouse gas  (Ciais et al.
, 2010). The development of optical sensors has allowed the measurement of phytoplankton fluorescence onboard ARGO buoys  (Johnson et al. , 2009; Claustre et al. , 2010)  providing a new tool to monitor biological productivity, and thus the carbon cycle, within the ocean interior. The increase in the number of bio-optical ARGO buoys in forthcoming years will complement available satellite ocean color data of surface chlorophyll concentration. Furthermore, the advent of pH and nitrate sensors provides the potential to expand the suite of measurements to assess the trophic status as well as ocean acidification  (Johnson et al. , 2009). A number of carbon-related ( pCO 2 , TA, pH, DIC) sensors for ocean use on autonomous vehicles are being developed and tested.
More recent sensors such as MODIS (Moderate-Resolution Imaging Spectroradiometer)  (Franz et al. , 2006)  and MERIS (MEdium-Resolution Imaging Spectrometer)  (Rast et al. , 1999)  have strengthened and extended this space-based ocean observing system (Fig. 5 ). A third satellite data product of high importance for ocean carbon-cycle studies are direct wind speed measurements from a range of scatterometers, such as Quick Scatterometer -QuikSCAT/SeaWinds (http://winds. jpl. nasa. gov/missions/quikscat/). Currently, estimating air-sea CO 2 fluxes from combined satellite and in situ measurements remains a challenge because the carbon content in the ocean surface layer depends not only on the surface temperature and phytoplankton biomass (that can be monitored from space), but also on the mixed layer depth and water-mass history.
Measurements of lateral carbon fluxes from land to the oceans are needed to accurately access carbon budgets of single countries or groups of countries for emission verification purposes. Recent data syntheses on riverine delivery data to the ocean (involving hydrological modeling on land) have significantly improved our ability to quantify land-ocean carbon fluxes  (Mayorga et al. , 2010). Due to the spatiotemporal heterogeneity of riverine carbon transport to the ocean, intermediate filtering processes  (Borges, 2005)  and associated release of CO 2 to the at-mosphere by freshwaters and estuaries, to the complexity of the interaction between shelf-seas and the open ocean  (Walsh, 1991) , a quantification of land-ocean carbon transport is a substantial challenge  (Regnier et al. , 2013). A rough estimate of total land-ocean carbon transfer amounts to ca.
Estuaries For estuarine carbon releases/burial, no global consistent observational data set exists as yet. A global synthesis taking into account spatially explicit typologies of estuaries and continental shelves estimated an estuarine CO 2 flux to the atmosphere of 0. 27 ± 0. 23 Pg C yr -1 (Laruelle et al. , 2010). Only for specific estuaries, regional observational carbon data sets are available (e. g. Frankignoulle et al. , 1998; Green et al. , 2006; Salisbury et al. , 2008).
River carbon fluxes to the ocean/carbon budget of coastal ocean Recent data syntheses (involving hydrological modeling on land) have improved our ability to quantify carbon delivery from land to estuaries and oceans  (Mayorga et al. , 2010). The CMTT (Continental Margins Task Team from the International Geosphere-Biosphere Programme (IGBP) core projects JGOFS, LOICZ and IMBER) synthesized carbon budgets from all major continental margins including the proximal as well as distal domains  (Liu et al. , 2010). The ongoing international Surface Ocean CO 2 Atlas (SOCAT) project (see Sect. 2. 4. 1) includes a specific surface pCO 2 data synthesis for coastal seas.
Remote sensing of terrestrial carbon-cycle parameters Remote sensing is an essential component of current carbon observation assets, for detecting trends and variability in land components of the carbon cycle. Past studies in vegetation dynamics have had to rely on vegetation indices such as the normalized difference vegetation index (NDVI) using the Advanced Very High Resolution Radiometer (AVHRR) data since the 1980s  (Tucker et al. , 2005; Myneni et al. , 1997)  Satellite radar data, both C-band and L-band, have been used over the past several decades to document land cover and disturbance. They have some sensitivity to canopy volume and have been used to estimate aboveground biomass in low to medium biomass ecosystems. Passive microwave measurements for freeze-thaw conditions have been used to estimate growing season length and infer GPP in northern high-latitude ecosystems.
The data shows good correlation with GPP. Chlorophyll fluorescence provides therefore direct observational constraints on GPP which opens a new viewpoint on the global carbon cycle. Future missions which cover the spectral range of the O 2 A-Band and includes solar Fraunhofer lines with sufficient high spectral resolution like OCO-2, GOSAT-2 or CarbonSat will also provide this information when launched. The cross validation or calibration of different data products may help to explore more uniformed vegetation traits and key satellite-based parameters of terrestrial productivity in the future.
FRE products from SEVIRI are generated every 15 min over Africa, Europe, and part of South America; with operational production at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Land Satellite Applications Facility (Land-SAF) (http://landsaf. meteo. pt/). Such products are already being used for fire emission calculations within the European Medium Range Weather Forecasting (ECMWF) system and steps are being taken to integrate FRE data from the constellation of geostationary meteorological satellites into the system  (Kaiser et al. , 2012). Integrated fire emission products are based upon satellitebased fire data incorporated in ecosystem models. The Global Fire Emission Database, GFED (Van der  Werf et al. , 2003) ; www. globalfiredata. org), is an integrated product, combining MODIS, ATSR, and VIRS satellite products of fire activity with the Carnegie-Ames-Stanford Approach (CASA; http://geo.
5. Concentration in the soil of dissolved and particulate inorganic and organic carbon (DOC and POC) monitored at at least 100 stations located in different biomes and ecosystems. Stations may be equipped with erosion traps, wells or lysimeters and tensiometers to continuously monitor chemical composition and fluxes of carbon in groundwater. In the longer term, over the next 20 years, production of DOC and POC should be monitored at stations located in different biomes and ecosystems (likely collocated with FLUXNET stations). These future stations should be equipped with wells, lysimeters and tensiometers that monitor chemical composition and fluxes of carbon in groundwater. Data could be disseminated through existing infrastructure (e. g. Glo-RiCh -Global River Chemistry Database) for hydrochemical modeling.
Hurdles and cross-cutting issues in the deployment of a global carbon observing system Developing and operating an integrated global carbon observing system will require a coordinated effort, spanning many partner countries and organizations to support instrument development, data and model validation, sustained observations, quality assessment and control, data assimilation, database management, carbon-cycle modeling, fossil fuel inventories, high-performance computing resources, decisionsupport analyses, and systems engineering. Advanced dataintensive analysis and visualization methods will be required to evaluate the information collected in the integrated observ-ing system. The system will have to be built in successive steps. Figure  8  gives a notional vision of the different accuracy and sampling requirements that could be associated with each successive step over the next two decades.
Integration of multiple data streams and model-data fusion The task of carbon data integration has several objectives: 1. Spatial integration, in which measurements from different regional programs are coherently combined, and databases interoperable. 2. Completeness, in which we try to ensure that important processes of the carbon cycle are observed with clear error assessment. 3. Temporal integration, to be able to use long-time series for improved model prediction, as well as evaluation of impacts associated with climate policy decisions. 4. Process integration, in which data are combined to form a consistent view of the carbon cycle given inferences from atmospheric, oceanic, terrestrial and socioeconomic data, and their uncertainties. It is obvious that any coherent observing strategy must take account of how observations will be used.
P. Ciais et al. : Current systematic carbon-cycle observations Acknowledgements. This study was carried out as part of the GEO task on integrated carbon observations (GEO Carbon Strategy Report; available for download at http://www. falw. vu/~dola/downloads. html coordinated by  H. Dolman  and  P. Ciais) , and the  FP7 European Commission  funded project  GEOCARBON  (www. geocarbon. net). The writing of the manuscript is sponsored by  Climate-KIC/European Institute of Technology  (www. climate-kic. org/)through the  Carbocount  project. RJA was sponsored by  US Department of Energy, Office of Science, Biological and Environmental Research (BER)  programs and performed at  Oak Ridge National Laboratory (ORNL)  under  U. S. Department of Energy  contract  DE-AC05-00OR22725. P. Rayner  is in receipt of an  Australian Professorial Fellowship  ( DP1096309 ).
Ecosys- tem models used to calculate CO 2 and CH 4 fluxes and carbon stock change from observations will need to as- similate remote-sensing and in situ data at the scale where the data are collected. 3. Pursue ecosystem models inter-comparison, bench- marking and evaluation programs such as the Interna- tional Land Model Benchmarking Project -ILAMB (www. ilamb. org) and dynamic global vegetation model projects -TRENDY (www. dgvm. ceh. ac. uk), and link to plant trait databases to exploit most up-to-date eco- logical information relating to biodiversity (Plant Trait -TRY database). In terms of terrestrial carbon observation access and infras- tructure, the needs include 1. Data access tools for operationally robust forest and car- bon monitoring systems such as the GEO Forest Carbon Tracking Task (www.
In §2 we summarize the relevant observations and photometry. In §3 we present the photometric redshifts, lens models, and results, discussed and summarized in §4. We assume a ΛCDM cosmology with Ω M = 0. 3, Ω Λ = 0. 7, and H 0 = 100 h km s -1 Mpc -1 with h = 0. 7. Lotz). These data consist of 70 orbits with WFC3/IR in the F105W, F125W, F140W, and F160W near-infrared filters, and 70 orbits with ACS/WFC in the F435W, F606W, and F814W optical bandpasses. These observations were supplemented with archival ACS data, ∼ 13 -16 ksec in each of these optical filters, taken between 2009 Oct 27-30 (GO 11689, P. I. , Dupke). We also use one orbit imaging in each of the F105W and F125W bands, and 1. 5 orbits in the F160W band, obtained in 2013 Aug and 2014 Jun-Jul (GO 13386; P. I. , Rodney). A detailed description of our data reduction and photometry can be found in  Zheng et al.
Low redshifts, however, are very clearly excluded. Bottom: similar prediction pattern for image C again showing the high-z preference. Lyman break using the color cuts given in  Zheng et al. (2014) , supplemented by careful visual inspection. For sources of interest lying near cluster members, such as JD1B and JD1C here (see below), we first run GALFIT  (Peng et al. 2010)  to remove the nearby members, before running SExtractor. Similarly, for JD1A, a nearby star was removed prior to the photometry (see §3). In addition to the HST observations, we also utilize Spitzer /IRAC imaging of A2744 obtained as part of Program 90257 (P. I. , Soifer) between 2013 Sep and 2014 Feb, supplemented with archival imaging from 2004 (Program 84; P. I. , Rieke). We process the IRAC Basic Calibrated Data (cBCD) images using standard methods implemented in MOPEX  (Makovoz & Khan 2005) , and create a final mosaic in each channel with a pixel scale of 0.
As an additional check, we also inspect the archival WFC3/IR imaging of A2744 from GO 13386 (P. I. , Rodney), which is rotated by approximately 9 • relative to the HFF mosaics, and find that both JD1A and JD1B are present (although only within the noise level due to the shallowness of this imaging), again suggesting these are not artifacts related to the spikes. Finally, we verify that neither JD1A nor JD1B are moving, foreground objects by creating custom mosaics from the first and second half of the individual F160W exposures obtained as part of the HFF observations. JD1A and JD1B are both clearly detected in both mosaics. Furthermore, subtracting the two mosaics causes both sources to disappear, again indicating that these are bona fide extragalactic sources. http://www. stsci. edu/hst/campaigns/frontier-fields/ FF-Data 4.