dataesr/scientific-paragraphs-categorization huggingface dataset:
Terrestrial ecosystems management data Global geo-referenced information about harvest and use of carbon in food and wood products is required to close regional terrestrial carbon budgets, and integrate ecosystem-based carbon budgets into regional budgets. Harvest information should be determined regularly from agricultural and forestry statistics, with the best geospatial resolution at which statistics are collected (for instance, counties). Basic notional requirements for management data are: 1. Geospatial information on crop and wood biomass harvest updated each year, at resolution of at best 1 km to 1 county in regions with detailed agricultural and forestry statistics, and at worst 100 km in regions with less detailed statistics. 3. Geospatial information about the use of wood and food products, including burning for energy, cooking, consumption by the population and by animals in case of food products, decay in landfills for wood products.
Development and evaluation are required for models to include land use and land use change (cohorts), forestry, fire/insect/wind throw disturbance (including the scale, frequency and severity of each type of disturbance and subsequent lagged effects, forest growth and demography changes, and agricultural management). Models need to incorporate, in addition to water, energy and carbon cycle processes, the interactions between nutrients and carbon cycling (both limitations and fertilizations resulting from increased nutrient supply), export of dissolved/particulate carbon from ecosystems to river headstreams, and transformation of carbon fluxes in aquatic systems. Ecosystem models used to calculate CO 2 and CH 4 fluxes and carbon stock change from observations will need to assimilate remote sensing and in-situ data at the scale where the data are collected.
In terms of terrestrial carbon observation access and infrastructure, the needs include: 1. Data access tools for operationally robust forest and carbon monitoring systems such as the GEO Forest Carbon Tracking Task (www. geo-fct. org) -spatial data infrastructure and web-delivery systems to transfer data holdings and present maps. 2. Closer working relationships with space agencies, e. g. through the Committee on Earth Observation Satellites (CEOS) to provide continuous supply of satellite data, to annually monitor areas of forest, deforestation and forest degradation, as well as afforestation, reforestation, and areas of rising biomass density. Because MRV reports are required for both national and sub-national level (e. g. REDD+) scale, and to avoid 'leakage', border-to-border remote sensing data with a spatial resolution of 50 m as a minimum will be required.
3. Access to inventory data and accuracy assessment (e. g. web-based) including remote sensing data products (e. g. aboveground biomass) and model input parameters and output (e. g. net carbon uptake, carbon stocks) with version documentation. 4. Statistical characterization of errors of all information used in the data integration, including ecosystem models, and their forcing data, in terms of bias and random errors. Random errors should be described by space-time covariance matrices. regions, where CH 4 fluxes are sensitive to climate or land use change (wetlands, permafrost, landfills, fire-prone ecosystems, geological gas storage areas, some dams) can also be considered as hotspots. The consequences of their destabilization will likely be increased natural emissions, loss of sink capacity, with positive feedback on climate change.
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, decision-support analyses, and systems engineering. Advanced data-intensive analysis and visualization methods will be required to evaluate the information collected in the integrated observing system. The system will have to be built in successive steps. 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 inter-operable. 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.
Quantitative network design and observing system simulation experiments Given knowledge of the error statistics of potential measurements, it is possible to quantify the value of a given dataset in terms of the new information it brings. This always requires some kind of model of the underlying statistics. The approach is described in  Kaminski et al. (2012)  for network design. The approach has been used to quantify the value of in situ measurements of concentration for constraining surface fluxes and to assess the likely value of satellite measurements of CO 2. One can even attempt to design an optimal network (e. g. Rayner et al. , 1996) , but the task of defining optimality and the assumptions of prior knowledge suggest that the more conservative option of testing potential networks is more reliable  (Hungershoefer et al.
Integrated data products and services The goal of a policy-relevant carbon observing system is to generate data products that are of value for the user communities. Raw observations are rarely adequate on their own. To create usable products, in situ measurements from a variety of sources with the vastly different spatial, temporal and process resolutions used in the modeling approach need to be integrated with remote-sensing observations within a modeling framework. To achieve this objective the following actions will be needed. 1. Ensure documentation and harmonization of in situ data from diverse sources. The current problems with in situ data include inconsistent parameter definitions of state variables, different data formats, incomplete data and estimates of uncertainties, various spatial and temporal scales, and sampling bias in measurements.
Close coordination between data managers, those making the observations, modelers, and other data users is critical. Integration and dialogue will be required between the research teams and the data systems to define and implement data product requirements. It is almost impossible for large data users to communicate with each individual data provider. Frameworks like NOAA/ESRL/GMD, WMO-WDCGG and ICOS are crucial as an intermediary. The continuous improvement of a carbon observing system should periodically take stock of the data and information by documenting its character and quality in ways that are responsive to the needs of its end users, now, for both basic and applied uses, and into the future as they provide the climate-quality, long-term records of Earth system change. As the system provides information for policy-makers (e.
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 datasets are available, e. g. Frankignoulle et al. (1998), Green et al. (2006), Salisbury et al. (2008)). Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | River carbon fluxes to the ocean/carbon budget of coastal ocean.
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. htmlcoordinated 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  US Department of Energy  contract  DE-AC05-00OR22725. P. Rayner  is in receipt of an  Australian Professorial Fellowship  ( DP1096309 ).
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 datasets are available, e. g. Frankignoulle et al. (1998), Green et al. (2006), Salisbury et al. (2008)). Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | River carbon fluxes to the ocean/carbon budget of coastal ocean.
Flow cytometry and fluorescence microscopy Cells were washed with PBS and dissociated by treatment with trypsin-EDTA. For cell cycle analysis, 1•10 6 cells were incubated with 20 μl of Hoechst 33342 (SIGMA B2261) in the dark for 30 min at 37 °C. For SSEA1 immunostaining, 5•10 5 cells were incubated for 15 min at room temperature in GMEM + 10% fetal calf serum. Then 10 to 30 ng of SSEA1-Alexafluor 647 (sc21702, Santa Cruz) antibody was added and cells were further incubated for 30 min at room temperature. Cells were analyzed using a FACS LSR II (Becton-Dickinson) equipped with 355, 488, and 561 nM lasers or were sorted with an Aria cell sorter (Becton-Dickinson) equipped with 405, 488, and 633 nM lasers. Data were acquired and analyzed using DiVa software.
Acknowledgments This work was supported by the  LabEX DEVweCAN  ( ANR-10-LABX-61 ). DC is a recipient of a fellowship from the  Association pour la Recherche contre la Cancer (ARC). We are grateful to  Peter Sicinski  for the gift of the pCS2cycE WT plasmid, and  Jim Maller  for the gift of pINDCycE WT and pINDCycE S180D plasmids. We are grateful to  Sébastien Dussurget  and  Thibault Andrieu  ( IFR Bioscience-Gerland ) for their assistance with flow cytometry.
The CoRoT and Kepler missions provide us with thousands of red-giant light curves that allow a very precise asteroseismic study of these objects. Before CoRoT and Kepler, the red-giant oscillation patterns remained obscure. Now, these spectra are much more clear and unveil many crucial interior structure properties. For thousands of red giants, we can derive from the seismic data precise estimates of the stellar mass and radius, the evolutionary status of the giants (with a clear difference between clump and RGB stars), the internal differential rotation, the mass loss, the distance of the stars. Analysing this mass of information is made easy by the identification of the largely homologous red-giant oscillation patterns. For the first time, both pressure and mixed mode oscillation patterns can be precisely depicted.
Data preparation and processing The basic structure of LIRIC is shown in Fig. 1. In our study we used the LIRIC version from autumn 2012. The lidar database for LIRIC consists of background-corrected, elastic-backscatter lidar signals P (λ j , z i ) for different laser wavelengths λ j : P (λ j , z i ) = E 0 (λ j ) O(λ j , z i ) z 2 i [β aer (λ j , z i ) + β mol (λ j , z i )] × exp    -2 z i 0 α aer (λ j , z) + α mol (λ j , z) dz   . ( 1 ) E 0 is the system constant and considers, e. g. , the outgoing laser pulse energy, collection area of the telescope, optical efficiencies of the transmitter and receiver units, photon detection efficiency, and vertical thickness z of the backscattering range cell. O(λ j , z i ) describes the incomplete overlap of the laser beam for wavelength λ j with the RFOV.
From photometric measurements (see Fig. 1 , yellow box) the retrieved fine-mode and coarse-mode volume concentrations V m,s , the complex refractive index (real part n r , imaginary part n i ), the size distribution, and the volume fractions of spherical particles (denoted as sphericity in the AERONET database) are required to solve Eqs. (  9 )-(  12 ). These quantities are obtained by means of the AERONET inversion algorithm. The nonspherical particles are described with the spheroidal-particle model after  Dubovik et al. (2006). Several simplifying assumptions are made. The refractiveindex characteristics are the same for fine and coarse particles. The ratio of spherical to spheroidal particles (in terms of column volume concentration) is also assumed to be the same for fine mode and coarse mode.
Variations of the reference height and minimum measurement height in the LIRIC data analysis by ± 1 km lead to variations in the backscatter and extinction coefficients by up to 50 % in the lowermost 1500 m and around 15 % in the aerosol layers. However, many of these resulting LIRIC profiles then show, e. g. , a constant but unrealistic offset above the main aerosol layers (around the reference height) and can thus be sorted out by visual inspection. Nevertheless, a careful study of the impact of the overlap correction and setting of the minimum and reference heights is an important part of the lidar data analysis and an important prerequisite for high-quality LIRIC products. Figure  4  shows a comparison of the LIRIC backscatter coefficients at 532 nm with respective backscatter profiles directly retrieved from the Raman lidar observations (total backscatter coefficient, for simplicity denoted as POLIPHON curves) and derived by using the separation method of  Tesche et al.
(2009a)  for the spherical and non-spherical particle fractions. As can be seen, the total backscatter coefficients agree well. The respective 532 nm extinction coefficient in Fig. 3  is however overestimated by 10-20 % in the dust layer when compared to the direct Raman lidar observation (not shown), and thus also the dust   extinction-to-backscatter ratio is overestimated (more details are given below). In the central and left parts of Fig. 4 , the LIRIC backscatter coefficients for non-spherical and spherical particles are shown and compared with the respective POLIPHON result. In addition, the LIRIC backscatter profiles for the fine mode and coarse mode are given. The sphericity as provided by the AERONET retrieval plays an important role in the LIRIC data analysis. The spherical particle volume fraction is 1.
7 % after AERONET and finally 0. 1 % in the LIRIC data set. According to the POLIPHON volume concentrations the sphericity is 8. 5 %. As a consequence, the LIRIC nonspherical particle backscatter coefficients are slightly larger than the respective POLIPHON values in the lofted Saharan dust layer and considerably larger in the haze layer below 700 m height. By comparing the LIRIC coarse-mode backscatter profile with the one for non-spherical particles, we obtain an impression as to how much of the backscatter is caused by non-spherical fine-mode particles. Because of the low AERONET sphericity value the backscatter coefficient for spherical particles is almost zero at all heights. In contrast, the respective POLIPHON curve shows that spherical particles considerably contributed to backscattering in the haze layer.
6. These values are typical for the western Saharan dust  (Tesche et al. , 2009b; Schuster et al. , 2012). The LIRIC backscatter and extinction profiles lead to dust lidar ratios of 78-80 sr (355 nm) and 60-62 sr (532 nm) in the height range of 1500 to 3000 m (center part of the dust layer). However, the lidar ratio (or better the particle backscatter coefficient) depends in a complicated way on the chemical composition (refractive-index characteristics), size distribution, particle shape, and aspect ratio (particle lengthto-width ratio) distributions. the observed systematic bias in the Ångström exponents and lidar ratios. Furthermore, the a priori AERONET input data are provided as column values, ignoring the observed aerosol layering with a lofted dust aerosol layer above the central European haze layer.
Volcanic ash In April 2010, an aged volcanic aerosol layer consisting of a mixture of volcanic dust, volcanic sulfate particles, and anthropogenic haze occurred over central Europe. These aerosol conditions provided a unique opportunity to apply the LIRIC approach to another type of irregularly shaped aerosol particles. In contrast to desert dust, volcanic dust is hygroscopic  (Lathem et al. , 2011) , so changes in the shape characteristics by water uptake cannot be excluded when relative humidity is high (i. e. , > 70 %)  (Ansmann et al. , 2012). As a consequence, spherical particles may occur not only in the fine-mode but also in the coarse-mode fraction, and thus may complicate the interpretation of POLIPHON results and the comparison with LIRIC results. The contribution of spherical particles to the particle volume concentration was about 15 % according to the AERONET data analysis.
LIRIC calculations were performed with cloud-screened lidar signal profiles for the time period from 14:35 to 15:36 UTC on 19 April 2010. The considered photometric measurements were taken at 14:49 UTC. Lidar signals were only used up to 8. 25 km height (reference point) because of cirrus cloud evolution above this height. The minimum measurement height z N 0 was set to 600 m. According to the volume concentration profiles in Fig. 9 , a strongly varying mixture of fine-mode and coarse-mode particles was observed throughout the troposphere. Cumulus cloud development at around 13:45 UTC at 1 km height (see Fig. 8 ) indicates high relative humidity of > 80 % close to the top of the boundary layer. Thus particle water-uptake effects must be taken into account in the data interpretation. The relative humidity was about 60 % in the lofted layer from 1.
5 km height agree well. The spherical particle volume fraction was about 15 % after AERONET, finally 16. 8 % in the LIRIC data set, and 55 % after POLIPHON. This means that the non-spherical particle fraction is much higher in the case of the LIRIC profiles compared to POLIPHON. As a consequence, the LIRIC non-spherical particle backscatter coefficients are, on average, larger in Fig. 10  (central panel). A pronounced overestimation occurs in the layer from 1 to 2 km height. Consequently a strong underestimation is found in the case of the backscatter profile for spherical particles. The comparison of the LIRIC coarse-mode backscatter profiles with the LIRIC non-spherical particle backscatter profiles indicates that the coarse-mode values are dominated by spherical particles in the lowermost 1 km of the atmosphere.
, 2005; Nicolae et al. , 2013). In such aerosol situations, the lidar ratio at 355 nm is significantly lower than the one at 532 nm. LIRIC retrieves the opposite, a positive lidar-ratio-related Ångström exponent. Such a complicated aerosol case can obviously not be adequately handled by the LIRIC/AERONET data analysis scheme. The found disagreement in the intensive aerosol parameters corroborate our statement at the beginning of this section that many basic assumptions of LIRIC are not valid under these conditions.
Acknowledgements. The research leading to these results has been performed in the framework of ACTRIS Workpackage 20 ( ACTRIS Joint Research Activity "Lidar and sun photometer ") and received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 262254. Discussion within ACTRIS Workpackage 20 is gratefully acknowledged. Edited by:  V. Amiridis.
Elemental and Organic Carbon (EC and OC) collected at Harwell Two datasets, both involving sampling at the Harwell site were analysed. The main dataset  (2006) (2007) (2008) (2009) (2010)  was collected daily at the Harwell site by the National Physical Laboratory using a Partisol sampler with a PM 10 inlet and a flow rate of 16. 7 L min -1 on quartz fibre filters. Collected samples were analysed using the QUARTZ protocol using both reflectance and transmittance corrections for pyrolytic carbon on a Sunset Laboratory thermal-optical carbon analyser. For consistency with the second dataset (from Harwell and EROS -see below), transmittance correction was preferred, and for data points for which only reflectance corrected data were available, the data were converted to transmittance using relationships derived from linear regression (i.
Data collected at Harwell and EROS site The second dataset was collected simultaneously at the Harwell and EROS sites from July 2010 to November 2010 using a Partisol 2025D dichotomous sampler so as to collect separate coarse (PM 2. 5-10 ) and fine (PM 2. 5 ) particle fractions on QMA quartz fibre filters, after correction of the coarse particle load for a small fine particle component. The samples were analysed on a Sunset Laboratory Thermal-Optical instrument using the EUSAAR 2 protocol  (Cavalli et al. , 2010). The two Harwell datasets were intercompared, showing an acceptable level of agreement given the different protocols and lack of simultaneous sampling (sample start times were out of phase by 12 h). EC, y = 0. 97x + 0. 09 µg m -3 ; r 2 = 0. 31 and for TC, y = 0. 90x + 1. 07 µg m -3 ; r 2 = 0.
Then, the primary PM 10 is calculated as followed: Primary PM 10 = measured PM 10 -secondary OM -(NH 4 ) 2 SO 4 -NH 4 NO 3 This fails to account for sea salt sulphate which, based upon a Cl -/SO 2- 4 ratio of 0. 14 amounts on average to < 0. 2 µg m -3 , or about 8 % of sulphate in the main Harwell dataset.
Meteorological data Wind speed and wind direction data measured directly at the Harwell sampling site are used in this study. These data were available from January 2006 to December 2008 on the UK National Air Quality Archive. The temperature data used in this study were measured at the Benson station which is the closest (16 km) from the Harwell monitoring site. These data have been supplied by the British Atmospheric Data Centre (BADC). Air mass back trajectories supplied by the BADC were used to estimate the pathways followed by air masses arriving at Harwell. BADC 3-dimensional backwards trajectories were calculated from interpolations of 6h-ECMWF input wind fields.
Concentration field map method In this study the concentration field map method of  Charron et al. (2008)  has been applied to 2006-2010 data associated with corresponding 5-day backward trajectories (the largest datasets available are used, see Table  1 ). Concentration field map methods (CFMM) combine air pollution data and backwards trajectories to establish concentration field maps of likely contributing sources. A number of CFMM have been used to identify regional sources of pollutant measured at a receptor site e. g. the Ashbaugh method  (Ashbaugh, 1983) , often called Potential Source Contribution Function or PSCF, the Seibert method  (Seibert et al. , 1994) ; the Stohl method  (Stohl, 1996)  and the Simplified Quantitative Transport Bias Analysis or SQTBA  (Zhou et al. , 2004). They are purely statistical models and as a consequence, no physical or chemical processes are included.