dataesr/scientific-paragraphs-categorization huggingface dataset:
slopes. The method is relatively insensitive to the parameter N F ; that is, a variation of the number of frequencies used to generate the sample time series does not significantly alter the result, suggesting that application of the Blackman window minimizes aliasing. Results differ for the structure function, as illustrated in Fig. B1a , which shows a 2D histogram that indicates the slopes of the structure function for separations from 5 to 256 grid elements for varying spectral slopes of the synthetic dataset and parameter N F. Generally, the slope of the structure function is less than predicted by relation (1); however, slopes increase and approach the prediction in the limit N F /N / '. This result is expected since in that limit (1) holds exactly. The difference between the estimated slope and the one predicted from (1) also increases with the spectral slope of the synthetic time series.
It illustrates that the estimate of the slope quickly degrades with increasing length of the gap, and more significantly with increasing g. Next, we vary the coherence C of the missing data. Figure  B2  shows 2D histograms of the difference between the slopes of the synthetic time series and those of the DFT estimate as a function of Q and C for g 5 2:0, 2:5, and 3:0. Again, estimates of average spectral slopes are precise for complete time series, but they deteriorate with decreasing Q. Figure  B2  also indicates that estimates improve as C decreases for fixed Q, that is, as average gap lengths decrease. Guided by these tests, we use a ''Q-C standard,'' which takes into consideration both, the fraction of good data points available (Q) and the structure of gaps (C), when selecting sections best suited for the spectral analysis of a given dataset.
Acknowledgments. We acknowledge support by the  Oleander Project  and thank  Tom Rossby ,  Charly Flagg ,  Alejandra Sanchez-Franks ,  Baylor Fox-Kemper , and two anonymous reviewers for their valuable feedback related to this work. We are grateful to  NOAA AOML  and the  VIIRS  project for making their data available. The work was also supported through  NSF  Grants  OCE 0825845  and  OCE 0851794. Salary support for P. C. was provided by the state of Rhode Island and Providence Plantations. B. B. was supported by  Le Collège doctoral international de l'Université européenne de Bretagne.
Trends in deep-sea coral data The scleractinian corals analyzed in this study are solely Enallopsammia sp. In contrast, the deep-sea corals presented in  Ghosh et al. (2006)  and  Thiagarajan et al. (2011)  are scleractinian Desmophyllum, Enallopsammia, and Caryophyllia sp. All scleractinian species precipitate a skeleton composed of aragonite although growth habits vary between species. Desmophyllum sp. , also known as cup corals, grow in a rosette shape, whereas Caryophyllia and Enallopsammia sp. have a dendritic growth habit. The E. rostrata specimens measured in  Thiagarajan et al. (2011)  allow comparison between deep-sea corals of the same species measured in this study. The scleractinian deep-sea coral data from this study have values that overlap with those reported by  Thiagarajan et al.
(Karl and Lukas, 1996)  and cruises extracted from the National Ocean Database  (Boyer et al. , 2013). (Ghosh et al. , 2006). Note that mixing of DIC from different sources results in curved trajectories (in δ 18 O space, mixing is linear whereas in ∆ 47 space can be non-linear)  (Eiler and Schauble, 2004; Thiagarajan et al. , 2011; Defliese and Lohmann, 2015). Diffusion results in enrichment of clumped species and depletion in δ 18 O  (Thiagarajan et al. , 2011). CO 2 hydration reactions are strongly temperature pH-dependent and also result in enrichment of clumped species and depletion in δ 18 O  (Affek, 2013; Tang et al. , 2014; Tripati et al. , 2015). At any given temperature, equilbrium DIC species have differing clumped and oxygen isotope signatures, potentially also giving rise to pH effect  (Hill et al.
Acknowledgements. This work was supported by a  NSF Graduate Fellowship ,  NSF   OCE-1437166 ,  DOE BES  grant  DE-FG02-13ER16402 , and the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ". We thank the  Tripati Lab Group  for their assistance with analyses and discussion of this work, and  Ben Elliott  for assistance with drafting figures.
9 µm, only data at lower sizes are considered in this paper. The uncertainties on the particle diameter were estimated to be 5 and 10 % for the SMPS and UHSAS respectively  (Wiedensohler et al. , 2012; Cai et al. , 2008). The number size distribution in the supermicron range was measured by the combination of two different optical particle counters (OPCs). A wing-mounted forward scattering spectrometer probe (FSSP, Particle Measuring System, model 300) measured the optical size distribution in the nominal size range of 0. 28 to 20 µm  (Baumgardner et al. , 1992). Data were recorded in 30 size classes at 1 s intervals. The FSSP-300 is based on the measurement of the light scattered between 3 and 12 • at 632. 8 nm. The FSSP has an uncer-tainty in diameter of about 30 % according to  Baumgardner et al.
Aerosol scattering and extinction coefficients The particle scattering coefficient (σ scat ) was measured at three wavelengths (450, 550 and 700 nm) with an integrating nephelometer (TSI, model 3563), which integrates light scattered by particles at scattering angle between 7 and 170 • relative to the incident and scattered radiation. The instrument operated at a volumetric flow rate of 30 L min -1 and the data were acquired at 1 s time resolution. The instrument was calibrated with free-particle air and high-purity CO 2 prior to and after the campaign. Uncertainty in σ scat measured with the nephelometer is estimated to be 5 %  (Muller et al. , 2011a). Measured values were corrected for the angular truncation error in the nephelometer measurements at angles smaller than 7 • and greater than 170 • as described in Sect.
(2015). As a complement, synoptic conditions and sea level pressure composite anomalies over the Mediterranean basin during the campaign were analysed using reanalysis data sets, such as the NCEP/NCAR Reanalysis  (Kalnay et al. 1996)  and the NCEP Climate Forecast System (CFS) reanalysis  (Saha et al. , 2010). Source regions and atmospheric transport times of the dust plumes were determined through the combination of satellite products and backward trajectory analysis. The potential source regions active during the observational period were identified using the images from SEVIRI on board the MSG satellite. The NOAA HYbrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT, http://www. arl. noaa. gov/HYSPLIT. php) using the Global Data Assimilation System (GDAS) meteorological input was used to calculate whether an air mass sampled by the aircraft could have originated from one of the identified active dust sources.
Our result on the moderate absorption properties of the dust plumes is thus likely relevant to dust events in the western Mediterranean in the absence of intense pollution or biomass-burning emissions and can be used for constraining modelling studies and satellite retrievals that make assumption on dust optical properties. We compared our measurements on dust absorption properties with values published in the OPAC aerosol database that is widely used by modelling and remote-sensing communities. The result of this comparison indicates an overestimation of dust absorption properties in the OPAC database. The n i value achieved in the OPAC database (n i = 0. 006-0. 008 at 550-500 nm respectively) are high compared to values observed for Saharan mineral dust in source region and over the Mediterranean during ADRIMED (n i between 0.
The data set obtained during the ChArMEx/ADRIMED airborne campaign can also be used for constraining satellite retrievals that make assumptions on dust properties in order to derive water vapour profiles, surface temperatures and greenhouse gas concentrations. The results presented here suggest that the aerosol particle size and optical properties of the dust plumes could be assimilated to those of native dust in satellite retrievals in the western Mediterranean. A straightforward comparison of our results with values published in the OPAC aerosol database, which is widely used by the remote-sensing communities, suggests that the OPAC database overestimates dust absorption. Moreover, this important data set provides opportunities for evaluating satellite aerosol products (size, absorption properties, vertical profiles) over the Mediterranean through comparison with our in situ airborne measurements.
Acknowledgements. This research work has been supported by the  French National Research Agency (ANR)  through the  ADRIMED program  (contract  ANR-11-BS56-0006 ). This work is part of the  ChArMEx  project supported by  CNRS-INSU ,  ADEME ,  Météo-France  and  CEA  in the framework of the multidisciplinary program  MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ; http://mistrals-home. org/). The aircraft deployment was also supported by  CNES. We thank the instrument scientists, pilots and ground crew of SAFIRE for facilitating the instrument integration and conducting flight operations. We acknowledge  Pierre Nabat  and the ENM students, especially  Damien Serça ,  Jonathan Guth  and  Valentin Seigner  for their meteorological forecasts during the campaign. We thank the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model used in this study.
Data LCA was used to estimate several important midpoint impacts of dairy farming systems. It was conducted using the DIAPASON database resulting from a partnership involving voluntary participation of farmers, the Chambers of Agriculture (France) and the French Livestock Institute. This database contains detailed information on technical and economic operations of nearly 500 farms each year throughout France  (Charroin et al. , 2005). Environmental performance was assessed by indicators of pressure from agricultural activity on the environment considering midpoint impact indicators of LCA. The frontier of the farm system was limited to the farm, considered as a system dedicated to agricultural products (crops, milk, meat) at farm gate. Impacts associated with these products beyond the farm gate were not considered in this study.
The impact values of inputs were derived from the LCA database 'ecoinvent'  (Nemecek and Kägi, 2007)  and  Gac et al. (2010a). Because the whole farm was chosen as the functional unit, all farm products were considered simultaneously in this analysis, therefore no allocation of emissions to the different products was applied. Measuring eco-efficiency with LCA and DEA Lorraine, Rhône-Alpes). The second dimension of the typology, crossed with the first dimension, concerned the type of feeding strategies, based on the area of maize silage in the total forage area of the farm: <10%, 10% to 30%, >30% maize. Other farm classes were not considered due to insufficient number of farms in the class. Table  2  summarizes the five EIs and three outputs used in this study per system for the years 2007 and 2008.
) Therefore, these tests are not affected by outliers or skewed data. See  Conover (1999). Examining the effect of region on eco-efficiency The bio-physical conditions under which dairy farms operate largely differ between West and Continental France. Regional differences in eco-efficiency were therefore tested. It would seem appropriate to pool farms from both regions in one dataset, run the RAM model and then test for differences between regions with the Kruskal-Wallis test. Such practice, however, would reveal any differences between regions under the observed levels of EIs and output (i. e. the EI and output values outlined in Table  2 ). This means that inefficiencies attributed to both managerial and regional factors would not allow inefficient farms to operate under their full potential.
Acknowledgements The authors would like to express their gratitude to two anonymous referees whose constructive comments helped significantly improve an earlier version of this manuscript. The authors take responsibility for any errors that may remain. We acknowledge feedback from staff at  SRUC (Scotland) ,  INRA  ( St Gilles, France ) and  Idele (Le Rheu, France)  and also from  Dr Philippe Lecomte  ( CIRAD, France ) and  Dr Marc Metzger  ( University of Edinburgh ). Likewise, thank you to  Professor Mette Asmild  ( University of Copenhagen ) and  Professors Jean-Philippe Boussemart  and  Hervé Leleu  at  IÉSEG (Lille, France ) for the fruitful discussions on DEA, as well as to  Dr Jiayi Liu  and  Dr Giles Innocent (BioSS, Scotland)  for their tips in statistics. This project is jointly funded by  SRUC (via Scottish Government 's Rural Affairs and the  Environment Strategic Research programme ) and the  PHASE division of INRA.
9. 5 used for the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations). The system relies on a stepwise approach that assimilates each data stream in turn, propagating the information gained on the parameters from one step to the next. Overall, the ORCHIDEE model is able to achieve a consistent fit to all three data streams, which suggests that current LSMs have reached the level of development to assim-ilate these observations. The assimilation of MODIS-NDVI (step 1) reduced the growing season length in ORCHIDEE for temperate and boreal ecosystems, thus decreasing the global mean annual gross primary production (GPP). Using FLUXNET data (step 2) led to large improvements in the seasonal cycle of the NEE and LE fluxes for all ecosystems (i. e. , increased amplitude for temperate ecosystems).
The assimilation of atmospheric CO 2 , using the general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMDz; step 3), provides an overall constraint (i. e. , constraint on large-scale net CO 2 fluxes), resulting in an improvement of the fit to the observed atmospheric CO 2 growth rate. Thus, the optimized model predicts a land C (carbon) sink of around 2. 2 PgC yr -1 (for the 2000-2009 period), which is more compatible with current estimates from the Global Carbon Project (GCP) than the prior value. The consistency of the stepwise approach is evaluated with back-compatibility checks. The final optimized model (after step 3) does not significantly degrade the fit to MODIS-NDVI and FLUXNET data that were assimilated in the first two steps, suggesting that a stepwise approach can be used instead of the more "challenging" implementation of a simultaneous optimization in which all data streams are assimilated together.
In this context, the main questions that we aim to answer in this paper are as follows: i. How and to which extend the optimization of the OR-CHIDEE model allows one to fit the three data streams that are considered? ii. Does the stepwise optimization result in a degradation of the fit to other data streams used in the previous steps? iii. What are the main changes in the optimized parameters when using sequentially these three data streams in a global CCDAS and which processes are constrained? iv. What are the improvements for the land C cycle in terms of net/gross fluxes and stocks as a result of multidata stream optimization? What preliminary perspectives can we draw that may help us in improving model predictions of trends, variability and the location of terrestrial C sources and sinks? Following these objectives, the paper first describes the new ORCHIDEE-CCDAS including the concept, the observations, the models and the optimization approach.
The system relies on two models: the ORCHIDEE global LSM, whose main C cycle parameters are optimized (see Sect. 2. 3); the GCM of the Laboratoire de Météorologie Dynamique, LMDz (see Sect. 2. 3), to relate the surface carbon fluxes to atmospheric CO 2 concentrations. The framework combines the different observational data streams within ORCHIDAS in order to optimize selected model parameters using a variational data assimilation system, described in Sect. 2. 4. Figure  1  illustrates the structure of the CCDAS and the different components that are involved. Such a framework distinguishes (i) the assimilated observations, (ii) an ensemble of forcing and input data streams, (iii) the models and optimization framework, as well as (iv) an evaluation step, where independent data sets are compared to the optimized model stocks and fluxes.
Atmospheric CO 2 concentrations Atmospheric CO 2 concentration measurements were taken from an ensemble of selected surface stations around the world (Fig. 3 ). The spatial concentration gradients relate to the integral of the fluxes over large areas and thus allow the optimization of large-scale global patterns of carbon fluxes. These data were taken from the NOAA Earth System Laboratory (ESRL) GLOBALVIEW-CO2 collaborative product (GLOBALVIEW-CO2, 2013) and averaged to monthly means. We assimilated the monthly values for 53 sites for the 2002-2004 period inclusive in the last step of the assimilation system. Such a restricted period (3 years only) was chosen for practical reasons (computing resources) while constructing the ORCHIDAS system. The station locations, indicated in Fig. 3 , favor the background conditions; i.
The site selection and the choice of a daily time step are described in more details in  Kuppel et al. (2014). These sites cover seven of the PFTs in ORCHIDEE (see Table  2 ). The posterior parameter values of the four phenology parameters derived in step 1, and their associated uncertainties, are input as prior information in step 2. For the additional parameters, the default ORCHIDEE values are used for the prior and the uncertainties are set as described in Sect. 2. 3. 3. A multi-site optimization is performed for each PFT independently as in step 1. Global parameters, i. e. , those that are not PFT-dependent, were optimized for each PFT and the mean across all PFTs was then calculated to define the prior parameter vector in step 3 of the assimilation with atmospheric CO 2 data (at global scale).
, 2013). For step 3 we used a different approach, given the large bias in the model a priori concentrations, and therefore followed the methodology of  Peylin et al. (2005)  based on the observed and modeled temporal concentration variability at each site. Overall, data uncertainties in the optimization procedure are between 0. 1 and 0. 45 for NDVI (step 1), around 3-6 mg C m -2 d -1 for daily NEE, and 15-30 Wm -2 for daily LE (step 2) and between 0. 1 ppm at remote oceanic stations and 4 ppm at continental sites (step 3). The determination of the optimal parameter vector that minimizes J (x) is performed by successive calls to a "gradient-descent" minimization algorithm L-BFGS-B  (Byrd et al. , 1995) , which is specifically dedicated to solving large nonlinear optimization problems that are subject to simple bounds on the parameter values.
The derivation of the time profiles relies on different data sets (e. g. , Eurostat; ENSTO-E, https://www. entsoe. eu/about-entso-e/Pages/ default. aspx; UN monthly bulletin) as well as correlations between recorded emissions and climate variables. Currently, the temporal profiles are derived mostly from data sets over Europe that were extrapolated using information on climate zone, average monthly temperature for the seasonal cycles and similarity in socio-economic parameters like population and gross domestic product (GDP). The annual mean emission for the period 2002-2004 is 7. 14 PgC yr -1.
Fire emissions Fire emissions data from the Global Fire Data (GFEDv3; https://daac. ornl. gov/VEGETATION/guides/global_fire_ emissions_v3. 1. html) are prescribed in the ORCHIDAS (except during the model spin-up). The GFEDv3 data are broken-down into six sectors (deforestation, peat fires, savanna fires, agriculture, forest fires, and woodland) that are further grouped into three main types. We generated fluxes of CO 2 relevant for typical "burning-regrowth" processes, as detailed in Appendix A2. The first type corresponds to deforestation and peat fires with carbon permanently lost to the atmosphere, the second to agriculture and savannah fires, which are assumed to be compensated by a sink during the regrowth period (i. e. , with zero annual net emission for each pixel), and the third to woodland and burnt forests, which are assumed to be at steady state for a given region (10 sub-continental-scale regions) over the period covered by GFEDv2 (i.
Step 3: assimilation of atmospheric CO 2 data The final optimization step with the atmospheric CO 2 concentrations provides a large improvement of the fit to the observed concentrations at most stations. The cost function J was reduced through the minimization by a factor of 5. 7 within 37 iterations. Figure  6  illustrates the simulated concentrations for four stations (representative of different conditions), over the assimilation period, with the standard prior parameter vector (used in step 1), the posterior vector from step 2 (used as prior in step 3) and the posterior vector from this last step. The improvement in the fit to the observations can be quantified with the reduction in RMSE (from the prior to the posterior of step 3) -the largest reduction is at the South Pole station (73 %) and is on average around 25 % across all sites.
5 at 45°S. 2,3  and 4 )  18, 21, 25, 26, 32, [56] [57] [58] [59]. Core sites are given by the black dots; locations are plotted in Fig. 2d  and Supplementary Fig. 1. Depths from 0 to 1,000 m have been left blank due to a lack of data. Figure created using Ocean Data View software  22. The largest differences between the LGM and modern Atlantic profiles occur below 2,500 m (Fig. 3 ); the deep North Atlantic shifts from e Nd values around À 10. 5 in the glacial to À 13. 5 in the modern ocean, whilst the deep South Atlantic shifts from À 5. 5 to À 8. 5. This coherent spatial pattern of changes in e Nd values in the Atlantic between the LGM and the Holocene (Fig. 3b ) is consistent with a change in water mass advection and indicates there was a greater influence of southern-sourced waters in the deep Atlantic under glacial conditions.
Acknowledgements Sample material was provided by the  Godwin Laboratory for Paleoclimate Research at the University of Cambridge , the  International Ocean Discovery Program the GeoB Core Repository  at the  MARUM-Center for Marine Environmental Sciences, University of Bremen and Petrobras. We thank  Jo Kerr  and  Aurora Elmore  for providing additional samples. We thank  Thiago Pereira dos Santos  for providing the unpublished age model data for GL1090; we thank  Jo Clegg  and  Vicky Rennie  for technical support and  Natalie Roberts  for helpful discussions. Radiocarbon analyses were supported by  NERC  radiocarbon grant  1752. 1013  and Nd isotope analyses by  NERC  grants  NERC NE/K005235/1  and  NERC   NE/F006047/1  to A. M. P. J. N. W. H. was supported by a  Rutherford Memorial Scholarship.
Insurance loss data Insurance data on losses to residential buildings were provided by the German insurance association, Gesamtverband der Deutschen Versicherungswirtschaft e. V. (GDV). These comprise of daily data on administrative district level, with areas ranging from about 40 km 2 for urban municipalities ("Kreisfreie Städte") to about 3000 km 2 for rural districts ("Landkreise"). In contrast to pointwise measurements from meteorological stations, the available insurance data represent records with an area-wide coverage of windstorm and thunderstorm losses making it most valuable for various weather impact studies. The data however contain some limitations and uncertainties that need to be kept in mind. Un-certainties in daily losses arise from the fact that the exact time of loss occurrence is indistinct in some cases, especially if an event has occurred at night.