dataesr/scientific-paragraphs-categorization huggingface dataset:
lmd. jussieu. fr/le-projet-lmdz-en-bref-en (last access: April 2016,  Hourdin et al. , 2006). Data availability. This work used eddy-covariance data acquired by the FLUXNET community (https://fluxnet. org/data/ la-thuile-dataset/, last access: September 2016,  FLUXNET, 2016). The NDVI data are derived from the MODIS MOD09CMG collection 5 daily global reflectance products  (Vermote and Kotchenova, 2008). The surface atmospheric CO 2 concentration data use measurements from the NOAA Earth System Laboratory (ESRL) GLOBALVIEW-CO2 collaborative product (GLOBALVIEW-CO2, 2013). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-20-1089-2023-supplement. Author contributions. CB, NM, PP and FC conceived the research. CB developed the data assimilation system with contribution from FC (coupling with LMDz) and SL (parallelization and post-processing).
Acknowledgements. This work has been supported by the  CAR - BONES  project, within the  EU's Seventh Framework Programme for Research and Development. This work used eddy covariance data acquired by the  FLUXNET community  and in particular by the following networks:  AmeriFlux (U. S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program  ( DE-FG02-04ER63917  and  DE-FG02-04ER63911 )),  AfriFlux ,  AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada  (supported by  CFCAS ,  NSERC ,  BIOCAP, Environment Canada and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia and USCCC. We acknowledge the financial support for the eddy covariance data harmonization provided by  CarboEuropeIP ,  FAO-GTOS-TCO, iLEAPS ,  Max Planck Institute for Biogeochemistry , the  National Science Foundation , the  University of Tuscia, Université Laval, Environment Canada  and the  US Department of Energy  and the database development and technical support from  Berkeley Water Center ,  Lawrence Berkeley National Laboratory, Microsoft Research eScience ,  Oak Ridge National Laboratory , the  University of California -Berkeley  and the  University of Virginia.
Code availability. The ORCHIDEE model code is open-source (http://forge. ipsl. jussieu. fr/orchidee, last access: April 2015,  Krinner et al. , 2005) , and the associated documentation can be found at https://forge. ipsl. jussieu. fr/orchidee/wiki/Documentation (last access: April 2022). The ORCHIDAS data assimilation scheme (in Python) is available through a dedicated website (https://orchidas. lsce. ipsl. fr/, last access: April 2022, Bastrikov et al. , 2018). Information about the LMDz model, source code and contact is pro-.
Oxygen isotopes (18, 16) have been implemented in the three-dimensional model of intermediate complexity iLOVECLIM, allowing fully coupled atmosphere-ocean simulations. The detailed implementations of oxygen isotopes (18, 16) in iLOVECLIM and the evaluation against observed data in water samples and carbonates can be found in  Roche (2013) ,  Roche and Caley (2013)  and  Caley and Roche (2013). In the present manuscript, we present the design and the validation of δ 2 H, δ 17 O water isotopes as well as deuterium excess and 17 O-excess in the coupled iLOVECLIM climate model for the atmospheric and oceanic components. The agreements and differences from the direct comparison between modelling results under preindustrial conditions and multiple datasets will be discussed to determine the potential and the interest of using iLOVECLIM for paleoclimatic studies.
Observational data To allow for comparison and discussion with iLOVECLIM results, global hydrogen and d-excess isotopic datasets for the atmosphere  (IAEA, 2006 and Masson-Delmotte et al. , 2008)   (Vimeux et al. , 2005; Landais et al. , 2008 Landais et al. , , 2010 Landais et al. , , 2012;; Risi et al. , 2008; Luz and Barkan, 2010; Winkler et al. , 2012; Tian et al. , 2021)  have also been used. The GISS global seawater isotope database  (Schmidt et al. , 1999)  has been used to compare the δ 2 H and d-excess with the ocean component in the model. available. This could be due to one of the well-known iLOVECLIM biases that is the overestimation of the precipitation in these regions. The west coast of South America also presents discrepancies between the model and the GNIP data (Fig. 1a ). This could be related to the coarse model resolution that may not perfectly reproduce the observed δ 2 Hprecipitation since the value is representative of a larger area.
001). This attests of a good representation of the d-excess in the model (excluding Antarctic values). This is also supported by the modelled d-excess in LMDZ that presents similar values than in iLOVECLIM (Fig. 5 ). Considering the large uncertainties associated with measurements, the relationship between model and data might vary and get closer to the expected 1:1 line. Comparison can be done between model and observations for the δ 17 Oprecipitation (Fig. 6a ) and the 17 O-excess (Fig. 6b ) to investigate their relationship, even if few data exist. Figure  6a  presents the relationship between modelled and measured δ 17 Oprecipitation (excluding values in Antarctica). Most of the values modelled in iLOVECLIM are grouped around enriched isotopic values, but the correlation remains low. The p-value is calculated to 0.
Similar comparison can be done for the 17 O-excess (Fig. 6b ). A wide dispersion of the but statistically significant with a p-value of 0. 0023 (<0. 05). 17 O-excess has also been previously modelled in LMDZ  (Risi et al. , 2013)  and presents similar isotopic composition than the iLOVECLIM results between 10 and 40 ‰ (Fig. 6b ). LMDZ modelled 17 O-excess is however different than iLOVECLIM and the expected data value (considering the 1:1 line) for extrema values: higher values than data are modelled for depleted values and lower values than data are modelled for enriched values. Considering the confidence interval associated with the model results and the uncertainties listed above on the measurements, our results could get closer to the 1:1 line.
Seasonal variations We compare the seasonal model results to the GNIP monthly data at several locations in South Africa (Pretoria), South America (Belem), eastern Mediterranean (Ankara) and northern Atlantic (Reykjavik) to have a global overview. We extracted the model results at the corresponding locations but due to the coarse resolution of the model, regional biases exist as depicted in previous section. The seasonal evolution of precipitation and isotopic composition in the model is then not expected to perfectly reflect the measurements. We then present the normalized values for both model and GNIP data. There is a good agreement in precipitation at Pretoria and Ankara between the observation and the model that correctly reproduce the seasonal cycle (Fig. 7 ). For Belem and Reykjavik stations the model shows some differences, namely higher precipitations in September and October at Belem and higher monthly amplitude at Reykjavik.
7 ). At Ankara, the modelled d-excess is delayed during summer compared to observations and shows too low values in October. At Pretoria, even if the δ 2 Hprecipitation is correctly reproduced in the model, the d-excess presents differences with enriched values between May and September, whereas the data indicates lower values during this period. However, taking into account the uncertainties associated with the GNIP data, our model results fit within the range of values obtained for the measurements. The hydrogen isotopic composition has also been modelled in the oceanic component for the sea water. We first look at the surface distribution of the isotopes using modelled values for the first oceanic layer at 5 m depth. For quantitative evaluation of the model results, we selected GISS sea water values between 0 and 10 m to be representative of the surface  (Schmidt et al.
Code availability. The iLOVECLIM source code and developments are hosted at http://forge. ipsl. jussieu. fr/ludus (IPSL, 2023)    but are not publicly available due to copyright restrictions. Access can be granted on demand by request to D. M. Roche (didier. roche@lsce. ipsl. fr) to those who conduct research in collaboration with the iLOVECLIM user group. Financial support. This research was supported by the ANR HYDRATE project, grant ANR-21-CE01-0001-01 of the French Agence Nationale de la Recherche. Acknowledgments. T. C. is supported by CNRS-INSU.
https://doi. org/10. 5194/gmd-2023-149 Preprint. Discussion started: 17 July 2023 c Author(s) 2023. CC BY 4. 0 License. Hprecipitation The annual mean modelled distribution of δ 2 Hprecipitation is presented in comparison to observations from the GNIP database (IAEA, 2006) and from Masson-Delmotte et al. (2008) data over Antarctica on Fig. 1a. The latitudinal gradient from the poles to the equator is correctly reproduce in the model with depleted values at high latitudes (cold and dry regions) and enriched values at lower latitudes. Differences with the observations are however observed for specific regions. In central Africa and northern region of South America, the model underestimates the δ 2 Hprecipitation, which has higher values in the few measurements.
Masson-Delmotte et al. (2008) data (in orange and red respectively). Regression lines are shown with a solid black line for the two datasets and with a dashed black line for the model. https://doi. org/10. 5194/gmd-2023-149 Preprint. Discussion started: 17 July 2023 c Author(s) 2023. CC BY 4. 0 License.
Characterization methods Atomic force microscopy (AFM) images were collected in tapping mode with a Multimode 8 AFM (Bruker) with a Nanoscope V controller (Bruker) using gold-coated silicon probes (Tap300GB-G probes by BudgetSensors). To analyze the morphology of the nanoparticles, a drop of the prepared water colloid was deposited onto a silicon wafer. To estimate the size distribution of the prepared nanoparticles, the diameters of 50 particles were measured using the Nanoscope Analysis software. The ferroelectric response of the nanoparticles was observed by Piezoresponse Force Microscopy (PFM). PFM measurements were carried out by the same NanoScope V AFM (Bruker), in the piezoresponse mode. In this mode, a conducting AFM tip is used as a top electrode while a bias is being applied. Since ferroelectric materials are also piezoelectric, the electric field generated in the sample causes the domains with the polarization parallel to the field to extend, and the domains with opposite polarization to contract  [43].
The X-ray beam wavelength was set at λ = 0. 1 nm (E = 12. 4 keV) using a Si(1 1 1) channel cut monochromator. The beamsize at the sample position was 60 × 100 μm 2 (vertical × horizontal). GIWAXS patterns were collected by a WAXS LX255-HS 2D (Rayonix) area detector placed at 109 mm. Sample to detector distance, detector tilts and reciprocal space were calibrated using Cr 2 O 3. Data were normalized by the incident photon flux and the acquisition time. Flat field, polarization, solid angle and efficiency corrections were additionally applied to the GIWAXS images. The scattering vector q, (q = (4π/λ)sin(θ), where 2θ is the scattering angle) was defined with respect to the center of the incident beam. Azimuthal integration on the 2D images led to 1D scattering patterns, that are presented in this work after background subtraction.
Acknowledgments The authors gratefully acknowledge the financial support of the  Spanish Ministry of Science and Innovation (MICINN)  through projects  PID2019-107514GB-I00/AEI/10. 13039/501100011033  and  PID2019-106125GB-I00/AEI/10. 13039/501100011033. Also, ' Programa Operativo de Empleo Juvenil' (YEI) Comunidad de Madrid  (CAM,  PEJ-2019-AI/TIC-15699 ). The synchrotron experiments were performed at  NCD-SWEET beamline at ALBA Synchrotron with the collaboration of ALBA  staff. The authors acknowledge also  J. P. Sánchez  for technical support.
2 TeV;  Furniss et al. (2015)  with E > 0. 2 TeV;  Acciari et al. (2020a)  with E > 0. 15 TeV;  Cologna et al. (2017)  with E > 2 TeV;  Arbet-Engels et al. (2021)  with E > 0. 75 TeV; only significant measurements > 2σ are displayed); Swift-XRT data; Fermi-LAT data in 14 day bins; OVRO data. The vertical black lines mark the 4 yr period featured in this work, and the gray area marks the identified period with a very low activity (see Section 3 for details). For the data from  Cologna et al. 2017  no original data set could be organized. Therefore the data points were extracted from the portable document format (pdf) and are displayed without error bars due to the lack of precision of this method. (The data used to create this figure are available. ) instruments, provide us with the unprecedented opportunity to investigate with accuracy the broadband emission of this archetypal TeV blazar during a period of extremely low activity.
(2017a Ahnen et al. ( , 2018)) , and a structured jet was used to explain the evidences for a narrow spectral feature at 3 TeV observed in the VHE emission of Mrk 501, as shown in  Acciari et al. (2020a). Additional claims for a stratified structure in the jet of Mrk 501 are strongly supported by very-long-baseline interferometry (VLBI) images taken in the radio regime indicating a transverse structure  (Giroletti et al. 2004 ). The emission mechanisms of Mrk 501 are far from being understood, and there are already several observations that suggest the need for more complex scenarios than those related to a single emission region. Our detailed 4 yr MWL data set and in particular its detailed characterization of the historically low activity now allows us to investigate the capability of these different models to explain the emission during and around the low state.
MAGIC MAGIC consists of two IACTs separated by a distance of 85 m. It is located at the Roque de los Muchachos Observatory, on the Canary Island of La Palma at an altitude of 2243 m above sea level. The telescopes work in an energy range between 50 GeV and tens of TeVs, with a sensitivity above 100 GeV (300 GeV) of about 2% (about 1%) of the Crab Nebula flux at low zenith angles (< 30°) after 25 hr of observations (see Figure  19  of  Aleksić et al. 2016). With this performance, the MAGIC telescopes are very well suited to perform blazar observations in the VHE range. During the 4 yr period from 2017 to 2020, Mrk 501 was observed by MAGIC for around 160 hr, yielding around 120 hr after the data selection based on the atmospheric transmission and night-sky background (NSB) levels. The data are analyzed using the MAGIC Analysis and Reconstruction Software package  (Zanin et al.
2013; Aleksić et al. 2016). Owing to Mrk 501 being one of the brightest sources in the MAGIC source catalog, it is possible to observe it even during moon conditions. The analysis is adjusted accordingly to the high NSB levels as prescribed in  Ahnen et al. (2017b). The VHE flux light curve is computed with a minimum energy of 0. 2 TeV in order to minimize the impact of the various observing conditions considered in this study, which can increase by a factor ∼2 the analysis energy threshold at zenith. Our selected energy threshold is compatible with the applied data selection (zenith<50°and exclusion of bright moon levels). We bin the observations once night-wise and once on a weekly basis. For bins with a significance of less than 2σ, the upper limits (ULs) with 95% confidence according to the Rolke method  (Rolke et al.
Fermi-LAT The LAT on board the Fermi Gamma-Ray Space Telescope is constantly monitoring the high-energy sky since its launch in 2008. As a pair conversion instrument, it is sensitive to an energy range from 20 MeV to beyond 300 GeV, and covers the whole sky every ∼3 hr  (Atwood et al. 2009; Ackermann et al. 2012). Owing to the low activity of Mrk 501, as well as the need to characterize its γ-ray emission on relatively short-time intervals, we decided to use the unbinned-likelihood tools provided by the FERMITOOLS software 107  (v1. 0. 10), which is more suitable than the binned analysis for situations with low-event statistics. Table  1  summarizes the basic analysis settings that were used. The usage of 0. 3 GeV as the minimum energy for the analysis (instead of the conventional 0. 1 GeV) reduces the detected number of photons from the source.
The parameters obtained are summarized in Table  10. For the spectral data points the number of spectral bins is chosen according to the time intervals and flux level. For part of the correlation analysis, a long-term Fermi-LAT light curve is used including all data since its launch. We therefore apply the same procedure as described above to the data from MJD 54,688 (2008 August 10, 00:00:00) to MJD 59,126 (2020 October 4, 00:00:00), also using 14 day bins. The starting MJD is the earliest possible in 2008 that is in line with the time bins used for the 4 yr epoch featured in this paper, and results in 317 time bins for the 2008-2020 period.
NuSTAR NuSTAR is one of NASA's Small Explorer satellites, sensitive in the hard-X-ray band. It has two multilayer-coated telescopes, focusing the reflected X-rays on the pixilated CdZnTe focal plane modules, FPMA and FPMB. The observatory provides a bandpass of 3-79 keV with spectral resolution of ∼1 keV. The field of view of each telescope is ~¢ 13 , and the half-power diameter of an image of a point source is ~¢ 1. This allows for a reliable estimate and subtraction of instrumental and cosmic backgrounds, resulting in an unprecedented sensitivity for measuring hard-X-ray fluxes and spectra of celestial sources. For more details, see  Harrison et al. (2013). The data reported here (Table  12 ) cover three observations in 2017 and 2018 that were planned as part of two dedicated NuSTAR proposals (from PI Balokovic and PI Paneque).
After screening for the South Atlantic Anomaly passages and Earth occultation, the observations yielded roughly 5 hr of on-target data per pointing. The raw data products are processed separately for each pointing with the NuSTAR Data Analysis Software (NuSTARDAS) package v. 1. 3. 1 (via the script nupipeline), producing calibrated and cleaned event files. Source data are extracted from a region of 45″ radius, centered on the centroid of X-ray emission, while the background is extracted from a ¢ 1. 5 radius region roughly ¢ 5 north of the source location. The spectra are binned in order to have at least 20 counts per rebinned channel. We consider the spectral channels corresponding nominally to the 3-30 keV energy range, where the source was detected. The mean net (background-subtracted) count rates in the modules FPMA and FPMB are consistent with each other.
We perform spectral fits to the data, using the standard NuSTAR response matrices and effective area files, via the NuSTAR data analysis package nuproducts. We adopt a ote. a http://fermi. gsfc. nasa. gov/ssc/data/access/lat/BackgroundModels. html simple power-law model, absorbed by intervening material with solar abundances and the Galactic column of 1. 55 × 10 20 cm -2  (Kalberla et al. 2005). In all cases, the spectra are adequately described by such a simple model, but in all three cases, we see a modest improvement to the spectral fit when we attempt a more complex model, such as log parabola. While this is not significant in any of the three pointings reported here, this spectral behavior is fully consistent with that reported in  Furniss et al. (2015).
Swift The study reported in this paper makes use of two instruments on board the Neil Gehrels Swift Gamma-Ray Burst Observatory  (Gehrels et al. 2004 ); namely, XRT  (Burrows et al. 2005 ) and the Ultraviolet/Optical Telescope (UVOT;  Roming et al. 2005). The related observations were organized and performed within the framework of planned extensive multi-instrument campaigns on Mrk 501, which occur yearly since 2008  (Aleksić et al. 2015a). In this study we consider all observations within the years 2017 and 2020 for both the UVOT and XRT instruments with an extension using the all data from 2005 to 2020 for the long-term studies on the XRT light curve. The Swift-XRT observations are performed in the Windowed Timing (WT) and Photon Counting (PC) readout modes, and the data are processed using the XRTDAS software package (v.
Differently to the optical bands, the emission in the UV is not affected by the emission from the host galaxy, which is very low at these frequencies. We perform aperture photometry for all filters using the standard UVOT software within the HEAsoft package (v6. 23) and the calibration included in the latest release of the CALDB (20201026). The source photometry is evaluated following the recipe in  Poole et al. (2008) , extracting source counts from a circular aperture of 5″ radius, and the background ones from an annular aperture of 26″ and 34″ for the inner and outer radii in all filters. The count rates are converted to fluxes using the standard zeropoints  (Breeveld et al. 2011 ) and finally dereddened considering an E(B -V ) value of 0. 017  (Schlegel et al. 1998; Schlafly & Finkbeiner 2011)  for the UVOT filters effective wavelengths and the mean galactic interstellar extinction curve from Fitzpatrick (1999).
Owing to the low flux variability in the radio, optical, and X-ray bands, the weighted average spectral points are used as a good proxy of the emission of Mrk 501 during this 2 yr long low state. The hard-X-ray spectrum derived with NuSTAR data from 2018 April 20 (MJD 58,228) is also depicted, showing a good agreement (within 30%) with the weighted average Swift-XRT spectrum in the overlapping energy range. This agreement is consistent with the relatively low flux variability at X-rays mentioned above and suggests that the NuSTAR spectrum from 2018 April 20 is a good approximation of the hard-X-ray emission of Mrk 501 during this 2 yr long epoch. As a consistency check, Figure  28  shows the broadband SED during the abovementioned 2 yr long period of low activity, together with the SED around the NuSTAR observation from 2018 April 20, using MAGIC and Fermi-LAT spectra derived with data within ±1 week of the NuSTAR observation.
In radio, optical and soft X-ray data the sensitivity is good enough to use the individual observations (typically less than 1 hr long). The good agreement among the spectra further supports the use of the NuSTAR observation from 2018 April 20 as a good proxy of the hard-X-ray emission of the 2 yr long integrated (weighted average) spectrum of Mrk 501. For comparison purposes, Figure  9 (a) also depicts the typical (nonflaring) state of Mrk 501 from  Abdo et al. (2011a). This helps visualize the historically low-activity state of Mrk 501 during the 2 yr time interval that goes from 2017 June 17 to 2019 July 23  (MJD 57, 921 to MJD 58, 687). For the first NuSTAR observation (NuSTAR-1) on 2017 April 28 (MJD 57,871) no simultaneous VHE observations are available. Therefore we choose to use all nights inside ±1 week for the computation of both the MAGIC as well as the Fermi-LAT spectra.
The corresponding multi-instrument SED is shown in Figure  9(c). Additionally, we added infrared (IR) points taken by the Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) mission 112   (Mainzer et al. 2014). For the low state all simultaneous data were averaged as done for the other lowenergy wave bands. No simultaneous data were available for the two NuSTAR time intervals. We chose the closest NEOWISE observation, which took place on MJD 57,807. However, NEOWISE only makes use of two (W1, W2) of the four filters of the Wide-field Infrared Survey Explorer (WISE) spacecraft. In order to include also information about the other two filters, we added archival WISE data from 2010  (Wright et al. 2010; Cutri et al. 2021 ) to all three SEDs from Figure  9  and used them for our spectral studies based on the observation of very low variability in the IR band.