dataesr/scientific-paragraphs-categorization huggingface dataset:
Sun photometer pre-processing and data quality PLASMA sun photometer data is included in the AERONET database (instrument #650) and the instrument is calibrated by 30 Service National d'Observation PHOTONS, French branch of AERONET, using Langley method at Izaña Observatory (28. 3° N, 16. 5° W; 2400 m asl) following the AERONET calibration protocol for the AERONET reference master instrument. PLASMA sun photometer is intercalibrated regularly against a CIMEL CE318 master sun photometer from shows excellent agreement between the two instruments with RMS AOD differences better than 0. 005 for all channels. Regarding data processing for mobile measurements, the data is filtered over time steps of 10 to 30 seconds with a criterion 5 of 2% maximum difference for the measurements in the selected time interval.
This filter rejects the artifacts from measurements through clouds or when passing by obstacles (trees, buildings, bridges, tunnels, etc. ). Filtered measurements are then submitted manually to AERONET processing system to derive spectral AOD. However, this method takes some time, up to 1 day, to get the calibrated AOD. Therefore, the internal PHOTONS AOD computation processing has been rather considered for campaigns to produce real time AOD. The desktop-based acquisition software shows the AOD not 10 calibrated in real time, useful for checking PLASMA measurements along the road. This monitoring will be improved in order to produce AOD level 1. 5 data (cloud screened) in the future STrAP (Système de Traitement des AOD de Plasma) web-based processing system. This will allow the visualization of real-time calibrated PLASMA AOD.
Spatial variability of AOD and comparison with MODIS data A strong variability was observed between the two end points, Lille and Dunkerque. High AOD values in the range 0. 6 -0. 8 at 440 nm were recorded at Lille and surroundings between 11:00 UTC and 12:00 UTC and decreased rapidly to 0. 37 when arriving at Dunkerque around 13:07 UTC. The Ångström exponent, between 1. 4 and 1. 6 at Lille, decreased to 1. 2 along the route to Dunkerque. The values recorded at Lille are characteristic for fine particles, typical for urban sites, while the 5 decrease of the Ångström exponent along the road indicates the presence of larger particles. During one hour of stationary measurements starting from 13:15 UTC, the AOD levels remained stable around 0. 4. On the way back to Lille, AOD and Ångström exponent values increased to 0.
75 and 1. 6, respectively. AOD at 440 nm and Ångström exponent from PLASMA measurements were compared with data from closest AERONET sites (Lille and Dunkerque) and a mean absolute difference of 0. 04 was found, proving the reliability of PLASMA measurements. One must take into account that for this comparison 10 the closest measurements (in space and time) were considered and that they are not taken at the exact same location as AERONET sites. As an example of the applications of the mobile system, MODIS AOD data has been evaluated by comparison with ground mobile sun photometer measurements. We used the MODIS deep blue (DB) product at 10 km over land  (Levy et al. , 2013)  for the comparisons. MODIS AOD product with higher resolution (3 km) would have been preferred to better address 15 pollution gradients, but not enough pixels coincident with our mobile transect were available for this day.
Dust transport in the range 2-5 km altitude, added to a significant local pollution event at surface level, illustrate an interesting case of spatial and vertical variability. This case 25 exemplifies the use of the synergy of lidar-sun photometer measurements. The second case study, focused on in situ data and comparison with air quality model, showed a clear horizontal gradient in PM 10 concentration between Lille and Paris, which was consistent in both observations and model. The utility of the mobile system for applications such as intercomparison with other lidars and sunphotometers in operational networks, i. e. EARLINET and AERONET, has been shown. The validation of satellite-derived products, e. g. MODIS AOD, 30 has been exemplified through comparison with mobile sun photometer measurements.
Acknowledgements We wish to thank  ANRT France  and  CIMEL Electronique  for supporting the research development on the mobile system and the  CaPPA project (Chemical and Physical Properties of the Atmosphere)  for the financial support. CaPPA  project is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract  ANR-11-LABX-0005-01  and by the  Regional Council Hauts-de-France  and the  EU. We also thank  ATMO Hauts-30 de-France  and AIRPARIF for the PM 10 data,  Service National d'Observation PHOTONS/AERONET  from  INSU/CNRS  for the Cimel CE318 sun photometer data processing and calibration and we thank  Sara Basart  from  BSC  for providing the dust model data. The ACTRIS-FR research infrastructure is also acknowledged for financial support.
Such disposition allowed us to conduct high-resolution measurements around the selected vine plant (Fig. 2 ). Field measurements were conducted in March 2017, using a 10-channel resistivity meter (Syscal Pro Switch 72, IRIS Instruments). For the 3-D scheme of ERT, a complete skip-two dipole-dipole scheme was adopted and produced some 5000 measurements, including reciprocal measurements used to estimate and reject bad data quality  (Binley et al. , 1995; Daily et al. , 2004). A pulse duration of 250 ms for each measurement cycle and a target of 50 mV for potential readings were set as criteria for the current injection. The R3t code  (Binley, 2013)  was used for data inversion. 2. 3 MALM acquisition, modeling and processing.
MALM acquisition and forward modeling The MALM acquisition used the same electrode arrangement as described above, with only a couple of necessary changes: the two remote electrodes for current (B) and potential (M)see Fig. 2  -were located at a large ("infinite") distance, more than 20 times the maximum distance between the other elec-  trodes, as suggested by  Robain et al. (1999). An additional electrode was placed near the stem (Fig. 2 ). Two different datasets were acquired depending on the position of the current injection electrode A, as described in the workflow in Fig. 3 : (i) the first case was a real MALM acquisition where the injection electrode A was planted into the apparent conductor (i. e. , the plant stem); (ii) the second case is a reference (or false MALM) case, with the injection electrode A planted in the soil very close to the stem.
Synthetic MALM data were produced (in forward mode) using the R3t code  (Binley, 2013)  and the same unstructured tetrahedral mesh used for ERT inversion. The quality of the meshing was checked by the comparison between a uniform 100 •m forward modeling with the corresponding analytic solution (Fig. A2 ). Inverted resistivity from 3-D ERT acquisition was considered as a resistivity distribution needed for the MALM forward modeling.
The number of current sources to invert for Ns is primarily dictated by the desired input mesh quality (Fig. A2c ). This is determined by the required computational time. For this small-scale prospection, we adopted a mesh composed of 23 700 nodes (including remote electrodes, e. g. , Fig. A2a ). The inversion region was limited to 3618 nodes (Fig. A2b ). Furthermore, as shown in Fig. 3 , the strategy is to use the F1 and F2 optimizations sequentially. In order to guide the physically sound F2 inversion, initial values of α io = [α 1o , α 2o ,. , α Nso ] were set using normalized F1 values (between 0 and 1). This is equivalent to applying a regularization based upon the initial F1 search upon the F2 optimization. A global optimization using a constrained nonlinear optimization algorithms method (fmincon solver using a gradient-based method associated with the sequential quadratic programming, SQP, optimization algorithm) implemented in MATLAB ® (R2016b) software was then used to minimize F2.
Testing of the inversion procedure: a synthetic data example In this synthetic example, we used the same configuration, mesh and protocols as for the real case (see Sect. 2. 2). Figure  4  shows the initial model with the location of a cubic resistive anomaly (500 •m) embedded in a 100 •m background. The anomaly is slightly shifted compared to the acquisition domain. A dipole-dipole skip-two protocol was adopted for ERT acquisition (Fig. 3 , step 0). The same mesh was used for ERT and MALM simulations. The resistivity distribution obtained from ERT was used, as necessary in real cases, as the background resistivity through which the current, induced by the MALM experiment, flows. In this synthetic example, the MALM datasets (see Fig. 3  -ERT Model) are obtained hypothesizing current source locations (at the FE mesh nodes) within the given theoretical root zone -the current intensity is assumed the same at all nodes.
In practice, a double inversion is carried out: (1) ERT data are inverted to give the estimated electrical resistivity distribution; (2) assuming that the ERT-derived resistivity is correct, stem injection MALM data are inverted only for the locations of current sources. The procedure above is not free from uncertainty, particularly when it comes to the following: -The identification of current source locations is inherently an ill-posed problem, as the number of candidate locations is potentially very large and the current intensity for each injection point is of course unknown. Given that the MALM normalized voltage is only measured at a very limited number of electrodes, we cannot expect that a unique solution is possible. However, the space of possible solutions can be constrained and volumes of B.
Acknowledgements. The authors wish to acknowledge support from the projects " Water Saving in Agriculture: technological developments for the sustainable management of limited water resources in the Mediterranean area" (ERANET-MED WASA ) and " Hydro-geophysical monitoring and modeling for the Earth's Critical Zone " ( CPDA147114 ) funded by the  University of Padua. In addition, the information, data or work presented herein was funded in part by the  Advanced Research Projects Agency - Energy (ARPA-E) ,  U. S. Department of Energy , under work authorization number  16/CJ000/04/08. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Luca Peruzzo  and  Myriam Schmutz  gratefully acknowledge the financial support from  IDEX (Initiative D'EXellence, France) , the  European regional development fund Interreg Sudoe -Soil Take Care , no.
Data A large part of the data presented here are from SBE21 and SBE45 thermosalinographs (TSG) installed on ships running along the AX01 transect between Denmark and western Greenland and along the AX02 transect between Iceland, Newfoundland and the northeastern USA (Fig. 1 ). Along AX01, TSG data were collected on M/V Nuka Arctica between  July 1997 July -2017 July (with intake temperature in 2005 July -2017)). Along AX02, TSG data are available between  April 1994 and December 2007 and between March 2011 and March 2016 (with intake temperature during April 1994 -1996). The first installation on Nuka Arctica was done on a pumped water circuit in the bow thruster room of the ship, with little warming, but frequent interruptions during and after bad weather. In 2006, it was moved to the engine room at approximately mid-ship, roughly 5-m below the water line.
In addition T from the TSG on Nuka Arctica (AX01) were also used to adjust T along AX02 where there were crossovers of the AX01 and AX02 ships route. Validation of T and S data from the thermosalinographs is discussed further in App. A. For AX02, additional T and S data originate from seasonal surface sampling, in particular in July1993,  January and April 1994 , in 1995 , and in 2007 -2017. TSG data from several research cruises were also included. Upper-level data (near 5-7 m) of profiles from Argo, earlier PALACE floats (since 1996) and from CTD casts were also considered, as well as data from drifters equipped to measure precise temperature and salinity.
Methods We construct monthly binned T, S, and density time series starting in mid-1993 along two standard sections intersecting near 59. 5°N/32°W: B-AX01 between the North Sea and South Greenland and B-AX02 between Iceland and southern Newfoundland (Fig. 1 ). The B-AX01 section extends from the south-east of Cape Farewell (excluding the shelf or its vicinity) to the northwestern North Sea north-east of Scotland over the shelf. A separate binning G-AX01 is done on the Greenland shelf between southern Disko Bay (near 68. 2°N/54°W) and northwest of Cape Farewell, but only since July 1996 (with no data north of 64. 5°N in 1996-1997). We also binned data on an alternate route often used by Nuka Arctica across the Irminger Sea and Iceland Basin (N-AX01), to the north of B-AX01. For B-AX02, we include two bins over the Newfoundland shelf and two bins further to the north-east over the continental slope, followed by more regular bins along the standard section.
This is seen here as the negative SST anomalies near the beginning and end of the time series with warmer temperatures in the 2000-2009 period, roughly corresponding to SSS variability of the same sign. Variability is slightly larger along B-AX02, as expected from the known westward increase in SST variability portrayed for example in the Hadley Centre SST data set (HADSST3). Altogether there is not a large spatial variability in the temperature signals along these transects, at least on seasonal or longer time scales, except for some differences on the southern part of B-AX02 compared to other regions. Density anomalies (Fig. 2 , lower panel) are a result of both temperature and salinity anomalies. Except in the southern part of B-AX02 (south of 54°N), temperature variability tends to have a larger contribution than salinity variability to density (in particular east of the Reykjanes Ride or north of 60°N).
Results and data presented here highlight the importance of repeated ocean observations from volunteer ships, and the value of complementary data to better assess and monitor the state of the ocean and its variability from seasonal to interannual time scales. mostly surface water samples following  Alory et al. (2015). An intake temperature measurement was used since late 2004 to adjust the temperature measurements reported by the TSG. Before that, ad hoc adjustment was made on Nuka Arctica TSG temperature based on comparison with nearby data, but showing often very small differences, of less than 0. 1°C. We checked the consistency of these T-S data of Nuka Arctica with other upper ocean data. The TSG temperature and salinity data do not present significant biases with the upper level of Argo profiles, close to 5-8 m depth.
Because data at large spatial scales seemed reasonable during these weak-flow instances, including the 5 events does not change significantly the average bias. Thus, we retained these data in the data set, despite the likely time delay. In summary, although there can be errors on individual transects, the comparisons suggest high consistency between TSG data and other validated data a few meters below the surface. We carried a similar comparison for TSG data AX02 data since 1994, but although average results are similar, scatter is larger. The comparisons are also more difficult to interpret, because of many changes in how and where the TSGs were installed on different ships during the 1994-2016 period, frequent insufficient flow through the instrument, and also because XBT and Argo data were used to adjust the TSG temperatures when there was no intake temperature measurements.
Notice also that for 6 crossings  (in July 1993, January and April 1994, as well  as in 2016-2017), temperature was measured by the bucket method, taking care of leaving the bucket long enough in the sea and measuring T quickly (within 30 seconds) after retrieving the bucket. The data were compared for two crossings with intake temperature measurements, suggesting small negative biases (at most -0. 1°C), except during high wind conditions, which were not frequent. correlation in SSS with EN4, except in the western Iceland Basin, and RMS variability is much higher in EN4 surface fields (often by a factor of 2). Amplitudes are closer in CORA-AX01 and Armor3D-AX01, although there are smaller than those observed in the eastern Irminger Sea, and correlation is high except near the slopes. Interestingly, when averaging vertically EN4 salinity over the 0-500m layer, correlation with B-AX01 strongly increases everywhere (with coefficients often larger than 0.
6), and becomes significant and comparable to what is found for CORA-AX01 or Armor3D-AX01, except in the central and western parts of the Irminger Sea. Although this is a region where it is known that surface low frequency variability tends to be correlated at depth  (Reverdin et al. , 2018  or the old one?), one expects a decrease of correlation between the surface and greater depths. The better correlation with vertically integrated quantities than with analysis at the same (5 m) level in EN4 suggests that 'noisy' or 'erroneous' data are not properly filtered in the EN4 surface analysis. It is also found that CORA analyses are more correlated vertically that EN4, which points in the same direction. To a large extent, CORA and EN4 products rely on the same data, largely to Argo (and earlier PALACE) floats as well as research cruise CTD data, whereas B-AX01 strongly relies on TSG data (to a large extent from Nuka Arctica).
These mapped products differ in how they are produced. EN4 will tend to stick more to local data, whereas CORA analysis scheme is a classical objective mapping of deviations from a guess field. Thus, it will damp variability when there is not enough data within the radius of integration  (Gaillard et al. , 2009). This is likely to have often been the case before the Argo float deployments in 2001-2002. Thus, CORA will underestimate the variability, but be less noisy. The lack of data probably also explains the absence in this product of the low salinity signals in 1993-1995. For SST, the correlation of B-AX01 with all the gridded products along this zonal section is quite large (larger than 0. 80 everywhere, albeit a little smaller for Armor3D) with RMS variability of the same magnitude to the one in B-AX01 in the different products (although slightly smaller in CORA).
Acknowledgements This is a contribution to the French SSS observation service, which is supported by  French agencies INSU/CNRS ,  IRD ,  CNES  and  IPEV. We are very grateful to the crews of the different vessels on lines  AX01  and  AX02  from which the salinity data have been collected, in particular under the EIMSKIP and  Royal Arctic Line  (RAL) managements. We acknowledge the strong support to this operation by  Lars Heilman in Nuuk ,  Magnus Danielsen in Reykjavik ,  Hans Magnussen in Aalborg ,  Denis Pierrot  and  Francis Bringas. NOAA/AOML  and  NOAA/CPO Ocean Observing  and Monitoring Division have contributed by maintaining the TSGs along AX02 and providing XBTs on the different ships that have operated along the AX01 and AX02 transects. Coriolis contributed by providing XBTs to M/V  Nuka Arctica  and by supporting the production of the CORA dataset.
Conclusion The validated data presented here are able to characterize the seasonal variability of surface temperature and salinity along two transects crossing the North Atlantic subpolar gyre (along 59°N and from south-west Iceland to south-east Newfoundland) from July 1993 to December 2017. The time series presented here describe the interannual variability at seasonal resolution over this 21 to 25-year period except for some winter gaps over the Newfoundland shelf and along west Greenland, as well as until 1996 in the Iceland Basin along 59°N, and until mid-1997 along parts of west Greenland. To describe this variability these time series are better than current SST or SSS ocean data gridded analyses such as those provided in EN4 or CORA, in particular before the Argo period. These time series provide added information, in particular on the shelves and continental slope regions that is not available from Argo float data, despite Argo reaching nominal.
Microthermometric parameters of fluid inclusions were characterized at temperatures between -170 • C and +600 • C while using a LINKAM MDS 600 (LINKAM scientific instruments, Tadworth, Surrey, UK) heating-freezing stage equipped with a video camera. The microthermometric stage was thermally calibrated using Synflinc CO 2 and H 2 O synthetic fluid inclusion (FI) standards. The accuracy of measurements is ±0. 2 • C at low temperature and ±2 • C at high temperatures. Homogenization temperatures were observed at 1 to 5 • C/min rates, depending on the inclusion size. Densities and pressure-volume-temperature-composition (P-V-T-X) properties of FI used to determine the isochores were calculated using the software package FLUIDS 1 (University of Leoben, Leoben, Austria)  [18]. Minerals 2018, 8, 430 6 of 21 Salinities were calculated using the final melting temperature of ice, or halite, when present  [19] , and from clathrate dissociation temperatures, in CO 2 -rich inclusions  [20].
P-T Pathway Coexisting H 2 O-NaCl and CO 2 -H 2 O-NaCl fluid inclusion types were generated by phase separation of an initially homogeneous fluid. In this case, trapping occurred along an immiscibility surface in the CO 2 -H 2 O-NaCl system, with T h(Total) of the purest end-members being a close estimate of the trapping temperatures  [33, 34]. Corresponding pressures were calculated while using R. Bakker's FLUIDS software (Version of 2003) (University of Leoben, Leoben, Austria) package  [18]. Isochores were calculated combining results from microthermometry and Raman spectroscopy analyses (Table  2 ). The fluid trapping history established above allows for the reconstruction of the relative timing of formation for the El Callao vein system, along a pressure-temperature path (Figure  10 ).
Acknowledgments: We wish to thank  A. Graterol  and  O. González  for their help in the field, and the  Gold Mining Company of Venezuela (MINERVEN)  for allowing access to the site and sampling of their drill core, and for their logistical support. Financial support for this study was provided by the  French CNRS  and the  University of Toulouse. Constructive comments by three anonymous reviewers contributed to the quality of this manuscript.
, 1991; Ramaswamy et al. , 1992). To improve the accuracy of, and to automate, TCO measurements, the Brewer spectrophotometer was developed in the early 1980s  (Kerr et al. , 1981 (Kerr et al. , , 1988)). In 1988, the Brewer was designated (in addition to the Dobson) as the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) standard for TCO measurement. By 2017, there were more than 230 Brewer instruments installed around the world. Brewer instruments can provide TCO values via four types of observations: direct-sun, direct-moon, zenith-sky, and spectral UV irradiance  (De Backer and De Muer, 1991; Fioletov et al. , 1997 Fioletov et al. , , 1999;; Labow et al. , 2013). The most accurate ozone data products from Brewer instruments are their direct sun (DS) measurements, which have a typical accuracy of 1%  (Fioletov et al.