dataesr/scientific-paragraphs-categorization huggingface dataset:
For the ESEM analysis the cores were sawed to cubes a couple of centimetres in diameter. A few filamentous structures were removed with a forceps of stainless steel and placed on a plate with silver glue for ESEM and EDS analyses. ESEM. An XL30 environmental scanning electron microscope (ESEM) with a field emission gun (XL30 ESEM-FEG) was used to analyse the minerals and the filamentous structures. The ESEM was equipped with an Oxford x-act energy dispersive spectrometer (EDS), backscatter electron detector (BSE) and a secondary electron detector (SE). The acceleration voltage was 20 or 15 kV depending on the nature of the sample. The instrument was calibrated with a cobolt standard. Peak and element analyses were made using INCA Suite 4. 11 software. Since most EDS analyses were performed in situ and subject to variations in focal depth the EDS analyses should be regarded with caution.
All analyses were made through a 1003 objective, using 514 nm excitation of an Ar-ion laser adjusted to an on-sample intensity of ca. 2. 5 mW. Raman acquisition was performed in ''multi-window'' mode with 2 3 5 seconds running time and a spectral range of 100-2000 cm  21  , using an edge filter for 514 nm excitation wavelength at 100 cm 21 cut-off, a 100 mm entrance slit, a 1800 l/mm grating, and an air-cooled (270uC) 1024 3 256 pixel CCD array detector. The obtained spectral data was treated with the software ''Lab Spec version 5. 58. 25''. Spectra were baseline-subtracted by automatic polynomial fitting in order to remove the background fluorescence. Subsequent peak fitting and characterization was performed by peak-de-convolution using a Gauss-Lorentian function with 1000 iterations per fit.
Acknowledgments We wish to thank  Heike Siegmund  at the  Department of Geological Sciences, Stockholm University , for the stable isotope analysis and  Per Andersson at the Laboratory for Isotope Geology (LIG), Swedish Museum of Natural History , for discussions about isotope geochemistry. Marianne Ahlbom  at the  Department of Geological Sciences, Stockholm University , is acknowledged for the ESEM/EDS analysis. Funding was provided by the  Deep Carbon Observatory  (sub award to PI Andrew Steele), the  Postdoctoral fellowship program of Geophysical Laboratory, Carnegie Institution of Washington  and the  Swedish National Space Board  (Contract No. 83/10  and  121/11 ).
The material parameters involved in the above equations have been determined according to the following procedures. First, physical parameters, such as the porosity or the grain density, have been taken from available data in Table  1. Then, the parameters p, p s , and s involved in the capillary pressure curve, have been identified from available data related to drying paths  (Mu ñoz et al. , 2003). The intrinsic permeability K 0 , as well as the relative permeability parameter , has been calibrated by laboratory drying tests performed on argillite samples  (Garitte et al. , 2010). During these tests, the weight loss was continuously recorded, and the water content was measured at three different times, thus enabling an accurate determination of K 0 and. Since the bedding planes of the samples were oriented along the sample axis, which was the only possible direction for water movement, this calibration supplies the value of the permeability along the bedding planes.
Acknowledgements The work described in this paper was conducted within the context of the international DECOVALEX Project (DEmonstration of COupled models and their VALidation against EXperiments). The authors are grateful to the Funding Organizations who supported the work. Quintessa Ltd. and  University of Edinburgh  were supported by the  Nuclear Decommissioning Authority  (NDA), UK; CEA was supported by  Institut de Radioprotection et de Sûreté Nucléaire (IRSN). The  Japan Atomic Energy Agency (JAEA)  and the  Institute of Rock and Soil Mechanics, Chinese Academy of Sciences (CAS)  funded DECOVALEX and participated in the work. The views expressed in the paper are, however, those of the authors and are not necessarily those of the Funding Organizations. The data used in this work were obtained in the framework of the  EC  project  NF-PRO  (Contract number  FI6W-CT-2003-02389 ) under the coordination of  ENRESA  ( Empresa Nacional de Residuos Radiactivos ).
The rationale behind this idea is that the number of missing data in the microwave based dataset is much smaller. All deduced reconstructions will be validated using satellite-independent in situ data. A principal component analysis (PCA) will then be applied to the reconstructed and validated SST images to identify the surface signal of the IPC on the SST images. The first EOF and its time expansion series will be shown to properly characterise the IPC-Navidad phenomenon by both the comparison with previous studies and the use of in situ temperature and salinity data. Once the IPC-Navidad time evolution time series has been established, its extreme positive and negative values will be used to deduce the related atmospheric and oceanic conditions. Accordingly, the objectives of this study are the following ones.
First, to obtain as many as possible missing data free wintertime SST images for the Bay of Biscay area applying the DINEOF reconstruction technique. The second objective consists in testing if the combined reconstruction of microwave and infrared SST images leads to any improvement compared to reconstructing only infrared images. The third objective tries to identify the IPC-Navidad signal on the reconstructed datasets by deducing a daily frequency series of its occurrence for the winters in the 1981-2010 period, validating it based on satellite independent in situ data. Finally, the fourth objective consists in deducing the atmospheric and oceanic conditions that enhance or prevent the major IPC episodes. They are analysed within the context of the IPC driving mechanisms proposed in previous studies.
To this end, the variety of the datasets used and the DINEOF reconstruction technique are described in Sect. 2 and the results are presented in Sect. 3. Sect. 3. 1 deals with the reconstructions of SST datasets and the validation of the reconstructed data, whilst Sect. 3. 2 shows the deduction and validation of the IPC-Navidad time series. Finally, Section 3. 3 deals with the atmospheric and oceanic conditions related to the IPC. A discussion follows in Sect. 4 and the final conclusions are given in Sect. 5.
Data The variety of the data sources used in this study is described in the following. First the satellite SST datasets to which the missing data reconstructing technique will be applied are described, followed by the description of the in situ measured SST dataset used for the verification of such reconstructions. Next, the reanalysis products used to study the influence of the atmosphere on the major SST variability modes are described. The properties of a satellite sea level anomaly (SLA) dataset used for the same purpose are also detailed. Finally, an in situ profile dataset used to obtain the vertical distributions of temperature and salinity related to the main SST variability pattern is detailed. Two sources of remotely sensed nighttime SST images covering the southern Bay of Biscay study area (Fig.
1 ) have been used in this study. One is based on measurements in the infrared spectral band. The second is based on the microwave band. The differing characteristics of the two datasets and their relative advantages and disadvantages are summarised in the following, paying special attention to their spatial resolutions and time coverages. The first source of SST images is the AVHRR (Advanced Very High Resolution Radiometer) Pathfinder v5. 2 product  (Casey et al. , 2010) , which offers nearly 30 complete years of 4 km resolution daily SST images retrieved from infrared band radiometer measurements for the 1981-2010 period. The product also offers a full set of eight quality flags describing the reliability of each of the SST retrievals, going from 0 (lowest quality) to 7 (highest quality).
Wintertime daily nighttime SST images were chosen for this study (ftp://ftp. nodc. noaa. gov). This choice strongly conditions the number of available SST retrievals but ensures the use of the best possible dataset in the application of the SST image reconstruction technique. The second source of SST images is the v7 product derived from measurements by the Advanced Microwave Scanning Radiometer (AMSR-E) on board the Aqua satellite (http://www. ghcc. msfc. nasa. gov/AMSR/). With a 0. 25 • spatial resolution, daily images are available starting in June 2002. Files for the 2002-2010 period were obtained (ftp: //ftp. ssmi. com) and processed to obtain winter (Nov-Dec-Jan-Feb) nighttime daily images for the study area. The Pathfinder SST image series, compared to those from AMSR-E, have the advantages of covering a longer period  (1981-2010 compared to 2002-2010)  and having a higher spatial resolution (4 km compared to 0.
25 • ). However, these images, derived from measurements in the infrared band also have a major disadvantage compared to the microwave retrievals since SST estimations are missing in the presence of clouds. Although AMSR-E images will contain missing values due to the occurrence of rain, strong winds (> 20 m s -1 ), sun glint or in areas near the coast (∼ 75 km), the overall percentage of missing data is considerably smaller compared to the Pathfinder dataset. Despite their lower resolution and lack of measurements in locations near the coast, this property makes them a potentially valuable source of information to complement other higher resolution SST images with high percentages of missing data. Accordingly, instead of filling in missing values in AMSR-E data, the technical approach applied here is to use that available information in the reconstruction of missing Pathfinder data.
Once the reconstruction technique was applied to SST images and missing data was filled in, reconstructed SST fields have to be verified, preferably against independent data. In situ SST measurements from the International Comprehensive Ocean-Atmosphere Data Set, ICOADS  (Worley et al. , 2005; Woodruff et al. , 2011) , were used for that purpose. Although observations from this source had already been used as an ensemble in the statistical procedure applied in the calibration of the algorithms used to retrieve SST from satellite measurements  (Kilpatrick et al. , 2001) , and thus are not fully independent from satellite SST retrievals, individual in situ SST casts corresponding to the study area were used as a satellite independent source of information in the verification processes. To check the oceanic vertical structure and the properties of water masses related to the main SST variability mode given by the PCA of the reconstructed Pathfinder SST data, vertical temperature and salinity profiles from the World Ocean Database (WOD) (http://www.
aviso. oceanobs. com) for the 1992-2010 period. The selected variables were the SLA and the related geostrophic current anomalies, but the absolute dynamic topography (ADT) was also obtained. The selected branch comprises delayed time data from different satellites (Topex/Poseidon, ERS-1/2, Jason-1, Envisat and OSTM/Jason-2) that are merged following a criteria for maximum temporal homogeneity ("reference" data). A detailed description of this dataset can be found in  AVISO (2012). The seasonal cycle was removed from both variables along with the long-term trend.
Methodology Initially described by  Beckers and Rixen (2003)  and  Alvera-Azcárate et al. (2005) , the DINEOF technique is an iterative methodology to fill in missing data in geophysical datasets. The technique initially sets missing values to zero value. Then the dataset is iteratively decomposed by means of a principal component analysis  (e. g. von Storch and Zwiers, 1999; Wilks, 2006)  and recomposed afterwards using a fixed number of EOFs until convergence is achieved. The iterative process is repeated using a different number of EOFs in each realisation. The optimal number of EOFs is determined by means of cross validation using part of the initially nonmissing data that is put aside for this purpose. Once the optimal number of EOFs is determined, a final reconstruction is conducted using all available non-missing data and the optimal number of EOFs previously identified.
The methodology was additionally improved by  Alvera-Azcárate et al. (2009)  to reduce spurious time variability by including a filtering of the time covariance matrix that leads to better quality reconstructions. Examples of the application of the DINEOF technique can be found for the Adriatic Sea in  Alvera-Azcárate et al. (2005) , for the Ligurian Sea in  Beckers et al. (2006) , the Gulf of Mexico in  Alvera-Azcárate et al. (2007)  or the Black Sea in  Alvera-Azcárate et al. (2009)  among others. In the case of the Bay of Biscay and the surrounding areas, examples of DINEOF application can also be found in  Sánchez et al. (2007) ,  Ganzedo et al. (2011)  or in  Esnaola et al. (2012). When applying the DINEOF technique to a dataset containing high percentages of missing data, it must be taken into account that the technique fills in missing values based on the available non-missing data.
It was previously stated in the methodology section that only SST images with less than 95 % of missing data were reconstructed. That means that only days with at least 5 % of available data were reconstructed for the two Pathfinder subsets (at least 5 % of quality flag 7 data available). In the case of the combined Pathfinder and AMSR-E reconstruction, the days that did not match that criteria but that had a joint proportion of 5 % available data were also reconstructed. The overall percentage of missing data (lower than quality flag 7) in the Pathfinder dataset in the study area in wintertime over 1981-2010 is 93. 2 %, in the set over 2002-2010 it is 92. 6 % and finally in the AMSR-E dataset it is 57. 1 %. The number of days in each of the reconstructions matching the 5 % percentage threshold are shown in the top row of Table  1  (days), together with the percentage they represent with respect to the total amount of days in the period (in brackets).
These percentages show that for the wintertime period, even allowing a threshold as high as a 95 % of missing data, the percentage of days trespassing the criteria remains low. However, the combined case shows the advantage of increasing that amount by approximately 4 %. For all the reconstructions, the percentage of the overall variance contained in the original datasets that was explained by their reconstructed counterparts was above the 99 %. The rest of the Table  1  lists the values of the verification indices computed using the in situ SST measurements from the ICOADS dataset. Based on the date and geographic location of each individual ICOADS observation, a check for the presence of a non-missing value was performed in the nearest neighbour satellite pixel and a verification sub-sample of N pairs of coincident values created.
For each of these sub-samples two verification indices were then computed: the bias (BIAS) and the median absolute deviation (MAD), which is qualitatively similar to the root mean squared error (RMSE) but less sensitive to outliers  (Wilks, 2006). For each of the reconstructions, two verification sub-samples were selected. The first compared the originally non-missing satellite values with the available ICOADS counterparts (SATEL-LITE). A second comparison was made between all coincidences of the reconstructed and the in situ datasets (DI-NEOF). The BIAS and MAD values in the SATELLITE columns give the approximated expected background differences between the in situ measurements and the satellite retrievals. It can be seen that DINEOF introduces a small negative BIAS in the 0. 12-0. 14 • C range.
The MAD values of the reconstructed datasets remain nearly unchanged with respect to the original satellite and in situ value differences. Consequently, the verification   1981 -2010 Pathfinder, 2002 -2010 Pathfinder and the 2002 -2010  combined Pathfinder/AMSR-E DINEOF reconstructions using ICOADS SST data. In all three cases in situ observations are compared with the originally non-missing satellite values (SATELLITE) and with the fully reconstructed dataset (DINEOF). The number of reconstructed days together with the percentage of the total they represent in brackets, the sample size of each verification sub-sample (N), the bias (BIAS) and the median absolute deviation (MAD) are shown. Three additional analogous reconstructions were also computed (not shown) jointly using Pathfinder quality 5, 6 and 7 data.
Considering, as in Table  1 , the comparison with in situ observations from ICOADS dataset, Fig. 3  shows the monthly evolution of the error parameters. Again, the background error, i. e. the comparison of the originally non-missing satellite values and ICOADS observations (SATELLITE), and the errors of the reconstructions versus ICOADS observations (DI-NEOF) are shown (blue and red lines respectively). The time axes are arranged so that each space between the dashed vertical lines shows a November to February period (both included), and the same time axis is used for the 1981-2010 and 2002-2010 cases. BIAS values related to both reconstructions are mostly negative and lie between 0. 2-0. 4 • C values. However, there are important exceptions with considerably higher BIAS values in the case of the 1981-2010 reconstruction, specially in the initial years, and also in some months in the 1992-1998 period.
In most cases, peaks with large MAD values in the SATEL-LITE original data are smoothed in the DINEOF reconstruction. Table  1  and Figs. 2 and 3 show that the error estimations of the reconstructions based on the Pathfinder quality flag 7 data computed using the DINEOF technique are similar to the ones of the original raw data. These reconstructed datasets, however, offer a much larger amount of data compared to their original raw data analogues. These results support the use of the reconstructions in the forthcoming steps in this study. The main wintertime SST variability mode and its.
Sea surface temperature variability and the surface signal of the IPC The main wintertime SST variability mode, obtained from a PCA analysis of the reconstructed datasets, will be analysed in this section. In relation to this mode, the surface temperature pattern and the time evolution at different timescales, as well as the water mass properties related to them, will be shown to match the properties inferred from several previous studies for the Iberian Poleward Current. Due to its time span of nearly 30 winters, and the approximately 1250 SST images it contains, the analysis will be mainly based on the 1981-2010 Pathfinder reconstruction. The characteristic SST patterns related to a well developed IPC episode, and the presence of the surface signal of this phenomenon in the reconstructed datasets, is evident in the results from two selected previous studies based on gappy SST data which are reproduced here.
The two dashed horizontal lines in Fig. 7  show the 80th and 20th percentiles of the 1981-2010 PC series. Days in the time series above the 80th percentile are those related to the most developed IPC-Navidad events according to the PC and will be denoted P80. Meanwhile, those below the 20th percentile represent the periods without, or with very weak, poleward flow and will be denoted P20. The properties related to these two groups will be used as indicative of the conditions related to well developed IPC-Navidad events and to their opposite conditions in terms of the PCA mode. For each set of days, P80 and P20, the available WOD vertical temperature and salinity profile data were selected for each of the 4 area boxes (I, II, III, IV) and the corresponding temperature and salinity means were computed for each depth.
This procedure will allow us a more general characterisation of the atmospheric circulation anomalies related to the Navidad-IPC as the analysis will not be limited only to the most common teleconnection patterns. The extremes of daily time series of the IPC-Navidad occurrence have already been used to infer the water mass characteristics related to these extremes. The same P80 and P20 day sets will also be used to identify the atmospheric circulation anomaly patterns that occur prior to the strongest anomalies of occurrences and non-occurrences of the IPC-Navidad. Assuming that time lagged relations may well occur, lagged versions of P80 and P20 are consequently created by displacing the dates by a given lag. Accordingly, two composite patterns for each variable (from ERA-Interim or AVISO datasets) will be computed by averaging the spatial fields of the variable over the dates with extreme IPC-Navidad, one for the positive (related to P80) and another for the negative (related to P20) cases.
For this purpose the AVHRR Pathfinder v5. 2 data (quality flag 7 unreconstructed) was retrieved for those days belonging to the P80 and P20 sets. To observe both the developing and decaying phases of the process, negative lags (days after a major IPC-Navidad event) were also taken into account. Figure  11  shows the (C P-N ) patterns for the -15-0, 0-15 and 15-30 day lag ranges. The SST patterns show an inten-sification and then a decay of the SST signal in the eastern sector. The overall pattern is very consistent with those of the SLA and the surface turbulent fluxes, although the signature also shows marked local differences on the European shelf and more specifically in the Bay of Biscay. For instance, in the north-eastern Atlantic a large-scale heat-flux pattern is observed while the SLA pattern is located on the European shelf.
Discussion The first part of this study, devoted to the reconstruction of gappy SST data using the DINEOF technique and the verification of such reconstructions, is of crucial importance for the analysis on the IPC-Navidad carried in the second and third parts, but it is also a valuable scientific result on its own. Although the percentages of missing data were initially very high (up to 93 %), it was possible to reconstruct between 35 and 45 % of the total days, depending on the reconstruction, with acceptable verification index values, as shown in Table  1. A similar reconstruction based on the 1981-2010 Pathfinder data used here can be found in  Esnaola et al. (2012) , but for the whole year in that case. The percentage of reconstructed days was higher (above 50 %) as data for the entire year were considered and the verification indices were also slightly better due to the same reason.
3. The joint use of infrared and microwave SST images in the framework of the DINEOF reconstruction technique is a novel approach that was applied in this study for the Bay of Biscay area. Previous multi-variable approaches have been developed in the context of DINEOF  (Alvera-Azcárate et al. , 2007) , but not for the combination used in this study, that had only been applied in the two-dimensional variational data assimilation (2DVAR) context  (Chao et al. , 2009). This combination allowed us the reconstruction of an additional 4 % of the days without any impact on the verification skill indices (Table  1 ). In the second and third parts of the present study, where the analysis was focused on the IPC-Navidad, this combined reconstruction lost prominence in favour of the considerably longer 1981-2010 reconstruction.