dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were placed on a KBr window under N 2 purge and areas were selected for analysis using the microscope. Initially a background im- age of the KBr window was collected, which was subtracted from the sample image. New background images were taken approximately every 300 min. Images were processed using Varian Win-IR Pro software (v3. 3. 1. 014). Individual spectra for use in transects were extracted from the images and H 2 O concentrations were calculated in the normal manner by entering the height (absorbance) of the relevant peak above a linear background into the Beer-Lambert law  (Stolper, 1982b). H 2 O t was calculated from the peak at ∼3500 cm -1 and H 2 O m from the peak at ∼1630 cm -1 , using respective molar absorptivity coefficients of 90 ± 4 l mol -1 cm -1  (Hauri et al. , 2002)  and 55 ± 2 l mol -1 cm -1  (Newman et al.
Acknowledgements We thank  R. Hinton  and  J. Craven  for SIMS assistance at the Edinburgh IMF,  L. Bowen  for SEM training and support at the  GJ Russell Microscopy Facility at Durham University , and  D. Wertheim  and  G. Gillmore  for undertaking sample imaging with confocal microscopy at the NPL. The  JSPS Summer Program  provided financial support to enable I. M. M. to visit Japan. SIMS data acquisition was funded by  NERC IMF  award  IMF455/1011. I. M. M was supported by  NERC  PhD Studentship  NE/H524606/1. M. C. S. H. is supported by a  Royal Society University Research Fellowship. A. B. and C. I. S. acknowledge support from  ERC  grant  202844  under the European FP7. We thank  A. Proussevitch  and  C. Martel  for their detailed and constructive reviews and  Tim Elliott  for editorial handling.
(Pebesma 2004 , Akima et al. 2013). In this study, the front was primarily delineated by salinity, and defined as the location of the greatest rate of change in salinity values. Isohalines between 33 and 34 were inspected, and the locations where the isohalines were closest together were delineated as the front. Physical data (non-interpolated environmental variables and location data) and biological counts were both placed into 1 m depth bins and then matched with each other using the sample time stamp. The concentration of each organism per bin (c b ; no. of ind. m -3 ) was calculated from counts (a b ) and volume sampled (vol b ): where 0. 13 is the ISIIS vertical field of view in m, 0. 5 is the ISIIS depth of field in m, v b is the average instrument velocity per time bin (m s -1 ), t b f and t b i the final and initial times when ISIIS crossed each depth bin.
Species distribution modeling Species-environment relationships were evalua ted using boosted regression trees (BRTs), an ensemble statistical method that combines regression trees with boosting, a machine learning technique  (Elith et al. 2008). The R package 'gbm' was used  (Ridgeway 2013). BRTs optimize predictive performance using the iterative development of a large group of small regression trees constructed with random subsets of the data. We chose to use BRTs because most organisms in our dataset exhibited non-monotonic, non-linear relationships to environmental gradients, thus rendering linear models inappropriate. Tree-based methods, which include BRTs, also provide an estimate of the relative influence of each environmental variable to species distributions  (Friedman 2001 , Ridgeway 2012).
We analysed in situ CTD data collected on 36 joint Tunisian-Italian oceanographic cruises in the Tunisia-Sicily Channel (Cap Bon-Mazara del Vallo section) from 1995 to 2009 in order to identify the water masses in the region and to estimate the interannual variability of their hydrological characteristics. Besides the well-known AW (Atlantic Water) and LIW (Levantine Intermediate Water), other water masses have been identified though their presence is neither as steady nor as stable as the aforementioned ones. The WIW (Western Intermediate Water) flows beneath the AW towards the eastern basin while the IW (Ionian Water), with a highly intermittent character, flows within the subsurface layer towards the western basin. The flow of subsurface water (WIW and IW) is affected by intense mixing which modifies the water masses, tending to make them disappear.
, 1999)  sampling was conducted over 12 h at 7 equidistant stations (5 mi). In 2003, we conducted a very high spatial resolution sampling at the 12 stations by reducing the distance between them (from 5 to 2. 5 mi) and sampling over 24 h (data described in Ben  Ismail et al. , 2012). It is noteworthy that we have kept this sampling strategy over the following years. In 2005 we similarly sampled 3 additional stations. Starting in 2006 we extended sampling to the eastern Tunisian coast in an effort to describe the newly identified water masses before reaching the Tp in the western sill of TSC (Fig. 2B ). A SeaBird electronics SBE 19/plus CTD profiler was used to collect all the data sets used in this study. The Tunisian probe was calibrated by the manufacturer each year, while the Italian one was calibrated at the NURC in La Spezia before and after each cruise.
Mooring data Two mooring Fig. 3  stations were deployed in the two passages of the western sill of the Tunisia-Sicily Channel at coordinates 37°22′ 54″ N/011°35′ 37″ E and 37°17′ 13″ N/011°29′ 59″ E, respectively  (C01 and C02, hereinafter). Simultaneous data collected between October 2004 and December 2010 at both stations have been used in this study. These stations are equipped with an upward-looking moored ADCP to monitor the upper part of the water column, three pointwise current meters in the lower layer and an autonomous CT probe at 100 m above the seafloor to monitor the hydrological properties of the deeper waters flowing into the western basin.
Vessel mounted ADCP data The hydrographic data set collected from the R/V Urania during the SESAME project (2008 cruises) has been integrated with direct current measurements, carried out with a vessel-mounted ADCP. During the whole campaign a VM-ADCP (RDI Ocean Surveyor, 75 kHz) was used for direct measurements of the currents along the whole ship track. The depth range of the current profiler is about 700 m. A revealing index of the quality of the ADCP data is the so-called percent-good, i. e. the percent of pings received with a noise-to-signal ratio below a certain threshold. In our case, with optimal sampling conditions, the percent-good is very high above 500 m (98-100%), decreasing slowly down to 600 m (~90%) and more rapidly from then on, reaching the lowest rates at 700 m (~85%). The ADCP data has been submitted to a post-processing with the CODAS3 Software System  (Firing et al.
Intermediate and deep layers The complex bathymetry that characterises the entire channel is a driving deep force of EIW flows. It divides into two veins which proceed to cross the channel in two streams whose cores can be found on each side of the ridge. Fig. 4A  shows that the core of the LIW is at about 230 m depth and located 35 km from the Tunisian coast. According to analysis of data available from 1995 to the present  (Ben Ismail Hamouda et al. , 2010)  we confirm that the LIW core is present in the wider Tunisian passage as well as in the eastern passage. Fig. 4B  also shows the presence of a deep water mass, much colder than the LIW, at the bottom on the Tunisian side of the channel. The tEMDW is characterised by an absolute minimum of temperature and relatively fresher salinity within the 100 m above the seafloor near the Tunisian slope and thus does not mix with the bottom EMED.
The data are centred (relative to the mean in blue) in Fig. 7B  and we have added a 10th order polynomial regression so as to obtain the interannual signal. An interannual increase in minimum salinity from S min = 36. 94 in 1995 to S min = 37. 11 was recorded in 2009. The annual mean (not shown) highlights this trend which is more significant after 2003. This increase may be due in part to the fact that we sampled the channel more during the summer cruises (see Fig. 2B ), when most of the data was collected. In fact, the S min is well correlated with the temperature and we observe that the S min record follows the evaporation rate, rising considerably due to the high temperature recorded in the study region. Furthermore, our results are in agreement with a recent study  (Millot, 2007) , which examined the interannual variability of the Mediterranean inflow at the Strait of Gibraltar and reported an increased salinity in the inflowing water between 2003 and 2007.
Acknowledgments The authors are deeply grateful to the  Captains  and the crews of both the  R/V Hannibal  and  R/V Urania  for continuous support throughout the measurement phase. The authors want to thank  Dr. Antonio Sanchez-Roman  ( University of Malaga, Spain ) for preparing the data of C01 and C02 as well as Fig. 12. Part of the data elaboration was also supported by the  SESAME  project,  EC  Contract No. GOCE-036949 , funded by the  European Commission 's  Sixth Framework Programme  under the priority ' Sustainable Development, Global Change and Ecosystems '. This study is part of the  PhD of Madame Ben Ismail Sana.
Even then, there has been relatively poor agreement between the various approaches for estimating net air-sea CO 2 fluxes in the Pacific Ocean  (McKinley et al. , 2004; Peylin et al. , 2005). In this work, we begin with a review of what is known about air-sea CO 2 fluxes over the sub-basins of the Pacific Ocean. We then present a synthesis of state-of-the-art assessments of net air-sea CO 2 flux over the past two decades spanning the years from 1990 through 2009. This effort brings together CO 2 flux estimates from a wide range of available approaches: a synthesized climatological pCO 2 sw data set, diagnostic models that use empirical interpolation schemes applied to the data of pCO 2 sw, oceanic inversion methods from measurements of ocean-interior dissolved inorganic carbon (DIC) and ocean circulation models, prognostic ocean general ocean circulation models coupled with biogeochemical models (OBGCMs), a data-assimilation model with pCO 2 sw, and atmospheric CO 2 inversion systems with measurements of atmospheric CO 2 mixing ratio and atmospheric transport models.
Climatological pCO 2 sw data and pCO 2 sw diagnostic models The evaluation of the air-sea CO 2 flux through gridded pCO 2 sw data products was originally developed in the studies built on the database of T. Takahashi for shipboard pCO 2 sw measurements  (Tans et al. , 1990; Takahashi et al. , 1997). The database and the gridded data products have been repeatedly updated and widely used since. The analysis here draws upon the data set of gridded monthly climatological pCO 2 sw in the reference year 2000  (Takahashi et al. , 2009a)  (hereafter referred to LDEO V2009; the climatological mean values for 4 • × 5 • pixel areas are listed in http: //www. ldeo. columbia. edu/res/pi/CO2). Diagnostic models of pCO 2 sw proposed by  Park et al. (2010)  and  Sugimoto et al. (2012)  are also used to estimate seasonal and interannual variations in air-sea CO 2 fluxes.
Ocean pCO 2 sw data assimilation The pCO 2 sw data set of LDEO V2009 has also been assimilated into an offline tracer transport model (OTTM;  Valsala and Maksyutov, 2010). This assimilation system minimizes the model biases in the surface ocean pCO 2 through a weak constraint given to its gridded monthly climatology of LDEO V2009, while a strong constraint is given to the in situ ship-observed pCO 2 sw measurements whenever they are available in the LDEO database  (Takahashi et al. 2009b ). The weak constraint is further weighted by the inverse of the model interannual variance, which ensures that the model is constrained to the monthly climatological pCO 2 sw only in regions where the interannual variability is small. Assimilated data of pCO 2 sw and air-sea CO 2 flux were constructed from 1996-2008 using this method, while here we present an extended record of the data starting from 1990.
It should be noted here that they differ in the atmospheric CO 2 data sets (i. e. , observational constraints), atmospheric transport models, spatial resolution of the optimized flux and inversion methods. Most of the inversions used climatological air-sea CO 2 flux data from some versions of the LDEO monthly climatology as a prior air-sea CO 2 flux estimate, and therefore regionally integrated or seasonal variations of posterior net air-sea CO 2 fluxes have been constrained by it to a greater or lesser extent depending on the inversion method.
We note that the southern boundary at 44. 5 • S lies in the vicinity of the Subtropical Convergence Zone and the surface water in this zone is the large sink of the atmospheric CO 2  (Metzl et al. , 1999; Inoue et al. , 1999; Takahashi et al. , 2009a). Therefore, the estimate of air-sea CO 2 flux in the South Pacific extratropics is expected to depend largely on the choice of the southern boundary. We will examine this using LDEO V2009 climatological fluxes in Sect. 5. 3. For each sub-basin, regionally integrated air-sea CO 2 fluxes were calculated as monthly means from each product: monthly climatological pCO 2 sw data of LDEO V2009, pCO 2 sw diagnostic models, Ocean BGC models, a pCO 2 sw data assimilation, and atmospheric CO 2 inversions. Decadal and longer-term mean fluxes were then calculated over the intervals  1990-1999 and 2000-2009 , as well as over the combined period 1990-2009.
They were subsequently compiled for each of the approaches: the average ± range for pCO 2 sw diagnostic models and the median ± median absolute deviation (MAD) for OBGCMs and atmospheric CO 2 inversions were calculated for decadal and longer-term means. In addition, skill-weighted mean values have been given for ocean CO 2 inversions. However, no remarkable differences are seen in decadal mean flux between 1990-1999 and 2000-2009 in all approaches (Fig. A1 ). Therefore, in the following sections, we will not mention the decadal means but present only longer-term means for the period 1990-2009 and interannual variability and mean seasonal variability for this period. For the pCO 2 sw data assimilation and the atmospheric CO 2 inversions, mean fluxes were calculated for 1990-2008 since modeling products in the year 2009 were not available.
In the cases of pCO 2 sw diagnostic models and OBGCMs, differences in the mean fluxes between for 1990-2009 and for 1990-2008 were minimal (< 0. 01 Pg C yr -1 ). Therefore we assume that the comparison of mean and median fluxes for 1990-2009 of diagnostic models and OBGCMs and those for 1990-2008 of pCO 2 sw data assimilation and atmospheric CO 2 inversions are not problematic in the following discussions.
The amplitude of interannual variability is somewhat larger in the flux estimate from pCO 2 sw data assimilation (0. 19 Pg C yr -1 ). Valsala et al. (2012)  posited a "four-region" structure in the North Pacific air-sea CO 2 flux variability related to the PDO in the data assimilation product. However, the correlation of the air-sea CO 2 flux with PDO is not clear when averaged over the North Pacific extratropics. Most of the atmospheric CO 2 inversions show negative anomalies around the late 1994, 1997, 2001 and early 2007 and positive anomalies around early 1994 and 2004. However, the correlations with the PDO index are also not obvious. No consistent pattern of interannual variability of air-sea CO 2 flux is seen among the different approaches in the North Pacific extratropics, suggesting that the various methods have not yet converged in their representation of interannual-to-decadal variability over the North Pacific extratropics.
With regard to the seasonality of air-sea CO 2 flux in the North Pacific extratropics, results from the three diagnostic models are consistent in that they all show very small net air-sea CO 2 flux in summer (July-September: +0. 03 ± 0. 10 Pg C yr -1 ) and a larger influx into the ocean in winter (January-March: -0. 86 ± 0. 20 Pg C yr -1 ) (Fig. 9 ). The difference in the net annual air-sea CO 2 flux among these diagnostic models is mainly attributable to the difference in the flux estimates in the cold time of year (December-April). The phase of seasonality in the pCO 2 sw data assimilation product is consistent with diagnostic models but does show a net CO 2 efflux in summer (+0. 56 Pg C yr -1 in July). All OBGCMs presented in this work also show well-defined seasonality with large CO 2 sink in winter (-1.
04 Pg C yr -1 in all months. As in the North Pacific extratropics, they all show a very small net air-sea CO 2 flux in summer (February: -0. 00 ± 0. 02 Pg C yr -1 ) and a larger influx into the ocean in winter (August: -0. 56 ± 0. 01 Pg C yr -1 ). However, the amplitude of seasonal cycle in the South Pacific extratropics is about 2/3 of that in the North Pacific extratropics (Fig. 9 ). The phase and amplitude of seasonality in the flux from the pCO 2 sw data assimilation product is also consistent with LDEO V2009 and diagnostic models. Most OBGCMs presented in this work also show well-defined seasonality with a large CO 2 sink in winter (-1. 20 to -0. 60 Pg C yr -1 ) and slightly negative or moderately positive fluxes in summer (-0. 13 to +0. 50 Pg C yr -1 ). The median ± MAD of the amplitude of seasonality is 1.
All Pacific Ocean regions 44. 5 • S-66 • N The time-averaged air-sea CO 2 flux for 1990-2009 described in the previous sections reveals a quite large range of variation among the different approaches when integrated over the Pacific Ocean between 44. 5 • S and 66°N (Table  4  and Fig. 6 ). It ranges from the weak CO 2 sink of -0. 22 Pg C yr -1 that was estimated from LDEO V2009 climatological pCO 2 sw to a stronger sink of -0. 57 ± 0. 12 Pg C yr -1 from OBGCMs. The results from diagnostic models (-0. 27 ± 0. 13 Pg C yr -1 ), pCO 2 sw data assimilation (-0. 33 Pg C yr -1 ) and atmospheric CO 2 inversions (-0. 26 ± 0. 10 Pg C yr -1 ) that are more or less constrained by the measurements of pCO 2 sw are consistent with the estimate from the climatological pCO 2 sw. However, the estimate from the ocean-interior CO 2 inversions (-0.
Dependence of air-sea CO 2 flux on wind product The accuracy of wind field products over the ocean is fundamental to evaluating air-sea CO 2 fluxes in all approaches considered in this study. In atmospheric CO 2 inversions, the wind field directly controls the transport of CO 2. The wind field in the marine boundary layer is also required to drive ocean circulation in the prognostic OBGCMs, the pCO 2 sw data assimilation, and the ocean-interior CO 2 inversions. In addition, various gas transfer velocities for CO 2 at the air-sea interface have been given empirically as a function of wind speed at 10m above sea surface and are needed to calculate air-sea CO 2 flux from the diagnostic models, forward OBGCMs and pCO 2 sw data assimilation. Several wind products are available and have been used for these purposes.
The model with pCO 2 sw data assimilation gave the smallest efflux in the tropics (+0. 27 Pg C yr -1 ) and the smallest influx into the extratropics of the North Pacific (-0. 37 Pg C yr -1 ) and the South Pacific (-0. 24 Pg C yr -1 ) among the approaches considered in this study. The phase of interannual variability in the tropics is not always consistent with the results from diagnostic models and OBGCMs. Atmospheric CO 2 inversions gave the largest variety of estimates of time-averaged air-sea CO 2 flux in each of the North Pacific extratropics (-0. 48 ± 0. 08 Pg C yr -1 ), tropics (-0. 53 ± 0. 08 Pg C yr -1 ), and the South Pacific extratropics (-0. 29 ± 0. 08 Pg C yr -1 ). The median of the estimates is fairly consistent with the estimate from the LDEO V2009 climatological pCO 2 sw and pCO 2 sw diagnostic models, possibly because of the use of the climatological CO 2 flux from pCO 2 sw in the flux priors.
Acknowledgements. This work has been done as one of the synthesis efforts of the project  Regional Carbon Cycle Assessment and Processes (RECCAP) of Global Carbon Project. We thank  J. G. Canadell ,  P. Ciais ,  C. Le Quéré , and  C. Sabine  for coordinating the RECCAP, and  F. Perez  and an anonymous reviewer for constructive suggestions to this manuscript. We also thank the captains, officers, and crew of all the many ships on which carbon measurements have been made and contributed to this work. All the modelers' contribution by providing the data sets of their model runs are also much appreciated. The OBGCM results are provided in RECCAP OBGCM site at http://lgmacweb. env. uea. ac. uk/lequere/reccap/. We thank the  Bjerknes Centre for Climate Research (University of Bergen and Uni Research AS)  for providing data of the MICOM-HAMOCC model and  ETH Zurich  for providing the data of NCAR CCSM3 ocean model with  BEC  with the gas exchange formulation of global mean velocity of 15 cm h -1 for CO 2 (ETHk15).
The French Agence Nationale de la Recherche ANR VANISH (program ANR-07-VULN-013 VANISH), NSERC-Canada and Sherbrooke University also provided financial support. The manuscript was written, in part, during a visit to Sherbrooke University supported by the grant CMIRA 2012 ExploraPro, Region Rhône-Alpes. Measurements in 2012 were conducted in the framework of the ANR MONISNOW.
Vertical profiles Figure  6  shows the vertical profiles of CH 4 as measured by AIRS (750-100 hPa) and GOSAT (1000-100 hPa) and as calculated by MOCAGE (1000-100 hPa) averaged over the EMB and the WMB depending on the four seasons: winter (DJF), spring (March-April-May, MAM), summer (JJA) and autumn (September-October-November, SON) 2010. There is a good agreement to within 20-30 ppbv between AIRS and GOSAT data in the vertical domain 750-200 hPa. GOSAT is systematically greater than AIRS by about 20-30 ppbv for pressure greater than 300 hPa, whilst for pressure less than 300 hPa, AIRS is systematically greater than GOSAT by 20 ppbv degrading to 50-200 ppbv at 100 hPa. But the shape of the vertical spaceborne profiles is consistent between AIRS and GOSAT. Separately, whatever the season considered, the MOCAGE low-to-mid tropospheric CH 4 is low biased compared to the measured profiles by ∼ 150-200 ppbv.
The minimum in January-February is close to zero but slightly positive in October (5-10 ppbv). The much stronger maximum in August calculated by MOCAGE compared to IASI CH 4 total columns might be attributable to the sensitivity of spaceborne measurements in the middle troposphere whilst the MOCAGE tropospheric columns cover the entire troposphere from the surface to the top of the model atmosphere, namely 5 hPa. In the upper troposphere (300 hPa), the spaceborne instrument data sets show an E-W maximum in summer of ∼ 12 ppbv in August for AIRS and an E-W wide maximum of ∼ 5 ppbv in July-September for GOSAT. An E-W peak of ∼ 10 ppbv in July-August is also calculated by CNRM-AOCCM although, in the LMDz-OR-INCA data set, the E-W maximum is slightly positive in August (∼ 2 ppbv). The minimum in the satellite data sets is observed in March-April and is negative (from -15 to -20 ppbv) consistently with the LMDz-OR-INCA data set whilst the CNRM-AOCCM E-W minimum is less intense.
3  and 8 ), we may have too much and/or too rapidly CH 4 transported over the Mediterranean Sea to the east compared to the west, leading to a too smooth E-W gradient in the models compared to the measurements. Recall that statistically the number of spaceborne measurements used in our analysis (see Sect. 2) is ∼ 5 times greater in IASI compared to AIRS, and ∼ 30 times greater in AIRS compared to GOSAT. Consequently, GOSAT monthly averaged data appear noisier than AIRS monthly averaged data. Note that IASI total columns are not and cannot be directly compared with AIRS or GOSAT profiles in our analysis. Nevertheless, although IASI data are not operationally produced, the IASI E-W seasonal variation is very consistent with the E-W seasonal variation as deduced from all other data sets. The monthly random error attributed to the E-W IASI CH 4 is about 0.