dataesr/scientific-paragraphs-categorization huggingface dataset:
Their diving behaviour seems to reflect this generalist feeding strategy even at the dive scale, exhibiting several foraging episodes within a dive, which are most often above the bottom phase of the dive  [21]. Southern elephant seal and Weddell seal foraging behaviour has been extensively studied in several locations  [29] [30] [31] [32] ; however, the new information on finescale behaviour by Dragon et al. [14]  and Heerah et al. [21] , in addition to the increasingly large number of lowresolution data available, highlights the value of re-visiting the common approach of identifying foraging activity within low-resolution dives. We used high-resolution time depth recorder (TDR) and acceleration datasets from two SES that visited the peri-Antarctic margin during their post-breeding foraging trip, and a high-resolution TDR dataset from a Weddell seal during six winter months.
Methods Two adult female SES (length: 266 and 255 cm) were captured at Kerguelen Island (49°20′ S, 70°20′ E) in early November 2010 before their post-breeding foraging trip. One adult female Weddell seal was captured in February 2008 after its annual moult at Dumont D'Urville (66°40′ S, 140°00 E) (length 230 cm). Similar capture and tagging procedures were used for both species and are fully described in  [21]. A TDR combined with an accelerometer (TDR Mk 10 X, Wildlife Computers) and a TDR (Mk 10, Wildlife Computers) was head-glued to the SES and to the back of the Weddell seal, respectively. The TDRs all recorded depth at 1 Hz. For the SES, acceleration was recorded in all three axes at 16 Hz. The number of prey capture attempts (PrCA) was calculated for each dive from the concurrent high-resolution acceleration data  [33, 34].
Fine-scale analysis of foraging behaviour The high-resolution dive data were processed with a new approach which accurately identifies the parts within a dive where a diving predator displays foraging behaviour based on the vertical sinuosity in the dive profile  [21]. Only dives below 20 m were analysed for both species. The method is fully described in  [21]  and briefly summarised below. Each dive was summarised by an optimised broken stick algorithm which (1) iteratively selected a series of inflexion points for individual dives, and multiple summaries were made for each dive based on a varying number of inflexions; (2) from this suite of summaries, the number of broken stick points (inflexions) that best summarised the dive shape was selected; (3) subsequently, the complete set of dives independently of their depth, duration and complexity was described; and (4) for each dive part (i.
9 ≤ vertical sinuosity ≥ 1). The time spent in the hunting mode within a dive was summed for each dive and used as an index of the foraging effort (hereafter, "hunting highres time"). Dives with more than 1500 s spent in hunting highres time were scarce and therefore not considered in further analysis (9 and 10 dives for the SES and the Weddell seal, respectively). As described in  [21] , dives of the Weddell seal dataset containing segments associated with abnormally high vertical ascent/descent rates were removed (23 dives in the Weddell seal dataset).
Simulation of a low-resolution dive dataset and calculation of foraging effort indices To quantify the foraging effort in low-resolution dives, it was necessary to find an index similar to the hunting highres time. First, we used the high-resolution dives to generate the equivalent low-resolution profiles provided by CTD-SRDLs (conductivity temperature depth-satellite relayed data loggers) using a broken stick algorithm  [11]. The SRDL datasets were created by selecting six data points for each high-resolution dive: the two surface points marking the beginning and end of each dive, the maximum depth point and the three other most informative inflexion points. This is the same method used to summarise dive data transmitted by SRDLs  [11]. Then for each dive, we calculated five different indices that could be potentially used to infer foraging effort: 1.
[11]. The index takes values close to 1 when the area enclosed by the dive profile is maximal (i. e. "square-shaped dive") towards values close to 0. 5 when the dive area is minimum (i. e. "V-shaped dive") given a set speed. The V-shaped dive represents dives where equal time is spent at all the depths encountered; conversely the square-shaped dive represents dives where a seal maximises its time at depth. TAD values close to 1 indicate that a seal has spent more time at a given depth reflecting potential foraging activity. 4. Hunting time calculated using low-resolution dive data (i. e. hunting lowres time). A recent study has shown that hunting mode in high-resolution dives (calculated using vertical sinuosity, see "Fine scale analysis of foraging behaviour" and  [21] , Fig. 1a, c ) was associated both with more PrCA and decreased vertical velocities (SES: 0.
The latter is likely to be related to the occurrence of rare "drift" dives that would be associated with "low-speed" segments in low-resolution dives, but not with an increased vertical sinuosity in high-resolution dives. In agreement with our assumption, these dives were also associated with lower than average PrCA (3 ± 6 PrCA instead of 9 ± 0. 05 PrCA). These dives were excluded from the dataset before repeating the analysis described above.
From high-resolution to low-resolution dives: estimation of foraging effort Comparison between foraging effort metrics Of all the low-resolution dive foraging effort indices, the hunting lowres time (see "Simulation of a low-resolution dive dataset and calculation of foraging effort indices") was best correlated with the hunting highres time for both species (R 2 SES = 0. 67 and R 2 Weddell seal = 0. 94, Table  2 ). Two dives for each species are presented as an example of the concordance between the parts of the dive where foraging occurred for high-and low-resolution dives, respectively (Fig. 1 ). For the SES, the second best correlated index with hunting highres time was bt60 (R 2 = 0. 4) followed by bt80 (R 2 = 0. 3, Table  2 ). The ascent rate correlated least with hunting highres time (R 2 = 0.
Discussion We present a new method for identifying areas of hunting activity within low-resolution dive data, which can be used at the scale of individual dives. Our results show that (1) of five potential indices, the hunting lowres time was the most correlated of the indices to the hunting high- res time, (2) times allocated to foraging at the dive or trip scale were quite similar when estimated by hunting highres and hunting lowres time, (3) 77 % of total PrCA occurred in hunting highres mode segments of high-resolution dives and despite dive information being much more degraded in low-resolution dives, 68 % of total PrCA occurred in the hunting lowres segments which were also associated with four times more PrCA than transit lowres segments. Importantly, the concurrent prey capture attempts (PrCA) estimated from high-resolution acceleration data for SES supported the low-resolution foraging effort index identified with our method.
unpublished data). Other projects like the Tagging of Pacific Pelagics (TOPP) has also deployed thousands of similar tags including SRDLs  [8]. These numbers are impressive and show that millions of low-resolution dive profiles were or are to be analysed. Our study showed that despite degraded information, insights on foraging activity (i. e. detection of withindive intensive foraging occurrences and quantification of foraging effort) could be obtained when using lowresolution dive datasets as long as using a metric that is based on the detection of changes in predator's diving behaviour. Our results were supported by independent PrCA, but the integration of complementary sensors (e. g. video recorders, stomach/oesophageal temperature sensors) from which feeding success could be inferred  [43, 47, 57]  would allow to further validate the method.
Number of prey capture attempts (PrCA) and rate of PrCA per segment according to the foraging behaviour (i. e. hunting or transit) associated with the corresponding segment (mean ± SE from data pooled for the two SES). Percentages of total PrCA were calculated on the whole dataset for both resolutions independently. Hunting phases of high-resolution dives (vertical sinuosity <0. 9) and low-resolution dives (segments associated with vertical velocity ≤0. 4 m s -1 ) indicate parts within a dive where a seal intensifies its foraging behaviour.
In the framework of the MOZAIC-IAGOS program, ozone and carbon monoxide vertical profiles are available since 1994 and 2002, respectively. This study investigates the variability and trends of both species at several tropospheric layers above the Frankfurt and Munich airports where about 21 600 flights have been performed over the 1994-2012 period, which represents the densest dataset in the world (about 75 flights per month on average). Over that period, most mean ozone trends are positive but insignificant at a 95 % confidence level, except in winter where significant upward trends (around +0. 38 ± 0. 24 ppb yr -1 ) are found. Conversely, a significant increase of annual ACPD 15,.
background ozone is highlighted, mostly during winter and autumn. Mean annual ozone trends increase with altitude, the largest increase being found in summer due to a noticeable decrease of highest ozone concentrations observed in the lower troposphere during the second half period. Over the 2002-2012 period, most mean CO trends are significantly negative, the decrease being higher in the lower troposphere compared to the mid-and upper troposphere with again, major differences in summer. Trends in the ozone seasonal cycle are also investigated, with a focus on the phase. Ozone maxima occur earlier and earlier with a shift around -10. 6 ± 2. 9 days decade -1 in the lower troposphere, in agreement with previous studies. The analysis of other ozone datasets in Europe (including surface stations and ozone soundings) confirms this trend, but highlights strong heterogeneities in the phase change.
However, results in boundary layer will be also presented to give a full picture of the troposphere. This study will also investigate the variability and trends of carbon monoxide, the other chemical compound measured in the framework of the MOZAIC-IAGOS program. acterizing carbon monoxide in troposphere may thus help investigating the variability and trends affecting ozone. Section 2 presents the MOZAIC-IAGOS dataset used in this paper and describes the treatment applied on vertical profile data. Ozone and carbon monoxide variability and trends are analysed in Sect. 3, while long-range transport is investigated in Sect. 4. Results are finally discussed in Sect. 5.
MOZAIC-IAGOS dataset In the framework of the MOZAIC-IAGOS program (www. iagos. fr), ozone and carbon monoxide measurements (among other parameters) are performed by commercial aircraft along various trajectories in the world (most of them from or to European airports) since 1994 and 2002, respectively. Until October 2014 (date of the last MOZAIC aircraft flight), both ozone and carbon monoxide have been measured using the same instruments in all aircraft, thus ensuring the dataset consistency during most of the period. Ozone measurements were performed using a dual-beam UV-absorption monitor (time resolution of 4 seconds) with an accuracy estimated at about ±2 ppbv/±2 %  (Thouret et al. , 1998). Carbon monoxide was measured by an improved infrared filter correlation instrument (time resolution of 30 seconds) with a precision estimated at ±5 ppbv/±5 %  (Nedelec et al.
, 2003). Note also that comparisons between the MOZAIC ozone dataset and other types of data (e. g. mountain surface stations, ozonesonde) have been performed in Europe, showing a good consistency  (Logan et al. , 2012). As The new IAGOS instrumentation is extensively described in  Nédélec et al. (2015). Briefly, the O 3 and CO measurements are based on the same technology used for MOZAIC, with the same estimated accuracy and the same data quality control. The nearly 4 years overlap of "old" MOZAIC and "new" IAGOS systems operations allowed to prove that new IAGOS systems provide the same data quality, which is especially important for trends analysis. The present study will focus on the Frankfurt airport where the longest (from 1994 to 2012) and densest (18 945 flights) record is available.
In order to fill a large data gap in 2005, this dataset is combined to data from the Munich airport  (2703 flights, mostly between 2002 and 2005) , approximately 300 km south-east from Frankfurt, as done in several previous studies  (Logan et al. , 2012; Zbinden et al. , 2006 Zbinden et al. , , 2013)). Note also that no measurements are available during a part of 2010 due to instrumental problems. An illustration of the dataset density by month and year is shown in Fig. S1  and S2 in the Supplement.
15 ± 6. 34 ppb yr -1 against -1. 72 ± 1. 92 ppb yr -1 for the 5th percentile). This may be partly due to a persistent influence of aircraft emissions -on tarmac and/or during the takeoff/landing phases (in case the MOZAIC-IAGOS aircraft closely follows other aircraft) -above 1 km, which may add some variability depending on the local dispersion conditions. But actually, the interannual variability found here in the LT with MOZAIC-IAGOS vertical profiles appears consistent with CO surface measurements obtained at four German stations from the WMO Global Atmosphere Watch (GAW) database (see Fig. • N, 13. 0 • W), Schauinsland (47. 9 • N, 7. 9 • W) -which highlights the regional dimension of that interannual variability. The large variability of CO concentrations has already been noticed in other studies and may be linked to the variability of biomass burning emissions  (Wotawa et al.
ozone maxima. Thanks to vertical profile observations, it brings an interesting contribution by highlighting that these seasonal changes above Frankfurt/Munich clearly depend on altitude, with the highest shift in the lower troposphere and much lower ones in the mid-and upper troposphere. It is worthwhile noting that MOZAIC-IAGOS observations above Frankfurt/Munich likely represent the densest dataset of ozone vertical profiles in the world, which gives robustness to our results. Qualitatively, a similar behavior is observed in ozonesonde observations at two sites among three, but the reliability of these results is limited by a much lower number of vertical profiles. However, a high variability from one site to the other in Europe affects both the rate of decrease with altitude and the shift extent itself, as confirmed by results obtained at ground stations.
Summary and conclusions An extensive database of ozone and CO vertical profiles above worldwide airports is available from the MOZAIC-IAGOS program since 1994 and 2002, respectively. In this Discussion Paper | Discussion Paper | Discussion Paper | study, we investigate the climatology, variability and trends of ozone and CO observations obtained above the Frankfurt and Munich airports whose combination represents the densest and longest MOZAIC-IAGOS dataset. We focus on troposphere, each vertical profile being subdivided in three tropospheric sublayers -the lower, mid-and upper troposphere (LT, MT and UT, respectively) -based on the potential vorticity extracted from ECMWF meteorological data. Main results are given below (all trends are.
5 Summary and conclusions An extensive database of ozone and CO vertical profiles above worldwide airports is available from the MOZAIC-IAGOS program since 1994 and 2002, respectively. In this Discussion Paper | Discussion Paper | Discussion Paper | study, we investigate the climatology, variability and trends of ozone and CO observations obtained above the Frankfurt and Munich airports whose combination represents the densest and longest MOZAIC-IAGOS dataset. We focus on troposphere, each vertical profile being subdivided in three tropospheric sublayers -the lower, mid-and upper troposphere (LT, MT and UT, respectively) -based on the potential vorticity extracted from ECMWF meteorological data. Main results are given below (all trends are.
Underneath the PC, the deep poleward flow incorporates the Mediterranean Undercurrent (MUC), which vertically splits into upper and lower cores  (Ambar, 1983; Ambar et al. , 2008). Presently there are several global and regional databases covering the study region. The most known is the World Ocean Atlas 2009 (WOA09), where a minimum radius of the gridding window of 444 km is used and the grid resolution is 1 • × 1 •. During the course of this work, the World Ocean Atlas 2013 (WOA13) was being published. The WOA13 has a radius of the gridding window of 214-321 km and a maximum grid resolution of 0. 25 • × 0. 25 •. It shows a significantly improved climatology in the study region as compared to WOA09. At the same time, the gridding procedure with the radius of influence of more than 200 km significantly oversmoothes the frontal zones, especially near the Iberian continental slope, where the spreading of the MW is characterized by very sharp temperature-salinity gradients.
Materials and methods Data from the World Ocean Database (WOD) were downloaded from the National Oceanographic Data Center (NODC, http://www. nodc. noaa. gov/): CTD (conductivitytemperature-depth, high vertical resolution of typically 1 m or less) and PFL (profiling float, medium vertical resolution of typically 1-10 m, mainly obtained from the ARGO profiling float array), and OSD (ocean station data, low-vertical resolution of typically more than 10 m) from 1950 to 2011. Bathymetry data, used for conditioning of some data filters and for gridding procedures, are obtained from the ETOPO2 data set (http://www. ngdc. noaa. gov/mgg/fliers/ 01mgg04. html). Within the study region, there are over 54 000 profiles (Table  1 ). The spatial distribution densities of CTD (and to a lesser extent of OSD) profiles show a tendency to decrease from the continental margin seawards.
If only a part of the profile deviates from the WOA09 over 5 standard deviations, the profile is considered "dubious" and the data reliability is verified by eye-checking. The expert decision is made based on persistence of the same dubious structures in other local profiles. This filter is not applied for the upper 100 m layer and the MW layer (700-1500 m), since extreme natural deflections from the WOA09 climatology in those layers significantly exceed the 5 mean standard deviations used as the filtering criterion. The next step consists of testing the profiles for static stability. In the parts of the profiles where a density inversion exists and exceeds 0. 02 kg m -3 over a less than 200 m depth range, an attempt to correct the inversion by removing spikes in temperature or salinity is made.
, 2008). To evaluate the effect, temperature and salinity deviations of the OSD pro- files from WOA09 between 1500 and 2000 m are summed in each of the 100 × 100 km areas. For a normal distribution of the data deviations from climatology, we expect the sum of the deviations to be close to zero, at least within the limits ± 1. 96n × SD/ √ n , where n is the number of data points and SD is the mean standard deviation of temperaturesalinity from climatology (see above). For the typical n = 30, we exclude OSD casts whenever the sum of the deviations from WOA09 climatology exceeds 1 in salinity or 4 • C in temperature. In addition, consecutive profiles of a PFL (an Argo float) are removed when a degradation of the salinity sensor with time is noticed. Sensor degradation is detected whenever two criteria are satisfied.
Firstly, at the end of the registered float's trajectory, the salinity values show a persistent decrease with time. Secondly, the salinity values differ from a reference value to more than 5 climatic standard deviations. As the reference salinity, we used the mean salinity computed from the other PFL observations in the same area during the period of the possible sensor degradation. If there are no PFL data available, the WOA09 climatology is used as the reference. The filtered data undergo a gridding procedure. The gridding is done on neutral density surfaces  (McDougall, 1987; Jackett and McDougall, 1997). For this study, pressure, temperature and salinity distributions are obtained on 53 neutral density surfaces (G) 1. The G surfaces are selected to be fairly uniformly distributed in depth, keeping the mean distance between the surfaces less than 50 m in the upper 1500 m layer, and less than 100 m below.
To get the highest possible resolution with the decreasing data density away from the Iberian Peninsula, we used spatially varying R. For G less than or equal to 27. 96 kg m -3 (in the upper 1900 m layer), R increases from 70 km near the Iberian Peninsula to 200 km in the western part of the study region. Downwards, R increases 1. 5 times for G from 27. 97 to 27. 98 kg m -3 (in the layer 1900 to 2000 m) and doubles for G greater than or equal to 27. 99 kg m -3 (below 2000 m). A smooth transition between the areas with the minimum and the maximum radii is assured by gradual variation of R across the study region (Fig. 4a-b ). In Fig. 3d , the Barnes response functions, are presented for different R. Therefore, in the deep ocean, the data set does not permit gridding with a higher resolution than that of WOA13, while above 1900 m an improvement can be achieved.
11d ). There are more similarities to the geostrophic currents estimated from WOA13 (Fig. 11c ). However, the latter gives less evidence of the AzC meandering and periodic along-path intensification that is observed in AVISO and MEDTRANS. In addition, the WOA13 climatology does not reproduce the cyclonic gyre in the Gulf of Cadiz (Fig. 11a-b ) as well as the MUC (Fig. 11e-f ) along the northern slope of the Gorringe Bank and the southern slope of the Galicia Bank  (Iorga and Losier, 1999). Figure  11a  shows that the AzC decelerates as it turns north in quasi-stationary meanders and accelerates as it turns south. This feature is also apparent in AVISO results (Fig. 11b ), but is absent in WOA13 results (Fig. 11c ). The strongest stationary meander is situated at 21-22 • W (in AVISO data -at 23 • W) and the second one at 16-17 • W (the same in AVISO data).
Conclusions The MEDTRANS climatology is based on the Barnes optimum interpolation procedure on 53 neutral density surfaces. Due to lower along-surface gradients of temperature and salinity, as compared to the isobaric surfaces, the isoneutral gridding provides a better representation of sharp thermal and haline fronts for the same gridding radius. This is further proven when computing geostrophic current velocities in comparison with the existing high-resolution isobaric data sets (WOA09 and WOA13). The hydrological data used for compiling the climatology have undergone several filtering procedures, including filtering on neutral density surfaces. Reducing eddy-related and instrumental noise, the filtering allows sufficient smoothness of the final thermohaline fields at the enhanced spatial resolution.
The gridding window radius decreases towards the northeast, allowing the best use of the large number of data near the Iberian Peninsula. In particular, this leads to a better representation of thermohaline fronts of the Iberian upwelling and the MUC. The final climatological maps of temperature and salinity distributions are further vertically re-gridded to the isobaric levels to have a 25 m depth interval with around 30 km horizontal resolution. The MEDTRANS gridding procedure can be regarded as a development of some of the ideas used for obtaining the MIMOC data set  (Schmidtko et al. , 2013). However, there are a few important differences, which allow the MEDTRANS data set to show several advantages compared to MIMOC. Based on vorticity considerations, the topographic influence on the dominant along-slope direction of water mass propagation for steep slopes is taken into account through the shape of the gridding function.