dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We wish to thank the crew of the  French R/V Atalante  during the cruise in the framework of the  AMOP  project (Activity of research dedicated to the  Minimum of Oxygen  in the eastern Pacific). We also thank  Sergio Larios  for his input in the data processing. Sea surface level anomaly data were produced by  Segment Sun Altimetrie et Orbitographie/Developing Use of Altimetry for Climate Studies (Ssalto/Duacs ) and distributed by Archiving Validation. The interpretation of satellite Oceanographic Data (Warning) was carried out with the support of the  Center National d'Etudes Spatiales (CNES).
In post-shift urinary samples, more quantifiable levels of VOCs were measured and these could be directly linked to the occupational exposure, with significantly higher medians for ethylbenzene and toluene recorded in the paints, varnish and resins plant, dichloromethane and toluene in the organic synthesis plant, and all BTEX except ethylbenzene in the oil refinery. For the other VOCs, the urinary levels could be attributed to environmental exposure. Examples of data obtained for a non-smoking control worker and for a non-smoking exposed worker from the refinery plant for a post-shift urinary sample are given respectively in Figures  3  and 4. The organic synthesis plant was a particularly interesting example due to a simultaneous use of both aromatic and chlorinated compounds. Thus, the urinary concentrations of toluene and dichloromethane measured in the urinary samples were clearly linked to the occupational exposure, with very few values that were below the LOQ.
Cirrus clouds and their potential formation regions, so-called ice-supersaturated regions (ISSRs) occur frequently in the tropopause region. It is assumed that ISSRs and cirrus clouds can change the tropopause structure by diabatic processes, driven by latent heating due to phase transition and interaction with radiation. For many research questions a threedimensional picture including a sufficient temporal resolution of the water vapour fields in the tropopause region is required. This requirement is fulfilled nowadays by reanalysis products such as the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. However, for a meaningful investigation of water vapour in the tropopause region a comparison of the reanalysis data with measurement is advisable, since it is difficult to measure water vapour and to assimilate meaningful measurements into reanalysis products.
Here, we present an intercomparison of high-resolution in-situ measurements aboard passenger aircraft within the European Research Infrastructure IAGOS (In-service Aircraft for a Global Observing System; http://www. iagos. org) with ERA-Interim. Temperature and humidity data over the North Atlantic from 2000 to 2010 are compared relative to the dynamical tropopause. The comparison of the temperature shows a good agreement between measurement and ERA-Interim. While ERA-Interim also shows the main features of the water vapour measurements of IAGOS, the variability of the data is clearly smaller in the reanalysis data set. The combination of temperature and water vapour leads to the relative humidity with respect to ice (RH i ). Here ERA-Interim deviates from the measurements concerning values of larger than RH i = 100 %, both in number and strength of supersaturation.
, 2007)  Meanwhile, the IAGOS data set now spans about 20 years and also allows trend analysis, for example with regard to the temperature. Here, a difference between in-situ and modelled data arise. While IAGOS exhibits a neutral temperature trend in the LS, the reanalysis data ERA-Interim of the ECMWF shows a temperature trend of +0. 56 K decade -1  (Berkes et al. , 2017). This underlines the importance of a thorough comparison between measurements and modelled data. To investigate the mutual influence of water vapour in the UTLS region in the future, properties regarding the water vapour such like relative humidity with respect to ice, fraction of ice supersaturated regions or pathlengths of ISSRs are compared between IAGOS measurements and ERA-Interim output. A companion study by  Neis et al (in preparation)  will focus on the physical interpretation of the water vapour distribution in the UTLS region.
The aim of this work is to asses the quality of the desription of water vapour in the UTLS region in the ERA-Interim data set. Therefore, the data sets ranging from 2000 to 2010 are compared. Sect. 2 describes the data sets and the methodology. In Sect. 3 the comparison of IAGOS and ERA-Interim is presented in different ways. The conclusion can be found in Sect. 4.
Starting from August 1994 more than 60000 flights (May 2019) have been performed, including data from the IAGOS predecessor MOZAIC (August 1994 to December 2014) and data from the IAGOS project starting in July 2011 until present. ° S 0 From Figure  1  it is obvious that the global data distribution is not uniform in every region. The subset covering the North Atlantic flight corridor shows the highest coverage of flights. Therefore, in our study we focus on this region (40N-60N, 5-65W). For the evaluation of the reanalysis data we use the geographic position of the airplane (lat/lon), the time, data quality flags, ambient pressure and temperature  (Berkes et al. , 2017) , relative humidity and water vapour volume mixing ratio. In this study we use the data collected during January 2000 to December 2010.
Results The aim of this study is to compare and quantify the difference of relative humidity with respect to ice (RH i ) between the in-situ measurements provided by IAGOS and the reanalysis data of ERA-Interim. According to Equation 1 the relative humidity depends on temperature and available water vapour. Figure  2  shows the temperature probability density function (PDF) in the seven vertical layers for IAGOS and ERA. With exception of layer UT3, the distributions show a quasi-Gaussian behaviour. As expected by definition, the tropopause layer shows the coldest mean temperatures with 216. 1 K and 216. 5 K for IAGOS and ERA, respectively. The warmest mean temperatures are visible for the lowest layer UT3 with about 225 K in both cases. The origin of the "wave-like" structure of the distribution at the UT3 layer is not clear.
Temperature Overall, the comparison between IAGOS and ERA shows a good agreement as far as the statistical values are concerned (see Table  2 ). Figure  3  shows the vertical profile of the temperature for IAGOS and ERA in the form of a Box-and-Whisker plot. The boxes are bounded by the 25 % and 75 % percentile, while the median is marked with a black vertical line. The whiskers are defined as 1. 5 • IQ with IQ the interquartile range, being equal the distance between the 25 % and 75 %. Outliers, values exceeding the whiskers, are marked as black circles. A very good agreement between the temperature in IAGOS and ERA-Interim is seen, highlighted by the symmetry of both profiles. Additionally, the boxes of the box-and-whisker plots are equal in size, pointing to a similar statistics of the data set.
Layer Median  The probability densitiy function of the water vapour volume mixing ratio is presented in Fig. 4. Here, a clear dependence of the distribution with height is visible. Note, due to the broadening of the distribution with decreasing height, the absolute probabilities are decreasing in this log-log illustration. The lowest mean and median values of the water vapour volume mixing ratio are observed, as expected, in the uppermost layer LS3. In contrast, the lowermost level UT3 shows the largest mean values of the water vapour mixing ratio. The comparison between IAGOS and ERA shows a good overall agreement (see Table  3 ). However, very low volume mixing ratios seem to be hard to achieve for the ERA model, which is visible for the two uppermost levels LS2 and LS3. It is also noteworthy that for the upper layers, beginning from the tropopause layer up to LS3, the left part of the distributions show always a much steeper behaviour in the ERA data set compared to IAGOS.
Hence, ERA shows less variability of water vapour in the tropopause region and above. Figure  5  shows the vertical structure of the water vapour mixing ratio. For the upper layers in both data sets starting from the tropopause level (TL) up to LS3 the variability, i. e. the size of the boxes, is significantly lower compared to the tropospheric layers (UT1 to UT3). Overall it can be stated that the variability is increasing with decreasing height. As mentioned before, the variability of the reanalysis data set is also smaller for the upper layers compared to the in-situ data. When focussing on absolute numbers it is also visible that IAGOS data set shows higher maximum values in the stratosphere compared to ERA. In summary, the reanalysis data is in good agreement with the vertical distribution and the seasonal cycle of the IAGOS data beside the smaller variability and extreme values of the in-situ data.
Relative humidity with respect to ice Since cloud formation is governed by the relative humidity rather than the water vapour mixing ratio the relative humidity w. r. t. ice RH i is now investigated. As mentioned in the introduction, RH i depends on both temperature and available water vapour. Hence, relative humidity is a convolution of both variables. In Fig. 6  the vertical structure of RH i is depicted. The  50 % and 100 % RH i. The highest median values are found in the layer UT1 for both data sets. In the tropopause layer still a significant amount of the data are exceeding values of RH i > 100 %, both in the in-situ data as well as in the ERA data set. However, the whisker in Fig. 6  indicate that ERA has less data points with a higher supersaturation compared to IAGOS. In the stratospheric layers the median of the RH i values is decreasing strongly.
However, ice supersaturation is still possible, especially in the lowest stratospheric layer LS1  (Müller et al. , 2015). The statistics of ISSRs in higher levels provided by the 5 ERA data set shows clearly less occurrence of this feature. This has important implications on the radiative impact of these layers on the Earth's radiative budget, if climate models underestimate the abundance of ISSRs. It is obvious that ERA and IAGOS show a good agreement for situations below ice supersaturation. However, in this study we focus on the situation where ice supersaturation exists. For a more distinct look on the occurrence of ISSR we illustrate the statistic evaluation with cumulative probability distributions. Figure  7  shows the cumulative distributions for IAGOS and ERA. 10 Each layer is depicted with its own color.
It is clearly visible that ERA and IAGOS behave differently for RH i > 1, especially for the tropospheric layers. The ERA distributions of the latter layers snap off as soon as they reach ice supersaturation. As mentioned before, the IFS-model allows the existence of ice supersaturation, but only in cloud free conditions. As soon as ice clouds are present in the models grid cell the supersaturation is adjusted to RH i = 100%. Unfortunately, the IAGOS data set of the investigated time frame cannot distinguish between cloudy and non-cloudy areas. 1 Nevertheless, also in ice clouds ice supersaturation is present  (Krämer et al. , 2016). Therefore, the behaviour of the cumulative distributions for ERA, especially in the layers from UT3 to TL, points to a missing process in the representation of ice supersaturation in the underlying IFS model.
Another way to compare the representation of the water vapour is the fraction of ISSRs. Figure  8  presents the vertical profil of the ISSR fraction. It is clearly visible that the measurements by IAGOS show a higher fraction of ISSR. Only for the two uppermost layers the fraction of both data sets are of comparable magnitude. Here, the very dry conditions produce only in very few cases supersaturation. The largest difference between measurement and reanalysis data occur in the the tropopause layer and the flanking UT1 layer, where a high percentage of ice clouds can be expected. IAGOS shows in the latter layer an ISSR fraction of up to 40%. Since ISSR are a prerequisite for the formation of in-situ cirrus clouds, a misrepresentation of the feature can lead to great deviations in the local radiative budget and can build up to large errors in the local dynamics.
Horizontal scales of ice supersaturated regions The horizontal scale of ISSRs offers another possibility to compare the measurements of the IAGOS project with the reanaylsis data from ERA. Former studies  (Diao et al. , 2014; Spichtinger and Leschner, 2016)  showed that the horizontal pathlengths can reach from the very small scale in the order of hundred meters to up to 1000 km. Here, small scale variability will lead to very 1 Nowadays, the IAGOS setup includes an optical sensor for registration of clouds on the flight path. Red shows the ERA data obtained from the given flight track and green shows a 100 km running mean of IAGOS data to mimic a coarse resolution comparable with the ERA spatial resolution. On the left side IAGOS and ERA show a good agreement. While the magnitude of the ice supersaturation cannot be found in ERA, the reanalysis data shows also small scale features of IAGOS.
Histograms of ISSR Although ISSRs are three-dimensional fields the measurements only provide one-dimensional pathlenghts. For a reasonable comparison between IAGOS and ERA we only evaluate ISSRs on a constant flight level. Figure  10  shows the histograms for the pathlengths of IAGOS, ERA and different running means of the IAGOS data. On the right side, a cumulative distribution is shown. As it is expected from the different underlying resolutions of both data sets, IAGOS shows much more small pathlengths  means with the ERA data set it is also noteworthy that the behaviour of the ERA data set changes for pathlengths of 100 km and lower. Again, as a reminder, this is the scale of the spatial resolution of ERA. It is clear that the ISSR with pathlengths smaller than 100 km are not represented in the ERA model in the same way as the running mean of IAGOS shows it.
Conclusions This study compares the in-situ measurements of temperature and water vapour and subsequent relative humidity with respect to ice in the UTLS region obtained by IAGOS with the reanalysis data set ERA-Interim from the year 2000 to 2010 over the North Atlantic. ISSRs are of special interest in this investigation due to their abundance and importance on the local radiation budget when transformed to a cirrus cloud. Both data sets are separated according to their relative height compared to the dynamical tropopause (2PVU). The comparison of the temperature shows a good agreement between measurement and reanalysis data. The structure and variability of the vertical temperature distribution is very similar, shown by a very good accordance in median and mean values as well as in the standard deviation.
The water vapour was analyzed using the water vapour mixing ratio. Both data sets show the clear decrease of water vapour with increasing height. In contrast to the measurements ERA shows clearly less variability, indicated by smaller standard deviations in all levels. The convolution of water vapour and temperature leads to the relative humidity with respect to ice, which governs the cloud formation. Both data sets reproduce two different regimes. In the UT layers including the tropopause layer, the statistics cover the whole range of possible saturation values, where most of the data lie between 50% and 100% RH i. However, ERA deviates from the RH i measurements concerning values of larger than RH i = 100 % by showing less data points and weaker supersaturations, impressively depicted by the comparison of cumulative distributions.
This is an important finding, because it points to a misrepresentation in ERA of ice supersaturation in the UT and tropopause region, which is the formation region of in-situ cirrus. Moving up to the stratospheric layers, as expected, the RH i values are much lower in both data sets. Again, ERA shows less and weaker supersaturations through all levels. Since the LS is very dry, supersaturation occurs rarely and therefore the difference between both data sets is smaller regarding the cumulative distribution. Nevertheless, ERA shows clearly less extreme events. The strong differences between IAGOS and ERA with respect to ISSRs is also shown by the fraction of ISSR. In the UT and the tropopause region the measurements show a significantly larger fraction of ISSR in the measurements compared to ERA.
He attributed those events to snow thermal contraction associated with the formation of few-cm-wide thermal cracks penetrating the surface down to ∼1 m. More recently, re-analyzing the TAMSEIS and GAMSEIS seismic dataset,  Lough and others (2015)  found 'firnquakes' detectable at distances as large as several hundreds of km. They attributed these events to wind-glazed macrocracks formation in the firn layer. Despite the lack of information relative to temperature, the authors interpret these events as resulting from the cooling induced by persistent katabatic winds leading to firn thermal contraction. The present paper aims at complementing those studies, providing local observations of thermal stress-induced seismicity over a whole year (February 2012 to January 2013) by using data recorded by permanent seismic and weather stations installed near the Princess Elisabeth Antarctica base (71.
Detection of icequakes Since 2009, a seismic instrument has been operating near PEA, eastern Dronning Maud Land, East-Antarctica. It sits on a narrow, elongated granitic ridge as an extension of the imposing pyramid-shaped Utsteinen nunatak (Fig. 1 ) protruding 300 m above the surface of the ice sheet. The instrument is a broadband seismometer with a flat response within the 120 seconds to 100 Hz band, with 24-bit digitizer sampling at 100 Hz. The proximity of PEA (∼350 m) provides an easy ground access and reliable power supply to the seismic station. We investigated continuous seismic recordings of this permanent seismic station throughout the period February 2012 to January 2013, which is the longest available period of recordings with no data gaps. The data were bandpass filtered between 5 and 20 Hz.
50. In total, 4200 events were extracted using this technique. To classify the icequakes and to provide clusters of events, a hierarchical clustering analysis was performed. The waveform cross-correlation value based on a 1 second window encompassing the surface wave arrivals was used to construct a correlation matrix for the entire event dataset. Starting from single-event clusters, the procedure iteratively creates agglomerative clusters of correlated events using the average method to characterize the distance between clusters. This procedure allowed the extraction of five major clusters of events, each gathering at least 100 events, representing a total of 2200 events, i. e. 50% of the original event dataset (Fig. 2 ). Overall, these events show a weak P-wave arrival followed by larger amplitude surface waves in the 10-20 Hz frequency range and some variable energy above 20 Hz depending on each cluster.
For each month, the occurrence of the two major clusters gathering 1500 events, i. e. ¾ of the dataset is merged to consider the dominant source mechanisms. This reveals that the total number of events per hour is two times larger in summer time than in winter time. This first observation leads us to investigate in more detail the monthly and daily variations of these cryoseismic signals through the analysis of hourly mean surface temperature estimated from longwave radiation measurements recorded at an automatic weather station (AWS) located 500 m south-east of the seismic station and installed on a snow surface (Gorodetskaya and others, 2013, 2015; Fig. 1a ). Figure  3  shows that overall the highest number of events occurs when the snow surface temperature is the lowest. We also observe a clear diurnal modulation of the seismicity related to temperature for the summer months, the maximum amount of icequakes occurring at night.
98  (Wiscombe and Warren, 1980)  and σ is the Stefan-Boltzmann constant and equals 5. 6704 J. s -1. m -. K -4. The weekly moving average ice surface temperature is shown as dashed blue line. Antarctica  (Thiery and others, 2012; Gorodetskaya and others, 2013; Souverijns and others, 2018). For the time period considered here, the mean wind speed is 4. 5 m s -1 with a slight increase to 5. 0 m s -1 during the winter period. Indeed, the seismic dataset at PEA does not present any particularly higher noise due to wind perturbations in winter. Therefore, though it may lower the signal-to-noise ratio of the entire dataset, wind perturbations may affect summer and winter observations in a similar manner.
Icequake signal waveforms and locations Our procedure of detection leads to two major clusters of icequakes representing ¾ of our initial dataset. The most abundant cluster #1 clearly presents seismic signals of longer duration and lower frequency content than cluster #2, suggesting the former may be generated by a more distant source, yet at few km to PEA. However, both clusters exhibit signals dominated by surface waves suggesting that both originate from sources located at the surface though recorded from different epicentral distances. Icequake signals present a weak emergent P-wave, no S-wave and well-defined surface waves arriving at 1. 2-1. 3 seconds and 0. 2-0. 3 seconds after the P-wave onset for cluster #1 and cluster #2, respectively (Fig. 5 ). Overall, polarization analysis of the three components indicates a near horizontal propagation for the P-wave and back-azimuths pointing to ∼255°and ∼215°for cluster #1 and cluster #2, respectively  (Montalbetti and Kanasewich, 1970; Jurkevics, 1988).
Surface waves present retrograde elliptical particle motion with varying polarization from elliptical to linear suggesting a propagation in a rather heterogeneous or/and anisotropic medium  (Picotti and others, 2015; Diez and others, 2016). Due to the dominance of the surface waves, the events highlighted in our study resemble the ones documented by  Lough and others (2015). However, though weak, P-wave phases are visible in our dataset. Their absence in the observations of  Lough and others (2015)  was most likely due to attenuation over hundreds of km long epicentral distances. In the study of  Nishio (1983) , there is not enough information on the seismic waveform to allow such a comparison. Regarding our observations, the high-frequency content (i. e. 10-15 Hz) of the surface waves indicates they probe the first tens of meters, i.
Temporal occurrence Over the year-long dataset, 1500 seismic events were detected and categorized into two clusters based on their seismic waveforms and durations. Their source location on two BIAs and their dependence on large diurnal temperature variability mainly due to daily solar radiation in summer or cold katabatic regime in winter suggest they are associated with thermal contraction of blue ice. Our dataset shows a clear temporal pattern of icequake occurrence that deserves to be discussed in the light of the few previous analyses of similar events. In the study of  Lough and others (2015)  no systematic and long-term temporal analysis was unfortunately provided as the dataset originated from short-term experiments. However, a high rate of 'firnquake' activity was observed during the onset of the Antarctic winter consistent with thermal contraction.