dataesr/scientific-paragraphs-categorization huggingface dataset:
Multilinear Engine (ME-2) Source apportionment of organic aerosol components from Q-ACSM was performed using Positive Matrix Factorization (PMF,  Paatero, 1997; Paatero and Tapper, 1994)  via the ME-2 solver  (Paatero, 1999 ). An extended Q-ACSM dataset of 2 months (starting from 05/06 till 5/08/2013) was used here in order to obtain a wider range of atmospheric variability and In this study, we therefore applied separate factorization analysis to both VOCs and aerosol databases. Another approach consists in a factorization analysis of combined aerosol and gaseous databases  (Slowik et al. , 2010; Crippa et al. , 2013a). Thus, an attempt to perform such PMF analysis was conducted, using the gaseous database (42 VOCs) described above and full ACSM spectra as inputs and taking care of the homogeneity of the different inputs by applying a scaling procedure as proposed by  Slowik et al.
The temporal variability of OC and WSOC are reported in Figure  5b  and show very close patterns with, however, few periods with noticeable discrepancies (17/07; 28/07-30/07). There is a clear correlation between the two datasets (r²=0. 68; N=229) with slope of 0. 58 reflecting that more than half of OC is water-soluble. The correlation between OC (OCEC Sunset Field Instrument) and OM (Q-ACSM) shows a better agreement (r²=0. 86; N=229) with a slope of 0. 87 when using an OC-to-OM ratio of 1. 9. This slope close to one reflects the general good agreement between both instruments measuring OC in PM 2. 5 and OM in PM 1 , respectively. A closer look at the OM/OC ratio derived from these two instruments (not shown) shows a slight but systematic diurnal variability with minimum values around 09:00 LT and a constant rise in the course of the day with a maximum value at 21:00 LT.
Exploring the drivers of VOC variability at Cape Corsica Source-receptor models, such as PMF, usually aim at identifying and quantifying the contributions of sources of pollutants impacting a measurement site. In our case, the remote location of the site combined with the reactivity of the selected species does not allow a proper identification and quantification of primary sources. Our main objective, here, lies within the identification of co-variation factors of species which could be representative of aged or fresh primary emission but also of photochemical processes occurring during long range transport or occurring locally. For this purpose, PMF was applied to a large dataset (42 different species) including primary VOCs from anthropogenic or biogenic origins but also secondary products measured by three different techniques (PTR-ToF-MS, GC/FID-FID and GC/FID-MS, see section 2.
Sauvage et al. (2009)  applied PMF to a database of NMHCs measured at three background sites in France, leading to 5 common PMF factors including evaporative sources, residential heating, vehicle exhaust, remote sources attributed to aged background air and biogenic emissions. Therefore, we incorporated for the first time OVOCs in a database used for PMF analysis at a remote environment. It allows the first identification of PMF factors representatives of secondary processes in addition to factors related to primary sources. As it has been found in previous studies performed in such environments, we also found that primary anthropogenic PMF factors were separated according to the lifetime of compounds which composed them. As in the three studies described above, a clear primary biogenic factor is identified in our study.
Conclusions The ChArMEx SOP2 field campaign provided a unique opportunity to give insights in the various sources and fates of organic carbon in the Mediterranean atmosphere, thanks to the measurement of a large panel of gaseous and aerosol species at a remote site located at Cape Corsica in the western Mediterranean basin. The combination of gaseous and particulate organic databases, as collected during this campaign, is not common and has the potential to help improving our understanding of SOA formation. Moreover, the Mediterranean basin is an ideal location to characterize organics in the atmosphere since it is impacted by strong natural and anthropogenic sources and undergoes intense photochemical ageing especially during summer. The measurement site (Cape Corsica) offered ideal experimental conditions since it is surrounded by the sea and it is located at various distances from regional anthropogenic emissions hotspots (such as north of Italy, south-east of France, north-east of Spain or north of Africa).
g. up to 3. 8 ppb for acetone) were also measured during the campaign due to the oxidation of both biogenic and anthropogenic precursors. These OVOCs exhibit the largest contribution to the VOC budget. The aerosol chemical composition derived from Q-ACSM measurements shows a clear predominance of OM, which represents 55% of the total mass of NR-PM 1 on average, followed by sulphate (27%), ammonium (13%), and nitrate (5%). Furthermore, the temporal variability of OC and WSOC shows very similar patterns, leading to a clear linear correlation between the two datasets (r²=0. 68). The slope found is 0. 58, highlighting that more than half of OC is water-soluble. PMF was conducted to identify co-variation factors of VOCs that are representative of primary emissions as well as secondary photochemical transformations occurring during the transport of air masses.
This analysis was performed using a gas-phase database of 42 VOCs (or sum of VOCs) of anthropogenic and biogenic origins, including NMHCs and for the first time OVOCs. A 6-factor solution turned out to be optimum for this PMF analysis. In parallel, a concentration field (CF) analysis was conducted on 4 PMF factor to help in their identification through the localization of potential source areas. This combination of CF and PMF was particularly helpful to interpret factors associated to long-range transport of anthropogenic compounds. Three anthropogenic factors characterized by primary anthropogenic VOCs of various lifetimes were found. The CF analysis confirmed the anthropogenic nature of these factors by an identification of potential source areas in region experiencing intense anthropogenic activities (e.
g. Po valley and south-east of France). Two biogenic factors were also identified. Both factors exhibited clear diurnal cycles and were correlated to temperature. In addition to a primary biogenic factor, usually observed in VOC source apportionment studies, we also clearly identified, for the first time in PMF analysis, a secondary biogenic factor made of first-generation oxidation products of biogenic VOCs. A last oxygenated factor characterized by OVOCs of both biogenic and anthropogenic origins, was also derived from the PMF analysis. The identification of this unusual factor was made possible by the extension of the input database to secondary oxygenated VOCs. This factor was influenced by anthropogenic and biogenic sources showing elevated levels during both periods of intense local biogenic influence (e.
Acknowledgements This study received financial support from  Mistrals / ChArMEx  programmes, ADEME, the  French environmental ministry , the CaPPA projects and the  Communauté Territoriale de Corse  ( CORSiCA  project). The  CaPPA   project (Chemical and Physical Properties of the Atmosphere)  is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 " and by the  Regional Council Nord-Pas de Calais and the "European Funds for Regional Economic Development" (FEDER). This research was also funded by the  European Union Seventh Framework Programme  under Grant Agreement number  293897 , " DEFIVOC " project,  CARBOSOR/Primequal and SAF-MED  ( ANR , grant number:  ANR-12-BS06-0013-02 ). Greenhouse gases data were provided by the  ICOS-France monitoring network.
The detection limits for each species measured by all five techniques were determined as 3σ of the blank variation for PTR-ToF-MS and offline sampling methods and as 3σ of the baseline fluctuations for online GCs. The uncertainties for each species were estimated following the "Aerosols, Clouds, and Trace gases Research InfraStructure network" (ACTRIS) guidelines for uncertainty evaluation (ACTRIS Measurement Guideline VOC, 2012), taking into account precision, detection limit and systematic errors of the measurements. The range of uncertainties and detection limits for each technique is given in Table 1. Furthermore, systematic intercomparisons for compounds measured by different techniques (e. g. isoprene, monoterpenes, acetone, n-pentane, benzene, etc…) were performed to validate the database (not shown).
Literature Review We conducted a comprehensive survey of the peer-reviewed literature to compile a database of studies that investigated the effects of MPAs on alien species. We used the research engine Web of Science (https://www. webofknowledge. com). Eligibility criteria included any paper or review published between 1950 and the cut-off date 10 June 2016 with the terms: "marine protected area * , " "marine reserve * , " "no-take area * , " "no-take zone * , " "marine park * " in the title, keywords and/or abstract. Each of these searches where refined using the terms: "exotic, " "alien, " "invasive, " "non-indigenous, " "NIS, " "non-native, " "introduced species, " "foreign species. " The results summed up to 284 peerreviewed papers. Our review started with a screening of the 284 paper abstracts.
DISCUSSION Our review demonstrates the scarcity of published evidence on a crucial topic: the effects of MPAs on alien/invasive species. The available data support the hypothesis that alien species respond negatively to protection; the density of most alien species was found to be greater outside than inside MPAs (as shown in Figure  2 ). Our findings differ from the conclusions of the precedent review article on this topic  (Burfeind et al. , 2013) , according to which alien species do equally well or better within MPAs. This difference in results may be explained by the different methodological approaches and datasets used in each study. However,  Burfeind et al. (2013)  argue that their conclusions are based on too few data and caution is needed in interpreting their results. We also argue that our results should be considered with caution and that more data on various taxonomic groups across different biogeographic regions are needed to draw robust conclusions.
Data We use daily fields from the NCEP/NCAR reanalysis  (Kalnay et al. , 1996) , with a horizontal resolution of 2 •. The analysis is carried out over the whole Northern Hemisphere for all days of the year over the period 1948-2013. The observables of interest are sea-level pressure, 2 m temperature, and precipitation frequency. A previous study  (Faranda et al. , 2017)  has shown that the results obtained are largely independent of the dataset used and of its spatial resolution. Anomalies are defined as deviations from the long-term daily mean. So, for example, the anomaly of t2m at a given location on 5 December 2000 is computed relative to the mean value of all 5 Decembers in the dataset at that location. The relevance of the composite anomaly maps for the different variables is evaluated using a sign test.
Acknowledgements. Pascal Yiou ,  Davide Faranda , and  Gabriele Messori  were supported by  ERC  grant no. 338965 ,  M. Carmen Alvarez-Castro  was supported by  Swedish Research Council  grant no. C0629701 , and  Gabriele Messori  was further supported by a grant from the  Department of Meteorology of Stockholm University. Edited by: Bruce Tsurutani Reviewed by: two anonymous referees.
Atmospheric radiative transfer model ATMOTARTES TARTES requires as input the spectral direct-to-diffuse incoming irradiance ratio. In Crocus, it is computed using the newly developed ATMOTARTES scheme, a two-stream multilayer model for atmospheric radiative transfer based on the same two-stream code as TARTES  (Libois et al. , 2013). The inputs of ATMOTARTES are the following atmospheric characteristics: surface pressure and temperature, surface relative humidity, solar zenith angle, day of year, aerosol optical depth at 0. 55 µm, total ozone column (atmcm), cloud bottom pressure, cloud type (ice or water) and cloud optical depth at 0. 55 µm. The cloud optical thickness is diagnosed from the broadband diffuse and direct solar irradiance estimated from the Col de Porte measurements (see  Dumont et al.
Data and study site The model simulations and evaluation were carried out at the Col de Porte experimental site for the 2013-2014 snow year. This site is located at 1325 m altitude in the Chartreuse Mountains, France. The model is forced with in situ meteorological measurements from the Col de Porte study sitenamely air temperature, specific humidity, rainfall and snowfall rates, incident direct and diffuse shortwave radiations, longwave incoming radiation and wind speed. An exhaustive description of the measurement devices and datasets can be found in  Morin et al. (2012a). Hourly albedo at noon were calculated using spectral reflectance measurements described in  Dumont et al. (2017). Measured spectral reflectance measurements were first converted to spectral reflectance for a flat surface using Eq.
For this evaluation we accounted for two LAI species assumed to have the strongest radiative impact on snow: BC and mineral dust. We extracted aerosol deposition fluxes from the atmospheric model ALADIN-Climate and forced the snowpack model with these deposition values. We evaluate the relevance of using atmospheric aerosol with a physically based model in terms of near-surface impurity concentration, near-surface SSA, snow depth and SWE. It appears that the atmospheric model ALADIN-Climate as a forcing dataset simulates LAI deposition acceptably over a season despite a large underestimation of extreme dust outbreaks and an overestimation of BC deposition. Radiative transfer properties of a seasonal snowpack in the presence of dust and BC can be computed efficiently following a physically based approach coupled to atmospheric aerosol deposition fluxes.
Acknowledgements. CNRM-CEN and IGE are part of  Labex OSUG@2020  (investissement d'avenir - ANR10 LABX56 ). This study was supported by the  ANR  programs  1-JS56-005-01   MONISNOW  and  ANR-16-CE01-0006 EBONI , the  INSU-LEFE projects BON  and  ASSURANCE , and the  Ecole Doctorale SDU2E of Toulouse. The authors are grateful to the  Col de Porte and EDF-DTG  staff for ensuring the proper functioning of all the instruments and to  Lydie Mbemba  for the in situ measurements of impurity content. Edited by: Christian Haas Reviewed by: three anonymous referees.
cnrm-game-meteo. fr/projects/snowtools_git/wiki. The Col de Porte dataset is available on the PANGAEA repository (https://doi. org/10. 1594/PANGAEA. 774249; Morin et al. , 2012b)  as well as on the public ftp server ftp://ftp-cnrm. meteo. fr/pub-cencdp. Time series of snow spectral albedo and superficial snow specific surface area and impurity content are available through the PANGAEA database (https://doi. org/10. 1594/PANGAEA. 874272, Picard et al. , 2017).
g. Huybrechts and others, 2004 ;  Yan and others, 2014) , which consists of adding climate anomalies from a coarser climate model on top of a high resolution climate dataset. In the present work, the simulations of the regional climate model (RCM) MAR (version 3. 5, 25 km × 25 km,  Fettweis and others, 2013)   Note that all climate forcing fields (for an overview of the models, see Table  2 ) are downscaled on the ISM grid by means of a simple downscaling method, using an atmospheric lapse rate (6. 309 °C km -1 ) and a precipitation correction factor (7 %/°C) to correct temperature and precipitation for the difference in elevation (see Section 2. 1).
ACKNOWLEDGEMENTS We thank  Catherine Ritz , who provided the GRISLI ice-sheet model code. We also thank  Xavier Fettweis  who shared the MAR outputs with us, and  Nina Kirchner  who helped in developing the initialization simulations and whose comments improved the manuscript. We thank the two anonymous reviewers whose comments also improved the manuscript. We acknowledge the  CMCC  foundation for providing the supercomputer on which the simulations were performed.
The distributions from Fig. 1  show that the model reproduces between 35% and 90% of the daily O3 variation captured by IASI and that the residual errors varies between 0. 01 DU and 5 DU (i. e. the RMSE relative to the IASI O3 time series are of ~15% on global average and vary between 10% in the N. H. in summer and 30% in specific tropical regions). On an annual basis (data not shown), the model explains a large fraction of the variation in the IASI O3 dataset (from ~45% to ~85%) and the RMSE are lower than 4. 5 DU everywhere (~3 DU on the global average). The relative RMSE are less than 1% in almost all situations indicating the absence of bias. The seasonal distributions of the contribution to the total variations in TOCs from the adjusted harmonic terms and explanatory variables, which account for the "natural" variability, and from the LT term are shown in Fig.
e. monthly dataset; e. g. Wespes et al. , 2016). It is also worth to mention that the O3 changes calculated over the whole IASI dataset in summer are larger than the RMSE of the model residuals (increase of 5. 39±1. 86 DU vs RMSE of 3. 14 DU), underlying the statistical relevance of trend estimates. The robustness of the adjusted trend is verified at the global scale in Fig. 4  which represents the seasonal distributions of the relative differences in the RMSE with and without including LT in the MLR model, calculated as [(RMSE_w/o_LT -RMSE_with_LT)/RMSE_with_LT ×100] (in %). An increase in the RMSE when excluding LT from the MLR is observed almost everywhere in regions of significant trend contributions (Fig. 2 ), especially in mid-high latitudes of the S. H. and of the N. H. in summer where it reaches 10%.
Annual and seasonal trends The annual and the seasonal distributions of the fitted LT terms which are retained in the annual and the seasonal MLR models by the stepwise elimination procedure are respectively represented in Fig. 5  (a) and (b) (in DU/yr). Generally, the mid-high latitudes of both hemispheres and, more particularly, the N. H. mid-latitudes in summer reveal significant negative trends, while the tropics are mainly characterized by non-significant or weak significant trends. Even if trends in emissions have already been able to qualitatively explain measured tropospheric O3 trends over specific regions, the magnitude and the patterns of the trends considerably vary between independent measurement datasets (e. g. Cooper et al. , 2014;   for the reasons discussed in Section 1 and they are not reproduced by model simulations (e.
The differences in the studied period and in the spatio-temporal sampling might also imply differences in the natural influence on the measured O3 variations. While the impact of the natural contribution is taken into account in the MLR model, it might introduce an additional bias in the trend determined from SLR, making further challenging to compare trends estimated from a series of independent measurements. The limitation in using a SLR instead of a MLR model for determining trends is explored here by comparing the seasonal distributions of the trends estimated from MLR (see Fig. we show that it is not accurate enough considering that some dynamics have irregular or no particular periodicity (e. g. NAO, ENSO). Furthermore, accurate satellite measurements of tropospheric O3 at a global scale are quite recent, limiting the period of available and comparable datasets (e.
The results clearly demonstrate the possibility to determine such a trend amplitude from the available IASI dataset and the used MLR model at northern mid-high latitudes in summer, while much larger measurement periods are necessary elsewhere. In particular, the regions of the longest required period highlight, in the tropics, a series of known processes that are closely related to the El-Niño dynamic, which underlies the lack of associated parameterizations in the MLR model. The importance of using reliable MLR models in understanding large-scale O3 variations and in determining trends is further explored by comparing the trends inferred from MLR and SLR, the latter being still commonly used by the international community. The comparison has clearly highlighted the gain of MLR in attributing the trend-like segments in natural variations, such as ENSO, to the right processes and, hence, in avoiding misinterpretation of "apparent" trends in the measurement datasets.
This study supports overall the importance of using high density and long term satellite records, such as those provided by IASI, for accurately determining trends in tropospheric O3 -as required by the scientific community e. g. in the Intergovernmental Panel on Climate Change (IPCC, 2013) -and for further resolving trend biases between independent datasets (Payne et al. , 2017; the TOAR report -Tropospheric Ozone Assessment Report: Present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation, Lead Authors: A. Gaudel and O. R. Cooper). Determination, with IASI, of robust trends at the global scale in tropospheric O3 will be achievable in the near future by merging the homogeneous O3 profiles from the three successive instruments onboard Metop-A (2006); -B (2012) and -C (2018).
Acknowledgments IASI has been developed and built under the responsibility of the  Centre National d'Etudes Spatiales (CNES, France). It is flown onboard the Metop satellites as part of the EUMETSAT Polar System. The IASI L1 data are received through the EUMETCast near real time data distribution service. We acknowledge the financial support from the  ESA O3-CCI , Copernicus  O3-C3S  and Eumetsat AC SAF projects. The research in Belgium is also funded by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA Prodex IASI Flow and AC SAF). Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2017-904Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 23 November 2017 c Author(s)  2017. CC BY 4. 0 License.
[2010]  (see location on Figure  1 ), including well-correlated 3D stratigraphic horizons (grey lines) mapped on DEM-based topography from observations on satellite imagery and aerial photographs, checked carefully with systematic field observations. Here, the traces of the 3D layers are significantly refined by using high-resolution satellite images from the Google Earth database (Landsat 7, DigitalGlobe), digital elevation models (Aster DEM ~30m resolution) and new helicopter views (Figures  4  and 5 ). For simplicity, we only represent here horizons that correlate well over several kilometers in Abanico and Farellones formations. Additional details are provided as supplementary material, in particular finer stratigraphic horizons required for the projection on the cross-section (see Figure  S1  and S2 ).
The detailed structural map of the WAFTB presented here is based on  Armijo et al. [2010]. It integrates stratigraphic layers in 3D, which are now significantly refined by using (1) highresolution satellite images from the Google Earth database (Landsat 7, DigitalGlobe) and aerial photographs, together with (2) digital elevation models (Aster DEM ~30 m resolution) (Figures  3  and 4 ). Our refined, high-resolution and large-scale structural map and sub-surface cross-section as well as our geo-referenced 3D stratigraphic horizons are provided as supplementary materials  (Figures S1, S2 and Dataset S1). Such high-resolution is crucial to precisely derive the folding geometry of the Abanico and Farellones formations, and therefore to constrain the kinematics of folding and thrusting of the belt.