dataesr/scientific-paragraphs-categorization huggingface dataset:
3%/yr to some extent (from -0. 7%/yr to -2. 5%/yr). The fact that some models with fixed SST (e. g ECHAM-HAM) reproduce this decrease does not support the hypothesis of the SST changes. The decrease in dust could be caused by increased wet scavenging of dust after coating with anthropogenic sulfate aerosols. The production of high levels of readily soluble materials on the dust surface makes dust aerosols effective cloud condensation nuclei  (Fan et al. , 2004; Bauer and Koch, 2005; Bauer et al. , 2007; Neubauer et al. , 2019). -ASIA: CAMS-Rean captures the same trend as computed with the observations dataset. Like for AOD<1µm, no certain trend can be identified in this region with the CMIP6 model. -AE: the trends are usually smaller than for AOD in the respective regions, meaning that the amount of the particles is more subject to variations than the size (type) of these particles.
Global trends As discussed previously, the regional trends found are probably not always representative of the trends in the extended regions and over the whole study period. The reasons are the partial spatial and temporal coverage of the ground based observations. Moreover, the observation stations are obviously located on land. This does not allow for a depiction of a global aerosol trends and is unfortunate as sea salt particles are among the most predominant aerosols on Earth  (Schulz et al. , 2004). In order to provide an assessment of the aerosol trends at a global scale, we present, in this section, the trends computed with the NorESM2 data (CMIP6 group) using all grid boxes. The calculation of the global trend is made by averaging the absolute trends computed at each grid-point of the model.
-While the mountain sites were excluded from this study, it could be of interest to investigate the trends at higher altitude (which may be related to changes in long range transport) by including the in situ and remote sensing stations higher than 1000 m (Jungfraujoch, Mauna Loa Observatory,. ). -While assembling the dataset for this analysis, it appeared that more observations (σ ap in the US) could be utilized. Due to time limitations, these data could not be integrated in the study, but could be considered in the future to enrich both databases. In addition, more models and diagnostics from the AeroCom and CMIP6 ensemble should be added into the analysis when data become available to eventually confirm the regional and global trends for all parameters.
https://doi. org/10. 5194/acp-2019-1203 Preprint. Discussion started: 13 January 2020 c Author(s) 2020. CC BY 4. 0 License. representativity of the observation networks specifically for the purpose of computing the trends. These two perspectives on representativity might give different results, since a station associated with a bias, could still have a representative tendency in time. In order to evaluate the effect of the partial space and time sampling of the observations for the evaluation of the trends, two sensitivity studies, focusing on the time sampling and the space sampling, have been conducted using model subsets of data. For each of these studies, the trends are computed for one reference (Ref ) and one experiment (Exp) dataset, and compared with each other. -Time representativity study -Ref time : Collocation in space and time -Exp time : Collocation in space using complete time-series -Space representativity study -Ref space : Collocation in space using complete time-series (=Exp time ).
Acknowledgements. Data providers from all the regional and global networks are greatly acknowledged for sharing and submitting their data to be used. DN acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme project FORCeS  under grant agreement No  821205. The ECHAM-HAMMOZ model is developed by a consortium composed of  ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford , the  Finnish Meteorological Institute  and the  Leibniz Institute for Tropospheric Research , and managed by the  Center for Climate Systems Modeling  ( C2SM ) at  ETH Zurich. The  CESM  project is supported primarily by the  National Science Foundation (NSF). This material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  NSF  under Cooperative Agreement No.
On sub-seasonal timescales, that satellitederived ice-shelf height change records cannot resolve, GPS arrays on ice shelves can be used to identify processes, such as tides (  Padman and others, 2018 ) that cause high-frequency ice deformation and contribute to ice loss  (Brunt and others, 2010, 2014 ;  King and others, 2010, 2011 ;  Makinson and others, 2012). Atmospheric and oceanic variability around Antarctica also includes a strong seasonal component that is unresolved by satellite-derived ice-shelf mass-balance products and undersampled by precise GPS observations limited to summer deployments. We assume, therefore, that an ice-shelf's response may also include a significant seasonal component. Here, we use data from a large-scale GPS array deployed on RIS in  2015 and 2016  to examine spatial and temporal variabilities of ice-shelf velocities.
GPS data and processing A 13-station GPS array  (Bromirski and Gerstoft, 2017)  was installed on RIS in November 2015 as a component of the Dynamic Response of the RIS to Wave-Induced Vibrations (DRRIS) project. The GPS stations (Fig. 1 ) were collocated with seismic stations  (Bromirski and others, 2917) , and remained in place for ∼1 year. Three stations (DR01-DR03) were 2 km from the ice front, with DR02 being located on Nascent Iceberg  (Brunt and others, 2010). Nine stations (including DR02) were positioned along a north-south transect from the ice front to ∼430 km south (RS18), roughly aligned with the mean flow of the central ice shelf. Two additional stations were installed, one on the west side of this transect (RS03) and the other on grounded ice on Roosevelt Island (RS08). Twelve of the stations were powered only by solar panels, and consequently recorded data only during the two austral summers.
One station, DR10, was continuously battery-powered and recorded during most of the experiments, including the winter months. The intervals of data acquisition at each site are shown in Figure  S1. All stations recorded at 1 Hz (1 sample s -1 ) and were processed following a precise point positioning (PPP) approach (  Zumberge and others, 1997; Geng and others, 2012; Geng and others, 2019)  to generate, for every day, a 24-h time series with 1-s sampling. The horizontal movements were up to several metres per day. In order to merge daily time series into year-long continuous time series, we defined a reference position, per station, by estimating a static position (PPP) using the first 6 h of measurements of the first day of each time series. The 1-s time series were down-sampled to 30 s using a median filter, to create continuous time series over the entire time period.
MEaSUREs velocities The Making Earth System data records for Use in Research Environments (MEaSUREs) project includes an Antarctic Ice Sheet velocity map  (Rignot and others, 2017 ) that was derived from data acquired between 1 January 1996 and 31 December 2016 from RADARSAT-2, Sentinel-1, TerraSAR-X, and TanDEM-X synthetic aperture radar (SAR), and Landsat-8 optical imagery. This dataset includes a long-term velocity field averaged over the 20 years of satellite data (represented in Fig. 2a ), and annual velocity fields from 2005 to 2017.
Tidal analyses Tides in the Ross Sea produce metre-scale vertical displacements of RIS (e. g. Brunt and others, 2010, Brunt and MacAyeal, 2014 ;  Padman and others, 2008, 2018 ; Fig. S2 ). The horizontal components of displacements measured by GPS are dominated by the long-term averaged ice motion (Fig. 2 ; Table  1 ); however, in agreement with previous observations on RIS (e. g. Brunt and others, 2010, Brunt and MacAyeal, 2014) , the tidal signal is also significant (Fig. 3 ). We identified the fundamental tidal signals (see Table  S1  for the list of constituents) from the time series of vertical and horizontal displacements for each GPS record. To do this, we used the T_TIDE Matlab toolbox  (Pawlowicz and others, 2002) , which is based on the FORTRAN tidal analysis package developed by  Foreman (1977).
2b ) are given. No data were obtained during Su2 for stations  DR16 and RS18. Elmer finite element software developed at the Center for Science in Finland (CSC-IT). We used the vertically integrated shallow-shelf approximation  (MacAyeal, 1989) , in which vertical shear stresses are assumed to be small relative to the horizontal longitudinal stresses so that the lateral ice velocity is constant throughout the ice thickness at each point. We calculated the effective ice viscosity as a vertically averaged quantity with a nonlinear dependence on strain rate, assuming isotropic material properties: ID Longitude Latitude Mean (V N ) Mean (V E ) V N (Su1) V E (Su1) V N (Su2) V E ( h = h 0 1 (1-n)/n e. (1) In Eqn (1), ε e is the second invariant of the strain-rate tensor, η 0 is a vertically integrated apparent viscosity parameter (sometimes referred to as ice stiffness), and n = 3, the value most consistent with field data and most commonly used in ice-sheet modelling (  Cuffey and Paterson, 2010).
4 ). The largest discrepancies were observed for the ice-front stations, where GPS velocities exceeded the MEaSUREs values by 20-90 m a -1. These differences are most likely due to extrapolation from the MEaSUREs grid to the actual GPS positions. The grid for ice-shelf extent in MEaSUREs velocity products was based on data recorded prior to our measurement program when the ice front was farther south, causing the icefront station locations to appear north of the MEaSUREs ice front and precluding direct comparisons (Fig. 2 ). Changes associated with the widening of the rift behind Nascent Iceberg could also explain some of the velocity differences between MEaSUREs and DR02 estimates. At the southern end of the array, GPS stations RS18 and DR16 moved more slowly than the MEaSUREs values. We expect some decrease in the velocity over time at these sites as the ice shelf adjusts to reduced ice inflow from the Siple Coast ice streams  (Hulbe and Fahnestock, 2007 ;  Thomas and others, 2013; Campbell and others, 2018).
Summary and outlook We have presented results from high-rate GPS observations recorded at 12 stations on RIS over the period November 2015 to December 2016. Estimated annual-averaged ice-flow velocities agree well with the MEaSUREs satellite-based Antarctic ice velocity field for 2015-2016, except for stations within 2 km of the front where MEaSUREs values are not available. Our GPS data span common months of two summers, allowing for an interannual comparison. Differences from summer to summer are typically <1% of the long term mean ice velocity, which is consistent with results from satellite-based analyses of velocity and elevation that most of RIS is near steady state. Our dataset has sufficient temporal sampling to allow us to observe time-dependent motions of RIS relative to the annual mean velocity estimated from GPS, revealing a seasonal pattern of negative velocity anomalies during the austral summer and positive anomalies during the austral winter.
The seasonal velocity anomaly decreases with distance from the ice front, but is still observed more than 100 km away from the melt rate anomaly, suggesting that this process could be particularly important for smaller ice shelves. Although this study was focused on the observed seasonal pattern, detailed investigations are needed for other contributors to ice velocity variability. For example, the combination of the present dataset with the year-round broadband seismometer and barometer data also acquired during the DRRIS project, as well as with elevation measurements provided by ICESat-2 launched in late 2018, could help explain the role of local processes such as rift widening and ice-front calving. In future work, we recommend that ice-sheet models also focus on short timescales to assess the correlation between observed transient processes on ice shelves and different climate forcings, as well as their longerterm consequences.
Figures have been made with Generic Mapping Tools GMT5  (Wessel and others, 2013 ; https://github. com/GenericMappingTools/gmt/ ), MATLAB (https://mathworks. com) and Python module matplotlib (https://matplotlib. org). Ice images were exported from the Antarctic REMA Explorer (https://livingatlas2. arcgis. com/antarcticdemexplorer/), MODIS Mosaic of Antarctica 2008-2009 (MOA2009) Image Map, Version 1 (https://nsidc. org/data/ NSIDC-0593/versions/1), and Bedmap2 Project (https://www. bas. ac. uk/project/bedmap-2/). Elmer/Ice code is publicly available through GitHub (https://github. com/ElmerCSC/elmerfem); Gagliardini and others, 2013). All the simulations were performed with version 8. 3 (Rev: b213b0c8) of Elmer/Ice. The CATS2008 tide model is available at https://www. usap-dc. org/view/dataset/ 601235.
Acknowledgements. EK was supported by  NASA  grant MEaSUREs  NNH17ZDA001N. CM and HAF were supported by  NSF  grant  OPP-1443498  and CM used the  Extreme Science and Engineering Discovery Environment (XSEDE) , which is supported by  NSF  grant no. TG-DPP190003. PB was supported by  NSF  grant  PLR-1246151  and also received support from  NSF   OPP-1744856  and  CAL DPR-C1670002. LP was supported by  NSF  grant  OPP-1443677  and  NASA  grant  NNX17AG63G. GPS instruments and on-ice support were provided by  UNAVCO. We thank  Patrick Shore ,  Anja Dietz ,  Jerry Wanetick ,  Zhao Chen ,  Momme Hell ,  Alan Seltzer , and  Laura Stevens  for their invaluable assistance with field operations. Logistical support from the  U. S. Antarctica Program  and staff at McMurdo Station were critical, and much appreciated.
Over the last two centuries, the area went through a depopulation and agricultural decline period  (Folliasson, 1916; Onde, 1942; Jail, 1977) , followed by a flourishing of the tourism sector that transformed its landscape  (Rambaud & Vincienne, 1964; Jail, 1973 Jail, , 1977)). However, the increasing demand for touristic facilities, coupled with the scarcity of suitable areas for development raises the question of the safety of settlements against natural hazards in general and snow avalanches in particular. A holistic interdisciplinary approach based on the cross analysis of different data sources is proposed to asses the evolution of avalanche risk in the upper Maurienne (Figure  2 ). Prior to avalanche risk analysis, the co-evolution of the socio-economic and environmental system is assessed by cross analysing various data sources.
Population counts for each of the studied municipalities (Lanslevillard, Bessans, Bonneval-Sur-Arc) were obtained from 1861 to 2015. For the first century of the study  were obtained from published data  (Rambaud & Vincienne, 1964; Jail, 1969 Jail, , 1977)) , the agricultural census published by the Ministry of Food and Agriculture (recensement agricole), and the pastoral inquiries ("enquête pastorale", http://enquete-pastorale. irstea. fr ). Land cover data from 1860 until 2017 were produced using the methodology explained in section 2. 3. 1. The chronology of recorded avalanche events (EPA database) (Figure  3 ), the local avalanche cadaster (referred to as CLPA: Carte de Localisation des Phénomènes d'Avalanche,  (Bonnefoy et al. , 2010) ) and information on protection structures were used to further identify areas at risk and infer the evolution of avalanche risk.
, 2010; Linder, 2016). In order to successfully perform an orthorectification, several additional information needs to be known. The most essential ones are the type of camera used and its characteristics such as the interior, exterior orientation and the focal length. The majority of the required information is provided in the camera calibration reports acquired from the IGN. In addition to the calibration reports, the IGN's 1 meter resolution digital evelation model (DEM) was used as the vertical reference during the ortho-rectification process. The software used to complete this task is ERDAS LPS R (Leica Photogrammetry Suite)  (Intergraph, 2015). Additional information concerning the aerial photos are found in Appendix A, Table  A. 1. Object Based Classification:. Over the past years, basic pixel based classification methods were superseded by object based image analysis (OBIA)  (Ma et al.
Land cover change analysis in avalanche paths To asses land cover change in avalanche prone areas, a 50 m buffer was created around the contour of the 129 avalanche paths from the EPA database within the study area. Then, change detection analysis was applied within this newly created buffer following the methodology explained above. Mean evolutions within avalanche-prone terrain were then computed for further analysis. 1929 1929-1952 1952-1979 1979-2017 1860-1929 1929-1952 1952-1979 1979-2017  0. 0%.
Diachronic analysis of land cover Two series of historical maps and aerial photos were used to asses the evolution of land cover in the upper Maurienne valley. As for other of such studies, our analysis is subject to a range of uncertainties. Most common uncertainties associated with land cover analysis stems from the classification and change detection phase. Thus, to reduce the errors associated with both, we adopted the methodology suggested by  Petit & Lambin (2001)  that facilitate change detection analysis through the homogenization of spatial and thematic information across data sources. The methodology was applied to layers digitized from historical maps. Three types of errors are recognized when performing thematic and spatial equalization. First, aggregation errors caused by the aggregation from small to a broader level.
Within the CDP-Trajectories framework, this work is supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). INRAE/ETNA and CNRM/CEN are a part of  Labex  OSUG@2020. Three referees and the editor are acknowledged for their useful detailed comments that helped produce a better paper.
Even in the absence of data on the metabolic rates of these specimens, this result may be explained by a reduced activity of energetic metabolism resulting from the lower availability of trophic resources. Consistently, the concentrations of three (free amino acids, intermediate acidic metabolites and amines) out of the four defined metabolic families in insects from the 400 m sample from the horizontal transect were similar to the pattern that has been observed for adult M. soledadinus sampled along the altitudinal transects. The separation of the M. soledadinus' groups sampled along the two altitudinal transects was generally similar. First, carabid beetles sampled at the coast differed from the other sampled insects. Second, beetles sampled at higher elevation (200 m a. s. l. -St. Malo only -and 250 m a.
Statistical analyses. For the three transects, samples collected from the foreshore are referred to henceforth as the 'initial sampling point'. For each transect, the Euclidian distances between the initial sampling point and all other sampling points were calculated from the georeferenced coordinates. In order to investigate the effect of resource rarefaction with progression inland from the seashore, variables measured in individuals sampled from 0 to 200 m (coast proximity) were compared with measurements in individuals sampled beyond 200 m inland. This threshold was selected because it corresponds to the limit of seashore influence in similar environments  58  , which was corroborated by NDVI (Normalised Difference Vegetation Index) data from the Kerguelen archipelago, that decrease from the shore to 200 m inland, and stabilise further inland  59.
Site effect was not directly included as an explanatory variable because of its correlation with altitudinal variations; given the clustered coordinates along the coast, longitudinal effect was used as a proxy for inter-site differences. Linear models were used to assess the effect of altitude, coast proximity, sex (if available) and z-scaled spatial coordinates on each of our measured variables (body size, protein, glycogen and triglyceride amounts), including the interactions between sex and the other variables, as well as between coast proximity and longitude (inter-site variations of coast proximity effect were considered). Interpretations of interactions were carried out using the visreg package  60  and non-significant interactions were dropped. All morphological measurements were computed so that we obtained a general measure of body size.
This computed body size value corresponds to the coordinates of the projection of the morphological measurements of each individual on the first axis of a Principal Component Analysis (FactoMineR package  61  ), which accounted for 67. 4% of the total variance. Forty-six metabolic compounds were identified via the GC-MS procedure. Concentrations were homogenised (nmol. mg -  of individual dry mass) and four molecules (cadaverine, ornithine, quinate, and ribose) showing extreme variations of concentrations in various samples were removed from subsequent analyses. First, we tested the effect of altitude, coast proximity and z-scaled spatial coordinates on individual metabolite concentrations. MANOVAs were performed to test for potential differences among experimental groups. Second, total metabolite amounts were summed for four chemical families (free amino acids, N = 16; organic acids, N = 8; polyols, N = 8; and sugars + phosphorylated sugars, N = 5; Supplementary Table  S3 ) for each sampling site.
Linear models were then used to compare the amounts of each metabolite family according to altitude, coast proximity and z-scaled spatial coordinates. Third, the concentration of each metabolite was log-transformed to normalise the distribution of model residuals. Linear discriminant analyses (LDA, MASS package 62 ) were then run on individual metabolites sharing less than 80% correlation (N = 27 metabolites). Statistical significance of discrimination was confirmed using the Monte-Carlo permutation test (P < 0. 001; 10,000 permutations). In a first LDA, we plotted the metabolic phenotypes of adult M. soledadinus from the three transects. This LDA revealed that our dataset of 27 metabolic compounds was better discriminated transect by transect, with 52. 1% of the total variance being explained by the first axis (the between-group inertia being 10.
Acknowledgements The authors wish to thank  Maxime Dahirel  for statistical support and advice, and to the helpful  French Polar Institute (IPEV)  logistics team that greatly assisted field work. This research was supported by  InEE-CNRS (Enviromics call, ' ALIENS'; Zone Atelier Antarctique et Subantarctique) , and the  French Polar Institute  ( IPEV, program 136  ' Subanteco '). Metabolic analyses were performed using the facilities from the  PLAY platform (UMR CNRS Ecobio, Rennes, France). Peter Convey  is supported by  NERC  core funding to the  British Antarctic Survey's 'Biodiversity, Evolution and Adaptation' Team.
Image acquisition A Leica TCS SP8 laser scanning confocal microscope was used to image adult sections with a 25x or 40x water immersion objective. For clarified brains, the same microscope was used with a Leica HC Fluotar L 25x/1. 00 IMM motCorr objective. For all these acquisitions, fluorescence signal was detected through photomultipliers (PMTs) after sequential laser excitation of fluorophores at 405, 488, 552 nm. Steps along the Z-axis were set at 1 mm. Epifluorescence images were acquired using a Multizoom AZ100 (Nikon). Bright-field images were acquired with upright microscopes, either BX43 or BX60 (Olympus). Acquired images were adjusted for brightness and contrast using ImageJ/FIJI software  (Schindelin et al. , 2012).