dataesr/scientific-paragraphs-categorization huggingface dataset:
We used these datasets to perform linear regression analysis with the S1C1 δ 18 O and accumulation records. 4 ), ammonium NH + 4 , nitrate (NO 3 ) and δ 18 O. The black lines correspond to the December-January period of each year (solid lines are the layers for which all sources of information are consistent, while dashed lines are the layers for which we have contrasted results from one or more sources of information), the purple lines to the non-full retreat horizons and the red lines to the nuclear test horizons. δ 18 O data are shown as horizontal steps because one measurement corresponds to a value averaged over a 5 cm sample. Green lines stress the years which were added for the second dating, while the green arrows stress those which were deleted (see text for details).
Atmospheric simulations In this paper, we refer to two simulations, one by the AGCM ECHAM5-wiso and one by the regional atmospheric model MAR. ECHAM5  (Roeckner et al. , 2003)  has been equipped with stable water isotopes  (Werner et al. , 2011). This model, named ECHAM5-wiso, leads to realistic simulations when evaluated against observations of isotopic composition in precipitation and water vapour on a global scale. More specifically, the simulated distribution of annual mean precipitation isotopic composition over Antarctica is in good agreement with the spatial isotopic dataset compiled from snow and ice-core data  (Masson-Delmotte et al. , 2008). In addition to the most common stable water isotope H 2 16 O, H 2 18 O and HDO were implemented into the hydrological cycle of ECHAM5-wiso, in a more analogous manner than in the previous model releases of ECHAM3  (Hoffmann et al.
We identified remarkable years as those for which accumulation deviates from the mean value by more than 2 standard deviations. Remarkably high accumulation rates were encountered in 1981, 1983 and in 2001 (at respectively 35. 9, 37. 3 and 38. 0 cm w. e. yr -1 ). No remark-ably low accumulation rates were found. These remarkable years can be found neither in the stake average record nor in the stake data closest to the S1C1 site. For stake accumulation measurements taken at the stake closest to S1C1, the highest values were measured in 1979 and 1981 (75. 8 and 73. 8 cm w. e. yr -1 ). Moreover, small and non-significant linear correlation coefficients are found with the stake average record, and with the stake data the closest to the S1C1 site. Spectral analyses performed using the multi-taper method  (Paillard et al.
Other sources of information for δ 18 O regional variability Very few multi-year direct records of snowfall isotopic composition are available from Antarctica  (Fujita and Abe, 2006; Landais et al. , 2012; Schlosser et al. , 2016) , and none in our region of interest. Near S1C1, the single direct observation arises from a collection of 19 samples of precipitation at DDU, spanning the period from February to November of 1977 (J. Jouzel, personal communication, 2016). These data depict a mean δ 18 O value of -18. 0 ‰, a standard deviation of 3. 9 ‰, with the lowest value in August (∼ -27. 7 ‰) and the highest values in March (-10. 0 ‰) and November (-11. 5 ‰). Despite its brevity, this dataset shows less depleted values, a different seasonal pattern (i. e. maxima do not appear at the same months), and lower variability than at Dome C, where recent measurements are available for the period 2008-2010  (Stenni et al.
, 2016). At Dome C, precipitation isotopic composition values range from -80. 6 to -35. 5 ‰, with a mean δ 18 O value of -58. 0 ‰, a standard deviation of 8. 6 ‰ and a ∼ 37 ‰ mean seasonal amplitude, with maximum values in December-January, as for near-surface air temperature. This comparison is of course limited by the short dataset available for DDU and the fact that these measurements were not taken over the same time period. The distribution of δ 18 O data at DDU appears to be consistent with the range of values observed in a multidecadal  (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996)  record of surface snow isotopic composition obtained from the coastal station of Neumayer, with a mean δ 18 O value of -20. 6 ‰ and a seasonal amplitude varying from 14.
Comparison between the ECHAM5-wiso and the S1C1 data This comparison is limited by the fact that we only have one firn core record, and no estimation of the deposition noise. In the future, the latter may be estimated from signal-tonoise analyses of multiple ice-core records from the same Table  2. Statistical analyses: minimum (Min), maximum (Max), mean (µ) and standard deviation (σ ) of the precipitation (extracted from ECHAM5-wiso) or the accumulation (from the S1C1 ice core) in cm w. e. yr -1 , the δ 18 O (extracted from ECHAM5-wiso or from the S1C1 ice core) in ‰ and the 2 m temperature (2 m-T , extracted from ECHAM5-wiso or from the READER database) in   , 2015). Moreover, recent monitoring of surface snow and surface vapour isotopic composition in polar regions have suggested isotopic exchanges between surface snow and water vapour in between snowfall events  (Ritter et al.
, 2016; Steen-Larsen et al. , 2014). Such processes are not accounted for in the atmospheric models, and we can therefore only compare simulated precipitationweighted δ 18 O with our firn measurements. Similarly, we compare the ECHAM5-wiso model-simulated precipitation with the ice-core accumulation data, which may also reflect the impact of wind erosion. The ECHAM5-wiso simulation produces a large increase in δ 18 O and precipitation after 1979 (not shown). As such an increase is not observed in the S1C1 record, we suggest that it may arise from a discontinuity in the ERA reanalyses data used as boundary conditions. Indeed, the lack of sea-ice observations prior to the satellite era in the ERA-40 dataset leads to the unrealistic set of climatological sea-ice coverage values around Antarctica prior to 1979.
This deficit does not exist in the ERA-Interim dataset, which starts from the year 1979. Therefore, in the following, the model-data comparison is restricted to the period 1979-2006 (Fig. 6 ). The ECHAM5-wiso simulation underestimates the average seasonal amplitude of δ 18 O at the S1C1 grid point by a factor of 2. 0, and its interannual standard deviation by a fac-tor of 1. 4. No strong similarity appears for year-to-year values. Weak but significant decreasing long-term trends appear in both the S1C1 record and in the model outputs (r = -0. 36 and p = 0. 058 and r = -0. 43 and p = 0. 022 respectively). By contrast, the model precipitation amount (68. 4 ± 11. 7 cm w. e. yr -1 ) is much higher than observed (23. 1 ± 7. 3 cm w. e. yr -1 ) at S1C1 site on the same period. This difference cannot be explained by the impact of sublimation (in average, -4.
Our model-data comparison is of course limited by comparing two different datasets. For the modelling results, we have analysed a pure atmospheric signal (precipitation). The signal recorded in the S1C1 core is produced by precipitation, but also potentially altered by post-deposition processes (such as sublimation, wind scouring and snow metamorphism). Also, having just one ice-core record (also associated with age-scale uncertainties) precludes an assessment of the signal-to-noise ratio. Indeed, with similar mean accumulation values, it was shown that 4-5 shallow ice cores may be needed to extract the common climatic signal in northwestern Greenland  (Masson-Delmotte et al. , 2015).
Discussion Here, we propose an alternative ice-core chronology, given the annual layer counting uncertainties. For that purpose, we have screened peak-to-peak relationships between S1C1 record, stake accumulation measurements and ECHAM5-wiso outputs. We first tried to optimise the match between S1C1 accumulation and stake data. However, the interannual standard deviation of S1C1 accumulation is approximately three times lower than when calculated from either individual stake records, or from the average stake record. As a result, we could not identify unequivocally remarkably low or high accumulation years as a robust signal. For instance, all stake area data show very low (close to zero) accumulation in 1972 and 1988, but no such signal can be identified in the S1C1 record. We therefore used the ECHAM5-wiso δ 18 O to test an alternative chronology (Sect.
4. 1). We identified a high similarity between ECHAM5-wiso interannual precipitation variability and average stake data (Sect. 4. 2). We then discussed processes that can produce non-climatic noise (Sect. 4. 3). After assessing δ 18 O-temperature relationships (Sect. 4. 4), we finally explored the spatial relevance of climatic records from the S1C1 location (Sect. 4. 5).
However, we could have missed 1 year between the initial layers associated with the summers  1981-1982 and 1982-1983 , when peaks are identified in Na + , nssSO 2winter 4 , nssSO 2-summer 4 , NO 3-and NH4 +. We therefore built an alternative depth-age scale (Table  S4 ) following these two findings (Figs. 7  and 8 ), and discussed the implications for the S1C1 records. Consequently, we have produced a new annual mean accumulation and δ 18 O time series from 1979 to 2006, following the same approach (Sect. 2. 3. 3). With this alternative chronology, the resulting mean accumulation does not change, and the interannual standard deviation is slightly increased (21. 8 ± 7. 4 cm w. e. yr -1 ). It has no impact on δ 18 O or accumulation multi-decadal variations or trends. The resulting accumulation dataset remains nonsignificantly correlated with the stake average record and with the closest stake data.
In the alternative chronology, there is still no significant δ 18 O-temperature relationship between the S1C1 record and the DDU near-surface air temperature time series, and no relationship between S1C1 accumulation dataset and the ECHAM5-wiso precipitation output at  the annual scale. However, it improves the strength of the correlation (by construction) between the S1C1 δ 18 O and the ECHAM5-wiso δ 18 O from r = 0. 17 with p = 0. 39 for the initial chronology to r = 0. 57 with p = 1. 6 × 10 -3 for the alternative chronology.
Comparison between stake data and ECHAM5-wiso simulation No significant linear correlation is found between the S1C1 accumulation record (whatever chronology is used) and the stake accumulation measurements or between the S1C1 accumulation record and the ECHAM5-wiso simulated precipitation. We therefore compared the stake measurements and the ECHAM5-wiso output, with the goal of investigate whether there is a common climatic signal (for the period 1979-1991). We observe a high and significant correlation between the ECHAM5-wiso simulated precipitation and the average stake record at the interannual scale (r = 0. 62 and p = 0. 030). This finding shows that the nudged ECHAM5-wiso simulation is able to capture almost 40 % of the interannual average accumulation variance and that there is a clear synoptic climatic deposition (and not only local deposition) in the stake accumulation record.
At the decadal scale, as in the data, the correlation is higher. However, the slope is lower (0. 63) than in the data, being closer to the Rayleigh distillation. To understand this mismatch between data and simulations, we investigated alternative approaches for the δ 18 O-temperature relationship obtained using annual data through the combination of temperature extracted from the READER database and simulated δ 18 O, and the temperature extracted from the ECHAM5-wiso model and S1C1 δ 18 O record (Table  4 ). We found a high and significant correlation between interannual variations in temperature from the READER database and ECHAM5-wiso simulated near-surface air temperature (r = 0. 91 and p = 2. 2×10 -12 ) but no significant correlation between interannual variations in δ 18 O from the S1C1 core (from the first dating) and simulated by ECHAM5-wiso.
No long-term trend is detected in the stake data  (Agosta et al. , 2012)  and in the S1C1 accumulation record. This robust finding challenges a recent regional modelling study showing a decrease of accumulation in coastal Adélie Land from 1979 to 2010  (Lenaerts et al. , 2012). We are not confident in the signal of higher accumulation rates in the 2000s, because no such signal is detected in the comparison of recent and historical stake measurements. The average stake area record is significantly correlated with results from the ECHAM5-wiso model, but not with the S1C1 record. This finding may arise from post-deposition noise associated with, e. g. wind scouring. The lack of similarity between the S1C1 ice-core records and the stake measurements may also arise from the fact that the location of stake measurements may have moved through time  (Agosta et al.
, 2012) , and because the correction of height measurements used an average density value along the profile rather than simultaneous measurements of density, which were not available. The S1C1 δ 18 O record shows a remarkably small amplitude of the seasonal cycle, when compared to other isotopic datasets from coastal East Antarctica. But note that the accumulation rate is site dependent. We have stressed that remarkable years in δ 18 O and accumulation records do not coincide. Both records display a multi-decadal variability. The S1C1 δ 18 O record is significantly correlated with DDU near-surface air temperature only at the decadal scale, with a particularly large slope. We showed that the ECHAM5wiso model strongly underestimates the interannual variability in δ 18 O. This features calls for a systematic assessment of the skills of the isotopically enabled atmospheric general circulation models against the whole variety of data regarding the temporal variability.
Therefore, these variations are driven by largescale atmospheric circulation. We have highlighted the fact that model outputs cannot be used in a climate sense prior to 1979, probably due to the lack of sea-ice data used in the assimilation systems. In this paper, we have also used the ECHAM5-wiso model outputs to challenge our initial ice-core chronology. None of our key conclusions are affected by the alternative chronology implying a 2-year shift. Finally, we have used the ECHAM5-wiso model to explore the spatial relevance of precipitation and precipitation δ 18 O outputs at the S1C1 site, and stressed the differences between a large-scale representativeness of temperature (typically, 1000 km), and a smallerscale representativeness of δ 18 O (typically, 100 km around the S1C1 site). In the future, obtaining a network of shallow ice-core records in coastal East Antarctica will be critical to assess signal-to-noise aspects and test the validity of these findings.
Here the authors assumed a model for IAs, the non-linear alignment model of IAs  (Bridle & King 2007) , which has been shown to fit well at linear and quasi-linear scales. Using two-point ellipticity correlation function data from the CFHTLenS survey,  Heymans et al. (2013)  made a simultaneous fit to cosmological parameters and their IA model, which they parametrized by a single free amplitude parameter. Split by colour, the analysis showed that the IA of blue galaxies was consistent with zero, while IAs in red galaxies were detected at close to 2σ confidence. Both results are consistent with theoretical expectations. DES SV data are broadly comparable to CFHTLenS in terms of sky coverage and depth. The full DES 5 yr survey will increase the available area by more than a factor of 30, making it realistic to expect constraints on IA amplitude which are an order of magnitude tighter than those from CFHTLenS.
This increased constraining power will also allow more ambitious parametrizations of the IA model and the comparison of different models. Of particular interest are measurements of the IA signal as a function of luminosity, where a positive correlation has been reported, and redshift, where no evidence for evolution has been found  (Singh et al. 2015). In particular, the deep redshift baseline of DES, compared to the spectroscopic data sets mostly used in the past, is likely to improve knowledge of IA evolution; this is of importance in understanding the mechanisms which source galaxy shape alignments. The DES data set will be extremely useful for joint studies of IAs in combination with data from other surveys. Overlapping spectroscopic surveys (see Fig. 1 ) combined with high-quality shape measurements from DES will allow for high-precision IA measurements.
Overlap with the SPT provides around 1000 clusters detected via the Sunyaev-Zel'dovich (SZ) effect between the existing 2500 sq deg survey and a nearly complete SPTpol survey; thousands more are expected from the upcoming SPT-3G survey. Existing XMM-Newton and Chandra data likewise give X-ray coverage of approximately 1000 clusters. For these samples, DES provides both photometric redshifts and cluster confirmation through the existence of galaxy overdensities, and this overlap enables multiwavelength probes of cluster and cluster galaxy evolution. Clusters also act as signposts of the cosmic web, and both the large-scale galaxy distribution and weak lensing shear signal from DES data will allow the identification of supercluster and void regions and filaments joining clusters  (Vikram et al.
Cluster evolution DES data (optical richness + ICL and lensing masses) coupled with X-ray and/or SZ samples of the hot gas phase will enable the study of the full baryonic content of massive haloes. The combined data will enable the study of the joint scalings of stellar mass, gas mass, gas temperature, X-ray luminosity, etc. with halo mass and redshift. In particular, the data should allow the covariance of these properties at fixed halo mass to be determined with significantly higher precision  (Evrard et al. 2014). In cosmological hydrodynamic simulations, the statistical relationships among stellar and hot phase properties are sensitive to the details of the 'subgrid' astrophysical models employed for star and supermassive black hole formation and their attendant feedback channels. Empirical constraints from DES studies will thereby lead to improved subgrid prescriptions in simulations.
In addition, DES will allow studies of the stellar mass fraction in clusters. This has already been done with early DES data in  Palmese et al. (2016) , where they derived the stellar mass profile for the cluster RXC J2248. 7-4431, and compared it with the total mass derived from DES weak lensing analysis, resulting in a stellar mass fraction of ∼0. 7 per cent within r 200c ≈ 3 Mpc. This and similar techniques will be applied to ∼10 5 DES clusters to provide important information about galaxy evolution.
Cluster sub-populations DES will discover new clusters suitable for many diverse science programmes and will have the statistics to identify samples of relatively rare sources. DES will, therefore, provide follow-up targets for a wide variety of programmes including the following. Merging clusters. DES cluster samples combined with DES weak lensing and/or existing X-ray and SZ data will allow the identification of new bullet cluster-like systems suitable for tests of DM self-interaction (e. g. Markevitch et al. 2004; Harvey et al. 2015). The strong correlation of diffuse synchrotron radio emission with cluster merging activity (e. g. Cassano et al. 2013; Cuciti et al. 2015 ) also shows that energetic cluster mergers are effective particle accelerators; however, the mechanisms responsible, whether shock acceleration, turbulent re-acceleration or secondary hadronic production, are still poorly understood.
g. Allen et al. 2008 ). As highlighted above, the history of cooling and AGN feedback in cluster cores shape the evolution of the ICM. Current evidence points to a lack of strong cool cores at higher redshifts (e. g. McDonald et al. 2013 McDonald et al. , 2014)) , but larger samples and further study are needed to confirm this trend. Strong cool-core clusters such as the Phoenix cluster (SPT-CL  J2344-4243; McDonald et al. 2012)  stand out in the DES data as rich clusters with blue, star-forming central galaxies. The DES data will also allow studies of the central galaxy properties of cool-core clusters identified in X-ray or SZ. High-redshift clusters. In combination with existing infrared surveys such as Spitzer Infrared Array Camera and WISE within the DES survey area, we will also be able to detect very high redshift clusters in the range z = 1.
0-1. 5 and possibly beyond. The very deep data which DES is acquiring in the SN fields will also provide additional leverage for high-redshift cluster science. This sample will allow the study of clusters near the epoch of their formation. Little is known about clusters at redshifts much higher than z ∼ 1 as only small samples of very high redshift clusters have been identified thus far, and the discovery of new high-redshift clusters by DES will have significant impact. These clusters will allow us to extend many of the above-mentioned studies to higher redshifts. For example, while we observe significant evolution in the properties of cluster galaxies and the ICM from the present epoch out to z ∼ 1, many of the bulk cluster properties are already in place. To understand questions of ICM heating, the establishment of the red sequence and BCG formation, it is advantageous to push our observations closer to cluster formation.
12. The lensed quasar search will use colour and morphology information at the catalogue level as well as the variable nature of quasars. We have no u-band observations in DES, so to compensate we will need to use infrared bands, e. g. WISE. We have already carried out spectroscopic follow-up of some lens candidates from the DES SV data and have confirmed a number of systems  (Nord et al. 2015). In addition, we have confirmed the first lensed quasars in DES, identified using the colour and morphology information  (Agnello et al. 2015). We will be able to use the sample of strongly lensed systems to study DM mass profiles at scales from individual galaxies all the way to massive clusters. Cluster-scale lenses are particularly useful because they allow us to study the effects of strong lensing in the core of the cluster and weak lensing in the outer regions.
Q UA S A R S The DES photometric data will contain millions of new quasars (QSOs), and in particular new samples at z > 6 and z > 7 that can be used to study the reionization history of the Universe through measurements of the transparency of the intergalactic medium (IGM), the evolution of supermassive black hole masses, the chemical evolution of the IGM from the statistics of metal-line absorption systems, their space density evolution and many other topics. In addition, the regular monitoring cadence employed for the SN survey will include variability data that can be used to estimate supermassive black hole masses via the reverberation mapping technique, as well as to select quasar candidates. Fig. 13  shows a DES colourcolour diagram, as well as known quasars overlapping the DES SV observations.
Quasars at the epoch of reionization The very deep z-band data obtained as part of DES, combined with Y-band observations, make the DES data particularly well suited for the discovery of new quasars at redshifts of six and above. At these redshifts, quasars may be selected as i-band dropouts, or even z-band dropouts. The source of confusion for these dropout samples is very red Galactic stars and substellar objects, namely L and T dwarfs  (Fan et al. 2001 (Fan et al. , 2006)) , which have substantially higher surface densities at these magnitude limits. Quasars may be separated from stellar objects in colour space with the addition of a redder filter, as the intrinsic SED of quasars is generally much bluer than these Galactic objects. For z ∼ 6 quasars, z -Y colours are an effective discriminant, while for the z ∼ 7 quasars that will be z-band dropouts  (Mortlock et al.