dataesr/scientific-paragraphs-categorization huggingface dataset:
A robust understanding of Antarctic Ice Sheet deglacial history since the Last Glacial Maximum is important in order to constrain ice sheet and glacial-isostatic adjustment models, and to explore the forcing mechanisms responsible for ice sheet retreat. Such understanding can be derived from a broad range of geological and glaciological datasets and recent decades have seen an upsurge in such data gathering around the continent and Sub-Antarctic islands. Here, we report a new synthesis of those datasets, based on an accompanying series of reviews of the geological data, organised by sector. We present a series of timeslice maps for 20 ka, 15 ka, 10 ka and 5 ka, including grounding line position and ice sheet thickness changes, along with a clear assessment of levels of confidence. The reconstruction shows that the Antarctic Ice sheet did not everywhere reach the continental shelf edge at its maximum, that initial retreat was asynchronous, and that the spatial pattern of deglaciation was highly variable, particularly on the inner shelf.
This paper provides an overview of, and introduction to, a community-based reconstruction of the deglaciation of the Antarctic Ice Sheet. Reconstructing the Antarctic Ice Sheet through its most recent (post-Last Glacial Maximum; LGM) deglacial history is important for a number of reasons  (Bentley, 2010). Firstly, ice sheet modellers require field data against which to constrain and test their models of ice sheet change. The development of a practical approach to modelling grounding line dynamics  (Schoof, 2007)  has led to a new generation of models (e. g. Pollard and DeConto, 2009; Pattyn et al. , 2012 ) that require such field constraints. Secondly, the most recent millennia of Antarctic Ice Sheet history are important for evaluating the response of the ice sheet to various forcing agents (e.
, 2012a; Ivins et al. , 2013)  but it is not clear if these are comprehensive in their inclusion of all available marine and terrestrial glacial geological data. In addition, ice-sheet reconstructions are also important for constraining the location of biological refugia during glaciation  (Convey et al. , 2008)  and understanding climatic and oceanographic change during the glacialeinterglacial transition. Several decades of work have produced a large body of geological data constraining Antarctic Ice Sheet history. There have been a number of attempts to synthesise the data but many of these reconstructions have focussed only on LGM ice-sheet extent  (Denton and Hughes, 1981; Anderson, 1999; Bentley, 1999; Anderson et al. , 2002; Denton and Hughes, 2002; Wright et al. , 2008; Livingstone et al.
, 2012)  and in some places they have been superseded by new datasets. Importantly, the period between the LGM and present has not seen similar attention. Moreover, significant progress has been made in developing and refining the methods used to acquire and analyse data needed for terrestrial and marine records of past ice-sheet thickness and extent (e. g. mapping of subglacial bedforms on the continental shelf using multibeam-swath bathymetry). Many of these new datasets that have been acquired have yet to be incorporated into continentwide reconstructions of the ice sheet. The glacial geological literature is widely dispersed across journals and reports ('grey' literature), covers a broad range of techniques, is presented in many different formats, and is subject to various uncertainties (especially dating) that may be subtle, and have changed over time as techniques and understanding have developed.
The use of dated timeslices also has the advantage of avoiding terms like 'the LGM', which has been used rather variably both to refer to local icesheet maxima, and as a global chronostratigraphic term to refer to the period c. 26. 5e19 ka BP (see  Clark et al. , 2009 for discussion). This has led to some confusion in ice-sheet syntheses. Whilst the 20 ka timeslice can be a useful rough proxy for the global LGM, it is clear from  Anderson et al. (2002)  and this volume that the Antarctic Ice Sheets did not reach a synchronous maximum extent, and that Local Last Glacial Maximum (LLGM;  (Clark et al. , 2009) ) positions differ widely in timing. Each paper in this volume synthesises the available marine and terrestrial glacial geological datasets to determine the position of the ice-sheet grounding-line, the ice-sheet upper surface, and in some cases flow-directional features for that particular sector and timeslice.
We have made considerable efforts to be clear about uncertainty in the position and timing of retreat of the groundingline and, as such, it is intended to demonstrate where there are robust constraints for models as opposed to geographic areas or time intervals where the position of the grounding-line or ice-sheet surface is less certain. There are a number of challenges associated with dating the geological evidence of deglaciation around Antarctica: offshore this includes the marine-reservoir effect, and reworking of old carbon, and onshore the reworking of previously exposed erratics presents problems for cosmogenic dating. These uncertainties are assessed in full in each of the sector papers. The use of timeslices also allows future development of more closelyspaced reconstructions, as available datasets expand to address specific debates.
Availability of data Each of the sector reviews provides substantial datasets identifying critical chronological data that have been used to constrain the reconstructions e these are available online as supplementary datasets. We also include here, as a supplementary dataset, the Antarctic-wide timeslice reconstructions of grounding-line and icesheet surface (Supplementary Information). We emphasise that any use of the data should rest on careful reading, and citation, of the appropriate individual sector paper(s): these are where critical issues of dating uncertainties and calibration, alternative models and other issues are discussed in detail.
20 ka timeslice Around much of Antarctica the grounding line was close to the continental shelf break at 20 ka. However, there are important exceptions in the Ross Sea, Prydz Bay, and Weddell Sea regions. Moreover, in some areas the maximum extent was reached prior to 20 ka and retreat had begun by this time (e. g. Hillenbrand et al. , 2014). There is an ongoing debate about the extent of ice in the Weddell Sea at the LGM, and the post-LGM retreat history  (Hillenbrand et al. , 2014). In broad terms, the available marine geological data in the Weddell Sea have been interpreted as showing extensive ice on the continental shelf at 20 ka. However, data are sparse in the southern Weddell Sea where the confluence of ice flowing from the East Antarctic and West Antarctic Ice Sheets occurred. In this    2014 )).
, 2014). Based on the distribution of glacial landforms and subglacial sediments, palaeo-drainage of the ice sheet across the inner and middle shelf was partitioned into a series of ice streams flowing in cross-shelf bathymetric troughs. In the Belgica Trough, Bellingshausen Sea the grounding line was deeply embayed and ice-sheet retreat had begun already  (Larter et al. , 2014). In the Amundsen Sea Embayment, geomorphological features and a small number of radiocarbon dates from the outer shelf indicate that the grounding line extended to, or close to, the shelf edge. However, data constraining the earliest stages of grounding-line retreat are sparse. Foraminifera-bearing layers of LGM age in one core near the shelf edge suggest that either retreat started before 20 ka or the grounding line position fluctuated across the outer shelf at around this time.
Anderson et al. (2014)  demonstrate that over half of the ice that was grounded in the Ross Sea came from East Antarctica. In eastern Ross Sea, subglacial geomorphological features extend to the shelf margin, indicating that the WAIS extended across the continental shelf during the last ice-sheet expansion. However, the precise timing of this expansion remains unresolved and we are unable to constrain the limit at 20 ka. Marine radiocarbon ages, mainly acid insoluble organic (AIO) ages, indicate that the ice sheet probably retreated from the shelf prior to the LGM. Terrestrial and glaciological data from the margins of the Ross Sea embayment indicate that the ice sheet retreated during the Holocene. Ongoing research is focused on obtaining compound specific radiocarbon ages aimed at resolving this controversy.
Recommendations for future work It is clear from the reconstructions that the level of knowledge of Antarctic Ice Sheet history is extremely variable in time and space. Each of the sector reviews identifies suggestions for future work and we highlight some of those here. With the exception of Mac. -Robertson Land and the Lambert/Amery system where most work has been focussed, East Antarctica contains substantial regions where we still do not know the broad deglacial history, and these have prevented a full circum-Antarctic timeslice-based reconstruction. The acquisition of further terrestrial and marine data around the East Antarctic margin has to be a priority, and in particular we identify the need for robust geochronological information from onshore localities to constrain former East Antarctic Ice Sheet thickness  (Mackintosh et al.
, 2014). In the Weddell Sea, obtaining targeted data to distinguish between the two alternative scenarios A and B would go a long way to helping resolve a significant debate about ice sheet extent in this region  (Hillenbrand et al. , 2014). The glacial history of the sub-Antarctic islands is exceptionally poorly known e in many cases we do not even have a broad understanding of the maximum ice-sheet configuration at the LGM, let alone the subsequent deglacial history. Yet such islands can provide important information on sub-Antarctic environmental change and can be a useful test bed for understanding the mechanisms of ice-sheet retreat in the later phases of deglaciation  (Hodgson et al. , 2014). In contrast to the Bellingshausen Sea, whose deglaciation history is poorly constrained because it is entirely based on AIO dates from marine cores, the amount of geological and geophysical data in the Amundsen Sea has multiplied rapidly in recent years.
However, we still require further chronological control on the steps in retreat that are increasingly being identified in the geomorphological record of the region. This would aid efforts to better understand if the recent ice sheet change in this important area is exceptional  (Larter et al. , 2014). Although the ice-sheet retreat history of the Antarctic Peninsula sector, particularly along its western margin, is one of the best constrained in Antarctica, there remain major data gaps, most notably along the Weddell Sea margin. In line with several other sectors the constraints on the timing of ice-sheet retreat are poor, and even in comparatively well-studied areas of the Peninsula we still require further chronological control so as to assess the variability between different ice-stream catchments and retreat rate.
Whilst the 20 ka timeslice is relatively well known, at least around West Antarctica, the constraints on ice-sheet configuration reduce rapidly through the deglacial period, and in several areas we know surprisingly little about the Holocene configuration of the ice sheet. Whilst deglacial reconstructions have been published for the Ross Sea  (Conway et al. , 1999) , the eastern Ross Sea is still poorly constrained. We lack radiocarbon age constraints for ice-sheet retreat from the continental shelf and must rely heavily on terrestrial glacial-geological and glaciological data from the inner shelf. Understanding the Holocene is particularly important both for providing the context for recent ice-sheet change (understanding if it is unusual or a part of Holocene variability), and because ongoing GIA is particularly sensitive to the most recent changes in ice loading  (Ivins et al.
Acknowledgements Our thanks go to the many individuals who have studied Antarctic deglaciation and the people who have supported them in the field and at sea over the last several decades e our understanding of ice-sheet behaviour and this volume would not exist without their dedicated hard work, often in demanding conditions. We also thank all of the national funding agencies who have supported the work described in this volume (MJB acknowledges  NERC  grant  NE/F014260/1 ). We also thank the  Editors of QSR  for their helpful comments and editorial support and to  Chris Clark  and  Mike Hambrey  for reviews that helped improve the paper. Tim Horscroft was very helpful in keeping the various papers on schedule. The  Scientific Committee on Antarctic Research (SCAR)  research programme 2004e2011 ' Antarctic Climate Evolution ' (ACE) provided funding for an initial discussion workshop for which we are grateful.
Acknowledgements. The authors would like to thank the  German and French Science Foundations DFG  and  CNRS  for the joint funding of this work within the project ATMOCHEM -Secondary organic aerosol production in the lower free troposphere over  Western Europe. The work has also been partly funded by the  FP6 Integrated project EUCAARI , contract no. 36833. In addition, the availability of ECMWF analysis data is acknowledged. Edited by:  B. Ervens.
In Sect. 4, these derived variables are used to assess whether several stateof-the-art land surface models, some of them embedded in IPCC-standard climate models, correctly describe and quantify the snow regime in the Arctic and boreal latitudes. The discussion in Sect. 5 deals with two issues: we firstly investigate how the observed inaccuracies in modelled snow variables affect simulated soil temperatures, and consequently permafrost extent and heterotrophic respiration produced by a land surface model; secondly, we describe how field data can readily be used to optimize model parameters in order to improve its simulation of snow dynamics.
Field data This study uses the Former Soviet Union (FSU) Hydrological Snow Surveys data set  (Krenke, 2004) , which consists of snow measurements carried out from 1966 to 1996 in the proximity of 1345 World Meteorological Organization (WMO) stations spread throughout the FSU (Fig. 1 ). The number of stations in the data set reduced from 1345 in 1990 to about 200 in 1991, the majority of them located in the European sector of the FSU, with very little coverage over northern Siberia. Each station exhibits different and varying sampling frequency, but measurements were usually taken every 5 or 10 days. Three products are provided. The most detailed is the synoptic product, which contains records of transect measurements made in the vicinity of the stations. Several variables were recorded, of which the most important for this study were snow depth, snow density and snow course type (field, forest, gully or unknown), with field and forest types being the most frequent.
The transect product contains the measurements from the synoptic product (with snow water equivalent in place of snow density) that occurred during the three most common days of measurements (the 10th, 20th and final day of each month), so these data are standardized to a common time frame and hence are easier to handle than the synoptic product. Finally, the station product contains the mean snow depth records for the first 10 days, the second 10 days and the remaining days of the month at the locations of the weather stations.
Earth observation data GlobSnow  v. 1. 3 (Luojus et al. , 2011 ) is a monthly snow water equivalent (SWE) data set covering the period 1979-2010. Its novel algorithm  (Pulliainen, 2006)  uses forward modelling of brightness temperatures from three satellitebased radiometers  (Nimbus-7 SMMR (1979 -1996) , DMSP  SSM/I (1987 SSM/I ( -2002) )  and Aqua AMSR-E (for 2002-2010)) and assimilates field data on snow, including the field data used in this study, to produce a global SWE product. Its accuracy has been demonstrated, and it has been used to estimate trends of SWE over the last 30 years  (Takala et al. , 2011). Hancock et al. (2013a)  further show that, amongst a suite of Earth observation SWE products, GlobSnow offers the highest accuracy due to its assimilation of ground data, thus making it most suitable for testing land surface models.
Snow water equivalent Monthly averaged SWE outputs were acquired from the lowresolution transect product. Usually, and depending on the spatial resolution of the data set, more than one station falls within the spatial extent of each grid cell. Since the cover type for each station was provided, this allowed the SWE for forest and non-forest areas to be estimated. The SWE www. the-cryosphere. net/8/487/2014/ The Cryosphere, 8, 487-502, 2014 for the whole grid cell was then calculated as the weighted average of these two values, where the weights were given by the fractions of the grid cell covered by forest and nonforest. These fractions were derived from the MODIS Vegetation Continuous Fields (MODIS VCF) MOD44B land cover product  (Hansen et al. , 2003). A similar approach was applied for the other snow variables described in later subsections.
Fresh snow density The density of fresh snow provides the starting point for snow evolution, and so is an important property of the snowpack, especially early in the snow season. In order to estimate a value of fresh snow density applicable mainly to the beginning of the snow season, we scanned the entire synoptic data set, approximately 600 000 measurements, for pairs of measurements that were taken less than N days apart, in which the earlier record showed no snow but the latter had positive values of snow depth. It was then assumed that the age of the snow would be uniformly distributed on the interval [0, N ] days, with an expected value of N/2 days, and so the latter record represented the value of fresh snow density N/2 days after the initial snowfall. This is less likely to be correct as N increases, because snow undergoes densification immediately following its deposition, while newly deposited snow can dilute the snowpack and decrease its overall density.
To reduce the effect of such perturbations, only measurements acquired up to N = 3 days apart were considered. This was possible because, although most of the records in the synoptic data set were collected with a 5-or 10-day step, there were 60 pairs of measurements at the beginning of the snow season taken less than 4 days apart in which the earlier had a snow depth of zero and the later a positive value. To optimize the algorithm two restrictions were applied. As the transect measurements used in this retrieval would involve newly deposited and therefore shallow snowpacks, transects carried out in forests were not considered, in order to avoid potential perturbations caused by the heterogeneity of a shallow forest snowpack. Additionally, the Global Summary of the Day (GSOD)  (NCDC, 2013)  data set was used to identify the minimum air temperature (T min ) for the station location on the day of the transect; since fresh snow density is a function of air temperature  (Anderson, 1976)  and what is sought here is an estimate for the early stages of the snow season, any values acquired when T min < -10 • C were filtered out.
Snow depth and density (daily time step) Daily time series for both snow depth and density had to be created from the data records for exploitation by LPJ-WM in the permafrost analysis in Sect. 5. 1. Only stations overlying organic soils which had nearly complete temporal coverage with few data gaps were selected in order to ensure a more accurate construction of daily time series. This led to a data set containing nine stations, all located either in south-central Eurasia, where the permafrost boundary lies, or eastern Eurasia, where low air temperatures contribute to predominantly continuous permafrost; there were none in the western sector, where permafrost is generally absent. The station data set contains records of mean snow depth at the location of each station for the first 10 days, the second 10 days and the remaining days of the month, while the transect data set contains records of snow depth and snow density on the same days, but acquired along transects in the vicinity of the station.
In the former, snow depth was assigned a zero value when snow was absent, so no gaps occur, even during the summer and spring. In contrast, the transect measurements took place only when snow was present; in its absence, the records contain no data, limiting our ability to detect the start and end dates of the snow season. Hence we utilized both sets, acquiring snow depth from the station records and snow density from the transect records, after first ensuring that the snow depth for each station from the two data sets was well correlated (ρ ≥ 0. 9) and had the same mean. To form daily snow depth fields between the 10-day interval of the station records, an increase between two dates was attributed entirely to snowfall on the second date (a step function), while decreases were calculated using linear interpolation between observed values.
, 2012; Zhang et al. , 2005) , espe-cially late in the snow season  (Molotch et al. , 2007; Lundberg and Halldin, 2001; Pomeroy et al. , 1998). The magnitudes of S S and S C usually significantly exceed those of T and T S ; for continental Eurasia, simulations by both  Yang et al. (2010)  and  Dery and Yau (2002)  produced values of snowpack sublimation ranging from 5 to 60 mm annually, a range which encompasses values usually reported in the literature for field measurements. Here a novel and model-independent approach is presented in which the transect measurements are employed to retrieve values of the monthly snowpack ablation, A S , with melt excluded (A S = S S + T + T S ) in order to provide the basis for evaluating values calculated by models. As the available field data only contain information about the snowpack, intercepted snow is not considered; the retrieved values will therefore include the effects of sublimation, snow transport and blown snow sublimation exclusively from the snowpack, although, based on the literature presented above, sublimation will likely be the major contributor.
Snow water equivalent comparisons Snowpack SWE is calculated by all four models and is also given by the GlobSnow and LEGOS EO products. In order to compare these six data sets with the SWE values derived from the transect data, for every grid cell we defined vectors A k , k = 1-6, and B, whose elements are the monthly values of SWE for the six data sets and transect measurements respectively. These were used to define two statistics: i. ρ k , the correlation coefficient between the time series for data set k and the transect data, i. e. between A k and B; ii. k = ( Āk -B)/ B, (3) which quantifies the bias in data set k relative to the observed mean SWE, B; here the overbar denotes average. All comparisons between models and field records were carried out for the full available temporal range of the transect data  (usually 1966-1990 or 1966-1996) , while for the EO products the range was restricted to their current availability: GlobSnow from 1980 to 1996 and LEGOS from 1988 to 1996, but only for latitudes north of 50 • N.
Comparisons between the transect product and the data sets are presented in Fig. 2  for each data set, with the correlation coefficient ρ in the left column and , the relative bias, on the right. The months May to August were excluded and grid cells with less than 30 pairs available for correlation are not shown. GlobSnow achieved the best agreement with the transect data, with over 80 % of the grid cells having a correlation coefficient exceeding 0. 6; this is expected since the Eurasian data were used in the calibration of Glob-Snow. However, a region of low correlation occurs around the southern border of FSU, probably as the higher relief of the area is not accounted for by the retrieval. In contrast, only 54 % of the grid cells had ρ ≥ 0. 6 in the LEGOS EObased SWE product for which data were available only for latitudes north of 50 • N.