dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012)  and in melting snow areas  (Magand et al. , 2008). Here, we present an updated SMB database for Antarctica. An important part of the work was documenting and formatting so-called "metadata" (e. g. , time coverage, measurement methods, altitude) which is required when using data, especially to check the quality of the SMB values. In the next Sect. 2, we present this updated database, we describe the improvements in spatial coverage, and compare the data with the V99 dataset (Sect. 2. 2). A quality control allows us to reject data considered as unreliable (Sect. 2. 3). The impact of this quality control on the spatial distribution of reliable data over Antarctica is discussed in Sect. 2. 4. In Sect. 3, we compare the data with ERA-Interim reanalysis  (Simmons et al. , 2006) , and show the importance of the selected data for climate model validation.
The fully updated database Because the international polar year (IPY) recently provided a large amount of new SMB data, an update of existing SMB compilation is timely. We consequently updated the V99 database by including the large amount of new SMB data obtained since 1999 (Fig. 1b ). Important new information was obtained during the European EPICA and international TASTE-IDEA programs, when isolated measurements and traverses were performed (Fig. 1a ), including in Dronning Maud Land (e. g. ,  Rotschky et al. , 2007) , from Ross Sea to Talos Dome (French-Italian contribution to ITASE  (Frezzotti et al. , 2004). Measurements were also taken along the French traverse to Dome C  (Agosta et al. , 2012; Verfaillie et al. , 2012) , along the Norway-USA scientific traverse from South Pole to Dronning Maud Land  (Anschütz et al.
, 2009 (Anschütz et al. , , 2011;; Müller et al. 2010) , and along the Japanese-Swedish traverse from the Swedish Wasa station to the Japanese Syowa (also spelled Showa) station  (Fujita et al. , 2011). A large new dataset was acquired from Zhongshan station to Dome A by the Chinese Antarctic Research Expedition (CHINARE)  (Ding et al. , 2011). Some traverses have also been revisited like the Japanese traverse from Syowa to Dome Fuji (e. g. ,  Motoyama and Fujii, 1999; Motoyama, personal communication, 2011) , resulting in a major update and   Bull (1971)  which were directly excluded from the  Vaughan et al. (1999)  database due to their low reliability (digitalized from maps). Background map is elevation according to  (Bamber, 2009). (c) Location of reliable field data (black dots) and selected datasets for model validation.
Thus, we choose to not include the published GPR data V. Favier et al. : An updated and quality controlled surface mass balance dataset in the present paper which is dedicated to direct SMB estimates. In addition to SMB values, information essential for a quality control is also provided, i. e. , location, methodology, altitude, local mean temperature, distance to the coast, dates of measurements, SMB units in the primary data sources, time period covered by the SMB values, primary data sources. This primary information was retrieved for both new data and for previous V99 data, which enabled us to correct several data. For instance, correction of longitude for measurements on Siple Coast was possible thanks to the primary publication  (Thomas et al. , 1984; Bindschadler et al. , 1988). In some cases, if measurements were a short distance apart (within approx.
20 × 20 km 2 ), the V99 database only gives their averaged values. Instead, we documented each data point. This was mainly the case at the South Pole and along traverses around Lambert Glacier, in Wilkes Land and from Syowa to Dome Fuji (Table  1 ). This increases the number of available measurements by 1493 (Table  1 ) (even though these data did exist in the V99 database it was at a lower spatial resolution). Of these 1671 data, 215 from Lambert Glacier traverse to Dome A were updated using new measurements made since 1999. These data offer a more accurate description of small scale (1 to 2 km scale) SMB spatial variability. Other specific characteristics were also added to the database, for instance, the presence of blue ice and of megadunes (when available in primary sources). Retrieving the primary information was complex because the whole information is usually not available in one single publication.
After tracking down previous publications, we were able to select the most relevant data together with precise information on the method used and the location. This included digitalizing data from figures or maps when necessary, which is clearly indicated in the final database. Finally, when different time periods were available for a single location (for instance, when several layers were reliably dated in ice cores), SMB estimates are given for each period. This involved compiling and documenting more than 5800 SMB data distributed over the whole continent (Fig. 1b ). Following  Magand et al. (2007) , we rejected data that did not correspond to measurements of annual SMB. This was the case of 255 data provided by  Bull (1971)  for which metadata are missing (e. g. ,  Vaughan and Russell, 1997).
Several data, as for instance between Dome Fuji and South Pole, can be traced as probably originating from a traverse undertaken in the area before 1971  (Fujiwara and Endo, 1971). However, original publication suggests that data are not highly reliable, justifying their rejection. The full updated surface mass balance of Antarctica database (called the SAMBA-LGGE database) now contains 5548 data (Table  2 ). This database is fully and freely available on the GLACIOLIM-SAMBA Observatory website: http://www-lgge. ujf-grenoble. fr/ServiceObs/ SiteWebAntarc/database. php.
A reliable dataset extracted from the full database A first update and improvement of the V99 database was performed by  Magand et al. (2007) , who focused on a limited part of Antarctica (90-180 • East Antarctic sector). These authors applied a quality control to SMB estimates based on objective criteria of reliability, as initially suggested by  Bull (1971). We applied the quality rating based on measurement techniques provided by  Magand et al. (2007). We do not discuss the quality and reliability of the method here because this has already been done by  Magand et al. (2007) , but the main explanations for the data rating are summarized in Table 3. The quality control enabled us to select only reliable SMB values leading to a new subset, hereafter referred to as "A" rated dataset. The measurement techniques we considered very reliable are rated "A".
Techniques considered less reliable are provisionally accepted and rated "B", while those considered unreliable are rated "C" (Table  3 ). Like  Magand et al. (2007) , we also rejected data when information that was crucial for the quality control was missing, i. e. location, SMB value and unit, method and period covered (for stake data). Results rated "A" form a new dataset of 3539 reliable SMB values (Table  2 , Fig. 1c ). This is about four times more than the 745 reliable data obtained by  Lenaerts et al. (2012b) , who conducted a similar quality control on the V99 database. Since our aim was to retrieve a high quality dataset, our data filtering may be too restrictive. Note that the fully documented database is available on the GLACIOCLIM-SAMBA (hereafter referred to as GS) website, so that any other control can be applied to the data.
Analysis of the "A" rated dataset The impact of the quality control on the distribution of available data over Antarctica was tested by comparing the full database with the "A" rated dataset (Table  2 ). The quality control led us to remove data from large areas (Fig. 1c ), mainly in West Antarctica. In particular, measurement lacks for a large area between Marie Byrd Land and the coast. This is particularly important because models were initially suspected to have common positive biases (i. e. , overestimated SMB) compared to surface accumulation compilations (Genthon and  Krinner, 2001; Van de Berg et al. , 2006). Since data for this area are not reliable, it is difficult to know whether the models are correct or not. Data availability is also particularly poor for the region from the Filchner-Ronne ice shelf to the South Pole, and for the Pine Island glacier catchment, which was the site of considerable research in the past but where SMB values were usually obtained through snow stratigraphy studies (e.
g. ,  Pirrit and Doumani, 1961; Shimizu, 1964). Stratigraphy data are generally assumed to be ambiguous because precipitation is low, presents high annual variability, and is affected by strong surface snow metamorphism, resulting in partial or sometimes total obliteration   Bull, 1971). "A" rated data Strict quality control (see Table  3 ) , only "A" rated 3539 data are retained. For 20th century model validation Blue ice data, data covering more than 70 yr, and data with 3242 differences in elevation of more than 200 m from the digital elevation model from  Bamber et al. (2009)  were excluded. of annual layering (e. g. ,  Magand et al. , 2007). Other large datasets from traverses to and around the South Pole were also excluded because the data were originally obtained from digitalized maps (e.
The proximity of the main Antarctic station (McMurdo station) is an ideal opportunity to plan future studies since it is the departure point for scientific research on the Ross Ice Shelf. The removal of suspicious data considerably has modified the distribution of the SMB. Especially, the SMB-elevation relationship is different when calculated with only the "A" rated dataset or the whole dataset. There is a significant difference between 200 m a. s. l. and 2000 m a. s. l. over East Antarctica (Fig. 2a ), because few observations are made over this elevation range and removing incorrect data thus had a significant impact on the mean SMB. Because large A-rated datasets exist in the high interior of the 0-90 • E sector of Antarctica (Fig. 1 ), the difference between "A" rated dataset and the whole dataset was limited above 2500 m a.
The SMB significantly increases from 200 m to 1000 m a. s. l. , although with marked scattering (Fig. 3 ). At higher elevations, between 1800 and 4000 m a. s. l. , the SMB and its scattering decreases progressively as the SMB is very low over interior plateaus. The frequency distribution of surface elevation for the entire continent or for only the observation points differs (Fig. 4a ), which means that the observations are not equally distributed as a function of altitude. Indeed, the frequency of surface elevations in Antarctica peaks at around 0 m a. s. l. (ice shelves) and at 3200 m a. s. l. , with a very broad maximum between 1800 m a. s. l. and 3400 m a. s. l. , whereas a narrow  maximum appears at 2800 m a. s. l. in the case of SMB measurements. Although new data at low elevation were added to this dataset, low elevation areas are not sufficiently documented considering their contribution to the total SMB and to the high spatial variability of their SMB.
There is still insufficient available data and measurements were mainly made in East Antarctica. The low density of field measurements is a serious obstacle to accurately assessing the Antarctic SMB (e. g. ,  Van de Berg et al. , 2006). Each SMB value was measured over a different period of time. Ninety percent of the periods covered less than 20 yr and 43 % less than 5 yr (Fig. 4c, d ). The covered period is closely related to the method used to estimate the SMB. The major cause of the stair-like distribution of the histogram in Fig. 4d  is the presence of data from very large stake networks (e. g. , around Lambert Glacier;  Higham and Craven, 1997; Ding et al. , 2011) , that span only a few years. Dating known horizons in cores or snow pits (volcanic eruptions, nuclear tests) is accurate and provides good estimates of the SMB over long periods (15 to 60 yr).
For this reason, stake measurements generally cover periods of less than 10 yr. Hence, stake measurements represent the largest pro-portion (82 %) of observations, because several large stake networks (containing many stakes) exist, but were measured only a few times. For these reasons, the scientific community cannot rely only on this method to increase data density for continental scale. 4 Comparison of the "A" rated dataset with results of ERA reanalysis 4. 1 A subset of data used for the comparison Regional features like elevation, continentality, location of sites relative to major and minor ice divides, surface slope and so on, clearly impact SMB distribution in Antarctica. However, large-scale features do not have the same consequences on SMB distribution, because SMB is more precisely related to how depressions penetrate inland and provoke precipitation, and on how the wind affect snow distribution.
Although perfectible, model outputs are useful here because of their large scale coverage and their ability to predict geographical distribution of the current and future SMB. Thus combining observational data with model outputs is essential both to identify biases in the model but also biases due to heterogeneous data coverage. It is difficult to compare spotty field data and model outputs on a regular grid. For this reason, we defined a special dataset for a (basic) model validation. Because climatic models generally focus on climatic conditions at the end of the 20th century, we filtered the database for this period, to avoid possible long-term climate variations. Here, we only considered data covering the last 70 yr, leading to a slight reduction in the database (52 data were removed).
We are aware that this process does not remove the decadal bias of each datum, because data present distinct time coverage. Now, this sub-dataset should be rescaled to a reference time period to produce a homogeneous climatology. But our purpose here was not to provide an accurate SMB map at the scale of Antarctica, but to compare the available field information with ERA-Interim data to judge if their spatial distribution is sufficiently regular and dense to allow model validation. In a future work, data will be rescaled against a common period to remove regional trends caused by heterogeneous coverage of time. Several data were further left aside because the elevation (as given in published works) differed from the local elevation given by the 1 km resolution digital elevation model (DEM) of  Bamber et al.
, 2009; Agosta, 2012). Conversely, accumulation over interior plateaus is very low (less than 50 mm w. e. a -1 ) and rather homogeneous over long distances as the topography is flat. Thus, field observations at low elevation are most appropri- ate for model validation, as already demonstrated in coastal Adelie Land, where data from the GS observatory allowed us to identify a number of discrepancies in various models  (Agosta et al. , 2012). Because low elevation areas (that is, where high SMB values are observed: Fig. 4b ) are undersampled by field observations, a focus on these specific areas is necessary. We selected datasets starting from coastal regions and extending inland, in order to include a strong topographic contrast (between 0 m a. s. l. and 2000 m a. s. l. , and sometimes extending up to 3000 m a.
s. l. when data from a continuous traverse were available). These Data cover the peripheral regions and key catchments of Antarctica. We further selected homogeneous data in terms of temporal coverage and methodology, and gathered data resulting from the same initial publications and origin. This led us to select the 10 datasets listed in Table  5  and shown in Fig. 1c , corresponding to traverse lines in Adelie Land (GS dataset), around Law Dome, from Zhongshan to Dome A, around the west side of Lambert glacier (above Mawson station), from Mirny to Vostok and from Syowa station to Dome F. Considering the spatial density of measurements, these data are particularly appropriate for model validation in coastal areas. We additionally selected three datasets not from traverses but from points located in Byrd region, along the Antarctic Peninsula and in Dronning Maud Land.
For Dronning Maud Land, Mirny to Vostok and the Peninsula, these observations cover a wide range of elevations (Fig. 6a ) and present a very low spatial density. These values thus provide important information on the regional increase in the mean SMB but data are also highly impacted by small scale variability due to local erosion or deposition processes (e. g. ,  Eisen et al. , 2009; Agosta et al. , 2012). In addition, Byrd, Peninsula and Dronning Maud Land are atypical climate settings, but it is important to study these particular areas because considerable environmental changes are expected to occur there in the future. For instance, the Byrd dataset presents the particularity of low SMB values in low elevation areas (Fig. 6b ). Among these datasets, the GS dataset and the one from Law Dome are particularly appropriate for model validation, because they have a high spatial resolution and cover a long observation period.
Available SMB data from ERA-Interim reanalysis Because reanalysis provide valuable information to study climatic features during recent decades, these data were used to study whether the SMB database allows us to reconstruct an accurate description of the main SMB distribution features in Antarctica. Reanalysis have been largely used to estimate climatic conditions and the Antarctic SMB (e. g. ,  Monaghan et al. , 2006; Genthon et al. , 2005; Agosta et al. , 2012) , as well as to force regional circulation models (e. g. ,  Van de Berg et al. , 2006; Lenaerts et al. , 2012a; Gallée et al. , 2013). The reanalysis methodology is based on assimilating meteorological observations (e. g. ,  Bromwich et al. , 2011) , which provides more reliable outputs than classical atmospheric models. ERA-Interim  (Simmons et al.
, 2006)  likely offers the most realistic depiction of precipitation in Antarctica (e. g. ,  Bromwich et al. , 2011) , which justifies to focus on these data. In the following section, ERA-Interim SMB values are tested against the SMB values of our database. The aim is to evaluate the accuracy of the ERA-Interim reanalysis data, and conversely, to check whether some areas are insufficiently documented in the database to allow model validation and to evaluate an accurate SMB average. We focused on the datasets for elevations between 0 and 3000 m a. s. l. (Table  5 ). ERA-Interim is an improved operational analysis: efficient four-dimensional variational data assimilation (4D-Var) is performed by taking additional data into account. ERA-Interim data are produced by applying the IFS model (Cy31r2 version), running in spherical harmonic representation (T255, nominal resolution of 80 km).
Calculations are performed on 60 vertical levels (hybrid pressure-sigma coordinates) from the surface to the mesosphere at 0. 1 hPa or 65 km. Here, we used ERA-Interim outputs over the period 1989-2010, even though data are now available for the period 1979-1988. Data were interpolated over a 15 km Cartesian grid resulting from a stereographic projection with the standard parallel at 70 • S and the central meridian at 15 • W. The liquid phase (P L and RU; see Sect. 2. 1 for abbreviations) is assumed to refreeze entirely. The simulated SMB is thus the balance between precipitation (P S and P L ) and sublimation (SU). The model used for ERA-Interim does not account for wind erosion or deposition processes (ER). Snow drift and wind processes are expected to have significant effects on SMB when wind speed is high (e.
g. ,  Gallée et al. , 2013; Lenaerts et al. , 2012a). These processes introduce a major uncertainty in SMB computations by ERA-Interim in low elevation areas. Hence, in our study, we did not focus on areas where SMB is controlled by snow erosion over long  distances, in this case, large blue ice areas. However, these data are still available in the full database, and should be included if the atmospheric model or the studied processes include erosion. To compare simulated and observed SMB values, we extracted grid boxes including at least one field measurement. Each field datum was then compared to the simulated one of the corresponding grid cell. We also calculated the average of all observed values included in the same model grid cell, and compared it to the SMB simulated by ERA-Interim.
Comparison between the subset of SMB data and ERA-Interim outputs Averaging ERA-Interim simulated data over the grounded ice sheet leads to a value of 128 mm w. e. a -1 (4. 4 mm a -1 in terms of sea level equivalent). This estimate is among the lowest published values  (Monaghan et al. , 2006) , and is well below estimates by  Vaughan et al. (1999)  and  Arthern et al. (2006). This low value is mainly due to very low accumulation modeled at high elevations (above 2000 m a. s. l. ), where ERA-Interim is known to considerably underestimate the actual amount of solid precipitation, and also below 1000 m a. s. l. , where ERA-Interim overestimates ablation. The areas located below 1000 m a. s. l. cover a narrow belt around Antarctica, in mountainous regions (the Antarctic Peninsula, in Palmer Land, along the Transantarctic mountains at 160 • E and in Mary Byrd Land).
This elevation range is crucial for the Antarctic SMB because it concentrates most of the total accumulated SMB. In grid cells containing measurements, ERA-Interim values are close, although lower, than measurements (Fig. 7a ). This shows that SMB measurements are reasonably well reproduced by ERA-Interim. Performing the same comparison with non-"A" rated data (Fig. 7b ) shows a lower quality relationship between data and model, suggesting that the filtering process removed lower accuracy data. Nevertheless, for "A" rated data, each elevation range between 200 and 1000 m a. s. l. , the mean simulated SMB computed over all grid cells is significantly higher than the one computed over grid cells containing measurements (Fig. 7a : red circles versus red squares). With the hypothesis that ERA-Interim output is close to the real world also for areas with no observations, this means that field data mainly reflect the low SMB areas and poorly constrain areas where SMB values are high, suggesting that observations do not correctly sample the SMB between 200 and 1000 m a.
s. l. (as already suggested in Sect. 3. 1). Above 2500 m a. s. l. , this discrepancy does not hold, suggesting that the observations may be representative of the entire range of elevations over the icecap. Nevertheless, large part of the plateaus of the EAIS are characterized by wind-glazed areas where wind and sublimation removes the annual solid precipitation (e. g. ,  Scambos et al. , 2012) , creating a hiatus in accumulation which is problematic for ice core interpretation. Existing "A" rated data are thus located only across the wind glazed areas, leading to overestimates of the net mass accumulation (e. g. Scambos et al. , 2012)  which may partly justify the wet bias observed according to ERA-Interim output (Fig. 7 ). The datasets selected at low elevations also provide interesting information.
The ERA-Interim simulation fits observations acceptably despite significant differences (Fig. 8 ). In some cases (in Adelie Land (GS dataset), Syowa station to Dome F, from Zhongshan to Dome A (CHINARE), around Lambert glacier) ERA-Interim misses the mesoscale variability, while in other cases (Law Dome, Byrd, Peninsula) ERA-Interim is mainly too dry. A large proportion of SMB differences is due to biases in the surface elevation used by the model. In fact, temperature and all related energy fluxes directly depend on elevation. However, some of the differences are directly related to the model's inability to simulate accurate SMB values. For instance, ERA-Interim assumes too low albedo values at low elevations (values between 0. 1 and 0. 75) and calculates too high runoff and sublimation. Overestimation of melting by ERA-Interim has already been demonstrated  (Agosta et al.