dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The quality of DUACS Sentinel-3A SAR altimetric 1 Hz in the coastal band of the European Seas, estimated here through comparison with independent tide gauge measurements, revealed a mean RMSD between both datasets lower than 7 cm for the whole region, with mean values ranging around less than 4 cm in the Mediterranean basin and around 10 cm for the NWS region. Previous works have compared in situ measurements from tide gauges and altimetry data in the European coasts. The tide gauge records from the PSMSL-i. e. ,  [5, 20, 25]  or GLOSS/CLIVAR  [23, 24, 26] -have been mainly considered. The PSMSL repository presents a dense tide gauge network in the European coasts similar to that found in the cmEMS repository, but it is based on monthly average sea level records. [41, 42]  conducted a regional calibration of the Sentinel-3A data at higher temporal scales by using tide gauge measurements included in the cmEMS repository, but it was focused on the German coasts of the German Bight and of the Baltic Sea.
Thus, to our present knowledge, this is the first time that the dense cmEMS tide gauge dataset is used to compare with Sentinel-3A data in the whole European coasts. The performance of the Sentinel-3A data in the coastal zone of the Gulf of Finland (Baltic Sea) was investigated by  [43]  through the comparison with tide gauge records from the Estonian Environment Agency. Such records are not included in the cmEMS repository. These authors found an overall RMSD between both datasets of 7 cm based on the inter-comparison with three tide gauge sites. This RMSD is larger than the one obtained here for the Baltic Sea (5. 69 cm, Table  2 ). However, we used 88 tide gauge stations distributed along the whole basin, this allowing a more robust computation. Ref. [42]  compared, among others, the tide gauge sites of Kiel and Warnemünde with the 1 Hz Sentinel-3A data.
However, this is out of the scope of this paper, because the length of the time series analysed is very short for properly investigating seasonal variability, so in the following we briefly summarise the features found. related to some inter-annual variability. This rise in SSH is captured by the Sentinel-3A dataset and also by in situ tide gauge measurements. As a consequence, the annual minimum in SLA observed in previous years in March-April is located in 2018 in May. This signal has not been detected in the other sub-regions investigated by either altimetry or tide gauge measurements. The tide gauge time series in the Baltic Sea (Figure  5b ) show an annual cycle peaking close to December with an amplitude of around 60 cm; this is quite similar to that found for the NWS region (figure not shown).
The tide gauge time series exhibit much more inter-annual variability than that of the Mediterranean Sea. The larger seasonal signal observed in the Baltic Sea is attributed to water mass variations within the basin linked to steric changes in the nearby North Atlantic Ocean and river discharges, as well as meteorological forcing, and amplified due to the presence of shallow waters  [44]. The quality of the Sentinel-3A dataset was also assessed by comparing it with the Jason-3 performance (RMSD and variance) obtained for the inter-comparisons with tide gauges conducted for the entire European coast and the different sub-regions investigated. The results are reported in Table  1  for the whole domain and in Table  2  for the different sub-basins clearly show the superior performance of the Sentinel-3A dataset with respect to Jason-3 in the coastal band in terms of the lower along-track RMSD and lower variance in the differences (altimetry-tide gauge) against the same tide gauges, despite their different ground tracks.
The Sentinel-3A satellite mission improves both the RMSD by 13% and the variance (altimetrytide gauge) by 25% with respect to the Jason-3 dataset in the European coasts. Figure  6  shows an example of the comparison between Jason-3 and tidal residuals at the tide gauge site of Aranmore (IBI region). A low correlation between both datasets was obtained, thus leading to poorer results than those for the Sentinel-3A mission (figure not shown). Additionally, the mean of the distance between the tide gauge sites and the most correlated altimetry track points used to conduct the inter- A sudden increase in SLA is observed in spring 2018 (black square in Figure  5 ), promoting a second maximum around March 2018 which is not observed in the previous year, being probably related to some inter-annual variability.
This rise in SSH is captured by the Sentinel-3A dataset and also by in situ tide gauge measurements. As a consequence, the annual minimum in SLA observed in previous years in March-April is located in 2018 in May. This signal has not been detected in the other sub-regions investigated by either altimetry or tide gauge measurements. The tide gauge time series in the Baltic Sea (Figure  5b ) show an annual cycle peaking close to December with an amplitude of around 60 cm; this is quite similar to that found for the NWS region (figure not shown). The tide gauge time series exhibit much more inter-annual variability than that of the Mediterranean Sea. The larger seasonal signal observed in the Baltic Sea is attributed to water mass variations within the basin linked to steric changes in the nearby North Atlantic Ocean and river discharges, as well as meteorological forcing, and amplified due to the presence of shallow waters  [44].
The quality of the Sentinel-3A dataset was also assessed by comparing it with the Jason-3 performance (RMSD and variance) obtained for the inter-comparisons with tide gauges conducted for the entire European coast and the different sub-regions investigated. The results are reported in Table  1  for the whole domain and in Table  2  for the different sub-basins clearly show the superior performance of the Sentinel-3A dataset with respect to Jason-3 in the coastal band in terms of the lower along-track RMSD and lower variance in the differences (altimetry-tide gauge) against the same tide gauges, despite their different ground tracks. The Sentinel-3A satellite mission improves both the RMSD by 13% and the variance (altimetry-tide gauge) by 25% with respect to the Jason-3 dataset in the European coasts.
This is due to the reduction in the cross-track distance in the Sentinel-3A orbit with respect to Jason-3, which promotes a higher probability of finding a Sentinel-3A track closer to a given tide gauge station. Similar results were found for the different sub-regions investigated. Lanczos low-pass filtered SLA from Sentinel-3A and Jason-3 were compared with tidal records from the tide gauge sites common to both missions. Overall, the inter-comparisons between the filtered SLA and in situ measurements improved when using the altimetry data from Sentinel-3A in all the regions investigated (Table  6 ). For the entire European coast, the RMSD was reduced 12% more for the Sentinel-3A satellite mission than for the Jason-3 one. The variance of the differences between both datasets reduced 22% more for the Sentinel-3A mission.
NWS Region Baltic To further investigate this, the LWE correction applied to the altimetry was checked. We found that the LWE correction diminished the variance of the SLA time series from Sentinel-3A used to Lanczos low-pass filtered SLA from Sentinel-3A and Jason-3 were compared with tidal records from the tide gauge sites common to both missions. Overall, the inter-comparisons between the filtered SLA and in situ measurements improved when using the altimetry data from Sentinel-3A in all the regions investigated (Table  6 ). For the entire European coast, the RMSD was reduced 12% more for the Sentinel-3A satellite mission than for the Jason-3 one. The variance of the differences between both datasets reduced 22% more for the Sentinel-3A mission. Table  6. Summary of the improvements (%) of the Sentinel-3A mission in comparison with tide gauges in terms of the lower RMSD, lower variance of the differences (altimetry-tide gauge), and lower mean distance between the most correlated altimetry point and tide gauges with respect to Jason-3 in the European coasts and the different sub-regions investigated.
To further investigate this, the LWE correction applied to the altimetry was checked. We found that the LWE correction diminished the variance of the SLA time series from Sentinel-3A used to compare with tide gauges by 21% in the European coasts (Table  5 ). This fact translates to better results in terms of the RMSD and variance of the differences (altimetry-tide gauge) when compared with tidal residuals. Similar results were obtained from the Jason-3 dataset. If we compare the outcomes reported in Table  5  for both satellite missions, a larger improvement in statistics was obtained for the Sentinel-3A dataset with respect to Jason-3. This leads to an overall larger impact of LWE correction on SLA from the Sentinel-3A mission. If the LWE correction is not applied to both altimetry datasets, better results in terms of lower RMSD and variance (altimetry-tide gauge) were still obtained for the Sentinel-3A mission for all the regions investigated (Table  7 ).
However, the improvements obtained were only in the range of 1-2%. These results demonstrate that LWE processing contributes to reducing errors in altimetry, enhancing the consistency between the altimeter and in situ datasets. However, it does not explain alone the better results obtained for the SAR technology in the retrieval of SLA close to the coast. Nonetheless, these outcomes again confirm the better capabilities of SRAL with respect to the altimeters onboard Jason-3 in the retrieval of SLA close to the coast in the European seas. We have given the reasons why we decided to focus on the reference 1 Hz altimetry data instead of using high-rate (i. e. , 20 Hz) SLAs. We realise that the use of high-frequency 20 Hz products could produce better results, and this analysis is in our future plans when these products will be available for the whole oceanographic community.
Conclusions We have performed an assessment of the Sentinel-3A L3 along-track DUACS dataset in the coastal area of the European seas over a period of two and half years from May 2016 to September 2018. This validation was conducted by comparing the equivalent SLAs derived from 6-h sampled tide gauges over the same period in the whole domain and the following sub-regions: the Mediterranean and Baltic Seas and the IBI and NWS regions. Tide gauge records disseminated on cmEMS were used. The mean value of the RMSD between 1 Hz SLA from Sentinel-3A and tide gauges for the whole European coasts was 6. 97 cm. This showed some variability according to the different regions investigated: minimum mean values of 3. 41 cm were observed in the Mediterranean Sea and maximum ones (10. 72 cm) in the NWS region.
These results can be explained by the larger spatio-temporal variability observed in the NWS region with respect to that found in the Mediterranean basin. Non-tidal variance, which is also larger in the former, contributes to the larger RMSD obtained in the NWS region. The assessment was also conducted using altimetry data from Jason-3 for inter-comparison purposes. The Sentinel-3A dataset showed a lower RMSD and variance of the differences (altimetry-tide gauge) in the European coasts. The impact of the measurement noise on the SRAL instrument was checked by repeating the inter-comparisons but using filtered SLA. The results showed that the variance of altimetry data diminished by 2% when using filtered SLA from Sentinel-3A due to higher frequencies being subtracted from the SLA time series in the filtering procedure.
As a consequence, an error 0. 3% lower when comparing filtered SLA with tide gauge records with respect to that obtained when using the unfiltered data was obtained. Additionally, the variance of the differences between both datasets reduced by 1% when using the filtered data. The outcomes from the Jason-3 dataset confirmed the better results obtained from filtered SLA, although much larger discrepancies were found between filtered and unfiltered SLA when comparing with tide gauge records with respect to those obtained for the Sentinel-3A mission. This fact emphasises that the Jason-3 dataset is affected by a higher measurement noise than Sentinel-3A, and also that SRAL instrument onboard the Sentinel-3A mission better solves the signal in the coastal band. This was doubly confirmed from the computations conducted using only filtered SLA from the Sentinel-3A and Jason-3 missions, and also from the analysis of the signal degradation when we approach the coast.
Acknowledgments: This study has been conducted in the frame of the cmEMS SL-TAC and In Situ TAC (INS-TAC) projects. The CMEMS, led by Mercator-Ocean, is based on a distributed model of service production, relying on the expertise of a wide network of participating European organisations involved in operational oceanography. We acknowledge the regional in situ data production centres responsible for the collection and distribution of the tide gauge data used in this study:  Puertos del Estado (Spain)  for the  Iberia-Biscay-Ireland region; HCMR (Greece)  for the Mediterranean Sea;  IOBAS (Bulgaria)  for the Black Sea;  IMR (Norway)  for the Arctic;  SMHI (Sweden)  for the Baltic Sea;  BSH (Germany)  for the North-West Shelves region; Coriolis (France) for the global ocean. This work represents a contribution to  CSIC Thematic Interdisciplinary Platform PTI TELEDETECT.
INTRODUCTION Comprehensive and detailed glacier inventories provide important reference datasets for assessing the current status and estimating the recent and future glacier changes  (Paul et al. , 2013; Vaughan et al. , 2013). In recent decades, with the development of satellite remote sensing techniques, more precise and standardized glacier outlines have been produced from optical satellite imagery, permitting the mapping of glaciers at a regional scale with high accuracy  (Nagai et al. , 2016; Meier et al. , 2018; Mölg et al. , 2018). Argentina is the second country in the Southern Andes with the largest glacierized area  (WGMS, 2020). Previous inventories for the Argentinean Andes were mostly local  (Pitte et al. , 2010; WGMS, 2012; Masiokas et al. , 2015; Falaschi et al. , 2016). The recent National Glacier Inventory of Argentina (hereafter NGI), conducted by IANIGLA-CONICET in accordance with the National Law 26,639 for glacier protection in Argentina, provides a comprehensive dataset and detailed outlines of all Argentinean ice masses  (Zalazar et al.
Debris-covered ice and rock glaciers were delimited by visual interpretation and manual digitization. High resolution images were also used to improve the delineations (e. g. ALOS-PRISM, CBERS-HRC and Google Earth). The classification and morphological parameters of each ice mass are included into a database with a total of 38 attributes following international guidelines (UNESCO-  IASH, 1970; Rau et al. , 2005). The NGI includes an additional category termed "debris-covered ice with rock glacier" that represents a transitional phase where debriscovered ice gradually transforms into rock glaciers with no clear boundary between these surfaces  (Zalazar et al. , 2020). Additional fields were added to classify rock glaciers considering their origin, activity, form and structure. To improve the mapping quality and assess the activity of rock glaciers, a total of 598 ice masses where directly surveyed in the field during austral summers from 2012 to 2015.
See  Zalazar et al. (2020)  for more details on materials and methods used in the NGI. In this study, an additional field was included in the dataset to account for the percentage of debris-cover. This classification ranges from 0% for debris free glaciers, 10-50% for partly debriscovered ice (Figure  2A ), 50-90% for mostly debris-covered ice and >90% for completely debris-covered ice. The same classification was used for "debris-covered ice with rock glacier" category (Figures  2B, C ). Rock glaciers are considered as an independent category, completely covered by debris with no visible ice on the surface (Figure  2D ).
Randoph Glacier Inventory The RGI data were obtained from the GLIMS's website https:// www. glims. org/. We used the RGI version 6. 0 (hereafter RGI) to compare with the NGI outlines. According to the RGI documentation, the glacier outlines for the Central Andes were created from Landsat seven ETM + imagery acquired between 2000 and 2003. Version 6. 0 includes some upgrades such as images of better quality to reduce the presence of seasonal snow. However, the ice cover for this region may still be overestimated  (RGI Consortium, 2017). It is also important to note that rock glaciers are not explicitly included in this dataset as the main focus of the RGI is to provide a near global picture of glaciers for large-scale assessments.
Geodetic Mass Balance Dataset Ice mass changes for the periods  2000-2018, 2000-2009 and 2009-2018,  were calculated using the "ASTER monitoring of Ice towards eXtinction" geodetic method (hereafter the ASTERIX method,  Dussaillant et al. , 2019) , for both the NGI and the RGI inventories. Individual glacier mass balance rates reported here for the RGI are the same as those reported by  Dussaillant et al. (2019). Results for the NGI inventory were calculated from the original ASTERIX elevation change grids using the newer outlines. The ASTERIX method calculates surface elevation changes using times series of digital elevation models (DEMs) obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) stereo images  (Wang and Kääb, 2015; Berthier et al. , 2016). This methodology has been validated in different glaciated regions of the world with different climatic contexts and glacier types  (Brun et al.
Ice Mass Loss in the Central Andes of Argentina Based on the ASTERIX methodology and the NGI dataset we found a thinning trend of the ice surface in the Central Andes during the period 2000-2018, with a regional mean mass balance rate of -0. 18 ± 0. 19 m w. e. yr -1. From all ice masses, partly debris-covered glaciers and clean ice glaciers exhibited the most negative mass balance rates (-0. 36 ± 0. 19 and -0. 31 ± 0. 19 m w. e. yr -1 , respectively). Contrarily, mostly debris-covered glaciers, completely debris-covered and rock glaciers, underwent considerably lower mass losses and slightly negative or stable mass balances rates (-0. 12 ± 0. 18, -0. 07 ± 0. 19 and -0. 02 ± 0. 19 m w. e. yr -1 , respectively; Figure  5A ). The strongest negative mass balance rates of -0. 56 ± 0. 20 m w. e.
Differences between the National Glacier Inventory and Randolph Glacier Inventory Datasets In the Central Andes of Argentina, the RGI version 6. 0 contains 1,135 glaciers covering a total area of 1,085 km 2. In total, 6,941 more ice masses and 682 km 2 of inventoried area were identified in the NGI (Figure  7A ). For example, in the San Juan river basin alone, the NGI recorded 3,810 ice masses (i. e. , 47% of the total number of units in the entire region), almost three times more units than those represented in the RGI (Figure  7B ). As for the glacierized area, the RGI seems to have overestimated the glacier surface in some particular locations probably due to the use of scenes with presence of seasonal snow and the inclusion of some outcrops and avalanche contributing areas. Most of the glacier areas that were not included in the RGI correspond mostly to rock glaciers (69% of the area).
The remaining 31% of the areal discrepancies between the NGI and the RGI corresponds to the lower tongues of valley glaciers, mainly the category "debris-covered ice with rock glacier," and some mountain glaciers and perennial snowfields. Comparing the inventoried areas for each basin, the larger differences occur in the San Juan and Mendoza basins, where rock glaciers are more frequent (Figure  7B ). Further south, debris-covered ice and rock glaciers are less extensive and the differences between the inventoried areas in the RGI and NGI are smaller. As mentioned above, 98% of the area inventoried in the Llancanelo basin corresponds to rock glaciers and "debriscovered ice with rock glacier" category, and the RGI does not include ice masses in this basin. Despite the differences in area and number of ice masses recorded in the RGI and NGI datasets, the geodetic mass balances calculated for both inventories are in good agreement (Figure  8  and Table  2 ).
ACKNOWLEDGMENTS This study was conducted within the framework of the  National Glacier Inventory of Argentina (NGI)  and was supported by  CONICET  and the  National Ministry of Environment and Sustainable Development. We would like to thank  Argentina's National Commission of Space Activities (CONAE), Global Land Ice Measurements from Space (GLIMS)  and the  Japan International Cooperation Agency (JICA)  for providing some of the satellite images used to develop the  NGI. This research was partially funded by project  PICT-2018-03211. Etienne Berthier  acknowledges support from the  French Space Agency (CNES)  through the TOSCA and ISIS programs. We thank  Rachel Smedley  for English proofreading of a preliminary version of the paper.
DATA AVAILABILITY STATEMENT The complete NGI is available at http://www. glaciaresargentinos. gob. ar and at the GLIMS website (http://www. glims. org/maps/ glims). The ASTERIX elevation change maps for the Andes are available in the PANGAEA repository (https://doi. org/10. 1594/ PANGAEA. 903618). Individual glacier mass balance rates for the RGI glaciers in region 17 (Southern Andes) were published in the WGMS database  (Dussaillant et al. , 2019). The new mass balance estimates for the NGI ice masses in the Central Andes of Argentina are available upon request from the corresponding author, and will also be published in the WGMS database.
Summary and outlook A new age of upper ocean observations is now gradually emerging. Large to medium spatial-temporal observing and monitoring technologies that have proved their worth for oceanography and climate science (e. g. , satellite altimetry, Argo profilers) have reached maturity for present coupled data assimilation. The next generation of simulation codes will then also face new challenges that are generally largely beyond the capabilities of present deterministic predictions, e. g. dealing with local and intense extreme horizontal and vertical velocity shears, coupled with atmosphere coupled adjustments. Meeting these challenges will require dealing with uncertainty through advanced stochastic modelling and mathematical analysis, applied in concert with high-resolution observations, computational nested simulations, including improved geophysical retrieval algorithms more constrained with sensor physics and analysis of large datasets.
The mass resolution was set to ~7,000, and 54 Fe + and 56 Fe + were measured in multi-collection mode with two off-axis Faraday cups. Typical 56 Fe intensity was between 5 and 6 × 10 7 counts per second (cps). A typical analysis consisted of 2 min pre-sputtering followed by data acquisition in 30 cycles of 5 s acquisition time. The isobaric interference of 54 Cr on 54 Fe was corrected by monitoring chromium on masses 52, but chromium levels were negligible in all samples. The internal precision for δ 56 Fe values was typically better than ±0. 1‰ (2σ), and the external reproducibility based on multiple measurements of our pyrite reference material (Balmat with δ 56 Fe = -0. 399‰, (Whitehouse & Fedo, 2007) ) was better than ±0. 2‰ (2σ). Sulfur isotope compositions were measured on the Cameca ims 1280 HR2 (CRPG) by simultaneous measurements of 32 S -, 33 S -, and 34 S -in multi-collection mode with three Faraday cups.