dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, in order to compare the values from satellite altimetry with the in situ observations, it is important to have consistent assumptions about the snow depth and the densities of sea ice, snow and water. Despite these challenges and the many assumptions in the processing chain, recent studies (e. g. , References  [49, 52, 72] ) find relatively good correlations (0. 5-0. 9) with mean differences -0. 21 to 0. 12 m between the CryoSat-2-derived SIT products and the evaluation data in the central Arctic winter (October-March). Prior studies comparing ERS-1 and ERS-2 with draft from submarine cruises  [32]  found an almost one-to-one agreement. These results are within the expected uncertainties of measurements, and no systematic biases were found. Often the correlation between OIB and CryoSat-2 SIT estimates are found to be lower than with submarine, moored buoys and AEM observations.
Improvements in Processing Although the launch of CryoSat-2 in 2010 heralded a new era in Arctic altimetry, with its high inclination orbit and finer resolution through SAR processing, there still remains much to be done with the preceding two decades of LRM altimetry. Recent advances in data storage and computer processing power mean that many people now have the potential to process the entire ERS-1, ERS-2 and Envisat waveform datasets rather than always using the products derived by the space agencies. This has been greatly assisted by ESA's REAPER project  [132] , which has made the data from early missions more readily accessible, coupled with improved orbits and corrections. A further understanding of radar reflections from snow upon sea-ice is required, as the scattering horizon varies according to the temperature and salinity of the snow (see Section 4.
2. 3). This knowledge, especially for differences in snow properties on FYI and MYI, could be gained through both modelling and in situ studies in the Arctic (see Section 6. 5) and would also be complemented by improved datasets on the depth of snow. Although there have not been many papers providing a quantitative assessment of the classification strategies, there have been a number of new ideas for methodologies, especially for SAR waveforms. However, these still need to be thoroughly evaluated, especially for their ability to separate melt ponds from leads, as the former make it difficult to recover the sea level within the ice floes during summer. A number of new processing strategies have been developed  [34, 83]  which have yet to be applied to the full set of data on the 35-day ERS/Envisat orbit.
New Missions with New Capabilities Table 1 catalogued all the radar altimetry missions covering the Arctic beyond 72 • N, including Sentinel-3B launched in April 2018. The recent Sentinel-3 missions not only offer the prospect of many decades of further coverage through subsequent launches but also offer the potential to improve the historical record. This is because, early in their combined tandem mission observing the same ground track 30 seconds apart, one operated as a SAR altimeter with the other in LRM mode. This enables a much closer comparison of waveform classification and retracking in these two different modes. The understanding gained about their differential response to, say, small leads or melt ponds, can be used to develop a more consistent processing scheme to deliver the homogeneous datasets on the Arctic sea level and freeboard that are required for climate studies.
5 • N, as this regions contains most of the MYI. Future missions not only will help ensure the long-term recording of Arctic sea level and ice freeboard but also could improve the snow depth climatology through the joint exploitation of the Ku-band and Ka-band data  [48]. This review paper has focused on radar altimetry only. However, NASA's ICESat mission  [138]  carried a laser altimeter that has been operated between February 2003 and October 2009, with the capability to retrieve freeboard  [139]. However, in contrast to radar altimetry, laser altimetry is affected by clouds, and ICESat measurements were restricted to two periods per winter season (October/November and February/March). The combination of laser and radar altimtery is challenging because of the very different footprint sizes, effect of clouds on laser measurements and different characteristics of the penetration into the snowpack on top of the ice.
A major advance is likely to come from the MOSAIC programme  [150] , in which the RV PolarStern will act as the central observation site for an international 12-month multidisciplinary study that will encompass measuring the snow depth and morphology, as well as the ice draft, on a variety of scales. In particular, there will be surveys on at least a weekly basis at a number of sites spanning different sea-ice conditions, which will include the development of melt ponds during the thaw cycle. Such a large programme will not only furnish a better parameterization of the factors affecting these constants but also create a useful database of validation data. However, even such a campaign, which will be lodged within FYI, cannot fully address the diversity of conditions across the whole Arctic, and so, contributions from other drifting stations, icebreaker missions and dedicated cal/val exercises (e.
Conclusions This paper has provided a review of the technical aspects of radar altimetry over the Arctic for both sea level and sea-ice studies and complements the scientific review provided by Johannessen and Andersen  [1]. It has shown how the surface type affects the shape and strength of the return waveforms, both for LRM and SAR (delay-Doppler) altimeters. It has covered the challenges of robustly classifying the waveforms (and also of assessing such a classification) and then developing retracking approaches for deriving the height of the reflecting surface, especially with spurious extra signals due to off-nadir bright targets. A persistent issue is that of reliably classifying data according to surface type. Whilst there are a plurality of solutions (with many just having a visual validation for a selected scene), there have been few papers that compared several methods quantitatively against reliable independent datasets.
Even the statistical or machine-learning approaches typically rely on some subjective operator classifications as their reference. Effort is required to collate all the available ground truth data (whether in situ or from airborne or spaceborne sensors) that coincide with satellite altimetry. Open access to such an extensive database, plus protocols for systematic validation, would allow new approaches to be more reliably benchmarked. An important aspect of this is the need to also improve the quality of the corrections, especially the density of ice and the depth and density of the overlying snow and its microstructure, some of which will be addressed by a major international in situ campaign, MOSAIC  [150]. For the geophysical products (principally, sea level and sea ice thickness), further developments in the interpolation and mapping may be required that better accommodate the short-correlation lengths and the non-synoptic nature of each month's observations.
are made from data downloaded from the Radar Altimetry Database System (RADS) using default editing (for a retrieval of the sea level from the open ocean, not from within leads) and wet tropospheric corrections from the ECMWF. Data were downloaded for four different missions in two areas: the Greenland Sea (10 • W-10 • E, 75-80 • N) and the Beaufort Sea (150-130 • W, 75-80 • N). The seasonal availability of data is very different for the two regions.
The approach cannot be used effectively in locations where there are strong spatial gradients of δ 13 C-DIC data or where temporal changes are expected so that  Lauvset and Tanhua (2015)  recommend the "use of CRMs if at all possible. " This step is presently not possible for δ 13 C-DIC analyses. Our study demonstrates that use of a CRM-based data correction procedure could improve between-lab reproducibility to match the target level (AE0. 05‰) proposed by GOOS. The result is encouraging, as the target uncertainty was attained despite the use of a wide variety of analytical and standardization approaches, and the samples themselves being subject to several of the variables that can compromise their integrity, including overseas transportation. Application of RM-based corrections procedure also brought the DSW δ 13 C-DIC results from all groups involved in this study into reasonably close agreement (to within ca.
Acknowledgments This study was funded by the  Canada Excellence Research Chair in Ocean Science and Technology. L. C. and D. W. R. W. would like to thank all members of the participating laboratories for their assistance and cooperation. The authors thank  A. G. Dickson , especially, for sound advice and suggestions concerning ocean carbon data quality control. The GO-SHIP A02 cruise was an initiative of the  Marine Institute in Galway. The authors appreciate the cooperation and support of  Chief Scientist Peter Croot  ( National University of Ireland Galway ) and  Evin McGovern  ( Marine Institute ) as well as all the scientific staff and crew members who were aboard Celtic Explorer during the GO-SHIP A02 cruise. The cooperative efforts on the cruise represent a contribution to the work of the  Ocean Frontier Institute  (www.
g. , water resources monitoring [2,  30, 31]. In line with those improvements, NASA's (National Aeronautics and Space Administration) Modern Era Retrospective analysis for Research and Applications (MERRA;  [32] , and MERRA2;  [33] ) as well as ECMWF's (European Centre for Medium-Range Weather Forecasts) Interim reanalysis (ERA-Interim;  [34] ) were the most investigated. We take advantage of the recent development of ERA5, which was released in 2017 as the fifth generation of ECMWF global atmospheric reanalyses. At the time of this study, a time-slice of the ERA5 database was available from 2001 within 3 months of real time. ERA5 brings extensive changes compared to ERA-Interim, including higher spatial and temporal resolutions as well as a generally improved representation of, e. g. , precipitation and incoming solar radiation (SWin)  [4, 35, 36].
The performance of ERA5 and ERA-Interim precipitation and SWin is first investigated using in situ measurements over Burkina Faso (BF) before studying the quality of LDAS-Monde renalysis of LSVs forced by either ERA5 or ERA-interim. ). (b) GEOV2 leaf area index (for pixels covered by more than 90% of vegetation) from the Copernicus Global Land Service project averaged at 0. 25° × 0. 25° spatial resolution over the domain centred on Burkina Faso (latitudes from 9. 0°N to 16. 0°N and longitudes from 7. 0°W to 3. 0°E). The symbols, SH (Sahel), SS (Sudan-Sahel), and SG (Sudan-Guinea), represent the three agroclimatic regions across Burkina Faso (BF). Section 2 provides (i) a description of LDAS-Monde, including details on the CO2-responsive version of the ISBA LSM and the data assimilation system, (ii) information on the atmospheric reanalyses used to force the system, the assimilated remotely sensed observations along with the insitu and satellite datasets used to assess the sensitivity of the results to the atmospheric reanalysis, and (iii) the experimental set up and the evaluation strategies.
We take advantage of the recent development of ERA5, which was released in 2017 as the fifth generation of ECMWF global atmospheric reanalyses. At the time of this study, a time-slice of the ERA5 database was available from 2001 within 3 months of real time. ERA5 brings extensive changes compared to ERA-Interim, including higher spatial and temporal resolutions as well as a generally improved representation of, e. g. , precipitation and incoming solar radiation (SW in )  [4, 35, 36]. The performance of ERA5 and ERA-Interim precipitation and SW in is first investigated using in situ measurements over Burkina Faso (BF) before studying the quality of LDAS-Monde renalysis of LSVs forced by either ERA5 or ERA-interim. Section 2 provides (i) a description of LDAS-Monde, including details on the CO 2 -responsive version of the ISBA LSM and the data assimilation system, (ii) information on the atmospheric reanalyses used to force the system, the assimilated remotely sensed observations along with the in-situ and satellite datasets used to assess the sensitivity of the results to the atmospheric reanalysis, and (iii) the experimental set up and the evaluation strategies.
3 m 2 •m -2 for other vegetation types. Conversely, a lack of photosynthesis triggers higher mortality rates. The carbon uptake related to photosynthesis represents the gross primary production (GPP) while the CO 2 released by the soil-plant system constitutes the ecosystem respiration (RECO). The difference between these two quantities corresponds to the net ecosystem CO 2 exchange (NEE). In the model version used in this study, ISBA parameters are prescribed for 12 generic land surface types, which consist of (i) nine plant functional types (needle leaf trees, evergreen broadleaf trees, deciduous broadleef trees, C3 crops, C4 crops, C4 irrigated crops, herbaceous, tropical herbaceous and wetlands), (ii) bare soil, (iii) rocks, and (iv) permanent snow and ice surfaces. Those parameters are derived from the ECOCLIMAP land cover database  [46].
Data Assimilation LDAS-Monde routinely uses a simplified extended Kalman filter (SEKF,  [38] ) to assimilate observations of SSM and LAI. This is a sequential approach with a forecast step followed by an analysis step (see Figure  2  for schematic diagram describing how it works). The forecast step propagates the initial state over a 24-h assimilation window with the ISBA LSM. Then, the analysis step corrects the forecast by assimilating observations. This step involves an observation operator defined as the product of the model propagation of control variables over the 24-h assimilation window with the projection of those variables to observation equivalents. The analysis requires the calculation of the Jacobian of the observation operator. It is computed using finite differences obtained by perturbed model runs over 24-h assimilation windows.
It is envisioned that ERA5 will replace the release of the current ERA-Interim reanalysis, from 1979 to the near real time period (on a regular basis). Regarding climate information, ERA5 has numerous improved characteristics compared to ERA-Interim reanalysis. It presents one of the most updated versions of the Earth System Model and data assimilation techniques used at ECMWF, which enables the use of more sophisticated parametrization of geophysical processes in comparison to the previous versions used in ERA-Interim. Moreover, ERA5 has two other important features, which are the improved temporal sampling and spatial resolution: From 6-hourly in ERA-Interim to hourly in ERA5, and from 79 km in the horizontal dimension and 60 vertical levels to 31 km and 137 levels in ERA5. Eight variables from ERA5 and ERA-Interim have been used to constrain LDAS-Monde, including the lowest model level (about 10-m above ground level), air temperature, wind speed, specific humidity and pressure, and the downwelling fluxes of shortwave and longwave radiation as well as precipitation partitioned in the liquid and solid phases (the latter being null over the considered domain).
At the time of this study, ERA5 is a new product and to the best of our knowledge, only three other studies compared the performance of ERA5 and ERA-Interim. In  [4] , the authors assessed the two reanalysis ERA5 and ERA-Interim using them to force the ISBA LSM over North America. Better performances in the representation of evaporation, snow depth, soil moisture, and river discharge estimates were observed in the simulations forced by ERA5. They were attributed by the authors to the improved precipitation estimates. Urraca et al. [36]  compared SW in estimates from ERA5 and ERA-interim at a global scale, and observed a better performance with ERA5. Finally, Beck et al. [35]  highlighted the good performance of ERA5 precipitation with respect to 26 gridded subdaily precipitation datasets using Stage-IV gauge-radar data for the evaluation over the continental United States of America.
Observations are rescaled to match the spatial resolution of the two atmospheric forcing data-sets, 0. 25 • × 0. 25 • and 0. 50 • × 0. 50 • for ERA5 and ERA-Interim, respectively 2. 2. 4. Evapotranspiration, Gross Primary Production, and Sun-Induced Fluorescence Independent datasets of evapotranspiration and gross primary production (GPP) are used to assess the quality of the LDAS-Monde reanalysis of LSVs. Terrestrial evapotranspiration estimates are from the GLEAM (Global Land Evaporation Amsterdam Model) v3. 1. product  [61]. They cover the period of 1980-2016 and are available at a spatial resolution of 0. 25 • × 0. 25 •. The GLEAM dataset is widely used for investigating both trend and spatial variability in the terrestrial water cycle (e. g. ,  [62] [63] [64] ) as well as land atmosphere interactions (e.
Evaluation Using Independent Datasets For evapotranspiration and GPP, seasonal scores (RMSD and R values in Figure  14 ) are computed for the model and for the analysis in both the LDAS-ERA5 and LDAS-ERAI configurations. Only vegetated grid points (>90%) are considered. After the joint assimilation of SSM and LAI, a small positive impact on evapotranspiration is observed for the correlations, as found in  [40] , and it is interesting to note a small degradation of the RMSD values in the second part of the year (August to December). However, there is a clear improvement in GPP in terms of the RMSD and R scores, especially from January to August (Figure  14c, d ). While the impact on R values of the analysis is equally distributed over the domain for both evapotranspiration and GPP, a larger (positive) impact on RMSD values is found on the southern part of the domain (not shown).
LDAS-ERA5 generally performs better than LDAS-ERAI for both the openloop and the analysis. It is particularly true when considering RMSD values for evapotranspiration (Figure  14a ). Figure  14a  indicates, however, the degradation of the evapotranspitation estimation by LDAS-ERA5 and LDAS-ERAI when compared to the GLEAM dataset. It is worth noting that the GLEAM dataset is not a direct remotely sensed observation, but rather a remote sensing-based evaporation retrieved model with its own uncertainties. For instance,  Pagán et al. (2018)   [82]  have recently assessed the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF), normalized by photosynthetically-active radiation (PAR), to diagnose the ratio of transpiration to potential evaporation ("transpiration efficiency") from several state of the art models, including SURFEX and GLEAM.
1 on R for both configurations. For the rest of the year, the impact is rather neutral. Using ERA5 or ERA-Interim as atmospheric forcing does not have much influence on R. This was also the case for FLUXCOM GPP in Figure  14. This is due to a diminished impact of atmospheric forcing on the modelled GPP. The positive impact is of the same scale for the three agroclimatic areas covering BF (not shown). This is consistent with what was observed for correlations between simulated GPP and the FLUXCOM dataset. For SIF, seasonal scores (R values in Figure  16 ) comparing the observed SIF and simulated GPP for vegetated grid points are computed for model and analysis estimates in both the LDAS-ERA5 and LDAS-ERAI configurations. A positive impact of DA on R can be seen from January to August with an averaged improvement of 0.
1 on R for both configurations. For the rest of the year, the impact is rather neutral. Using ERA5 or ERA-Interim as atmospheric forcing does not have much influence on R. This was also the case for FLUXCOM GPP in Figure  14. This is due to a diminished impact of atmospheric forcing on the modelled GPP. The positive impact is of the same scale for the three agroclimatic areas covering BF (not shown). This is consistent with what was observed for correlations between simulated GPP and the FLUXCOM dataset.
Acknowledgments: We thank the  German Ministry of Education and Research (BMBF)  for funding the  West African Science Service Centre on Climate Change and Adapted Land Use  (WASCAL) that funds this work, and the  University of Abomey Calavi-Benin  that hosts the  Graduate Research Program of Climate Change and Water Resources. We are further thankful to the  Laboratoire mixte international  ( LMI-ECLAIR2 ) and the  Agence Nationale de la Météorologie du Burkina-Faso (ANAM). We are grateful to the  VEGEO research group of the Centre National de Recherches Météorologiques (CNRM Météo-France)  where this work was mainly conducted and for their various supports. Finally we thank the 3 anonymous reviewers for their comments and suggestions.
Thermodynamic Modeling Thermodynamic modeling was performed using the PerpleX software package  (Connolly, 2005; Connolly & Kerrick, 2002) , which minimizes the Gibbs energy to compute the most stable mineral assemblage at given P-T conditions. P and S wave velocities are then computed as Voigt-Reuss-Hill averages  (Hill, 1952). In this study we calculate P and S wave velocities for both eclogites and granulites at eclogite-facies conditions. To do so, the internally consistent thermodynamic database of  Holland and Powell (1998, updated in 2002)  was used, because it includes shear moduli, necessary for seismic velocity calculations. The calculations were done in the Mn-Na-Ca-K-Fe-Mg-Al-Si-Ti-H 2 O system using the following solution phase models: pyroxene and mica  (Holland & Powell, 1996) , garnet  (White et al.
g. , receiver function signal) and subsequently erase it entirely with time. to perform texture measurements. Anja Schleicher and Andrea Gottsche (GFZ Potsdam) are thanked for help with XRF analysis. Additionally, we thank Christian Schiffer and two anonymous reviewers for thorough and constructive comments and Douglas Schmitt for editorial handling and further comments. This research was supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority program SPP 2017 "Mountain Building Processes in Four Dimensions (MB-4D)" by grant JO 349/11-1. All additional data used in this study can be accessed in the supporting information, and neutron diffraction results are accessible via https://osf. io/ac4bk/.
Pb/ 206 Pb dates are preferred when older. The preliminary zircon U-Pb-Hf data obtained by Chelle-Michou et al. (2017) on sample TN46 were appended to our new dataset of 67 U-Pb analyses on this sample. The detrital zircon date distributions of all samples feature a dominant Ediacaran to late Cryogenian population in the range 550-720 Ma representing between 50 and 75% of the dates and defining a prominent peak at c. 635 Ma (Fig. 4, 5). The remaining analyses are mostly distributed over three main periods: 760-1050 Ma (Tonian to late Stenian), 1. 7-2. 1 Ga (Paleoproterozoic) and 2. 6-3. 2 Ga (Neo-to Paleoarchean). PIL-16-01 and CHA-16-02 show similar detrital zircon date distributions, with the Ediacaran-Cryogenian and Tonian-Stenian populations representing c. 70% and 14%of the dataset, respectively (Fig.
4a,c and 5a,c). Paleoproterozoic and Archean dates are relatively scarce (< 12% and 6%, respectively). Sample SEN-16-01 differs by a larger proportion of 760-1050 Ma and older-than-1. 8 Ga grains (18 and 23%, respectively, Fig. 5b). TN46 paragneiss contains very few Tonian-Stenian grains but an abundant Paleoproterozoic-Neoarchean population (36%, Fig. 5d), with dates up to 3. 27 Ga (spot z7). The zircon U-Pb date spectrum of PdB-16-01 contrasts with that of the other samples because: (i) Tonian-Stenian grains are more abundant than in any other sample, representing 38% of the dataset; and (ii) Paleoproterozoic and Neoarchean dates are very scarce (only 4% of the data) (Fig. 5e). CL-dark zircon grains yielding Variscan dates in the range 320-305 Ma were retrieved in sample TN46, as already described and discussed by Chelle-Michou et al.