dataesr/scientific-paragraphs-categorization huggingface dataset:
We then calculated the anomalies of the mean summer temperature and total winter precipitation for each year using the 1981-2020 period as a baseline. Lastly, to determine the relation between the glacier balance and regional climate variability, we compared the glacier-wide mass balance with the seasonal variations of temperature and precipitation, averaging the climate records for analogous sub-periods in which the geodetic mass balance was considered. Since ERA5-Land data are available from 1981, the initial 1962-84 sub-period could not be included in this particular analysis. Using data from Paso de los Indios gauge station (38°31 ′ S, 69°2 4 ′ W; 498 m a. s. l. ; not visible in Fig. 1b.
8 ), and located 340 km north of Volcán Domuyo. The correlation coefficients between both the annual time series are ∼0. 6, and all are statistically significant ( p-value <0. 05), indicating a strong large-scale climate forcing. Although this is an interesting development (especially considering the distance between Echaurren Norte and Domuyo), we stress here that the SMB results may be biased, and that this is not a calibrated model. As a final validation of the modelled SMB using the MGM, we compared our results against those provided by ten forcing datasets of the ensemble-based model of  Malles and Marzeion (2021)  for the four modelled glaciers (Fig. S5 ). Again, the correlation coefficient between the ensemble mean and our MGM values is high (r = 0. 8), indicating that the interannual variability is well represented, though large differences (∼1 m w.
investigated three glaciers in the Río del Yeso catchment (∼33. 55°S), and found almost no elevation changes between 2000 and 2013, yet an increase in glacier thinning of <0. 75 m a -1 between 2013 and 2015. Geodetic mass-balance records from 2000 to 2018 for all glaciers of the Randolph Glacier Inventory (RGI 6. 0; Pfeffer and others, 2014) in the Northern Patagonian and Central Andes area are available from Dussaillant and others (2019). From their glacier-specific mass-balance database, we extracted an areaaveraged mass balance of -0. 04 ± 0. 13 m w. e. a -1 between 2000 and 2009 and -0. 40 ± 0. 07 m w. e. a -1 between 2009 and 2018 for Volcán Domuyo (Figs 10a, b). We interpret that the differences between these values with our much more negative massbalance signals (-0. 54 ± 0. 34 m w.
Acknowledgements. We acknowledge funding by the  Agencia Nacional de Promoción Científica y Técnica  (Grant  PICT 2007-0379  and  PICT 2016-1282 ) and  CONICET  (Grant  PIP 11220110100200 ). EB acknowledges support from the  French Space Agency (CNES). We thank  Sergio Cimbaro  (IGN),  Martín Matula  (AEROMAPA),  Mario Rosas ,  Paola Banegas ,  Ricardo Manzur  ( SEGEMAR Mendoza ),  Luis Lenzano ,  Pierre Pitte  (IANIGLA) and David from  Taller 4  ( Mendoza City ) for handling and scanning the photographic material. We also thank  Tobias Bolch  and  Owen King  ( St. Andrews University ) for their assistance on the mapping of debris-covered ice and language editing, respectively. The Check Points were surveyed and postprocessed using equipment and software lent by the  Geomatics division of IANIGLA.
Finally, the windows detection is achieved in two main steps: potential window point detection and window localization. The authors of  Tuttas and Stilla (2011)  have proposed a new method for detecting windows in airborne multi-aspect laser scanning data which represent a sparse point clouds. They started by detecting facade planes using point normals and region growing algorithm. Then, the windows are detected using the indoor points (points lying behind the detected facade planes) which have a special interest due to their regularities in appearance. Another method for window and door detection has been proposed in  Nguatem et al. (2014) , which apply a model selection based on Bayes factor and use a Monte Carlo simulation to generate and score competing models. This method has proven its ability to distinguish between various window and door shapes.
In the Tara oceans dataset, representing 197 globally distributed pelagic oceanic metagenomes, the putative heterotrophic Planctomyces and Proteobacteria accounted for ~25% of the nifH reads obtained from the 180 to 2,000 μm size-fraction  (Karlusich et al. , 2021). Moreover, metagenome assembled genomes representing NCDs occurred in the 5-2,000 μm planktonic size-fractions  (Delmont et al. , 2021). Finally, one of the most widely distributed NCDs, BOX 1 | Key term definitions. Non-cyanobacterial diazotrophs (NCDs): From a phylogenetic, rather than a metabolic, point of view, diazotrophs can be divided into cyanobacteria and non-cyanobacteria. The term NCDs has therefore been widely used in the literature. NCDs are a diverse group of prokaryotes with potentially diverse metabolic pathways (see definition below).
These particles are formed by the aggregation of organic material suspended in seawater. Aggregates are often rich in labile carbon and nutrients, and are therefore hotspots of microbial activity. Gamma-A, showed a ubiquitous presence in nifH genes across the North Atlantic Ocean quantified by qPCR in the >3 μm fraction  (Benavides et al. , 2016). This Gamma-A was also found in metatranscriptomes from the 3 to 2,000 μm size-fraction in the Tara oceans dataset, suggesting a filamentous or aggregateattached lifestyle for this putative heterotrophic bacterium  (Cornejo-Castillo and Zehr, 2020). Hence, both PCR-dependent and -independent approaches suggest the presence and/or activity of NCDs on aggregates. Experimental data also suggest aggregate-associated N 2 fixation by NCDs. In an early study from the Chesapeake Bay, United States, experiments by  Guerinot and Colwell (1985)  suggested that isolated strains of NCDs could fix N 2 in the presence of plankton and particulate matter.
The pH meter was calibrated using NIST standards of pH 1. 679, 4. 006 and 6. 865. The water samples were filtered on site using 0. 2 or 0. 45 μm cellulose acetate filters and stored in polypropylene bottles under dark and cool conditions. Alkalinity was measured by titration using an automated Metrohm system: 888 Titrando with Tiamo software, using the pH probe LL Aquatrode with PT 1000. The concentrations of the major ions (i. e. Na + , K + , Ca 2+ , Mg 2+ , Cl -, SO 4 2-, NO 3 -and F -) were determined by ion chromatography (IC, Metrohm 881 Compact IC Pro system). Samples for trace element analysis were acidified with nitric acid (ultrapure grade) immediately after collection. The concentrations of trace elements were analysed by inductively coupled plasma mass spectrometry at Kumamoto University (NexION 300, Perkin-Elmer Co.
Acknowledgements Funding for this work has been provided by  German Research Foundation (DFG)  through Germany's  Excellence Strategy  ( EXC 2037  " CLICCS" -Climate, Climatic Change, and Society , project Number:  390683824 ), and the  Cluster of Excellence CLISAP2  ( DFG Exce177 ) contribution to the  Center for Earth System Research and Sustainability (CEN) of Universität Hamburg. Pierre Bürckel  is thanked for technical assistance during ICP-MS analyses at IPGP. Part of this work was supported by  IPGP multidisciplinary program   PARI  and  Region Île-de-France SESAME  Grant no. 12015908  and the  JSPS Grant-in-Aid for Scientific Research B  ( 17H01861 ) and  Fostering Joint International Research A  (  19KK0291 ). MEB wishes to thank  I. Schmiedinger  for expert isotope analytical support.
TH and JH thank for financial support from  IROAST, Kumamoto University. K. Ide  is thanked for helping to organize and conducting field sampling and laboratory analysis at  Kumamoto University. T. Jäppinen  and  P. Bartsch  from  Universität Hamburg  are thanked for field and laboratory support. This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001. The authors wish to thank the handling editor and the journal reviewers for their help to shape the final version of the manuscript.
(2011)  and  García et al. (2018). *: production rate and scaling scheme used in the discussion. Sample  Kaplan et al. (2011)   field data. We followed the seven-step workflow specifically designed by  Chandler et al. (2018)  for field mapping of glacial landforms and deposits: 1-3. Data procurement, pre-processing and reconnaissance mapping. We used the MicMac photogrammetry software (https:// micmac. ensg. eu/index. php/Accueil) to build a Digital Surface Model (DSM) of the area with a horizontal resolution of 50 cm from a set of tristereographic, panchromatic Pl eiades satellite images. Prominent landforms were mapped in a GIS in order to select the areas of priority interest for subsequent field mapping. 4. Field mapping. Cerro Benítez was covered with an extensive field survey after four campaigns between 2017 and 2020.
Identification criteria Risk of confusion Environment Previous mapping Scale of mapping Superficial deposits were characterised based on the field facies analysis of six existing natural outcrops and two additional test pits (Table  4 ). Orientation and dip of erosional landforms and beddings were measured with a compass clinometer. The orientation was systematically corrected by subtracting the magnetic declination. In support of the ground operations, we used aerial drone photography to investigate the southern side of Cerro Benítez. We used a DJI Mavic Pro for reconnaissance survey and oblique aerial photography and a DJI Phantom IV for nadir aerial photography. Landforms and deposits were mapped on hard-copy, orthorectified satellite images. 5-7. Data processing, digital mapping and map production.
Chronological modelling Bayesian statistics are increasingly used in chronological modelling applied to the study of landform development (e. g. Chiverrell et al. , 2009; Bendle et al. , 2017b; Thorndycraft et al. , 2019; Genuite et al. , 2021). Chronological modelling using Bayesian statistics aims to obtain absolute chronological models (or posterior dates) from a set of prior, absolute dates arranged in a hypothetical, relative chronology. In order to build a chronological model of deglaciation and lake regression from our set of surface exposure ages, we used the chronological modelling software Chronomodel v2. 0. 18 (https:// github. com/Chronomodel/chronomodel; Lanos and Philippe, 2015; Lanos and Dufresne, 2019). The posterior distributions of the dates were calculated with a Metropolis-Hastings algorithm using an adaptative Gaussian random walk for the terrestrial cosmogenic 10 Be surface exposure dates, and the posterior distribution of the calibrated date for the 14 C date.
Acknowledgments This work was funded by the  Chilean FONDECYT  (projects  1150845  and  1180272 ), the  French CNRS (PICS GEOCEBE) , the  Istanbul Technical University  (research project  TGA-2017-40610 ), a PhD studentship from the  university of Rouen  awarded to I. G. and a mobility grant from the  French Institut des Am eriques  awarded to  I. G. Pl  eiades satellite images were acquired from  Airbus DS Geo via Dinamis. We acknowledge  Valentina García Huidobro Mery ,  Yiara Vela-Ruiz Figueroa ,  Florentina P erez Martin ,  Erik Lukoviek  and  Jos e Díaz Tavie  for their assistance on the field, C eline Colange and  Olivier Gillet  for their assistance to collect and process the satellite images,  Kim Genuite  for his help in using Chronomodel and  Jo€ el Rodet  for our scientific exchanges.
Numerous glacier velocity observations, derived from spaceborne imagery, are available online, but it remains difficult to analyze them because they are measured with different temporal baselines, by various sensors. In this study, we propose a novel formulation of the temporal closure to fuse multi-temporal multi-sensor velocity observations without prior information on the displacement behavior and the data uncertainty. We establish a system of linear equations between combinations of displacement observations and fractions of estimated displacements. The proposed approach provides a velocity time-series with a regular and optimal temporal sampling, the latter representing a compromise between the temporal resolution and the signal-tonoise ratio. The proposed approach is first evaluated on synthetic datasets and second on Sentinel-2 and Venµs velocity observations over the Fox glacier in New Zealand.
I. INTRODUCTION W HILE numerous glacier velocity observations are avail- able online  [1] -  [4]  with a temporal resolution up to 2 days and a spatial sampling up to 50 m  [4] ,  [5] , intraannual variations of glacier velocity remain poorly understood at regional scales, especially in mountain areas. This is due to the difficulty to analyze scene-pair velocity observations since they span different temporal baselines  [6] , are based on images from different sensors, and still contain noise, artifacts, and missing data when outliers have been filtered out. Therefore, a question arises: how to exploit the amount of available multisensor and multi-temporal glacier velocities in order to study intra-annual velocity variations with a reasonable uncertainty at a global scale? A way to fuse multi-temporal velocity observations is to use a Temporal Inversion (TI) based on the temporal closure of the displacement observation networks, inspired from the Small BAseline Subset (SBAS) in the InSAR community.
Currently, most of the TI approaches consider mono-sensor datasets  [6] -  [9]  or apply TI separately to each sensor datasets  [10] ,  [11]. A few studies imply multi-sensor datasets inside the same system of linear equations  [12] ,  [13]  whereas it could improve the results by adding more redundancy as stressed L. Charrier, Y. Yan, Emmanuel Trouvé are with LISTIC, Université Savoie Mont Blanc, Annecy, France. E. Colin Koeniguer and L. Charrier are with DTIS, ONERA, Paris, France. J. Mouginot is with IGE, Université Grenoble Alpes, Grenoble, France. R. Millan is with Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen K, Denmark. by  [11]. In  [12] ,  [13]  irregular Leap Frog (LF) time series (time series of displacements between consecutive dates) are inverted.
This study is an extension of  [6]  in order to obtain a RLF with an optimal temporal sampling by taking advantage of every multi-sensor observation, even if outliers have been removed beforehand. For multi-sensor datasets, each sensor does not acquire images on the same temporal grid: hence, there is less redundancy and more disconnected subsets of observed displacements. This is even more true when outliers have been removed beforehand. Therefore, we introduce fractions of displacement in the TICO formulation proposed in  [6]. This extension is called Temporal Inversion using COmbinations of Fractions of displacements (TICOF). Besides, evaluation of observation uncertainties remain crucial to invert velocity time series  [6] ,  [8] ,  [12]. The shape of the similarity function used in image matching appear to be a robust estimation of precision for both off-and onglacier pixels  [6] ,  [12] ,  [14] ,  [15].
However, most of the online datasets do not provide the full similarity function values  [1] -  [4]. Hence, there is a need to get a robust estimation of uncertainty without any information on surface correlation values. Therefore, this study has two objectives: 1) to take into account that velocity observations have different uncertainties which are not necessarily provided by the authors 2) to propose a new system to invert a RLF time series with an optimal temporal sampling from multi-sensor datasets, which can present missing data. We apply the proposed TICOF approach to Sentinel-2 and Venµs velocity observations over the Fox glacier located in the Southern Alps of New Zealand.
The dates where acquisitions of both satellites exist depend on the common multiples of the repeat cycles of satellites 1 and 2, here ∆t 1 and ∆t 2. There are even less common acquisition dates between sensors when missing data exist due to outliers removal. Therefore, we propose an extension of TICO  [6]  for datasets containing multi-sensor displacements and missing data. When a displacement observation can not be included in the TICO formulation, a linear relation between d ti,tj and a fraction of displacements dτ k ,τ k+∆τ is established, assuming linear displacement in the considered temporal interval. For example, in Fig. 1, 2  5 dt0,t5 = d t2,t4. In a more general form, d ti,tj is written as: d ti,tj = k w k dτ k ,τ k+∆τ (3) with w k =                      0, if τ k+∆τ < t i 0, if τ k > t j 1, if τ k ≥ t i ∧ τ k+∆τ ≤ t j τ k+∆τ -ti τ k+∆τ -τ k , if τ k < t i < τ k+∆τ ∧ τ k+∆τ < t j tj -τ k τ k+∆τ -τ k , if τ k < t j < τ k+∆τ ∧ t i < τ k tj -ti τ k+∆τ -τ k , if t i > τ k ∧ t j < τ k+∆τ The TICOF formulation is: BY = A ′ X where A ′ is filled, in consequence, with 0, 1 and/or fractions (equation S.
IV. SYNTHETIC DATASET To assess the bias introduced by the use of fraction in the case of non-linear displacements (e. g. seasonal variation), synthetic simulations are performed. The synthetic instantaneous velocity is taken as: v(t) = a + b sin( 2π T t) + c cos( 2π T t) with T = 365. 25 as in  [20]. N 1 displacements based on Sentinel-2 repeat cycle and N 2 displacements based on Venµs repeat cycle are simulated. To make sure that the coefficients a, b and c represent well the data, instead of an arbitrary choice, these coefficients are estimated by an IRLS inversion by adding a regularization term corresponding to a displacement model with the coefficients a, b and c as parameters as performed in  [21]. The corresponding system of equations is given in equation S. 2. The system is solved for Sentinel-2 data on the point represented in blue in Fig S1.
2d ) are equal to 0 after October 2018. Moreover, the Root Mean Square Error (RMSE) between the synthesised and the estimated velocities is more than 80 m/y lower with the TICOF formulation than with the TI or TICO formulation, and about 20 m/y with TICOF than with the moving average, for different temporal sampling (Fig. 3 ). However, the mean residual of the inversion (the mean difference between AX and Y or A ′ X and BY ) characterizes the bias introduced by the assumption of a constant velocity over the considered temporal sampling. For a synthetic dataset without noise, the residual of the inversion should be null. This is the case for the traditional TI or TICO (Fig. S3 ). On the contrary, the increase of the TICOF residual which ranges from 0 to 3 m/y, for 0 to 120 days temporal sampling, is caused by the bias introduced by the fractions.
This bias is around 25 times lower than the benefit of using TICOF rather than TICO, in case of multi-sensor datasets (Fig. 3 ). The use of TICOF is advantageous: 1) for multi-sensor datasets and/or 2) datasets with a lot of missing data and/or 3) when the temporal sampling is not a multiple of the satellite repeat cycle (Fig. 3  and S4 ). In the absence of any of these conditions, Fig. S4  illustrates that the RMSE from TICO is up to 5 m/y better than the one from TICOF. This error also corresponds to the bias introduced by the fractions.
The bias caused by the validity of this assumption is evaluated on synthetic data. For a sinusoidal signal with a period of 1 year, the bias introduced is below 0. 1 m/y for a temporal sampling of 5 to 20 days. This is compared to the improvement in velocity uncertainty obtained after TICOF. For time sampling less than 20 days, the use of TICOF is still beneficial (the uncertainty is reduced by 13%). The 20-day LF velocity time series makes it possible to highlight the strong seasonality of the Fox glacier with lower uncertainty, fewer outliers, and no more gaps. The fluctuation of Fox surface velocities is on average 35 % between spring and autumn from 2017 to 2018. Since this approach does not require prior information, it can be applied to any kind of available velocity datasets computed by any research team.
The oxygen and carbon isotopic compositions of ECGs provide direct proxies for warmseason land-surface temperature (LSTws)  44  and precipitation  45. Our quantitative dataset based on coupled stable isotope composition and radiocarbon dating of ECGs provides a record of high temporal resolution, spanning 45,000-22,000 years cal BP (late Marine Isotope Stage 3 and MIS 2), from two temporally overlapping loess-palaeosol sequences. The Schwalbenberg  12, 50  and Nussloch  11, 29  sequences are located c. 180 km from one another along a northwest-southeast transect in the nonglaciated zone in the Rhine River valley of western Germany and therefore reflect terrestrial responses to oscillating climates of the last glacial period for central Europe (Fig. 1 ). We embed our climate reconstructions within Bayesian age models for both sequences, facilitating precise correlation between both one another and with other archives from the North Atlantic region.
5 to 0. 6 mm yr -1 ). Short-lived peaks (up to 1 mm yr -1 ) in dust accumulation during MIS 2 were constrained to c. 30, 25. 5, and 24 cal kBP. Stable isotope composition. ECGs were collected from 5 cm thick vertical slices according to published methods, 23 samples from Schwalbenberg and 18 from Nussloch. From each sample we extracted 30 granules, measuring the carbon and oxygen isotope values on 690 individual ECGs from Schwalbenberg (Fig. 4 , Supplementary Data 2, and Supplementary Fig. 1 ). The Nussloch record incorporates an already published stable isotope dataset (12 samples representing 360 analyses)  44, 45  , to which an additional 180 analyses from 6 samples were undertaken (Fig. 4 , Supplementary Data 2, and Supplementary Fig. 2 ). At Schwalbenberg, ECGs extracted from the interstadial phases (9 samples, representing 270 analyses) yielded mean values of -12.
(  2 ), (  3 ), (  4 ) and (  5 ). Since earthworms produced ECGs during the active layer thawing season  49  (May to September, the five warmest months of the year; data from Global Terrestrial Network for Permafrost http://gtnpdatabase. org), we propose that the oxygen isotope composition of ECGs records LSTws rather than mean annual land-surface temperature (LST). We reported the distribution of the 30 values for each sample of 5 cm thick at 1σ (Fig. 5 ). At Schwalbenberg, LSTws range from 8. 8 ± 4. 7 to 12. 9 ± 3. 3 °C, and MAP values from 215 ± 172 to 518 ± 270 mm yr -1 (Supplementary Data 2). Nussloch temperatures were cooler (5. 0 ± 6. 7 to 12. 0 ± 5. 5 °C) but recorded comparable annual precipitation (224 ± 175 to 490 ± 265 mm yr -1 ) (Supplementary Data 2). The Rhine Valley is particularly cold with a mean value of 7.
Our air circulation models for the present-day and LGM time slices help us contextualize the impact of extreme climate phases on terrestrial environments. Our dataset hints at significant correlation between changes in sea ice cover and extent of periglacial aeolian environments (such as the Rhine Valley deposits) over continental Europe during the second half of the last glacial period. Although the correlation between temperature and precipitation recorded within the Rhine Valley with higher latitude ice sheet dynamics suggests a passive, and more moderate response to hemispheric climate drivers, such environments provide an appropriate and quantitative illustration of changes occurring during abrupt climatic transitions. Overall, we observe comparable trends between loess records from western and eastern central Europe and isotopic proxies from the Greenland ice cores confirming that the Schwalbenberg and Nussloch and archives from central Europe were directly influenced by changes in the North Atlantic climate during the last glacial period.