dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was carried out, and data used in this manuscript were produced, as part of the international  Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC)  with the tag MOSAiC20192020. We thank all persons involved in the expedition of the  Research Vessel Polarstern during MOSAiC in   2019-2020  ( AWI_PS122_00 ) as listed in Nixdorf et al. (2021). A subset of data was obtained from the  Atmospheric Radiation Measurement (ARM) User Facility , a  U. S. Department of Energy (DOE) Office of Science User Facility Managed by the Biological and Environmental Research Program. Instrumentation from the  University of Leeds  was provided by the  Atmospheric Measurement Observations Facility (AMOF)  or the  UK National Centre for Atmospheric Science (NCAS).
, 2012; Celini, 2020)  propose that the fold formed due to salt tectonics and interpret the Vélodrome as a mini-basin. Thus, the Vélodrome complex tectonic structure requires a thorough understanding of the 3-D geometries to understand its tectonostratigraphic evolution. This study aims at understanding the emplacement and the tectonic history of the Miocene Vélodrome series using in-situ field observations and drone field data to realize a 3-D geometrical model (GeoModeller -©BRGM). More than 3000 structural data have been measured -both directly in the field and on 3D models obtained from drone image processing -and used in the GeoModeller to test the different hypotheses. The implicit approach offered by the GeoModeller and the field structural data-based approach bring an objective and new vision of 3-D geometries of the Vélodrome basin and confirm the Vélodrome as a syn-sedimentary fold.
Extrapolation of these data to zero thickness provided a more complete pressure history of the sample. Pressures quoted throughout this paper refer to the average pressure experienced by the sample, with errors accounting for both the pressure gradient and measurement uncertainty. The structure of the shocked material was probed using XRD with quasi-monochromatic (dE/E = 0. 2-0. 5%) pulsed X-rays of 50 fs pulse duration, 50 lm spot diameter, ~10 12 photons per pulse, and 8 keV energy. The optical laser and XFEL were spatially overlapped and operated in single-shot mode. Time delay was specified for each shot, with a jitter of 0. 3-0. 5 ns between the optical laser and the XFEL. An oscilloscope captured each shot in order to verify the time delay. Notably, the paper defines time zero as the point at which the shock wave exits the ablator and enters the plagioclase.
XRD patterns were recorded by Cornell-SLAC Pixel Array Detectors (CSPADs) which are charge integrating with 110 9 110 µm pixels operated in a single shot mode (selecting one pulse from the xFEL running at 120 Hz co-timed with the optical laser system). The XRD data were calibrated using CeO2 and LaB6 NIST powders. Analysis was completed with the Dioptas software  (Prescher & Prakapenka, 2015)  and masks created to eliminate the ASIC dead space between pads. A CPSAD pedestal (defined as dark current frame and bad/dead pixel map) background was applied and removed from each 2-D tiff image prior to analysis with Dioptas.
Acknowledgments-AEG and WM were supported by  NSF  Geophysics (EAR0738873) and AEG by  Los Alamos National Laboratory (LANL) Reines LDRD  and  FES, DOE ECA. DRR was supported by " Materials Science of Actinides ," an  Energy Frontier Research Center  funded by the  U. S. Department of Energy (DOE) Office of Science, Basic Energy Sciences  (Grant No. DE-SC0001089 ). This work was performed at the  MEC instrument of LCLS , supported by the  U. S. DOE Office of Science, Fusion Energy Science (FES)  under contract No. SF00515 , and was supported by  LCLS , a National User Facility operated by  Stanford University  on behalf of  DOE-BES. Use of the LCLS,  SLAC National Accelerator Laboratory , is supported by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences  under Contract No.
Topo-climatic descriptors Topo-climatic descriptors were derived from data of the Swiss Meteorological Network (resolution 25 m; average trends for the period 1961-1990; https:// www. meteoswiss. admin. ch) and Swisstopo (resolution 25 m https://www. swisstopo. admin. ch/). The descriptors were elevation (mnt25), slope steepness (slope), slope orientation (asp25) and the sinus of the orientation (aspvar), topographic position (topo), topographic wetness index (twi25), site water balance (swb), shortwave radiation per month (sumradyy), monthly mean precipitation sum (precyy), number of precipitation days per growing season (pday), monthly moisture index (mmind68), monthly average temperature (taveyy), annual degree-day above 0 • C (ddeg0), and number of frost days during the growing season (sfroy)  (Buri et al.
, 2020; Mod et al. , 2020 Mod et al. , , 2021;; Pradervand et al. , 2014; Yashiro et al. , 2016 Yashiro et al. , , 2018)). In addition to these descriptors, bioclimatic descriptors were added from the Chelsa database  (Karger et al. , 2017)  (Table  S1 ). For both edaphic and topo-climatic descriptors, the few missing values (1 rh, 4 spectroscopy, 3 mineralogy, 6 C/N when nitrogen content was below the detection limit) were inferred by nearest neighbour averaging (function preProcess, method 'knnImpute', package caret v. 6. 0-86  Kuhn, 2020). The dimensionality of the edaphic and topo-climatic datasets was reduced using a principal component analysis. In order to capture 80% of the total variance in each dataset, we retained the first ten and the first four PCA axes for edaphic and topo-climatic descriptors, respectively (Fig.
2. 5. 3 The taxonomic assignment of the OTUs was performed in two steps. The first part was aiming to identify and remove the OTUs not belonging to protists (i. e. Metazoa, Embryophyta and Fungi). This was done by using the best alignment from a pairwise local alignment (blastn v. 2. 9. 0,  Camacho et al. , 2009)  of the OTUs dominant sequences on the pooled PR 2 4. 12. 0  (Guillou et al. , 2013)  and Silva v. 138 (non-redundant at 99 %  Pruesse et al. , 2007)  databases. The second part consisted of the final assignment of the remaining OTUs by aligning their dominant sequences against the PR 2 database v. 4. 12. 0 using the best alignment of a global pairwise alignment  (ggsearch36 v. 36. 3. 8, Pearson, 2000). Prior to the alignment, the PR 2 database was trimmed according to the primers used for the sequencing after a multiple sequence alignment performed with ClustalW v.
Acknowledgements BF and CVWS acknowledge the  WISNA program  from the  German Federal Ministry of Education and Research. BF acknowledges the project "FunShift"  (project  FO 1420/1-1 ) awarded by the  German Research Foundation ,  DFG. EL acknowledges the program " Atracción de Talento Investigador " from the  Community of Madrid  (project  2017-T1/AMB5210 ) and the project Myxotropic  VI PGC2018-094660-BI00  awarded by the  Spanish Government.
Data Accessibility Statement All DNA sequences are available on the European Nucleotide Archive project number: PR-JEB30010 (ERP112373). Edaphic variables are available from Dryad repository: https://doi. org/10. 5061/dryad. fttdz08p7. Details about the topoclimatic data can be found at https://www. unil. ch/files/live/sites/ecospat/files/shared/PDF_ site/chclim25. pdf.
, 1976; Barton et al. , 1998; and Gómez-Gesteira et al. , 2008). Further south, over the SMUS region, UI sst shows a marked seasonality with positive values of UI sst in winter (upwelling season) and negative values of UI sst in summer. Figure  2  reveals that although the three observational data sets present a similar behavior of UI sst , substantial differ-ences in the amplitude emerge. The largest discrepancies are found in the sMoUS (whole year) and SMUS (winter) where UI sst , values are significantly lower in ERSST-v5 than in the other data sets. In the rest of the CUS subregions (i. e. , IP and nMoUS), a stronger observational agreement is found.
, 2006, and Benazzouz et al. , 2014) , CSET is strong in summer in the nMoUS and permanent throughout the year in the sMoUS. Finally, the SMUS is characterized by the existence of two well-marked seasons: an upwelling season from approximately November to May and a downwelling season from June to October. As for SST, the comparison between wind products also shows some discrepancies in terms of upwelling amplitude. Despite the CCMP wind data set covering a shorter time period (1992-2011), there is no major difference with respect to ERA5. In contrast, NOAA-20CR-v3 shows a slightly enhanced CSET with respect to the other two data sets, particularly for the SMUS and nMoUS from April to June. A conclusion emerging from this analysis is that the Ekman transport might therefore depend on the underlying size of the grid cell.
The objectives of this study are: (a) to evaluate an ensemble of CORDEX-SEA simulations with an ensemble of four observational datasets in order to assess the sensitivity of model performance to the choice of observational reference; (b) to determine differences between and identify the potential improvement of individual RCMs compared to their forcing GCMs in simulating the whole distribution of daily precipitation over both summer and winter seasons; (c) to further identify possible mechanisms for any identified differences between RCMs and GCMs. The remainder of the paper is organized as follows. Section 2 describes the observational datasets and simulations used in this study along with the different metrics for model evaluation. Section 3 then outlines model performance, a comparison of the precipitation distributions between RCMs and GCMs, and the possible mechanisms behind identified differences, followed by a discussion and the conclusions in Sections 4 and 5, respectively.
| Global and regional climate simulations In this paper, we use the daily precipitation data from 11 CORDEX-SEA simulations running at 0. 22 (about 25 km) horizontal grid spacing (Table  1 ). Following the modelling framework of the CORDEX project  (Giorgi et al. , 2008) , simulations from three RCMs are driven by boundary and initial conditions from the historical simulation of eight CMIP5 models. Only one simulation is available for each RCM-GCM combination. Further information on different physical schemes used in each RCM (i. e. , convection parameterization, land surface and planetary boundary layer schemes) can be found in Table  1. To investigate the potential improvement of CORDEX simulations compared to their forcing GCMs, RegCM4-3_CSIRO-MK-3-6-0 CSIRO-MK-3-6-0 1. 865 × 1. 875 RegCM4-3_EC-EARTH EC-EARTH 1.
| Observational datasets and domain To address observational uncertainties, we utilize four gridded observational products of daily precipitation from different sources (i. e. , three in situ and one satellite-based dataset). Three datasets of spatially interpolated in situ data are considered,: the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE version V1101; Yatagai et al. , 2012) a regional land-only product over Asia; two global land-based datasets that are the Rainfall Estimates on a Gridded Network  (REGEN version Allstns V1 2019; Contractor et al. , 2020)  dataset, and the Global Precipitation Climatology Centre  (GPCC version FDD_v2018; Schamm et al. , 2014)  dataset. We also consider remote sensed estimates of precipitation by using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS version 2.
0;  Funk et al. , 2015)  dataset, a quasi-global rainfall dataset that incorporates in situ climatology and satellite imagery. These datasets have been selected because they have at least 24 years of data (over the period 1982-2005) and sufficient coverage over the Southeast Asian domain (90 -145 E; 15 -25 N, Figure  S1 ). We do not consider those satellite products that do not account for a correction to rain gauges because these type of products are too inconsistent in climatology and trends in daily precipitation maxima over this region  (Nguyen et al. , 2020). Further information on the observational datasets is listed in Table  2. Note that we did not select the newest version of APHRODITE (i. e. , V1901) due to its shorter time period  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005)  compared to its predecessor  (i.
| Precipitation indices and evaluation metrics In this research, the RCMs performance is evaluated over the climatological period 1982-2005, as this is the longest period common to all observational datasets and simulations. RCM simulations are evaluated based on two aspects of precipitation: the mean state and daily precipitation distribution. The performance of a simulation in reproducing the observed climatology of seasonal daily mean precipitation is evaluated using different statistics including the spatial average (M), the root mean squared error (RMSE), and the spatial correlation (R). While M and RMSE measure the similarity between models and observations in terms of intensity, R provides information on the spatial distribution. Simulated and observed seasonal distributions of daily precipitation are compared by using quantile-quantile (Q-Q) plots (Figure  S2 ).
| Observed precipitation and its associated uncertainties We first characterize the spatial distribution of seasonal mean observed daily precipitation and its associated uncertainties (Figure  1 ) during the 1982-2005 period. We consider four observational datasets (as mentioned in Section 2. 2) and compare these distributions across the different products We find clear seasonal and regional contrasts in the precipitation pattern over SEA with generally high rainfall observed over Indochina during summer (June-July-August-September; JJAS) and less precipitation found in equatorial regions. On the contrary, Indochina receives less precipitation while the Maritime Continent receives more precipitation during the boreal winter (December-January-February; DJF). This "northto-south dry-to-wet" gradient is mainly due to the influence of the Asian-Australian monsoon  (Chang et al.
, 2005; Robertson et al. , 2011; Juneng et al. , 2016; Tangang et al. , 2020). The southwest monsoon from the Indian Ocean is typically from late May to September and particularly affects Thailand and Myanmar, resulting in the rainy season there during JJAS. Meanwhile, the northeast monsoon, typically from November to March brings rainfall in the southern part of SEA in DJF and is associated with relatively dry and cool air and little precipitation to the mainland  (Kamworapan and Surussavadee, 2019). Overall, the spatial contrasts and the seasonal shifts of intense rainfall regions are quite similar among the four datasets considered but substantial differences are found in the intensity of climatological precipitation, notably during DJF. APHRODITE has a drier climatology compared with other products, whereas the other three datasets (i.
Generally, from a quantile of 3 mm (i. e. , in the first half of the distribution), APHRODITE consistently shows the lowest estimates of precipitation whereas GPCC_v2018 presents the highest estimates among all considered observational datasets. In addition, the quantiles of CHIRPSv2 and REGEN_ALL are quite similar to each other up to the value of the 50th percentile in both seasons. Subsequently, the quantiles related to higher precipitation amounts from all datasets increasingly diverge from each other, with maximum variance observed in the highest quantiles (greater than the 99th percentile). This inter-comparison of seasonal climatologies in daily precipitation is in line with the previous regional studies on mean  (Juneng et al. , 2016; Tangang et al. , 2020)  or extreme precipitation  (Nguyen et al.
We first discuss the key features of summer season (JJAS; Figure  2 ). Models reveal good qualitative correspondence with the gross regional distribution. The spatial correlations between the simulated and observed patterns are around 0. 5 for most models (8 out of 12). Second, most models generally show a wetter bias in terms of regional-average but exhibit an inhomogeneous spatial distribution with mixing of dry and wet biases. Interestingly, the spread of the climate model ensemble (i. e. , summer biases relative to APHRO-DITE ranging from 0. 53 to 5. 68 mmÁday -1 , Figure  2 ) is greater than the spread of reference datasets (ranging from 5. 25 to 7. 14 mmÁday -1 , Figure  1a-d ). The corresponding results for boreal winter (Figure  3 ) reveal some interesting features. First, all models can reproduce the summer to winter shift in precipitation from Indochina to the Maritime Continent.
This shift reflects the 'north-to-south dry-to-wet' gradient which is F I G U R E 1 Seasonal mean (the boreal summer-JJAS and winter-DJF; row) of daily observed precipitation (mmÁday -1 ) from four considered observational products: APHRODITE, REGEN_ALL, GPCC_v2018 and CHIRPSv2 (column) during the climatological period of 1982-2005. ALL datasets are considered at a grid of 1 × 1 degree of resolution discernible in observed precipitation (Section 3. 1). Third, better spatial correlation between simulations and observations are found during winter, highlighting the dependence of model performance on season. In addition, the wetter biases are domain-wide, and they have smaller magnitude than in summer. Finally, it is interesting that the less prominent inter-model differences are found in the spatial distribution of winter daily precipitation intensity compared with that in summer.
First, we F I G U R E 3 Same as Figure  2  but for the boreal winter (DJF) note that simulations are similarly ranked (i. e. , with few exceptions) in both seasons when using different observational references, emphasizing their similarity in terms of intensity and precipitation distribution. REGEN_ALL and GPCC_v2018 shared the exact same ranking across models as this could be expected from their common data structure (building upon the same station network-see  Roca et al. , 2019). Second, some models indicate a consistent performance during both seasons, being always better (i. e. , ENS-RCM, RCA4_CNRM-CM5 and REMO2015_NorESM1-M) or always worse (i. e. , REMO2015_HadGEM2-ES and RegCM4-3_EC-EARTH) than others.
Number 1 indicates the best model performance and Number 12 indicates the worst model performance simulations and consider them as the model ensemble mean (ENS-RCM, Figures  6a  and 7a ), or average each quantile in the precipitation distribution across all simulations to calculate the quantiles for the ensemble mean (Figure  S7 ). The differences in ASM between an RCM and its forcing GCM can be very sensitive to the choice of observational dataset. In particular, when taking APHRODITE (or REGEN_ALL, CHIRPSv2; see Figures  S5  and S6 ) as a reference, RCM area scores are mostly larger (i. e. , RCM simulations are further away from observations) than that of their forcing GCMs. However, the reversed relative relationships between RCMs and GCMs are observed among 8 out of 11 pair simulations during both seasons (including RCA4, REMO2015 and some RegCM4-3 simulation, Figures  5  and 6 ) when taking GPCC_v2018 as a reference.
It is interesting that the position of ENS-RCM and ENS-GCM lines are also changed with respect to Note that there are some parts of precipitation distribution in some cases (e. g. , RCA4 and REMO2015 simulations) for which the RCM shows improvement compared to its driving GCM. To illustrate this point, three precipitation intervals are considered, including: 0-50th percentile (before the dashed line), 50-99th percentile (between the dashed and dot-dashed line), and greater than 99th percentile (after dot-dashed line) (Figures  6a  and 7 ). For the lowest two percentile ranges, the RCA4 and REMO2015 simulations have very similar distributions to their forcing GCMs. Interestingly, in this driest part of the distribution, RCMs are sometimes closer to the observed reference datasets than the GCMs.
This interesting feature can be observed in most RCA4 and REMO2015 simulations during both seasons, except REMO2015_MPI-ESM-LR. However, at the highest percentiles (greater than 99th percentile), the RCMs are very different from their forcing GCMs and reference datasets, highlighting difficulties with these RCMs in estimating precipitation extremes. Meanwhile, RegCM4 simulations stand out as being much wetter and having distributions further from observed distributions than their forcing GCMs across all precipitation percentiles. We further analyse how much improvement (or not) RCMs bring to the representation of daily precipitation distributions compared to their forcing GCMs by analysing the spatial distribution of differences in ASM using APHRODITE as a reference (Figures  8  and 9  for summer and winter, respectively).
In addition, there is no agreement across all simulations on these regions during both seasons. In general, all combinations show consistently worse scores for RCMs than GCMs. Interestingly, the maps of area score differences are quite consistent with the differences in daily mean precipitation in both seasons (spatial correlations are greater than 0. 5 in most cases for both seasons as illustrated in Table  3 ). This implies that the mean biases (as well as the ASM relative to different reference datasets, see more in Figure  S8 ) are worse in RCMs and that the whole precipitation distribution in the RCMs analysed is further away from observations. It is essential to explore whether the quality of RCM simulations relates to the internal skill of the RCM itself or the quality of its driving GCM.
, 2009). This also helps to explain the widespread amplification of the proportion of convective precipitation over land in RegCM4-3 (all greater than 0. 8), which is much higher than in RCA4 and REMO2015  (Figures S12 and S13). It is acknowledged that some of the differences in convective precipitation may be related to the differences in resolution between RCMs and GCMs  (Kyselý et al. , 2015)  even if the convective component is not resolved yet at the resolution of CORDEX-SEA RCMs (i. e. , 25 km) but at smaller sub-grid scales (horizontal resolution less than 4 km;  Weisman et al. , 1997; Prein et al. , 2015; Li et al. , 2018). F I G U R E 1 2 Seasonal (JJAS and DJF) mean of daily regionallyaveraged evaporation (colour-filled) and moisture convergence (colour dashed) (land only; in mm/day) simulated by seven available RCMs simulations (Table  1 ) (red) and their associated forcing GCMs (blue) during the climatological period of  The outcome of any model evaluation study is likely dependent on various factors including: the choice of reference dataset(s), the processes and metrics considered, the spatial scale and the domain used.
This is partly explained by the large discrepancies in the precipitation distribution among observations outlined in Section 3. 1. In addition, inconsistencies in the frequency of wet days within and between RCMs and observations might somewhat play a role. Among observations, GPCC_v2018 always has the lowest total number of wet days among observations (Figure  S14 ). The RegCM4-3 simulations produce precipitation too frequently while the probability distribution of wet-day frequency among the REMO2015 simulations are quite consistent with observational datasets (Figure  S15 ). The second aim of the research was to assess differences in each RCM-GCM pairing to capture the precipitation distribution of regionally-averaged daily precipitation. Somewhat surprisingly RCMs were generally "worse" at simulating the distribution of daily precipitation over the region than their forcing GCMs.
List of observational datasets of daily precipitation used in this study Product Temporal Original short name Product version coverage resolution Type of data References APHRODITE APHRODITE 1950-2005 0. 5 × 0. 5 In situ-based Yatagai V1101 et al. (2012) REGEN_ALL REGEN Allstns V1 1981-2019 1 × 1 In situ-based Contractor 2019 et al. (2020) GPCC_v2018 GPCC FDD v2018 1982-2019 1 × 1 In situ-based Schamm et al. (2014) CHIRPSv2 CHIRPSv2 1981-2016 0. 05 × 0. 05 Satellite with correction to Funk et al. (2015) rain gauge T A B L E 2.