dataesr/scientific-paragraphs-categorization huggingface dataset:
Mean annual maximum levels occur where tide and low-frequency components are both large, such as the coasts of the North East Pacific, North West European continental shelf and North West Australia, while high-frequency variability was shown to be relatively more important along the American Atlantic coast and in North West Europe and South Australia (see their Figs. 3, 4 ). One concern arises from the fact that the largest extremes (and so the events with the most catastrophic flooding) might not be represented adequately in tide gauge data sets. This reservation applies particularly to extremes caused by tropical storms. By definition, these events are rare ones and, when they do occur, the highest sea level may not be located exactly at the gauge, but some distance away. Therefore, this type of event may not be sampled adequately, even in a record spanning many decades.
Seasonal Variability in Coastal Sea Level The seasonal cycle is one of the most important non-tidal components of sea level records, especially in the mid-to high latitudes. 3 Seasonal variability in sea level is forced by a variety of mechanisms, including atmospheric pressure and winds, precipitation, river runoff, ice melting, ocean circulation changes and variations in steric height. The latter, driven by expansion/contraction of the water column above the seasonal thermocline in response to heat flux exchanges with the atmosphere, is the dominant contributor in most oceanic regions  (Gill and Niller 1973). The mean seasonal sea level cycle at the coast was investigated by Tsimplis and Woodworth (1994) using a data set of 1043 tide gauges distributed globally around the world coastlines.
2014 ). Similar variations have been observed in other shelf areas (e. g. , Yellow Sea,  Kang et al. 1995, and the Canadian Pacific, Foreman et al. 1995). As far as we know, such studies have not been made on a global basis. However, inspection of the Admiralty Tide Tables (UK Hydrographic Office, UKHO 2017) and other tidal databases shows evidence for similar seasonality in other shelf areas. One suspects that it is not so apparent at deep-ocean islands where tidal amplitudes anyway tend to be smaller than in continental shelf areas. Despite the seasonal sea level cycle often being considered as a steady signal, many studies have reported significant temporal changes in seasonality. Interannual to multidecadal variability has been related to changes in atmospheric pressure and wind fields (e.
Nevertheless, the temperature and sea level variability associated with them can result in coastal impacts in, for example, coral bleaching or flooding of low-lying coral islands (e. g. ,  Dunne et al. 2012 ). Finally, one can mention that the nodal astronomical tide will contribute to variability in MSL. If this has a magnitude comparable to its equilibrium expectations, then it will be at the centimetre level or less at most locations  (Woodworth 2012). Occasionally, larger nodal signals are reported in analyses of tide gauge data (e. g. ,  Baart et al. 2012 ). However, these anomalous findings are mostly likely due to localised sea level variability (e. g. , in river estuaries), or to genuine ocean variability with a similar period that is not adequately separable from the nodal tide in a short record.
2019 ) calls for an important upgrade to its core network by requesting the installation of continuous Global Navigation Satellite System (GNSS) stations at tide gauges. GNSS will thereby measure the local vertical land movement of whatever origin. Figure  10  provides a demonstration of how trends of MSL measured by different tide gauges can be made more regionally consistent when GNSS information is employed to measure vertical land movements. The modern geodetic techniques are also required to place the tide gauge data into the same geocentric reference frame as for the altimeter data as required for altimeter calibration studies  (Mitchum 2000; IOC 2016).
• With regard to waves, how much are historical MSL records contaminated by wave setup and can any contributions be modelled efficiently and so removed from the records? This question applies especially to composite records obtained from gauges at different locations within a bay or port. Should there be a comprehensive global coastal wave monitoring network to collect the data needed to assess the setup contribution to MSL? How will the wave setup contribution change in the future if wave climate changes? • Similarly, how much have historical MSL records been contaminated by contributions from river runoff, and therefore misrepresent open-ocean conditions? • There are several important questions in geodesy. How well can models of the solid Earth predict the motion (primarily vertical) of geodetic benchmarks at the coast over decadal to century timescales, specifically at tide gauges? Also, can the terrestrial reference frame be implemented in an accurate and stable manner in order to predict accurately the positions and motions of geodetic stations? Geophysical modelling and space geodesy observations have suggested that we should target vertical land motion predictability at the level of 0.
Acknowledgements We are grateful to  Déborah Idier  and  Chris Piecuch  for helpful comments. This work was supported partly by  Natural Environment Research Council National Capability  funding (for Woodworth, Hibbert and Huthnance) and by a research grant from  CNPq  (for Cirano). The paper arose from the Workshop on 'Understanding the  Relationship between Coastal Sea Level and Large-Scale Ocean Circulation ' held at the  International Space Science Institute (ISSI), Bern, Switzerland , on 5-9 March 2018. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
| Comprehensive sampling and DNA sequence compilation Studied specimens were sampled during several expeditions in the SO (Figure  1 ; Appendix S1); all were preserved in 96% ethanol or frozen and identified at species or genus level (either by Christopher Mah or Camille Moreau). Genomic DNA was extracted using either a salting-out protocol (modified from  Sunnucks & Hales, 1996 , with larger volumes and incubation at 70°C for 10 min to inhibit protein activity after digestion) or Qiagen DNeasy extraction kits. A fragment (612 nucleotides) of the mitochondrial gene cytochrome c oxidase subunit I (COI) was then amplified using the specific forward primer LCOech1aF1 for the class Asteroidea and the universal HCO2198 reverse primer  (Folmer, Black, Hoeh, Lutz, & Vrijenhoek, 1994). COI sequences amplified using the same primers were also obtained through The Barcode of Life Data System (BOLD,  Ratnasingham & Hebert, 2007)  in both public and private datasets, accounting for 460 of the 1,416 sequences analyzed in this study (Appendix S1).
As the Astropectinidae genera are also recorded outside the SO (under different nominal species), all available sequences for these from outside the SO were included within this study (Appendix S1). Reverse and forward sequences were edited and assembled using CodonCode Aligner v6. 0. 2 and translated using the echinoderm mitochondrial genetic code to ensure the absence of a stop codon. Sequences were aligned using the MUSCLE alignment process  (Edgar, 2004). Base compositional heterogeneity was examined using match-paired tests for symmetry  (Ababneh, Jermiin, Ma, & Robinson, 2006)  in SeqVis v1. 5  (Ho et al. , 2006).
Projection: South Pole Stereographic using a codon partitioned model and the GTR + G substitution model in RAxML v 8. 1. 2 (Stamatakis, 2014)  through the raxmlGUI interface  (Silvestro & Michalak, 2012). To assess branch support, 10 runs were realized with 1,000 thorough bootstraps each. The PartitionFinder v2 software  (Lanfear, Frandsen, Wright, Senfeld, & Calcott, 2016)  was used for the BA reconstructions on the CIPRES Science Gateway  (Miller, Pfeiffer, & Schwartz, 2010)  to select for best-fit partitioning schemes and models of evolution. An XML file was created with BEAUti v1. 8. 4  (Drummond, Suchard, Xie, & Rambaut, 2012 ) using a partition for each codon position as specified by PartitionFinder v2, a strict clock model with a lognormal prior for "clock. rate" such that the median reflected a universal divergence time of echinoderm COI of 3.
1%-3. 5% per Myr  (McCartney, Keller, & Lessios, 2000) , a Markov chain Monte Carlo run of 20 × 10 6 generations sampling every 1,000 trees, and a Yule process speciation prior. The XML file was used through the software BEAST v1. 8. 4 on the CIPRES Science Gateway  (Miller et al. , 2010)  to reconstruct time-calibrated trees. Tracer v1. 6 allowed us to ensure an appropriate effective sampling size (ESS > 200) as recommended by the software documentation. TreeAnnotator v1. 8. 4 calculated a consensus tree which was visualized using FigTree v1. 4. 3 (http://tree. bio. ed. ac. uk/softw are/figtr ee/).
| Species delineation Several single-locus methods of species delineation were used to delineate and explore diversity among the studied genera. Two of these methods, the Generalized Mixed Yule Coalescent (GMYC-  Pons et al. , 2006; Fujisawa & Barraclough, 2013)  and the multirate Poisson Tree Processes (mPTP-  Kapli et al. , 2017) , are tree-based methods requiring an ultrametric tree for the former and a maximum likelihood tree for the latter. Both single (sGMYC)-and multiple-threshold (mGMYC) models were investigated using the R package SPLITS  (Ezard, Fujisawa, & Barraclough, 2009) , and the ultrametric tree was obtained using BEAST during the phylogenetic reconstruction. The online web service (available at http://mptp. hits. org) was used for mPTP with the ML tree constructed using RAxML.
We also performed a distance-based analysis using the Automatic Barcode Gap Discovery (ABGD-Puillandre, Lambert, Brouillet, & Achaz, 2012) on the online server (http://wwwabi. snv. jussi eu. fr/publi c/abgd/abgdw eb. html) with default settings for the prior range (0. 001, 0. 1), K80-corrected distances, and a value of 1. 5 for the relative gap width (X). We also used the newly developed ASAP method (http://wwwabi. snv. jussi eu. fr/publi c/asap/; N. Puillandre et al. , in prep. )  with default settings and K80-corrected distances. For subsequent analyses, we have selected the method giving the smallest number of delineated entities as recommended to avoid false interpretation  (Carstens, Pelletier, Reid, & Satler, 2013). All molecular diversity indexes were calculated using the DnaSP v6 software  (Rozas et al.
ACK N OWLED G M ENTS The work was supported by a "Fonds pour la formation à la Recherche dans l'Industrie et l'Agriculture" (FRIA) grant to C. Moreau. This is contribution no. 32 to the vERSO project (www. recto verso proje cts. be), funded by the Belgian Science Policy Office (BELSPO, contract n°. BR/132/A1/vERSO). The authors thank M. Bauwens, R.
, Q. J. , B. D. , and T. S. conceived and designed the experiments; C. M. , Q. J. , C. S. , and G. A. performed the analyses; C. M. , Q. J. , A. A. , M. E. , B. D. , and T. S. collected the data; and C. M. , B. D. , Q. J. , M. E. , C. S. , G. A. , A. A. , and T. S. wrote the manuscript. DATA ACCE SS I B I LIT YAll data and metadata used in this study are publicly available on BOLD Systems (www. bolds ystems. org) under the Dataset Code.
1 ), grouped into four depth intervals above 3 m (see Methods). The vertical error bars indicate 25th-75th percentiles among the available years for each sitedepth. SOC densities (kg C m -3 ) are derived from two databases, WISE  25  (blue) and NCSCD  28  (red), integrated into the same four depth intervals. The blue and red solid lines indicate the linear regression lines of log 10 (D) vs. SOC from WISE (y = -0. 012 × -0. 35) and from NCSCD (y = -0. 016 × -0. 30), respectively, with 95% confidence intervals shown in the dashed lines has the strongest explanatory power for D variations among the different site-depths. Considering the interrelation between these soil properties, e. g. , SOC density is negatively correlated with bulk density (Supplementary Fig. 4 ), we conducted partial correlation analysis to test if the D-SOC relationship persists after removing the effect of other variables.
The partial correlation between D and SOC density remains significant after controlling the other properties separately or combined (Supplementary Table  1 ). This suggests that the impact of SOC on D is a composite result of soil composition and structure alterations such that its correlation with D cannot be fully accounted for by the variability in the texture-and porosity-related properties. Uncertainties in the results shown in Fig. 1  and Supplementary Fig. 2  include a spatial-scale mismatch: the local soil temperature observations are not accompanied by collocated records of soil texture, moisture and organic carbon; we therefore resort to the global (WISE 25 ) and regional (NCSCD  28  ) databases for these soil properties, which are not fully representative for site-level observations.
In addition, even at broad-scale, soil inventories bear substantial uncertainties  29, 30. The two databases used here differ in total carbon stock by about 20% for their overlapping areas (Supplementary Fig. 5ab , 690 Pg C in WISE vs. 825 Pg C in NCSCD for 0-2 m depth). Both databases derive their spatial distributions by linking field data of soil profiles to global/regional soil classification maps, their accuracy depending on both the accuracy of the up-scaling maps and the representativeness of samples within each soil class. Thus, the high landscape complexity in the northern permafrost zone and limited soil profiles available from Siberia  25, 31  add to uncertainties in this region. To partly reduce this uncertainty, we excluded the sitedepth data whose relative difference in SOC density between the two databases is higher than 25% (see Methods and Supplementary Fig.
8 and 1. 6 m depths for the sites in permafrost region in yesSOM compared to noSOM (Supplementary Fig. 10 ). Consequently, ALT decreases by 0. 6 m averagely across the region due to the presence of SOM (Fig. 3a, b ), and the permafrost area (ALT < 3 m) increases from 10. 4 in noSOM to 13. 8 million km 2 in yesSOM, closer to the empirical estimate of 15. 3 million km  2  for the continuous and discontinuous categories  33. For SOC, modelled total carbon stock for 0-3 m depth in the northern permafrost zones (22 million km 2 , according to IPA  33  including all permafrost categories) increases by 39%, from 590 Pg C in noSOM to 820 Pg C in yesSOM (Fig. 3e , f and Supplementary Fig. 11 ), which is still less than the estimate of 1030 Pg C in NCSCD database 1 over the same region. This discrepancy may in part be attributed to peat formation not being included in the current model  40.
If the real soil thermodynamics at a site significantly violates any of the above conditions, it is unlikely that the soil temperature function T(z,t) would take the simple sinusoidal form as Eq. [4]. We therefore assume that, if the measured soil monthly temperatures at two adjacent depths both conform to sinewaves (with a small error), the soil thermodynamics in this interval can be approximated by Eq. [2], and the calculated D using Eq. [5] is a valid value that describes the thermal diffusivity for the bulk soil in this depth interval at the site. In this study, we used two data sets of in-situ soil temperature measurements to calculate D: the Russian Historical Soil Temperature Data  23  (RHST), and the International Polar Year Thermal State of Permafrost 24 (IPY). RHST provides monthly soil temperature records at 263 sites since the 1880s until 1990, but the majority of data exist after 1960.
Here we used the last ten years' data, from 1981 to 1990. RHST contains measurements at multiple depths for near surface (0. 02-0. 2 m) and deeper (mostly at 0. 2, 0. 4, 0. 8, 1. 2, 1. 6, 2. 4, and 3. 2 m) soils, but the near surface measurements were taken in bare areas whereas the deeper soil measurements were taken under natural cover of grasses and snow  64  ; so we only used the data below 0. 2 m in our analysis. The IPY soil temperatures were measured at various depths dependent on the site, mostly for the years 2007-2013. The daily observations in IPY dataset were aggregated to monthly means. For both data sets, we conducted the following data processing: First, only the sites with available monthly temperature records for at least two nearby depths (depth difference less than 1 m) and for at least 12 consecutive months were used (in total 274 sites, 219 from RHST and 55 from IPY, Supplementary Fig.
1 ). Second, to determine whether the monthly soil temperatures evolve as a sinusoidal cycle, we first fitted the 12 months to a sine curve as Eq. [3] using nonlinear least squares regression, and then calculated the sum of square error (SSE) after normalizing by amplitude such that the SSE for different depths are comparable: SSE ¼ X 12 i¼1 ð T i À Ti Â Þ 2 ; ð6Þ where T i is the temperature for month i; Ti is the predicted temperature with the fitted sine curve; Â is the amplitude of the fitted curve. To derive an SSE threshold, we assume that the fluctuations of monthly air temperature, according to the CRU dataset  65  , generally approximate sine-waves, and calculated their SSE values for the northern hemisphere (Supplementary Fig. 13 ). The 75th percentile of the SSE values for air temperatures, 0.
34, was chosen to filter the soil temperature data: the observation for a site-depth-year is removed if its SSE is higher than 0. 34. After this filtering, 170 sites from RHST and 15 sites from IPY remained. Note that the retained sites do not necessarily fulfil the SSE threshold for all years or depths; those depth-years that exceed the threshold were also removed before the calculation of D. Most of the high SSE situations are associated with the so-called zero curtain effect, namely, the soil temperature stays near 0 °C during extended periods of freezing/thawing, maintained by the release/absorption of latent heat and by other non-conductive processes including vapor transport and evaporation/ condensation  66. Supplementary Fig. 14  presents four examples from the IPY dataset to illustrate the disparity or similarity between soil temperature observations and the theoretical sine-waves.
Third, for the retained site-depth-year data after step ii), we calculated the D value for each depth according to Eq. [5], using the observed temperature amplitude of this layer and of the adjacent upper layer, only if the two depths are nearby (<1 m). Since we try to correlate D with soil properties extracted from the global/regional soil databases which have fixed/limited layers (see below), the derived D values for different depths were integrated into four depth intervals: 0-0. 4, 0. 4-0. 8, 0. 8-2, and 2-3 m, taking the median D if multiple depths are available within each interval. Finally, if multiple years of D are available for a sitedepth, their median value is used for the regression analysis against soil properties, while their 25th-75th percentiles are shown in Fig. 1. Soil databases.
In order to identify the dominant influential factors on D, and considering that the soil temperature data sets do not report collocated measurements of soil properties at the Russian meteorological stations in the RHST dataset or the boreholes in the IPY dataset, we seek to extract soil information for these sites from the spatially explicit global/regional soil databases. To our knowledge, there are four such soil databases that are recent and/or widely used: HWSD  67  ; WISE30sec 25 (hereafter WISE); SoilGrids250m 68 (hereafter SoilGrids); and NCSCD  28. Among them, WISE is developed based on HWSD, but includes updates on the mapping unit composition, adopts a consistent taxotransfer procedure, and provides information down to 2 m 25. We therefore used WISE instead of HWSD in this study.
The SoilGrids product, unlike the other three databases that rely on soil classification maps to derive spatially explicit information, predicts soil properties using statistical methods from a list of covariates (climate, vegetation, topography, lithological units, etc), and thus can generate high-resolution maps (250 m) dependent on the resolution of input covariates  68. However, in a preprocessing, we found that SoilGrids gives a much higher SOC stock (in kg C m -2 ) than other databases in most grid cells in the northern hemisphere, and gives a global total SOC storage of 2757 Pg C for 0-1 m and 4570 Pg C for 0-2 m (calculated using their 250 m resolution map). This total SOC storage is almost twice the estimate by the majority of previous studies (see a short review in ref30. ). Therefore, we did not use SoilGrids in this study.
Compared to WISE, NCSCD provides SOC stock down to 3 m, and is based on more soil samples in permafrost region than WISE  25. However, NCSCD is a regional database for the northern permafrost zones only, and does not provide other properties like soil texture and bulk density. We therefore used both databases for SOC and used WISE for other soil properties. At their highest resolution (0. 012°for NCSCD and 30 arc second for WISE), we took the grid cells whose center is nearest to the coordinates of each observation site. The two databases provide different depth intervals (4 intervals down to 3 m in NCSCD, and 7 intervals down to 2 m in WISE), and we integrated their original layers into the same four depth intervals as D values mentioned above (0-0. 4, 0. 4-0. 8, 0. 8-2, and 2-3 m), using depth-weighted averages.
WISE does not provide information below 2 m, so its variables for the 4 th depth interval are non-valid value in all the regression analyses. Using the large-scale soil databases to indicate local-scale observations introduces uncertainties. To reduce this uncertainty, we compared the SOC density for the available site-depths from the two databases (Supplementary Fig. 5c ), and excluded those with a relative difference (calculated as SOC NCSCD ÀSOC WISE SOC NCSCD þSOC WISE ) larger than 25%. This filtering process removes 105 site-depth SOC values, leaving 100 pairs of D-SOC NCSCD and 278 pairs of D-SOC WISE as shown in Fig. 1 , spanning 184 sites (170 sites from RHST and 14 sites from IPY, Supplementary Fig. 1 red ). For consistency, the excluded site-depths were also removed from the analysis of the other soil properties as shown in Supplementary Fig.
1 ). We conducted two simulations for the northern hemisphere (>30°N) at 2°spatial resolution forced by CRUNCEP v8 climate: yesSOM, in which an observation-based SOC map was used in the thermal and hydrological modules to derive soil properties; and noSOM, in which the thermal and hydrological modules see zero SOC, i. e. , no impacts of SOC on soil properties. For yesSOM, the observational SOC map was constructed from NCSCD  28  for permafrost regions and from WISE  25  for non-permafrost regions, using linear vertical interpolation to convert their original soil horizons into ORCHIDEE-MICT vertical layers  40. Here we did not use the prognostically modelled SOC by the carbon module of ORCHIDEE-MICT, in order to exclude bias of the carbon cycle module for the analysis of SOM effect. For both simulations, spin-ups were first conducted forced by looped 1901-1920 climate and preindustrial CO 2 level of 286 ppm, covering 200 years of the full model and 20,000 years of the soil carbon sub-model to reach equilibrium for the pre-industrial period.