dataesr/scientific-paragraphs-categorization huggingface dataset:
B. 3. | Temperature error budget The error budget of the lidar can be obtained both experimentally or through numerical simulations. Experimentally, the lidar temperature profiles are compared with concomitant exogenous measurements (typically radiosoundings). The indicators detailed in Appendix C, in particular the MB and the centred root-mean-square error, allow to assess the systematic and statistical errors inherent to lidar measurements as a function of the altitude range and the spatiotemporal resolutions used. Note that the mean bias calculated between lidar-and radiosounding-derived temperature profiles is not as relevant as if it had been calculated with a dedicated radiosonde dataset (independent from the calibration). Nevertheless, it allows to assess the accuracy of the calibration process.
One should notice that the increasing error near the surface is due to the altitude-restriction of lidar profiles at low altitudes to prevent overlap factor uncorrected residual to bias the calibration. This lack of constrain at these levels and given the non-linear nature of the calibration function, a bias appears near the surface. To best contain this bias, the overlap factor should be the lowest possible, allowing a greater calibration zone, especially critical for daytime measurements. A PP E ND IX C: STATISTICAL PARAMETERS Statistical parameters are commonly used for comparisons between models and observations  (Tombette et al. , 2008)  or different instruments  (Chazette, Flamant, et al. , 2016). Here we choose to use the mean bias (MB), the Pearson correlation coefficient (COR), the root-meansquared deviation (RMSD) and the centred root-meansquared error (RMSE) to quantify the agreements and discrepancies between the different datasets.
ACKNOWLEDGEMENTS This work was supported by the  Commissariat à l'Energie Atomique et aux énergies alternatives (CEA)  and the  Centre National d' Etudes Spatiales (CNES). The authors would like to thank  Météo-France  for the availability of the weather station and radiosounding data. ECMWF data used in this study have been obtained from the  Copernicus Climate Change Service Climate Data Store  (https://cds. climate. copernicus. eu/cdsapp#!/home); the authors are grateful for the availability of ERA5 products and particularly acknowledge  Paul Berrisford  ( ECMWF ) for his help and explanations. The IASI data used in the paper have been obtained from the  EUMETSAT  website (https://www. eumetsat. int/website/home/Data/Products/Atmosphere/index. html).
Summary of the uncertainties of WALI computed experimentally against radiosoundings and the discrepancies calculated between WALI and ERA5, IFS and IASI datasets T A B L E 2 Statistical indicator Range (km) RS/WALI WALI/ERA5 WALI/IFS WALI/IASI COR 0-1. 5 0. 99 0. 97 0. 96 0. 88 1. 5-3 0. 99 0. 93 0. 98 0. 96 MB ( C) 0-1. 5 À0. 12 0. 97 0. 80 2. 2 1. 5-3 À0. 06 0. 52 0. 61 0. 01 RMSE ( C) 0-1. 5 0. 81 0. 97 0. 85 1. 6 1. 5-3 1. 0 1. 3 0. 89 1. 4.
Acknowledgements Thanks to the crew of the  Oceanographic Vessel Sepia II  for their help during sampling,  Rémy Boisserie  for the sorting of macrofauna from sediment and  Magalie Legrain  for the sediment analysis. Camille Hennion  for the CTD data,  Romain Abraham  and  Eric Armynot du Châtelet  for the sediment analyses and  Gwendoline Duong  for the organic matter data. This research was funded by the  Agence de l'Eau Artois-Picardie  and the  Agence de l'Eau Seine-Normandie  (Project  Foram-INDIC , and project  CHABIMANOR ). J. -C. P. PhD fellowship received funding supports from the  Agence de l'Eau Artois-Picardie  and the  University of Lille. Thanks to the two reviewers and to  Dr Amy Fowler , Deputy Editor-in-Chief of  BioInvasions Records , for their suggestions and remarks on the first versions of this paper.
Similarly, because the TMI is positively related to maximum angular head motion (Methods and Supplementary Methods), when behavioural activity increases, the TMI may have to increase to improve SDS function. As the SDS does not fossilize, we assembled a new dataset of 50 membranous labyrinths of various vertebrates to establish morphofunctional correlations between semicircular ducts and bony canals (Supplementary Data 2 and Supplementary Note 2). Subsequently, we computed the TMI of 362 specimens, including 68 extinct synapsids, from bony labyrinths visualized through microtomography (Fig. 1 , Extended Data Fig. 1  and 2  and Supplementary Data 2 and 3).
Comparative inner ear sample Three inner ear datasets were analysed for 341 species: 3D bony, 3D membranous, and 2D membranous (Supplementary Methods, 'Sampling rationale'). The 3D bony sample consists of 3D endocasts of bony semicircular canals (SC) of 234 extant species (7 amphibians, 67 lepidosaurs (69 specimens), 6 turtles, 9 crocodilians, 72 birds and 73 mammals), and 64 extinct species (1 diadectomorph, 3 non-saurian reptiles, 1 archosauromorph, 2 non-therapsid synapsids, 5 non-neotherapsid therapsids (6 specimens), 16 anomodonts (19 specimens), 5 gorgonopsians, 6 therocephalians (7 specimens), 9 non-probainognathian cynodonts (11 specimens), 3 non-mammaliamorph probainognathians, 9 non-mammalian mammaliamorphs (11 specimens) and 4 mammals). We used non-synapsid fossils to test the method outside the clade of interest at deep divergence time points.
1. 1  65  , no significant differences were found (paired Student t-test, n = 6, P = [0. 054-0. 93], two-tailed). The cross-sectional diameter of each SC was measured in Amira, using the SurfaceThickness tool, by selecting five distant points along the slender portion of the SC perpendicular to its plane and averaging their values (Extended Data Fig. 2 ). Cross-sectional diameters of problematic fossil specimens (for example, showing obvious segmentation artefacts) were measured using the 'straight line' tool of ImageJ on scaled screenshots where the plane of the SC was placed perpendicular to the screen plane. Seven fossil specimens (FMNH UR 161, BPI/1/375, MCZ 1161, GPIT/RE/7119, NHCC LB631, NHCC LB178 and NHCC LB387) had incomplete SCs. We estimated missing measurements of these specimens in R v.
4. 0. 3 66 with pls 2. 7-3  67  , using cross-validated partial least-square regressions on a set of 281 species with all measurements (Supplementary Data 2). Reproducibility was assessed in XLSTAT 2018. 1. 1 by comparing measurements taken by R. A. and R. D. on a subsample of 77 specimens. All 12 variables are left-skewed, and a two-tailed Kolmogorov-Smirnov test showed no significant difference (P = [0. 404-1. 000]). Repeatability was tested by comparing measurements taken by R. A. and showed no significant intraobserver variation (repeatability scoring between 2. 14 to 8. 67%; signal to noise ratio minimum of 16. 254 where a value of greater than 5 is considered adequate, Supplementary Data 5). Morphological and functional analyses of the 3D membranous sample were performed using Ariadne Toolbox  19.
Input data included surfaces and linesets representing individual components of the duct system (for example, slender parts and ampullae, and modelled cupulae  19  ). The average height of each cilia area was measured on the µCT scans. Endolymph density was taken as 1,000 kg m -3 , Poisson ratio as 0. 48 and cupula shear modulus as 1. 44 Pa  19. Output parameters included, among others, wall shape factor, 3D length and cross-sectional area of the slender portion of each SD, enclosed area of the projection of each SD torus on its maximal response plane, and a transfer factor linking endolymph volume displacement to cilia deflection (hereafter called cilia deflection factor). The cilia deflection factor corresponds to the ratio between average deflection of the cupula at the level of the height of the underlying cilia to the average endolymph volume displacement for a given angular head rotation.
Measurements of 2D images The 2D membranous sample consists of published photographs  56, 57, 58, 59, 60, 61  and measurements  27  of membranous labyrinths. We used ImageJ to process photographs and measure areas enclosed by SD tori, major and minor axes, lengths of slender portions, and inner duct radii (Supplementary Data 2). Photographs were generally taken in lateral view and show the planes of the anterior and posterior SDs at an angle, projected onto the sagittal plane. The resulting distortion can be corrected by multiplying the horizontal axis of the image by 1/cosine of the angle of a specific duct, providing a good representation of undistorted size and shape. Where available, the angles of the planes of the anterior and posterior ducts to the sagittal plane were measured in 'dorsal' view.
For specimens without a 'dorsal' view, we generally used an angle of 45° to undistort the views (Supplementary Data 2). The undistorted views of the anterior and posterior ducts were used to measure corresponding SDs. When a 'dorsal' view was available, measurements for the lateral SD were done on this view. For specimens without a 'dorsal' view, the maximum distance measured between any points on the lateral semicircular duct in lateral view was taken as its major axis. Missing measurements were estimated in R, using cross-validated partial least-square regressions on a set with all measurements (Supplementary Data 2).
Body temperature, body size and phylogeny Body temperatures of extant species were obtained from the literature (Supplementary Data 2), supplemented by Traitbank (https://eol. org/traitbank). For ectothermic taxa, preferred Tb were chosen. When both activity and preferred ranges of Tb were provided, we took the average of the latter. In a few instances (11 ectotherm species) we used online pet care information to estimate preferred Tb. Values for fish species were obtained from FishBase (http://www. fishbase. org). Three body size variables were used: condylobasal length, condyloanteroorbital length and body mass. Condylobasal length (posteriormost border of the occipital condyle(s) to the anteriormost tip of the snout, projected onto the sagittal plane) was used to represent cranial size and could be measured for most specimens (Supplementary Data 2).
To avoid bias introduced by snout lengths that are either exceptionally long (for example, gharials) or short (for example, anomodonts), we defined the condylo-anteroorbital length as the linear distance from the posteriormost border of the occipital condyle(s) to the anteriormost border of the orbit, projected onto the sagittal plane (see measurements in Supplementary Data 2). Body mass was measured whenever possible (Supplementary Data 2). We used typical adult values rather than extremes, and male and female values were averaged for sexually dimorphic species. For fossils and some specimens of the 3D bony sample, body mass was predicted using an allometric multiple regression of body mass and condylobasal and condylo-anteroorbital lengths (Supplementary Note 2, model 7). Predictions were refined by computing phylogenetically informed body mass residuals.
Therefore, we derive the TMI from SDS morphology and endolymph composition, using equations governing the saturating angular velocity and the upper corner frequency, but excluding Tb (Supplementary Methods, 'Biomechanics'). Consequently, when Tb increases, compensatory changes in the TMI, traceable in fossils, are required to keep semicircular duct function constant. In practice, as fossils do not preserve the membranous labyrinth, we need to predict the morphology of their SDS. To do so, we first used a dataset of 50 species (Supplementary Data 2), for which we had bony and membranous labyrinths, to regress morphometrical parameters of the ducts (including their cross-sectional area) against morphometric parameters of the bony canals (Supplementary Note 2, models 8-19). Next, we used these regressions to obtain fitted values of these duct parameters using morphometric parameters of the bony canals of fossil specimens.
Post hoc analyses We investigated whether high/maximum stride and impact frequencies (as a proxy for behaviourally induced angular head motion  94  ) were positively correlated to Tb, and if they explained part of the variance of the TMI left unexplained by Tb. To do so, we performed PGLS regressions (Supplementary Note 2, models 84-89), using stride frequency, impact frequency, body mass, and Tb, sampled for 106 taxa. Impact frequencies were obtained from stride frequencies, using multiplying factors estimated from the analysis of gait types (Supplementary Data 1). Similarly, we assessed if maximum anaerobic speed (as a proxy for behavioural activity) is higher in endotherms than ectotherms, and if it is positively correlated to Tb. To do so, we performed PGLS ANOVA and PGLS regressions (Supplementary Note 2, models 76-80), using maximum anaerobic speeds, body mass, and Tb, sampled in 428 taxa.
Data collection followed the same protocols as above and references are provided in Supplementary Data 1. We explored which thermoregulatory strategies (for example, aestivation) could have been adopted by fossil synapsids predicted as ectotherms, by comparing their predicted Tb to the palaeoclimate of their palaeolocation (Supplementary Data 4). To do so, we used the temperature curve of Scotese et al. 28. to obtain minimum and maximum global average temperatures (GAT) corresponding to the age range of each fossil specimen. These GATs were corrected for the palaeolatitude of each specimen (obtained from the Paleobiology Database: https://paleobiodb. org/), using Fig. 7  of Scotese et al. 28  , to obtain the latitudinal average temperature (LAT). In addition, we reviewed the literature to compile climate types that have been proposed for the palaeolocation of each sampled specimen  95, 96, 97, 98, 99, 100, 101.
Furthermore, we collected temperature data (for example, summer maximum temperature) for 150 locations and 16 different Köppen climates from https://weatherspark. com/. Climate type and LAT were used in R to predict seasonal temperatures at the palaeolocation of each fossil specimen, with a range of uncertainty. Palaeolocation temperature and predicted body temperature (Supplementary Note 2) were randomly sampled from corresponding ranges of uncertainty, and compared 1,000 times to address specific questions about thermoregulatory strategies (for example, whether predicted body temperature is higher than the maximum local night temperature-if true the specimen is probably obligatory diurnal). Results from these tests allowed us to compute probabilities for given thermoregulatory strategies to occur.
Acknowledgements Funding for this project was provided by the  Fundação para a Ciência e a Tecnologia postdoctoral  fellowship  SFRH/BPD/96205/2013 ,  FCT-AGA KHAN Development Network  grant number  333206718 ,  National Geographic Society  grant number  CP-109R-17 , the  Field Museum ,  NSF   EAR-1337291 , the  Max Planck Society  and the  Calleva Foundation. We acknowledge intellectual contributions from discussions with  R. Rabbitt. We thank  J. White ,  S. Walsh ,  P. Campbell ,  S. Pierce ,  C. Capobianco ,  S. Chapman ,  J. D. Cundiff ,  A. Wynn ,  P. Gill ,  E. Rayfield ,  J. Hopson ,  R. Asher ,  A. Neander ,  W. Simpson ,  A. Stroup ,  A. Resetar ,  J. Mata ,  J. -J. Hublin ,  D. Plotzki ,  H. Temming ,  W. van Gestel ,  J. Jansen ,  R. Allain ,  D. Silvestro ,  F. Condamine ,  C.
Scotese ,  R. Mundry ,  S. W. Evers ,  M. J. Mason ,  P. -O. Antoine ,  S. Hellert ,  C. Schultz ,  M. B. Soares  and  A. Schmitt. We also thank the  Institute of Veterinary Pathology  and the  Veterinary Clinic for Birds and Reptiles at Leipzig University , the  Leibniz Institute for Zoo and Wildlife Research  and the  German Primate Center at Göttingen. We acknowledge the  MRI platform member of the national infrastructure France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ,  «Investments  for the future»), the  labex CEMEB  ( ANR-10-LABX-0004 ) and  NUMEV  ( ANR-10-LABX-0020 ). IPFN activities received financial support from through projects  UIDB/50010/2020  and  UIDP/50010/2020. Some silhouettes were obtained from Phylopic. org.
Data availability The raw datasets used in this study are available in the Supplementary Dataset. Links for CT scan datasets and bony labyrinth 3D meshes obtained from https://www. morphosource. org/ can be found in Supplementary Data 3. Some bird skull measurements and fish lengths were obtained from https://skullsite. com/ and fishbase. org, respectively (Supplementary Data 2). Time calibrations between most extant species were obtained from timetree. org. Body mass and Tb of some extant species were obtained from https://eol. org/traitbank (Supplementary Data 2). Source data are provided with this paper.
The Atlantic Meridional Overturning Circulation (AMOC) is a key player in the global coupled oceanatmosphere climate system. To characterise the potential of an AMOC slowdown, a past and future trend probability analysis is applied using 16 models from the Coupled Model Intercomparison Project Phase 5. We determine the probability of AMOC annual to multidecadal trends under the historical period and two future climate scenarios ('business-as-usual' scenario -RCP8. 5 and 'stabilisation' scenario -RCP4. 5). We show that the probability of a AMOC decline in model data shifts outside its range of intrinsic variability (determined from the pre-industrial control runs) for sustained 5-year trend or longer. This suggests that interannual AMOC events are not significantly affected by future climate scenario, and so potentially neither by anthropogenic forcing.
This study aims to (i) determine whether there are any periods of a higher probability of a decline in the AMOC transport from 1850 to 2100 (i. e. , during the historical period and future climate projections), and (ii) identify whether salinity transports active in atmospheric and oceanic feedbacks are associated with the probability of a decline. From AMOC observations using data from the RAPID array,  Moat et al (2020)  suggest that from 2004 to 2008 the AMOC had a constant mean transport followed by a step change and a lower constant mean from 2008 to 2018. A longer-term (centennial) forced signal can potentially exhibit shorter (decadal) periods of intrinsic variability, with an opposing (or absent) trend  (Easterling and Wehner, 2009). For example, this can be seen in historical atmospheric temperature data where the multi-decadal trend experiences a hiatus or stabilisation period  (Sévellec et al, 2016).
McDonagh et al (2015)  on the other hand, focus on the respective contribution of the overturning or gyre transports in freshwater divergence between the Bering Strait and 26. 5 N. They show that the positive freshwater flux (of 0. 37 Sv) is dominated by the AMOC, which explains 91% of the variance, while the horizontal gyre component has a much smaller impact. Separating the circulation components (into overturning and horizontal gyre) can help to elucidate drivers of a flux change, and is therefore used in this study. We have organised the paper into two parts. We first look at the probability and intensity of a past or future decline in the AMOC using over two centuries of data in coupled numerical models. We quantify the magnitude of trends of different durations to aid in understanding the modelled AMOC's interannual to multi-decadal properties through time.
Data This study uses a selection of 16 CMIP5 models, namely: (1) ACCESS1-0, (2) bcc-csm1-1, (3) BNU-ESM, (4) CanESM2, (5) CCSM4, (6) CESM1-BGC, (7) CMCC-CM, (8) CNRM-CM5, (9) CSIRO-Mk3-6-0, (10) EC-EARTH, (11) GISS-E2-R-CC, (12) HadGEM2-CC, (13) IPSL-CM5A-MR, (  14 ) MPI-ESM-LR, (15) MRI-CGCM3, (16) NorESM1-M. Four datasets are used: (i) control simulations, which are based on pre-industrial atmospheric greenhouse gas emissions, (ii) historical simulations, which are based on post-industrial forcing from 1850 to 2005, (iii) the RCP4. 5 future 'stabilisation' of emissions scenario, and (iv) RCP8. 5 future 'business-as-usual' scenario; the latter two are from 2006 to 2099. The vertically-and zonally-integrated AMOC transport streamfunction is taken at 48 N and at a depth of 1,000 m. This latitude is chosen since the salinity transports (in the subpolar and subtropical regions) show a different behaviour north and south of 48 N (further explained in the Results section).
The original data are monthly and we remove the seasonal cycle by subtracting each month's overall means before estimating trends for each dataset separately; the control, historical, RCP4. 5 and RCP8. 5. The salinity  [psu]  and absolute potential temperature  [K]  profiles that vary in time and 3D space are extracted from the same 16 models and annual means are computed from monthly-mean data, only from the historical and RCP8. 5 scenario output data this time. The number of vertical levels among the 16 models vary from 31 to 70, with a resolution ranging from 4. 5 m to 15 m at the surface, and 206 m to 550 m at the bottom. The North Atlantic data are extracted between 96 W and 2 W (from the Gulf of Mexico, inclusive, to the eastern African coast) and 20 N to 60 N. The models are first re-gridded to a 2 ⇥2 resolution by finding the nearest neighbour longitude and latitude using even values.
By removing the mean over the beginning of the timeseries to t ref (from t 0 =1851 to 1975), this allows all fluctuations in the timeseries until 1975 to oscillate around zero, after which the future accumulated anomalies are more easily distinguishable  (over 1975 to 2099). Using one of the components, S ov , at any latitude as an example, the anomaly of S ov is: S ov | anom (t)=S ov (t) 1 t ref Z t ref t 0 S ov (t 0 ) dt 0. (7) The accumulated anomaly transport of the overturning component (which therefore recovers a measure of salinity content) then reads: Σ ov (t)= Z t t 0 S ov | anom (t 0 ) dt 0 , ( 8 ) where Σ is the accumulated anomaly of any transport and Σ (t ref ) = 0 by definition. Smoothing of the accumulated annual data is then performed using a 10-year moving-average. The mathematical derivations of the salinity budget above can provide insight regarding the components driving the salinity feedback loop associated with the AMOC.
Ezer (2015)  suggests that this so-called 'extreme event' is the last part of a longer, decadal trend, that started before RAPID (according to three observational datasets from 2000). It is worth noting that McCarthy et al (  2012 ) and  Roberts et al (2014)  suggest that the CMIP5 ensemble underestimates the observed interannual variability and trends (of the 2009-2010 magnitude, 4. 7 Sv, for example), though. However this ratio between the trends and the variability could still be accurate, therefore not affecting our conclusions.
Funding This study was jointly funded by the  department of Ocean and Earth Science, University of Southampton  and the  National Physical Laboratory. The research at the  National Physical Laboratory  was funded by the  National Metrology Programme of the UK Department of Business, Energy and Industrial Strategy (BEIS). This research was also supported by the  UK Natural and Environmental Research Council  ( SMURPHS ,  NE/N005767/1 ) and by the  DECLIC  project funded through the  French CNRS /INSU/LEFE program.
Heterotrophic bacteria were distinguished from photosynthetic bacteria (Prochlorococcus and Synechococcus) by their signature in a plot of red fluorescence (FL2) vs green fluorescence (FL1). Yellow-green latex beads (1 µm, Polysciences) were used as an internal standard. (Stolle et al. , 2009). Cell counts were determined with the CellQuest software (Becton Dickinson). For autotrophic pico and nanoplankton <20 µm, 2 mL samples were fixed with formaldehyde (1 % final concentration) Prochlorococcus, 120 fg C cell -1 for Synechococcus, 500 fg C cell -1 for eukaryotic picoplankton and, 3. 100 fg C cell -1 for eukaryotic nanoplankton after  Hernández-Hernández et al. , (2020). We report the autotrophic plankton biomass as the sum of eukaryotic pico-and nanoplankton and cyanobacteria (Prochlorococcus and Synechococcus) biomass.