dataesr/scientific-paragraphs-categorization huggingface dataset:
Requested variables A transient simulation of the PDG implies that the climate model will need to be integrated for 13,000 years, thus generating a large amount of data. Depending on the spatial resolution of the different model components, storage of data outputs can become an issue. We therefore suggest a list of variables that absolutely needs to be uploaded to the ESGF database to understand the results and perform inter-model and model-data comparisons. The CMIP6 conventions should be used for all the variable names and units. Table  3  presents a list of forcings, and variables related to the atmosphere, ocean, vegetation and ocean biogeochemistry realms (if relevant) to be saved either as annual or monthly means. In addition to this list standard global, hemispheric or zonally averaged variables would need to be included, such as northern and southern sea-ice extent and surface air temperature, maximum meridional overturning streamfunction in the North Atlantic, top of the atmosphere (TOA) energy budget, surface energy budget, carbon budget (if relevant), etc.
Tables 4 and 5 also detail the timing of the major changes (and associated uncertainties) as recorded in each timeseries. These major changes are identified using the RAMPFIT or BREAKFIT software programs  (Mudelsee, 2000 (Mudelsee, , 2009)). Age uncertainties (1σ) reported in Tables  4  and 5  include i) the "internal" error of the event given by RAMPFIT or BREAKFIT, ii) the relative error related to the climatic alignment method for marine sediment and Ioannina records and iii) the absolute dating error of the reference time scale. The selected ice-and sediment-based records are all displayed on AICC2012 or on a timescale coherent to this ice-core age scale, while terrestrial records are displayed on their own independent chronology. Among these paleoclimatic time series, several isotopic records are presented here and we strongly encourage transient simulations to be performed with oxygen and/or carbon isotope-enabled models to allow direct quantitative comparison between simulated and measured isotopic time series.
Acknowledgements. This study was performed as part of the PAGES-PMIP working group on Quaternary Interglacials (QUIGS) and was initiated during the second QUIGS workshop at the  Université du Quebec  in Montreal, 18-20 October 2016. PMIP is endorsed by the  World Climate Research Program (WCRP)  and  CLIVAR. We thank  J. -Y. Peterschmitt  for setting up the the PDG page on the PMIP4 wiki. LM acknowledges funding from the  Australian Research Council  grants  DE150100107  and  DP180100048. EC received funding from the  European Union 's  Seventh Framework Programme  for research and innovation under the  Marie Skłodowska-Curie  grant agreement nb  600207. EW is supported by a  Royal Society professorship. His part in this project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement nb  742224 ).
PCT and RFI acknowledge funding from from  NERC  ( NE/G00756X/1  to  PCT  and  NE/K008536/1  to RFI). MK is funded by the  CNRS. AD acknowledges U. S. National Science Foundation (NSF)  grants  1559040  and  1702740 , as well as the PALSEA working group. BOB's contributions are based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  U. S. National Science Foundation  under Cooperative Agreement No. 1852977 , with additional funding provided by an  NSF   P2C2 grant  ( AGS-1401803 ). F. H. was supported by the  NSF  ( AGS-1502990 ) and by the  NOAA Climate and Global Change Postdoctoral Fellowship program , administered by the  University Corporation for Atmospheric Research. XZ is supported by  Helmholtz Postdoc Program  ( PD-301 ) and the  Chinese "The Thousand Talents Plan" Program.
For precipitation, SAFRAN shows rather satisfactory results in terms of bias despite the high daily variability (Fig. A1b  and e ). However, the statistical analyses carried out on the monthly accumulations show very good results, confirming that SAFRAN is well adapted to inform the precipitation at local scale, at least for this area. The detailed results obtained from the two datasets are presented in Appendix A. The reasons for the discrepancies between direct observation and ERA-I/SAFRAN are out of the scope of this paper, but the presence of significant bias at this local scale motivates the use of observations and not reanalysis for the current issues.
( 5 ) We normalized the anomalies with respect to the entire 1979-2017 studied period because, according to Huntingford    2015 ), when anomaly normalization is performed relative to a reference period, then standardization tends to increase the variability and extremes. With a normalized anomaly, we compute the distributions for two periods  (1979-2002 and 2003-2017). The choice of separation between these two periods is mainly motivated by the fact that over the 2003-2017 period observations of various meteorological parameters are available at the SIRTA supersite (see Fig. 1 ) and have been reanalysed to produce the SIRTA-ReOBS dataset at an hourly timescale  (Chiriaco et al. , 2018). This dataset is not used in this study, but it will be used in a forthcoming paper focused on the understanding of the processes responsible for the changes detected in the current paper.
Temperatures in regimes favouring "cold conditions" warm up very clearly and even contribute for some years to very warm temperatures. Regimes favouring "hot conditions" continue to contribute very significantly to extreme heat events, such as heat waves. In particular, the Atlantic Low regime shows a very strong increase in the temperatures and a very strong decrease in the relative humidity, whereas precipitation and specific humidity show no trend but a change of their distribution. In a recent study,  Bastin et al. (2019)  analysed the spatial variability over Europe of the temperature thresholds over which the relative humidity starts to decrease using an integrated water vapour dataset from GPS stations. They suspect that the spatial variability of this threshold is strongly linked to local processes that drive moisture sources, in particular surface-atmosphere interactions and coastal/orographic circulations.
One of the perspectives of this study is to understand these current changes using the rather complete set of atmospheric observations from the supersite of  Chiriaco et al. (2018) , which has collected more than 50 meteorological and atmospheric parameters at hourly time steps since 2003 over the full boundary layer. The strong correlation between the stations as well as the average of the stations encourages us to use this dataset. Data availability. The meteorological station data used in this study are paid public data, but are accessible free of charge under a teaching-research license. The continuous time weather regimes data can be found via the A2C2 website at http://dods. lsce. ipsl. fr/ dase/REGIMES/  (Yiou et al. , 2008)    The ERA-Interim (ERA-I) reanalysis developed by ECMWF is a global atmospheric reanalysis available from 1979 to today, every 6 h and at a spatial resolution of 0.
75 • × 0. 75 •  (Dee et al. , 2011; Dee and Uppala, 2009; Simmons et al. , 2014). The ERA-I dataset contains both analyses and forecasts. Unlike T 2 m , which contains analyses four times per day (00:00, 06:00, 12:00 and 18:00 UTC), T max and T min series under ERA-I are built from daily forecasts. There are five time values per day: 00:00, 06:00, 12:00, 18:00, and 00:00 UTC, corresponding to the five forecasting steps  (12, 18, 24, 30 and 36 h)  starting at the reference time 12:00 UTC of the day before; thus, the daily value T max and T min of ERA-I are selected by selecting the maximum or minimum daily values from the five values available on the corresponding day. The configuration of the ERA-I grid imposes a grid point in the near centre of our study area, involving the presence of observation stations on four different ERA-I pixels (green square; Fig.
A2 SAFRAN For precipitation, we use a meteorological analysis system called SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie)  (Durand et al. , 1993)  developed by the Centre National de Recherches Météorologiques et le Centre d'études de la Neige (CNRM/CEN). The main characteristic of SAFRAN is its treatment of a limited area divided into non-regular and climatologically homogeneous areas. As input, SAFRAN uses vertical profiles derived from the meteorological model as well as numerous sources of observations. The data are analysed by altitude range (300 m steps) via optimal interpolation (6 h time steps and 24 h time steps for precipitation). The analyses are then interpolated at the hourly time step; then, a spatial interpolation is performed to project the data on a regular grid.
Acknowledgements. We would like to acknowledge  Météo-France  for the provision of observation measurements and the SAFRAN analysis. We also would like to acknowledge  Pascal Yiou , who provided the weather regime classification via the project  A2C2 , supported by  ERC  grant  338965-A2C2. To process the data, this study benefited from the  IPSL mesocenter ESPRI facility , which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL, CNES and  Ecole Polytechnique. Financial support. This research has been supported by the LABEX L-IPSL, funded by the  French Agence Nationale de la Recherche , under the  programme "Investissements d'Avenir " (grant no. ANR-10-LABX-18-01 ). Review statement. This paper was edited by  Hang Su  and reviewed by three anonymous referees.
An original source apportionment study was conducted on atmospheric particles (PM 10 ) collected in Metz, one of the largest cities of Eastern France. A Positive matrix factorization (PMF) analysis was applied to a sampling filter-based chemical dataset obtained for the April 2015 to January 2017 period. Nine factors were clearly identified, showing mainly contributions from anthropogenic sources of primary PM (19. 2% and 16. 1% for traffic and biomass burning, respectively) as well as secondary aerosols (12. 3%, 14. 5%, 21. 8% for sulfate-, nitrate-, and oxalate-rich factors, respectively). Wood-burning aerosols exhibited strong temporal variations and contributed up to 30% of the PM mass fraction during winter, while primary traffic concentrations remained relatively constant throughout the year.
During the past decade, the development of receptor models, especially Positive Matrix Factorization (PMF,  [6] ), has enabled the identification and quantification of PM pollution sources in many locations and different site typologies worldwide (e. g. , References  [7, 8] ). They have recently been proven to provide statistically robust outputs for the major pollution sources, and therefore essential information towards improved air quality management  [9]. However, the large spatial variability observed in terms of source distribution highlights the need of a spatially-resolved knowledge of PM pollution sources. Moreover, PMF filter-based analyses have been commonly conducted using datasets which include major chemical species, such as Organic Carbon (OC), Elemental Carbon (EC), water-soluble inorganics, and metals, but only a few organic molecular markers.
Although France is the second most populated country in Europe and regularly exposed to persistent pollution episodes, source apportionment studies were still scarce until recently. Nevertheless, a harmonized receptor model program conducted at the national level (so-called SOURCES project) provided a first glimpse of the spatial distribution of PM 10 sources in different French urban areas, based on 15 chemically-resolved datasets obtained in the last six years  [17]. Primary anthropogenic sources such as traffic and wood-burning were commonly identified, respectively contributing to 15 and 16% of PM 10 on average, with a significant spatial variability. Secondary pollution, notably ammonium nitrate and sulfate, was found to be the main contributor to PM 10 , with however, high SOA contribution emphasized in these factors.
Source Apportionment In order to apportion the sources of PM 10 in Metz, Positive Matrix Factorization has been applied to the chemically-resolved dataset. PMF can be summarized in the deconvolution of an input data matrix X into chemical profiles G and temporal contributions F: X = G•F + E where E is the residual matrix. Uncertainties associated with each sample and each compound were calculated following the methodology proposed by References  [11, 26]. σ ij =            u j •x ij 2 + LoD j 2 + x ij •a j 2 if x ij > LoD j 5 6 •LoD j if x ij ≤ LoD j 4•Geo. Mean if missing data (1) where x ij is the concentration of specie j at t i ; LOD j the limit of detection of specie j; u j , the extented uncertainty for specie j; and a j , an additional error coefficient for each j th specie.
Missing concentrations were replaced with the median value, and their corresponding uncertainties calculated with the geometric mean, multiplied by 4. The authors of Reference  [17]  have determined optimized a j values depending on the analytical procedure for each compound, and these are summarized in Table  1. The same values were applied because the same analytical procedures were used. The classification of "weak" and "bad" variables was based on signal-to-noise ratios, provided by the software; their calculation differs from EPA PMF v3 (Environmental Protection Agency, Durham, NC, USA), as it handles data below uncertainty more robustly. PM 10 was used as the total variable. OC*, used as input data, refers to OC concentrations subtracted by the carbon concentration of all organic markers (in ngC/m 3 ) of the database.
EPA PMF v5. 0 software package was used to control the PMF algorithm. Scaled residuals were found to be most well-shaped (centered around 0 between ±4) for the 9-factor solution. The 8-factor solution consists in a mixing of an oxalate and ammonium sulfate-dominated factor. Although bootstrapping exhibited satisfactory results, indicating a good stability, these 2 factors should have specific temporal variabilities as well as geographical origins. That is why the 9-factor solution is believed to have a higher atmospheric relevance, including the following sources: primary traffic, wood-burning, nitrate-rich, sulfate-rich, oxalate-rich, secondary sea salt, dust, primary biogenic and secondary marine biogenic aerosols. In order to explore rotational ambiguities, a minimum number of soft pulling equations were implemented.
Co-located multi-wavelength Aethalometer (AE33 model, Magee Scientific, Ljubjana, Slovenia) data was also available, providing complementary information on carbonaceous aerosols sources. The AE33 measurement principle is fully described in  [28]. Briefly, light attenuation caused by particle accumulation onto a filter tape is determined at seven wavelengths (370, 470, 520, 590, 660, 880 and 950 nm). This advanced Aethalometer version allows automatic compensation of the filter-loading effect using two simultaneous samplings at different flowrates. The mass concentration of equivalent black carbon (eBC) is also estimated from absorption coefficient at 880 nm. It is further discriminated between its two main combustion sources, i. e. , fossil fuel combustion (eBC ff ) and biomass burning emissions (eBC bb ) using light absorption coefficient at 470 and 950 nm, and default Angström absorption exponents respectively of 1 (α ff ) and 2 (α bb )  [29].
In this study, a correction factor of 1. 64 was applied, as recommended within the ACTRIS infrastructure. Based on this AE33 dataset and filter-based offline OC/EC measurements, we also applied a source apportionment approach to carbonaceous material (CM), where total CM (CM total ) could be primarily considered as the sum of brown carbon containing material (CM bb ), non-brown carbon containing material originating from fossil fuel combustion (CM ff ) and non-combustion OA (CM non-comb. ), as follows: CM total = EC + OM = CM ff + CM bb + CM non comb. ( 2 ) where CM ff = C 1 •b abs, ff, 950nm and CM bb = C 2 •b abs, bb, 470nm b abs, ff, 950nm and b abs, bb, 470nm respectively represents the absorption coefficient of CM ff at 950 nm, and CM bb at 470 nm; C 1 and C 2 relate to the light absorption of the particulate mass of both sources.
Primary Combustion Sources A primary traffic factor could be identified unambiguously thanks to a high share of EC (representing 44. 3% of total EC). This composition is consistent with traffic-related sources resolved in other urban areas (e. g. , References  [7, 47] ). The OC/EC ratio of 1. 43 ± 0. 53 is close to values found in other urban areas, such as Paris (1. 2,  [18] ) and Milan (1. 3,  [48] ) for instance, although a large variability can be obtained at the European scale (Specieurope database,  [49] ). The presence of nitrate in the profile may suggest a slight secondary feature (12. 5% of total NO3), but the concomitant absence of ammonium advocates that nitrate is not in the ammonium nitrate form, but more probably associated with other mineral cations. This is consistent with specific studies, allocating nitrate within road dust and/or vehicle exhaust and wear factors (e.
Primary Combustion Sources A primary traffic factor could be identified unambiguously thanks to a high share of EC (representing 44. 3% of total EC). This composition is consistent with traffic-related sources resolved in other urban areas (e. g. , References  [7, 47] ). The OC/EC ratio of 1. 43 ± 0. 53 is close to values found in other urban areas, such as Paris (1. 2,  [18] ) and Milan  (1. 3, [48] ) for instance, although a large variability can be obtained at the European scale (Specieurope database,  [49] ). The presence of nitrate in the profile may suggest a slight secondary feature (12. 5% of total NO 3 ), but the concomitant absence of ammonium advocates that nitrate is not in the ammonium nitrate form, but more probably associated with other mineral cations. This is consistent with specific studies, allocating nitrate within road dust and/or vehicle exhaust and wear factors (e.
This may be an edge effect of the model, but this region is, according to the European emission database, one of the most emitting of SO 2 in Europe, and exhibits, like Serbia, no decreasing trend over the years, as opposed to Poland and Germany (Figure  S4b ). The result for the NO 3 -rich factor is consistent with what has been observed in Paris or Lens for instance, highlighting higher ammonium nitrate concentrations associated with continental air masses. This transport feature does not however carry off local/regional formation of ammonium nitrate, as shown in  [56]. To this respect, the use of Chemistry-Transport Models (CTM) should provide more insights into the contribution of this region to secondary aerosols measured in France. Finally, the oxalate-rich factor has a significantly different seasonality, characterized by a strong increase during summer.
The fact that (i) it mainly contributes to moderate PM 10 concentrations, and (ii) its geographical origin is dispersed (Figure  5  and Figure  S1 ), suggests that this factor may be somewhat representative of highly oxidized background pollution. Although oxalate is a tracer of in-cloud processing, this clear increase during summer also emphasizes photochemical pathways. The corresponding precursor's sources cannot be identified from this dataset and should be a mixture of biogenic and anthropogenic emissions. However, as suggested above, a primary contribution from fuel combustion may not be excluded. This factor is the main contributor to PM 10 on average (21. 7%), which clearly highlight the role of SOA in this region, a particulate fraction that should be better characterized in the future.
Moreover, since the CWT analysis showed a rather local influence, these two sources should therefore represent an efficient leverage at a local scale in order to prevent extreme concentrations. Finally, substantial contributions of secondary factors are investigated. In particular, the nitrate-rich factor displays a sharp increase during spring and a clear decrease during summer due to its semi-volatile characteristic when a specific SOA factor is becoming predominant. Furthermore, oxalate-rich factor is one of the most original features of our study, underlining the role of atmospheric processes in this area of France. Its geographical origin is rather diffuse, which is consistent with highly oxidized background pollution. In its current form, this dataset is however unable to comprehensively characterize these secondary fractions or their precise geographical origins.
2 ). To do so, we used an infra-red camera (C. A 1950 Diacam 2 IR Camera IP 54; Chauvin Arnoux 190, rue Championnet -75876 Paris cedex 18 -France) which allows to record the superficial temperature of a targeted spot. The emissivity was set to 0. 95 as in  Farlow et al. (2010)  according to the infra-red thermography principles by  Speakman & Ward (1998). The data were plotted on colored images which were centered around a cross-hair that revealed the temperature of each targeted spot. The entire field of view of the camera was numerized so the surroundings' temperature variations were plotted by a gradient of colors from deep blue (cold) to deep red (warm). Since the value of the Noise Equivalent Thermal Difference (NETD) is 80 mK for this device when the environmental temperature is 30 °C, we deduced that the least detected temperature variations were under 0.
In order to understand the thermal behavior differences between the osteoderms and the nonossified dermis, a previous study has shown that the presence of the osteoderms does not modify the crocodylian skin thermal conduction if we do not consider the relative presence of the blood vessels  (Clarac et al. , 2017b). Therefore, as a complementary approach, using the ITIS foundation database  (Hasgall et al. , 2015) , here we have calculated that the thermal diffusivity of the blood is higher than the one of the tissues which either compose the soft skin or the osteoderms (i. e. the compact bone, the cancellous bone, the red and the yellow marrow; see supplementary data). Since the entire body of the crocodylians is covered with a continuous layer of keratin  (Trutnau and Sommerlad, 2006) , we deduce that the observed temperature contrast between the various anatomical regions must not either be due to variations in the skin reflectivity but indeed to the relative presence of blood vessels which has a main consequent influence on the global skin thermal diffusivity.
Dataset. Abbreviations: A-1: Aquatic-1; HT-1: Heating-Terrestrial-1; A-2: Aquatic-2; HT-2: Heating-Terrestrial-2; NA: Non-Attributed; S. table: Skull table; ost: osteoderms; C. P. : Caudal Paramedian; L. side. tail: the Lateral side of the tail (on the anterior side of the caudal crest symphisis; see text for details); Std dev. : Standard deviation; Max: Maximum value; Min: Minimum value.
Acknowledgments We thank the zoological  park "Planète Crocodiles"  for giving us access to their facilities and for housing all the experimentations. We address our respectful regards to  Johann Doray  (zoological director) and  Franck Doucet  (executive director) for their active participation in the set-up of an official collaboration between scientific institutions ( Muséum National d'Histoire Naturelle, Sorbonne Université ) and  Planète Crocodiles  (zoological park). We also address our thanksful thoughts to  Léa Pierrefitte  (animal keeper) who was helpful during the experimentations as she perfectly knows the crocodylian behavior and shows safety and carefulness in their handling. We also thank  Joffray Fomel  (director of  Biotope Conception ) for its technical advice in the set-up of the experimentation.