dataesr/scientific-paragraphs-categorization huggingface dataset:
5), but instead use the overlap period between the beginning of the satellite record  (1982)   2. 8 The role of advection, clouds and entrainment in large-eddy simulations (LES) While the MOSAiC observational data sets (partly addressed in Section 2. 2) are unprecedented in their coverage of the low level thermal structure in the central Arctic, various aspects that play a key role in the ALRF were not continuously sampled. These include processes such as turbulent entrainment driven by cloud top cooling across shallow liquid layers. To augment the observational data set, we conduct daily LES for the whole MOSAiC drift, at turbulence-and cloud-resolving scales. The fourdimensional output of these simulations is used as a virtual laboratory to address how small-scale boundary layer processes affect the thermal structure of the lower atmosphere within a heat budget framework.
Covering the full MOSAiC drift with such simulations is a significant computational effort and goes far beyond the more common application of LES for short, single case studies. The added value of this effort is that it allows for bridging the gap between small-scale, fast-acting atmospheric boundary-layer processes and long-term means at climate time-scales  (Neggers et al. , 2012). The daily LES experiments for MOSAiC were conducted with the DALES code  (Heus et al. , 2010). The simulated domain is Eulerian, situated around the location of the RV Polarstern. The domain size is 0. 8 x 0. 8 x 12 km 3 , discretised at a gridsize of 8 x 8 x 288. The horizontal grid-spacing is 100 x 100 m 2 , while for the vertical dimension a telescopic grid is used featuring a vertical resolution of 10 m across the lowest 2 km.
45/0. 51, respectively. All models used in this study are specified in Table  1 , including the model-specific AA and ALRF, corresponding to Fig. 1b. Again, we use different models for representing CMIP6/w and CMIP6/s model subsets, depending on the time resolution. The model usage is specified in Table  1  by a superscript in the model acronym. Note that individual time-resolution groups always apply the same models. For instance, Section 2. 2-2. 4 compare model and observational data at 6-hourly resolution. Thereby, the CMIP6/w subset includes data from the SAM0-UNICON, MPI-ESM-1-2-HAM and AWI-ESM-1-1-LR, and CMIP6/s from CNRM-ESM2-1, CNRM-CM6-1-HR and IPSL-CM6A-LR model, respectively.
385 During extended winter, the CMIP6/w models are in better agreement with the observations compared to the CMIP6/s subset (Fig. 4b ). During ON, the observations lie in between both sub-groups and better match the multi-model mean (not shown). During AMJ both subsets of the models tend to overestimate the inversion strength from the observations. However, the CMIP6/w subset is slightly closer to the observations. During JAS, in turn, both subsets show inversions that are too weak in comparison to MOSAiC observations. However, severe data gaps during spring and summer make the interpretation somewhat less reliable. It is also noteworthy that the atmospheric large-scale circulation during the MOSAiC winter 2019/2020 was characterised by an unusual and long-lasting occurrence of the positive phase of the Arctic Oscillation Index from January to March 2020  (Lawrence et al.
Temperature inversions from radio soundings at Utqiaǵvik (NSA) The regular radiosonde observations at the Utqiaǵvik site are complementary to the MOSAiC analysis in that they provide long-term statistics, albeit at one site, and are representative of a different geographical region in the Arctic. We present our results in comparison with the measurements conducted during MOSAiC. Correspondingly, the co-located model data cover the period of 2010-2014 and apply the same CMIP6 models defined in Table  1. During both ON and DJFM, CMIP6/w models, on average, show a stronger inversion compared to the CMIP6/s subset, and vice versa during AMJ, which is consistent with the findings for the MOSAiC data in Fig. 4. This agreement with the findings from MOSAiC suggests the same explanation also holds true for this longer-term analysis.
The comparison of the observed inversion data with the ones from models shows that the CMIP6/w model subset lies closer to the observations in ON. For the winter case, it is somewhat less clear than for the MOSAiC comparison: The observations lie in between CMIP6/w and CMIP6/s with regard to the 25 and 75-percentile of the data. However, although the CMIP6/w models overestimate the median inversion strength during DJFM, they better agree with previous findings for the NSA site by  Kahl (1990). This study reported that during winter (primarily in February) the inversion is stronger (median values of up to 11 K) Conversely in spring, the CMIP6/w models underestimate the inversion strength as compared to the observations, while CMIP6/s models fits the observations better. This is in contrast to our MOSAiC results, which suggest that both model groups overestimate the inversion strength at this time of the year.
Temperature profiles from dropsondes in the Fram Strait In order to assess the mediating effect of the surface type (open ocean or sea ice) on the temperature profile, we make use of dropsonde profiles launched from aircraft. Again, this analysis is complimentary to the results from the MOSAiC and NSA data comparison, embedded in the context of local influences on the Arctic LRF. We thereby apply the same models, but only include data during the end of extended winter (March), as discussed before in Section 2. 4. The comparison with co-located CMIP6/w and CMIP6/s model subsets is shown in Fig. 6. Panel a and b show the temperature profiles derived from observations and models over open ocean and sea ice, respectively, which are normalised to the temperature at 1015 hPa. Note that due to a lack of open ocean data in the CNRM-ESM2-1 model (number 25 in Table  1 ) domain, CMIP6/s only comprises two models.
The mean temperature profiles derived from both models and observations show an almost linear temperature decrease over open ocean, as also expected climatologically (Fig. 2a  and b ). In contrast, the profile over sea ice shows a near-surface temperature inversion for both observations and model data (again, in agreement with the climatological analysis in Fig. 2 ). Over ocean, the CMIP6/s subset shows slightly less stable conditions than the CMIP6/w data. Similarly, the CMIP6/w subset simulates a stronger inversion (on average 4. 35 K), compared to the CMIP6/s data (on average 3. 55 K) over sea ice. The stronger simulated stability in present-day temperature profiles as simulated in the CMIP6/w subset is in agreement with previous results from MOSAiC in the central Arctic, and the NSA site located near the coast during DJFM (and ON).
Note that the difference in stability between both subsets weakens when including campaign data from April (not shown). We attribute this to the fact that during AMJ, both MOSAiC and NSA show the opposite, i. e. , CMIP6/s models simulate stronger presentday inversions compared to CMIP6/w. This likely leads to less differences between the subsets in the dropsonde data through overlapping signals between March and April. Overall, both model subsets underestimate the inversion strength compared to the observations over sea ice. However, over both open ocean and sea ice, the CMIP6/w subset is closer to the observations, albeit being rather consistent with the multi-model average. To analyse the impact of sea ice retreat on the temperature profile, Fig. 6c  shows the difference in profiles between openocean and sea-ice areas.
Close to the surface, the temperature difference between ocean and sea ice is larger for the CMIP6/s subset (on average 13. 0 K) compared to CMIP6/w (on average 10. 5 K). This is mostly due to higher near-surface air temperatures over ocean in the CMIP6/s subset (not shown). However, above 1000 hPa the situation reverses, with a larger surface-type temperature difference for CMIP6/w models. Comparing to the observations, the warming expected through sea ice retreat is slightly better depicted by the CMIP6/w models very close to the surface. However, in higher layers, CMIP6/s models simulate a slightly more realistic temperature difference between profiles over ocean and sea ice (albeit the difference between models subsets is small). In summary, we conclude that in the context of simulated stability over sea ice and ocean, the dropsonde results representing the month of March are in agreement with the inversion data obtained from the central Arctic during MOSAiC, and at the coast of the NSA site during DJFM.
The role of advection, clouds and entrainment in large-eddy simulations (LES) To gain further insight in the role of clouds and boundary-layer dynamics, we now investigate the Arctic energy budget in more detail, using output data from year-long LES covering the MOSAiC drift. The results are shown in Fig. 10. Averaged over all 300 daily simulations we find that the full column heat budget is approximately in radiative-advective equilibrium (RAE; Fig. 10a ). Even if only a certain number of weather situations were sampled during the drift, and even if the scatter in particular in the advective heating remains large, this confirms what is expected for the large-scale energy budget of the ice-covered Arctic: In particular, the surface flux (SHF) and precipitation (P ; representing net condensation and freezing in the column) are negligible compared to the radiative cooling (F rad ) and vertically integrated advective heating (Adv).
Acknowledgements. We gratefully acknowledge the funding by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) - Projektnummer 268020496 -TRR 172 , within the  Transregional Collaborative Research Center "ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC ) 3 ". 29 https://doi. org/10. 5194/acp-2022-836  Preprint. Discussion started: 9 January 2023 c Author(s) 2023. CC BY 4. 0  License.
Code availability. The LES code (DALES) used in this study is open access and available on GitHub at https://github. com/dalesteam/dales. The current version of DALES (https://doi. org/10. 5281/zenodo. 5642477 : dales 4. 3 with extension for mixed-phase microphysics) is available on github as https://github. com/jchylik/dales/releases/tag/dales4. 3sb3cgn. Data availability. The CMIP6 data are available from the Earth System Grid Federation (ESGF) system: https://esgf-node. ipsl. upmc. fr/ projects/esgf-ipsl/, ESGF, 2022 (Eyring et al. , 2016). Data from radiosondes launched during the MOSAiC expedition can be downloaded from PANGEA: https://doi. pangaea. de/10. 1594/PANGAEA. 928656  (Maturilli et al. , 2021). NSA radiosonde data are available at the DOE ARM data repository https://adc. arm.
, 2022). Comparative developmental biology is now extended to molecular data. Classically, the expression of marker genes is studied in situ to establish the dynamics through which shape emerges and compare it between species. Since the last decade, the transcriptomes of embryos or organs have been used to quantify the divergence of development trajectories between species. This strategy uncovered periods of maximal conservation of embryogenesis (so-called hourglass pattern eg(H. Hu et al. , 2017; Irie & Kuratani, 2011; Kalinka et al. , 2010; Levin et al. , 2016)  or certified organ homology  (Fisher et al. , 2020; Tschopp et al. , 2014; Z. Wang et al. , 2011). Because these datasets often encompass large developmental windows and large evolutionary scales, they have brought limited insights so far into the principles of development evolution in relation with the acquisition of new shapes.
Gene expression dynamics are already divergent in the early steps of morphogenesis To quantify temporal variation in molar morphogenesis, we obtained RNA-seq data at high time resolution. Data was sampled and processed to be as comparable as possible between species (Figure  2A ). Yet, in a PCA analysis that separates samples according to the main axes of variation in the data, the first principal component is associated with mouse/hamster difference (47. 8% variance), followed by development time (10. 2%). Coordinates on this time axis confirmed the homology of samples chosen for homologizing the time series in this study. Upper and lower molar samples are only separated from each other on the sixth component, which carries 3% of the total variance. This PCA does not show an excess of variation at the end of the sequence of cusp formation, as expected for classical terminal addition (Figure  2B ).
To directly quantify differences along morphogenesis, we modeled temporal expression profiles in each molar with polynomials (Figure  2C ). For each gene we fitted four distinct curves, one per tooth. We measured the distance between pairs of curves in ten time windows over development. Among all possible pairs, the distance between upper molar transcriptomes is highest among all pairs of molars, as expected from the difference of morphologies. It remains rather constant during development, but shows a slight increase at the first third and toward the end of our dataset (Figure  2D ). Divergence in upper molar transcriptomes, like divergence in spatial and temporal patterns of cusp 8￼ addition, is therefore only partially in accordance with terminal addition. Hence, the whole transcriptome trajectory has evolved, particularly in early stages.
Increased polarization may result in an heterochrony of this mechanism in the upper molar, favoring supplementary cusp formation specifically on the lingual side. To test this idea, we quantified the proportion of naive tissues in mouse and hamster tooth germs, by deconvoluting the time series dataset with buccal and lingual tissue transcriptomes  (Gong & Szustakowski, 2013). As expected given the progressive nature of cusp formation, the proportion of naive lingual tissue decreases during morphogenesis in both species (Figure  3D ). But in mouse molars, and even more markedly in the mouse upper molar, the initial proportion of naive tissue is larger, and diminishes more slowly (in accordance with Sfrp2 expression, Figure  S3 ). Gene expression dynamics largely co-evolved between upper and lower molars.
) and chondrogenesis (Wnt3 and the Activin pathway: Inhba, Inhbb, Acvr2b…). It is also the case of most of the genes known to regulate webbing (Fgf8, Grem1, Bmp7, Ihh, Retinoic acid pathway: Aldh1a2, Cyp26b1). We note that several of these genes have been pointed in the literature as key for bat wing evolution. For three of them, we could compare the expression profile in the transcriptomic dataset with published in situ hybridization in both limbs, and they were consistent with co-20￼ evolution. The iconic Shh gene expression clearly peaks at the second stage in both bat limbs, but not in mouse limbs (Figure  S6 ), and peaking is exaggerated in the bat forelimb (Figure  S6 ). This is consistent with figure 2 in  (Hockman et al. , 2008). The new temporal profiles of Fgf8 and Grem1 in both bat limbs are also consistent with a previous study, which has reported the novel expression domain of these genes in the mesenchyme of both limbs at these stages and/or slightly later on during webbing  (Weatherbee et al.
27￼ R scripts corresponding to the main methods and processed data are available on GitHub (https://github. com/msemon/DriftHamsterMouse). Rodent breeding and embryo sampling CD1 (CD1) adult mice and RjHan:AURA adult hamsters were purchased from Charles River (Italy) and Janvier (France) respectively. Females were mated overnight and the noon after morning detection of a vaginal plug or sperm, respectively, was indicated as ED0. 5. Other breeding pairs were kept in a light-dark reversed cycle (12:00 midnight), so that the next day at 16:00 was considered as ED1. 0. Pregnant mouse females were killed by cervical dislocation. Hamster females were deeply anesthetized with a ketamine-xylasine mix administered intraperitoneally before being killed with pentobarbital administered intracardially.
Estimating embryonic age from embryo weight Embryo weight is well correlated with developmental age, allowing us to use it as a proxy in mouse and hamster, following  (Pantalacci et al. , 2009). We fitted age of development according to weight (in mg) for hamster and mouse data separately, based on 1047 mouse embryos and 636 hamster embryos respectively, collected over more than 15 years of research. We fitted generalised additive models (GAM) to the data after Box-Cox transformation of weight (libraries mgv version 1. 8-35 for GAM and MASS 7. 3-53. 1 for Box-Cox). These models were preferred to log transformations and linear models, because they allow to treat the data homogeneously between species, and because the relationship is not perfectly linear between weight and age (Figure  S1 ).
Examples of dissection are visible in  (Pantalacci et al. , 2017). Another embryo of the same litter and same weight was processed as indicated above for Fgf4 in 30￼ situ hybridization to check the exact developmental stage. Total RNA was prepared using the RNeasy micro kit from QIAGEN following lysis with a Precellys homogenizer. RNA integrity was controlled on a Bioanalyzer (Agilent Technologies, a RIN of 10 was reached for all samples used in this study). PolyA+ libraries of the large-scale dataset were prepared with the Truseq V2 kit (Illumina, non stranded protocol), starting with 150 ng total RNA and reducing the amplification step to only 12 cycles and sequenced on an Illumina Hi-seq2000 sequencer (100 bp paired end reads) at the GENOSCOPE (Evry, France). For the bucco-lingual dataset, we dissected the 4 first molars (left/right, lower/upper) from a unique mouse E15.
0 embryo (weight: 359 mg) as above, except that tooth germs were cut in two halves to isolate buccal and lingual side. Replicates were thus obtained by comparing the right and left side of this same embryo. Total RNAs were extracted and libraries were prepared as above, starting with 50-70 ng total RNAs, where an equal amount of AmbionR ERCC RNA Spike-In Mix1 had been added according to the AmbionR protocol (e. g. 1μL og a 1 :1000 dilution for each tube). A total of 8 libraries were sequenced (50bp single-end reads) by the Genomeast Sequencing platform, a member of the France Genomique program. For the epithelium-mesenchyme dataset, lower and upper mouse and hamster first molars were dissected as above and treated for 15 minutes at 37°C with Dispase (Roche) 10mg/ml in Hepes/KOH 50mM ph7. 7 ; NaCl 150 mM to separate the epithelial from mesenchymal parts which were stored in RNAlater.
Expression levels estimation using RNA-seq and differential expression analysis For the whole tooth germ data (64 samples) we obtained 100 bp paired-end sequences, with on average 46. 2M (millions) reads per sample. For epithelium/mesenchyme and bucco/lingual data (respectively 20 and 8 samples), we obtained 50 bp single-end sequences, with on average 93. 7M and 48. 6M reads per sample respectively. Raw data are publically available in ENA with project accession number: PRJEB52633. These reads were mapped by using Kallisto (version 0. 44. 0,  (Bray et al. , 2016) ) to custom reference sequences for hamster and mouse transcriptomes. To generate them, we retrieved mouse and hamster cDNAs from Ensembl (release 93, July 2018, assemblies GRCm38. p6 and MesAur1. 0  (Howe et al. , 2021) ), selected 14536 pairs of one-to-one orthologous transcripts, realigned pairs of sequences with Macse (macse_v2.
32￼ Differential gene expression analysis (DE analysis) was performed on smoothed expression profiles over relative developmental age. Developmental age was estimated with embryo weight (GAM models above). The two milestones defined above (see "milestones" section) were used to convert days of development into relative development age (0-10). The relevance of this choice was confirmed by PCA analysis of the transcriptome data (Figures  1  and S1 ) Expression profiles were fitted by third degree polynomial splines with 2 interior knots, for each tooth and species (bs function of spline R package (W. Wang & Yan, 2021) , independently or jointly within tooth and/or species, as explained below. Nested models were tested by DEseq2  (Love et al. , 2014)  and the best model was chosen for each gene by comparing the fit of these nested models (FDR adjusted p-value < 0.
If t2 but not t1: "upper/lower". Finally, if "lower/upper" or "hamster/mouse" and t4: complex. From this selection procedure, percentage of coevolution among genes was computed as the proportion of "hamster/mouse" models among the selected models as follows (related to Figure  4A ): "hamster/mouse"/("hamster/mouse"+"upper/lower"+"complex"). We then computed the intersection of the results with several lists of genes important for tooth development: 259 genes from the bite-it database (retrieved in July 2019), 187 genes with a phenotype in tooth development (100 "dispensable" genes, 87 "keystone" genes,  (Hallikas et al. , 2021) ), and 295 genes belonging to 8 pathways active in tooth development  (17 genes in ACTIVIN pathway, 81 in BMP, 10 in EDA, 69 in FGF, 32 in SHH, 9 in NOTCH, 11 in TGFB, 96 in WNT, courtesy Jukka Jernvall).
Measure of pathway activation in RNAseq samples ROMA was used to quantify activation of WNT, BMP and SHH pathways in the buccolingual samples (version rRoma_0. 0. 4. 2000, https://github. com/Albluca/rRoma and  (Martignetti et al. , 2016) ). ROMA is designed to compare pathway activity in transcriptomic samples based on expression levels of a list of targets for the pathway. Genes for the SHH modules were retrieved from GSEA (  (Mootha et al. , 2003) , 41 genes present in our dataset). Because BMP and WNT pathways are active both in the mesenchyme and the epithelium and they target different genes in each tissue  (O'Connell et al. , 2012) , we used two separate lists of targets to estimate both an epithelial and a mesenchymal activity, adapted from a "regulatory evidence" dataset established for first lower molar development.
ACKNOWLEDGEMENTS We acknowledge the contribution of several platforms of  SFR Biosciences Gerland-Lyon Sud  (UMS344/US8): the  Plateau de Biologie Expérimentale de la Souris (PBES)  (many thanks especially to  Jean-Louis Thoumas ,  Tiphaine Dorel ,  Céline Angleraux ,  Marie Teixeira ,  Myriam Prudent ), the  Plateau Technique Imagerie/Microscopie  ( PLATIM ) and the ANIRA/IMMOS platform with  Mathilde Bouchet ; as well as the computer resources from  PSMN (ENS Lyon). We acknowledge the technical help of  Anne Lambert ,  Alain Rubod ,  Mathilde Estevez-Villar ,  Claudine Corneloup  and the contribution of many students including  Coraline Petit ,  Alice Lorenc ,  Margaux Pillon ,  Ludivine Rotard  and  Asma Benahmed. We are grateful to several colleagues and their staff for sending plasmid probes:  Irma Thesleff  (Fgf4) ,  Hiko Ogura  ( Cv2/Bmper ),  D.
49￼ This work was supported by the  Agence Nationale pour la Recherche  ( ANR 2011 JSV6 00501  " Convergdent "), a grant from the  GENOSCOPE -Centre National de Séquençage  and a grant from  IDEX Lyon   ANR-16-IDEX-0005. Salaries were supported by the  Centre National de la Recherche Scientifique , the  Ecole Normale Supérieure de Lyon  and the  Université de Lyon ,  Université Lyon  1. Klara Steklikova  benefited from a  Barrand Fellowship program.