dataesr/scientific-paragraphs-categorization huggingface dataset:
Model evaluation In this chapter, we evaluate the model results with observations from commercial aircraft. It is important to verify the model's performance in the upper troposphere-lower stratosphere region, especially in the northern extratropics, where most of the trace gases are emitted and the downward stratosphere-to-troposphere transport (SST) occurs. The SST, including the trace gases emitted at stratospheric altitudes, is a very important step in the continuous process that eventually removes these trace gases from the atmosphere -a fact that is further emphasized by the results of this publication. IAGOS offers data particularly fit for this region of interest. In a forthcoming publication, we extend the evaluation to higher altitudes and compare EMAC model results to satellite data  (Pletzer and Grewe, 2022).
Observation dataset The research infrastructure IAGOS provides in situ measurements onboard a fleet of commercial aircraft. Observations of ozone and water vapor started in August 1994 and are still being collected to date. IAGOS mostly samples the UTLS in the northern extratropics, with cruise data spreading between 9 and 12 km above sea level. The ozone instruments are based on UV-absorption spectrometry, and their accuracy, precision, and time response are 2 ppb, 2 %, and 4 s, respectively. H 2 O is measured using a capacitive hygrometer. The latter's precision and time response are generally 5 % -or 6 % in the thermal tropopause at midlatitudes  (Smit et al. , 2014)  -for relative humidity and 5-300 s for H 2 O, respectively, with regard to ice  (Helten et al. , 1998; Neis et al. , 2015).
In order to allow a direct comparison between simulation outputs and the IAGOS data, the Interpol-IAGOS software  (Cohen et al. , 2021a ) used here first projects observations onto model grids and then derives monthly means. The subsequent products are called IAGOS-DM-INCA and IAGOS-DM-EMAC. The -DM suffix refers to the distribution onto the model grid. Since the outputs from the models have a daily resolution, a mask is applied with respect to the IAGOS sampling. The subsequent products are called INCA-M and EMAC-M. The -M suffix refers to the mask. In this way, the monthly means derived from both the IAGOS and the simulations datasets represent the same days for each grid cell. Seasonal and annual climatologies are then calculated on the 3D model grids. As in  Cohen et al. (2021a) , a grid point is filtered out if the total amount of IAGOS data are below a minimum threshold, the latter decreasing with latitude in order to account for the grid cell area.
The validated grid points are then averaged together as partial columns, with a 400 hPa lower bound in order to exclude the IAGOS data recorded during ascent and descent phases near airports. In order to ensure a correct vertical representation, we select only the columns derived from at least two grid cells. Our assessment focuses on the northern midlatitudes, since, firstly, the UTLS is far more influenced by the stratosphere in the extratropics than in the tropics, and, secondly, only the UT is sampled in the tropics. The seasons defined in the northern midlatitudes are thus typically extratropical. The scores used for the evaluation are the modified normalized mean bias (MNMB) and the Pearson correlation coefficient. For a set of N grid cells with an observed value o i and a simulated value m i , the MNMB is defined as follows: MNMB = 2 N N i=1 m i -o i m i + o i.
titude is already much smaller and since the models seem to agree very well for the region between 26 and 35 km. The assumed tropical maximum of the ozone mixing ratio of 31 km overlaps with this region and is very close to the maximum value. However, the altitude of emission often does differ from perturbation maxima of NO x and O 3. Be aware that we are only presenting two data points per model, and to come to conclusions regarding the largest value of ozone sensitivity might be inaccurate. Additionally, there are some differences between the setups, e. g. ,  Kinnison et al. (2020)  estimate ozone sensitivity based on NO x perturbations only and with a larger amount, while we look at the combined effects of NO x , H 2 O, and H 2 emission. Furthermore, the HIKARI data include a vertical distribution of emissions, in which take off and landing are present, and the fleet comprises not only hypersonic but subsonic aircraft as well, while they inject the emission in a single layer.
The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "The Modular Earth Submodel System (MESSy) (ACP/GMD interjournal SI)". It is not associated with a conference. Acknowledgements. The authors gratefully acknowledge Douglas Kinnison and Guy Brasseur for the helpful discussions on their results. MOZAIC, CARIBIC, and IAGOS data were created with support from the European Commission, national agencies in Germany (BMBF), France (MESR), and the UK (NERC), and the IAGOS member institutions (https://www.
iagos. org/organisation/ members/, last access: 2 August 2022). The participating airlines (Lufthansa, Air France, Austrian, China Airlines, Iberia, Cathay Pacific, Air Namibia, Sabena) have supported IAGOS by carrying the measurement equipment free of charge since 1994. The data are available at http://www. iagos. fr (last access: 2 August 2022) thanks to additional support from AERIS. The model simulations have been performed at the German Climate Computing Centre (DKRZ). The resources for the simulations were offered by the Bundesministerium für Bildung und Forschung (BMBF). The LMDZ-INCA simulations were performed using HPC resources from GENCI (Grand Equipement National de Calcul Intensif). The H2020 STRATOFLY Project and the H2020 MORE&LESS project have received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No.
769246 and 101006856, respectively. The main part of post-processing, data analysis, and plotting has been done using the module xarray  (Hoyer and Joseph, 2017, v0. 20. 1) and matplotlib. For interpolating hybrid model level to pressure level we used the interp hybrid to pressure function from geocat. comp (Visualization and Analysis Systems Technologies, 2021). Review statement. This paper was edited by Farahnaz Khosrawi and reviewed by three anonymous referees.
Figure2. Zonal sum of trace gas emission location; (a) depicts the subsonic reference scenario, (b) depicts the ZEHST scenario, with a hypersonic market penetration of 9. 8 %, and (c) depicts the LAPCAT scenario, with a hypersonic market penetration of 26. 0 %. A color bar with units is not shown, since the location (altitude, latitude) is the interesting piece of information here. For annual amounts of emissions, see Table2. The data is taken from the HIKARI project's emission inventory.
ipsl. fr/ipsl-climate-models/ipsl-cm6/(lastaccess: 2 August 2022). The distribution of the IAGOS data onto the EMAC and LMDZ-INCA grids is based on an updated version of the Interpol-IAGOS software, available at https://doi. org/10. 25326/81(Cohenetal. , 2021b). To access datasets of the LMDZ-INCA results, please contact Didier Hauglustaine. For access to datasets of the EMAC results, please contact Johannes Pletzer. The IAGOS dataset can be found at http://doi. org/10. 25326/20(Petzoldetal. , 2015), and more precisely, the IAGOS time series used in this study are available at http://doi. org/10. 25326/06(Bundkeetal. , 2018). Two video supplements are published with a DOI via Zenodo. The first supplement shows the time development of SWV and integrated H 2 O perturbation by LAPCAT aircraft at 32-38 km altitude (http://doi.
org/10. 5281/zenodo. 4455592,Pletzer,2022a)overatime period of 15 years(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). The second supplement shows the transport of H 2 O emission by LAPCAT aircraft in the first month of air fleet operation at stratospheric altitudes. The presentation is a world map view (http://doi. org/10. 5281/zenodo. 4475334,Pletzer,2022b). The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-14323-2022-supplement. Author contributions. JP set up the EMAC model simulations and analyzed and post-processed the EMAC model results. DH set up the LMDZ-INCA model simulations and analyzed and postprocessed the LMDZ-INCA model results, which JP plotted for a consistent presentation. YC contributed with the IAGOS observation validation of both models.
ACKNOWLEDGMENTS Gabriel Ferreira  ( University of Tübingen ) supported the segmentation process in Avizo. Richard Cifelli  ( Sam Noble Oklahoma Museum of Natural History ) and  Nicholas Czaplewski  ( Sam Noble Oklahoma Museum of Natural History ) kindly provided access to OMNH 44816. The scan was created by  Matthew Colbert  ( University of Texas at Austin ) and prepared by  Jessica Maisano  ( University of Texas at Austin ). We thank JF and DM for their valuable comments that enhanced our manuscript. Lastly, we thank  Robert Reisz ,  Sean Modesto , and two reviewers for comments/support with a former version of this manuscript.
The Ovalentaria is a taxon of teleosts that has been proposed based on molecular analyses only. Previously widely separated families are assembled in this taxon. For the first time, the Ovalentaria are analysed using a comparative morphological approach. The caudal-fin skeleton of 355 species covering all 48 ovalentarian families are examined in cleared and stained specimens, µCT datasets and X-ray images as well as from the literature. A total of 38 morphological characters are evaluated and used for ancestral character state reconstructions and phylogenetic analyses. Results provide hypotheses for a scenario of the evolution of the caudal-fin skeleton and its ground plan in Ovalentaria. An evolutionary trend towards the reduction of skeletal elements in the caudal fin is observed. Connections between the evolution of the caudal-fin skeleton and modes of locomotion found in ovalentarian taxa are discussed.
[6]  based on DNA sequence data from 10 nuclear loci. The taxon was retrieved again in subsequent analyses  [1, 2, 54]. The Ovalentaria comprises 42  [19]  to 48  [55]  families. However, the monophyly of some of these families (e. g. Grammatidae, Labrisomidae) has been questioned  [1, 2, 6]. The taxon Ovalentaria is well supported by molecular data, although the relationships of major ovalentarian taxa remain unresolved and support values for many basal nodes are very low  [1, 2, 6, 54]. For the first time, the phylogenetic relationships of the Ovalentaria are analysed using morphological data. This study aims to (i) compare the caudal-fin skeleton of ovalentarian taxa based on detailed descriptions, (ii) reconstruct the evolution of the caudal-fin skeleton within the Ovalentaria using the phylogenetic hypothesis of Betancur-R et al.
Taxonomic sampling and morphological analysis The caudal-fin skeleton of 355 species were examined and/or reviewed from literature in this study (electronic supplementary material, S1). We studied the caudal-fin skeleton of 275 species from a total of 556 cleared and stained specimens, X-ray images or µCT scans from the collections of the Australian Museum (AMS), the Deutsches Meeresmuseum (DMM), the Florida Museum of Natural History (FLMNH), the Muséum national d'Histoire naturelle (MNHN), the Phyletisches Museum Jena (PMJ) and the Zoologische Staatssammlung München (ZSM). Furthermore, data on the caudal-fin skeletons of 203 species were collected by reviewing literature (electronic supplementary material, S1)  [33, 45, 46, 50,. The taxon sample covers all ovalentarian families and, if possible, we selected taxa from different phylogenetic positions within each family.
During this study, specimens were cleared and double stained (bone stained in red and cartilage in blue) following the protocols by Dingerkus & Uhler  [98]  and Taylor & Van Dyke  [99]. Larval material examined in this study was cleared and stained following the protocol by Schnell et al. [100]. Pictures of the caudal-fin skeleton were taken either with a Canon EOS 80D and a Canon MP-E 65 mm macro lens, an Axiocam microscope camera attached to a Zeiss Discovery V20 stereomicroscope, or a Leica M205 stereoscope with a DMC 4500 camera. Pictures were processed in Adobe Photoshop and Zeiss ZEN software and plates were assembled in Adobe Illustrator. Thirty-eight characters of the caudal-fin skeleton were evaluated for each species during this study (data matrix available in electronic supplementary material, S1).
Ancestral character state estimation Ancestral character state estimation was performed in RStudio using the packages ape  [111] , Geiger  [112]  and phytools  [113]  and the phylogenetic tree provided by Betancur-R et al. [2]  as basis for the analysis. First, the phylogenetic tree provided by Betancur-R et al. [2]  was trimmed to only the Ovalentaria and the outgroup taxa. Then all ovalentarian taxa were further trimmed to family level except atheriniforms and cyprinodontiforms, which were reduced to their most-recent common ancestor, as the provided tree did not represent the full diversity of families of these taxa. Afterwards, the best-fitting parameters (i. e. model, pi-value and transformation matrix) for each character were determined using a customized script mainly employing the functions fitMk (phytools) and fitDiscrete (Geiger).
Phylogenetic analysis The 48 ovalentarian families were examined, of which all atheriniform, beloniform and cyprinodontiform families were condensed as Atheriniformes, Beloniformes and Cyprinodontiformes, respectively, as their monophyly was confirmed by both morphological and molecular-genetic data (e. g. [2, 7, 102, 104, 115] ). The compiled data were analysed with parsimony (MP), maximum likelihood (ML) and Bayesian inference (BI) approaches. Parsimony analyses were conducted with TNT v. 1. 5  [116, 117]. Heuristic searches were carried out with Traditional search (Wagner trees: 500 replicates and TBR: 350 replications) and New Technology search algorithms (RAS: 5000 additive sequences, Sectorial Search: RSS + CSS (500 rounds) with minimal sector size 5, Ratchet: 500 iterations, Drift: 500 cycles and Tree fusing: 20 rounds).
Further taxa, i. e. Plesiopidae, Pseudochromidae, Opistognathidae and Grammatidae, were retrieved in similar positions within both molecular-based and morphology-based analyses. While molecular data suggest that the two grammatid genera Gramma and Lipogramma are only distantly related, which results in paraphyletic Grammatidae  [1, 2] , the morphological data support monophyletic Grammatidae (figure  9 ).
Conclusion The present study shows that recent molecular phylogenies can contribute to new hypotheses in the evolution of morphological structures and morphological data can be used to independently test molecular findings. Both approaches work well together and can lead to new insights into the evolution of fish diversity. The phylogenetic analyses of the Ovalentaria using a dataset of one morphological complex, the caudal-fin skeleton, resulted in similar topologies as proposed by molecular data, which simultaneously support molecular findings and also demonstrate the power of morphological data. The well-known disadvantage, the time-consuming data acquisition, of morphological analyses should not be considered an obstacle as the results from such analyses bring forth new hypotheses useful in various disciplines, e.
Acknowledgements. We want to thank  Amanda Hay  and  Sally Reader  from the  Australian Museum  and  Jonathan Pfliger  and  Zora Gabsi  from the  Muséum  national d'Histoire naturelle for loan of specimens and X-rays. We are also grateful to  Fabian Herder  from the  Zoologisches Forschungsmuseum Alexander König  and  Manuela Schmidt  and  Bernhard Bock  from the  Phyletisches Museum  for loan of specimens. We thank  Ulrich Schliewen  and  Dirk Neumann  from the  Zoologische Staatssammlung München  for X-rays. We also want to thank  Zachary Randall  from the  Florida Museum of Natural History  for µCT scans. We are very grateful to  Eric Hilton  and  Adriana López-Arbarello  for providing helpful comments that improved the herein presented study.
| Data processing Acoustic data were processed using the open-source library EchoPY v1. 1, (2020), implemented in Python 3. Near-surface noise was avoided by excluding the signal from the transducer to a fixed distance of 10 m. Background noise was estimated and subtracted using the approach of De  Robertis and Higginbottom (2007). Low signal-tonoise regions were excluded in two steps. First, samples with signalto-noise ratios below 10 dB were set to -999 dB. Second, the depth range where background noise was above -80 dB was removed. Single-ping interferences from other instruments, and periods with either noise or attenuated signal due to inclement weather, were removed using the filters described by  Ryan et al. (2015). S v data were vertically and horizontally downsampled, by computing the arithmetic mean in the linear domain, to a resolution of 2 m by 2000 m, respectively.
| Daytime classification The first nine PCs of the daytime dataset explained 76% of the total variance contained in the acoustic profiles at 38, 70 and 120 kHz (Figure  4a ). These nine PCs were used to classify the acoustic profiles through hierarchical clustering (Figure  5b ). The resulting classification was projected along the first two PCs (Figure  5c ) and along geographical coordinates (Figure  5d, e ). In the PC space, we can see that these coordinates correspond to the acoustic seascape D1 (Figure  4c ), associated with slopeboundary waters (Figure  4d, e ). These differences were also evident Vertical integration values of acoustic backscatter and minor modes of daytime profile variance can be seen in supplementary material (Figures  S2  and S3 ).
| Nighttime classification The first nine PCs of the nighttime dataset explained 83% of the total variance contained in the acoustic profiles at 38, 70 and 120 kHz (Figure  4a ). As in the daytime analysis, these nine PCs were used to classify the acoustic profiles through hierarchical clustering (Figure  4b ), and the resulting classification was projected along the first two PCs (Figure  4c ) and along geographical coordinates (Figure  4d, e ).
| Alternative implementations of the method The present study provides a new application of FDA to classify acoustic seascapes using ship-borne multifrequency echosounder data. In particular, we propose a solution to analyse frequencies with variable depth range, we provide fully documented opensource code to implement the method (EchoPY v1. 1, 2020), and we show with ancillary environmental and biological data that the classification obtained is consistent with other regionalization methods. However, decisions on the depth range and frequencies used will be survey specific, depending on frequency availability and signalto-noise (weather) conditions. Decisions related to spatial resolution will depend on the objectives of the study. For instance, in the present work, we aimed at analysing regional-scale features, essentially sound-scattering layers which typically presented a vertical extent greater than 10 m.
parallel to the acoustic data presented here, Assunção et al. (2020)  defined three different thermohaline areas: the westernboundary and south-equatorial current systems (WBC and SEC) and the transition area in between (see Figures4d,c and 5d,c). In the present study, echoregions systematically distributed either in slope-boundary or open-ocean areas, matching these thermohaline systems. The only exception to this occurred in the nighttime Spring 2015 dataset, where the slope-boundary and the Rocas Atoll were classified together as the same echoregion, despite they were geographically separated by the WBC-SEC transition area.
The method proposed here and the associated opensource application can be implemented to any acoustic dataset to define echoregions in the aquatic environment. ACK N OWLED G EM ENTSWe acknowledge the French oceanographic fleet for funding ABRACOS I and ABRACOS II ship-time and the officers and crew of the R/V Antea for their contribution to the success of the operations F I G U R E 7 Day (a) and night (b) acoustic seascape regionalization compared to the distribution of current systems (c), pyrosomes (d), physonect siphonophores (e) and mesopelagic fish assemblages (f) in Fall 2017. Pyrosome and physonect abundances were analysed for the present study, circle colours indicate the sampling depth (epipelagic-mesopelagic) and time (day-night), circle size indicate abundance of colonies per 10 -3 • m -3.
, 2021). Finally, the interactions of solar radiation with the sea ice surface and clouds are likely oversimplified, leading to major issues in simulating this key surface coupling process with stark implications for sea ice properties  (Stapf et al. , 2020; Keen et al. , 2021). The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition was designed to address many gaps in our knowledge and to collect a wealth of data to study the changing Arctic climate system and improve model representations of this system. MOSAiC was a yearlong expedition in the central Arctic starting in September 2019, wherein the German research icebreaker Polarstern (Polarstern, 2017) was frozen into the sea ice north of the Laptev Sea, and drifted with that ice across the Transpolar Drift for most of the following 12 months.
Observational assets were onboard Polarstern, surrounding Polarstern in a Central Observatory (CO) ice camp, and across a Distributed Network (DN) of mostly autonomous observing systems, in addition to coordinated satellite and aircraft observations. A general overview of the expedition, including scientific design and logistical implementation, is forthcoming. In addition to that overview, more detailed overview papers aim to document the scientific drivers and field details for each of the primary MOSAiC science teams, including providing initial views into the data and preliminary results. These include the atmosphere (ATMOS, this paper), sea ice and snow (ICE;  Nicolaus et al. , 2022) , ocean (OCEAN;  Rabe et al. , 2022) , ecosystem (ECO; forthcoming), and biogeochemistry (BGC; forthcoming) teams.
Atmospheric physical structure measurements Measurements of the physical structure of the atmosphere-temperature, humidity, wind speed and direction, and turbulence-provide an essential context for interpreting many of the other measurements, far beyond their primary application to atmospheric dynamics. They span a hierarchy of temporal and spatial scales, and include continuous measurements, discrete but regularly scheduled, and sporadic sampling (see Figure  5  and Table  B1  in Appendix B). The backbone of the atmospheric structure measurements is provided by the radiosounding program. Radiosondes were launched at least every 6 hours throughout the entire duration of MOSAiC, including periods when Polarstern was in transit. Conforming to the requirements of Global Climate Observing System Reference Upper-Air Network postprocessing, additional prelaunch ground checks of the radiosondes were conducted, and data were transmitted to the Global Telecommunications System (GTS) for assimilation by operational weather forecast models.