dataesr/scientific-paragraphs-categorization huggingface dataset:
2019), around the limit between the MIZ and the OW sector. In general, FCM abundance data indicated higher pico-and nano-phytoplankton abundances within the MIZ and the OW sectors, respectively. Although the micro-phytoplankton size fraction was not counted by FCM, the amplicon data suggests a community structure shift from smaller to larger size fractions as the bloom progresses. FCM data shows that while UI pico-phytoplankton reached its maximum and was relatively well distributed down the euphotic zone of the water column, nano-phytoplankton community was particularly abundant right beneath the sea-ice, indicating a close association between the top of the water column and the bottom of the sea-ice (Figure  3 ). Interestingly, contrary to cell abundance, the relative abundance of the main genera did not seem to change with depth within the UI sector, except for a higher contribution of Phaeocystis spp.
2009). Such recruitment due to polar dark conditions might explain the dominance of Micromonas, an early-bloom taxa  (Lovejoy et al. 2007 ) with known persistence during winter  (Joli et al. 2017; Vader et al. 2015) , within the UI and MIZ sectors, which represented almost the total bulk of pico-sized plankton. Several abundant non-diatom ASVs were flagged as indicator ASVs from iceassociated sectors, especially in the smaller size fraction (Figure  6 ). Many of these indicator ASVs were found in datasets from the high Arctic (see ASV diversity subsection below), suggesting that UI and MIZ phytoplankton communities are highly diverse, probably low-light adapted populations of smaller organisms, which seem to be connected to higher-latitude communities  (Kalenitchenko et al. 2019)  probably via water mass intrusions from the Nares Strait and the Smith, Jones and Lancaster Sounds  (Bluhm et al.
2017; Tragin and Vaulot 2019). It exhibits a wide thermal niche and is considered a sentinel for polar  (Freyria et al. 2021 ) and global plankton diversity  (Demory et al. 2019 ) in relation to temperature changes in the oceans. The use of metabarcoding datasets combined with microdiversity approaches has previously allowed the discovery of new polar Micromonas ecotypes, such as the Micromonas B3 clade, which displays a wider distribution band towards lower latitudes than M. polaris  (Tragin and Vaulot 2019 ). The M. polaris CCMP2099 strain, isolated from North Water Polynya  (Lovejoy et al. 2007 ) and the RCC2306 strain from the Beaufort Sea (holotype of the species,  Simon et al. 2017 ) are 100% similar in the V4 region of the 18S rRNA to the M. polaris ASV_0003 from the present study. M.
polaris ASV_0003 has a widespread distribution pattern with a high abundance in all sectors, in accordance with its dominant role within the Arctic  (Balzano et al. 2012; Lovejoy et al. 2002 Lovejoy et al. , 2007;; Not et al. 2005 ). Although M. polaris ASV_0154 differs from ASV_0003 by a single nucleotide (Fig. S6 ), its significantly different distribution (Fig. S7 ), and the assignation of ASV_0003 as an indicator species, suggests it might represent a new ecotype. There is no 100% similarity match in GenBank with M. polaris ASV_0154, either to strains or environmental sequences. The distribution of M. polaris ASV_0154 is pan-Arctic, although it always contributes to a small fraction of Micromonas reads. M. polaris ASV_0154 has also been found in the Nares Strait (metaPR 2 set #42,  Kalenitchenko et al.
2020) , there was no 100% match between ASV_0244 and any strain or environmental sequence in GenBank. Pterosperma sp. ASV_0244 was only found in three other samples from the 41 global datasets in the metaPR 2 database, in the east coast of Greenland  (Kopf et al. 2015)  and the Nansen Basin  (Metfies et al. 2016). Although previous studies have identified sea-ice associated communities harboring a high relative abundance of Pyramimonas (Gradinger 1996;  Mundy et al. 2011) , our indicator analysis did not detect any specific distribution linked to sea ice for this genus. Haptophyta. P. pouchetii is ubiquitous throughout the Arctic  (Lasternas and Agustı 2010; Schoemann et al. 2005)  and has been reported to be capable of early blooms, even under snow-covered ice pack  (Assmy et al. 2017 ). Although P.
Acknowledgements We wish to thank officers and crew of  CCGS Amundsen. The success of the field campaign was widely contributed to  G. Bécu ,  J. Lagunas ,  D. Christiansen-Stowe ,  J. Sansoulet ,  E. Rehm ,  M. Benoît-Gagné ,  M. -H. Forget  and  F. Bruyant  from  Takuvik laboratory  and  J. Bourdon ,  C. Marec  and  M. Picheral  from  CNRS. We also thank  Québec-Océan  and the  Polar Continental Shelf Program  for their in-kind contribution in terms of polar logistics and scientific equipment. We thank the ABIMS platform of the  FR2424 (CNRS, Sorbonne Université ) for bioinformatics resources. We thank  Margot Tragin  for her help during sampling and onboard sample processing.
Data availability Physicochemical and biological data from the Green Edge project are available at http://www. obs-vlfr. fr/proof/ php/GREENEDGE/x_datalist_1. php?xxop=greenedge&xxcamp=amundsen and at https://www. seanoe. org/ data/00487/59892/ as described in detail in  Bruyant et al. (2022). Raw metabarcoding sequences are available under the Accession Code PRJNA810033 from GenBank SRA. Source code for sequence processing is available at https://github. com/vaulot/Paper-2021-Vaulot-metapr2/tree/main/R_processing.
The largest weights are assigned to mass conservation (i. e. , the mass conservation equation will be more relevant for the resolution of the EOMP) and to the parameters with the largest sampling accuracy (potential temperature and salinity). For potential temperature and salinity the assigned weight was 24. Nutrients, such as phosphate, nitrate, and nitrite have weights of 7. Due to a non-constant   S1-S3. N: Nutrients; TDS: Temperature, salinity, and density. * SO245  (Ferdelman et al. , 2019 ), SO211 (Martínez-Méndez et al. , 2013b , 2013c , 2013d, 2013e) , M93  (Lavik & Krahmann, 2016). **CIMAR21 data set from shallow water depth is available in  Farías and Troncoso (2021) , in this study we add information about deep stations. * and ** data sets are available in GLODAP and PANGAEA webpages.
Water Column Stable Isotope Data Water samples for δ 18 O, δD, and δ 13 C DIC were collected during several expeditions conducted between 1992 and 2018 in the SEP using Niskin bottles coupled in a Rosette with CTD device (see Table  S3  and S4 ). The RR-9702a expedition is an exception; a single Niskin bottle was coupled to the top of a multicorer device. To prevent contamination and/or micro-evaporation, water samples were immediately siphoned from Niskin bottles into 100 and 25 mL glass and polypropylene bottles avoiding bubbles. Water samples for δ 13 C DIC determinations were poisoned onboard with 0. 5 mL of ultra-saturated mercury chloride (HgCl 2 ) to avoid alternating the δ 13 C DIC signature by further remineralization. All δ 18 O, δD, and δ 13 C DIC samples were sealed with parafilm and kept at 4°C until analysis in the laboratory.
96‰) and was better than 0. 1‰. Working standards were calibrated against NBS 19 (δ 13 C: +1. 95‰;  Brand et al. , 2014) , NBS 18 (δ 13 C: -5. 01 ± 0. 03;  Brand et al. , 2014). All results were then corrected to VPDB. To complement our new data, we include in the database the published δ 13 C DIC values for the SEP (n = 1552, 4 expeditions, see Table  S4 ). This data was prepared following the WOCE protocols and the data are stored in the GLODAP database https:// www. glodap. info/. The burning of fossil fuels since the Industrial Revolution has led to the long-term depletion of δ 13 CO 2 in the atmosphere, which in turn is affecting seawater δ 13 C DIC values (e. g. , ref. Suess Effect;  Keeling, 1979; Suess, 1955). The maximum Suess effect in the SEP is found in surface waters reaching between -0.
Data Visualization and Processing For data visualization we use Ocean Data View 5. 2. 1  (Schlitzer, 2021 ; https://odv. awi. de/). The original data for the meridional section plots were gridded using interpolated variational analysis (DIVA, https://github. com/gher-ulg/DIVA) and weighted-average interpolation. The degree of gridding was maximum 35 in the coordinate axis (X) and 45 in the axis (Y). For the EOMP analysis, a MATLAB code was developed by  Llanillo et al. (2012 Llanillo et al. ( , 2013)). A Python code was developed for statistical analyses and data manipulation using elements of the free and opensource library SciPy for scientific and technical computing https://www. scipy. org/. For further information see https://github. com/Paleobiogeochemistry.
δ 18 O-S and δD-S relationships are statistically significant for the SAAW and ESSW, suggesting that the SAAW have an origin in high-latitudes with strong influences of high precipitation and melted-ice from the Patagonian Ice Sheet. Our study confirms that ESSW follows an opposite trend expected for tropical regions, possibly due to advection and mixing processes with surface waters. δ 13 C DIC can be used as a proxy to differentiate between STW/ESSW (oceanic section), SAAW/ESSW, ESSW/AAIW, and AAIW/PDW, but not between SAAW/STW and STW/ESSW (coastal section). The δ 13 C DIC , oxygen, and nutrients database for the oceanic section has a good spatial cover and can be compared with the new coastal data set, highlighting strong differences in water mass geometry above 1,000 m water depth in the SEP.
The relationships of δ 18 O, δD, and δ 13 C DIC with each other and with other parameters like temperature and oxygen offer a multiproxy database that can be used as present-day hydrological analogs for reconstructions of past water mass distributions. In addition, using the relationship of planktonic and epifaunal benthic foraminifera δ 18 O calcite versus δ 13 C calcite in specific time slices may enable the characterization of past SEP water column geometries from surface to deep waters.
(  1 ) Small individuals were scanned using a field of view of 2. 6 x 2. 6 mm 2 (5x magnification) resulting in a projected pixel size of 1. 3 μm, and a propagation distance of 148 mm. (2) Larger individuals were scanned using a field of view of ~12. 6 x 3. 3 mm 2 (1x magnification) resulting in a projected pixel size of 6. 17 μm, and a propagation distance of 500 mm. For individuals slightly wider than these field of views, the latter were extended horizontally by positioning the rotation axis off-centre and acquiring data over a 360° rotation of the sample. The volumes were reconstructed from the (vertically) combined radiographs using PyHST2 software  (Mirone et al. , 2014) , with a Paganin phase retrieval algorithm  (Paganin et al. , 2002). The huge resulting volumes (from 70 Gb to 1. 2 Tb) were reduced (crop, rescale 8-bit, binning) to facilitate 3D data processing.
Acknowledgments We thank  J. Joss  ( Macquarie University ) for providing Neoceratodus embryos. We are grateful to  Dr H. Seno  (curator of the  Ichthyology Collection of the Kanagawa Prefectural Museum of Natural History ) for the loan of Neoceratodus specimen  KPM-NI 11384. Funding:  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) -grant Finance Code 001  ( Programa Nacional de Pós Doutorado-PNPD) (CC) Programa de Apoio à Docência (PAPD)  grant  E-26/007/10661/2019 )- Universidade do Estado do Rio de Janeiro (CC)   Interdisciplinary Collaborative Research Program  of the Atmosphere and  Ocean Research Institute , the  University of Tokyo JP (CC, TS) Prociência  fellowship  CNPq  grant  310101/2017-4 (PMB) FAPERJ grant E-26/ 202. 890/2018  (PMB).
Quantification and attribution of long-term tropospheric ozone trends are critical for understanding the impact of human activity and climate change on atmospheric chemistry but are also challenged by the limited coverage of long-term ozone observations in the free troposphere where ozone has higher production efficiency and radiative potential compared to that at the surface. In this study, we examine observed tropospheric ozone trends, their attributions, and radiative impacts from 1995-2017 using aircraft observations from the In-service Aircraft for a Global Observing System database (IAGOS), ozonesondes, and a multi-decadal GEOS-Chem chemical model simulation. IAGOS observations above 11 regions in the Northern Hemisphere and 19 of 27 global ozonesonde sites have measured increases in tropospheric ozone (950-250 hPa) by 2.
, 2019; Gulev et al. , 2021) , but satellitedetected trends at mid-latitudes since the early 2000s are less certain due to instrument errors (e. g. , row anomaly), uncertainties in retrieval algorithms, and disagreements between the available products  (Gaudel et al. , 2018). Ozone measurements from the In-service Aircraft for a Global Observing System database (IAGOS)  (Petzold et al. , 2015) , which contains ozone profiles from more than 60 000 commercial aircraft flights worldwide since 1994, are a critical source of data for quantifying ozone trends in the free troposphere  (Cohen et al. , 2018). A recent study utilizing IAGOS observations identified remarkable ozone increases in the free troposphere since 1994 above multiple regions of the Northern Hemisphere  (Gaudel et al. , 2020). The IAGOS observations provide a new opportunity for checking the consistency of ozone trends derived from other observation platforms (e.
We evaluate the GEOS-Chem tropospheric ozone trends with the aircraft ozone observations from the IAGOS database which provides long-term ozone measurements in multiple regions, together with ozonesonde measurements; we also compare the GEOS-Chem results with CMIP6 models. We then conduct a series of sensitivity simulations to quantify the impact of anthropogenic emissions and climate change on global and continental tropospheric ozone trends and estimate the resulting radiative impacts.
Ozonesonde observations We also use ozonesonde measurements from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC; available at https://woudc. org/data. php, last access: 16 October 2022). WOUDC is operated by the Meteorological Service of Canada, within Environment and Climate Change Canada. For our study period of 1995-2017, the database contains ozone profiles from 130 globally distributed sites. Ozone from the surface to the stratosphere (from launch up to 35 km) is measured by balloon-borne ozone electrochemical concentration cell instruments  (Tarasick et al. , 2019)  with a vertical resolution of about 100 m and an accuracy of 5 %-15 % in the troposphere and 5 % in the stratosphere  (Sterling et al. , 2018; Witte et al. , 2018; Steinbrecht et al. , 2021). The sampling frequencies of ozonesondes vary across sites but are mostly lower than 2-4 profiles per week, posing a challenge for our ability to detect a trend.
GEOS-Chem model description and configuration We apply the global three-dimensional chemical transport model GEOS-Chem version 13. 3. 1 (available at https: //github. com/geoschem/GCClassic/tree/13. 3. 1, last access: 9 March 2022,  Bey et al. , 2001)  to interpret global tropospheric ozone and its trends for 1995-2017. The model is driven by Modern-Era Retrospective analysis for Research and Application version 2 (MERRA-2) assimilated meteorological fields from the NASA Global Modeling and Assimilation Office (GMAO), which has a native horizontal resolution of 0. 5 • (latitude) × 0. 625 • (longitude) and 72 vertical layers extending from the surface to 0. 01 hPa  (Gelaro et al. , 2017). GEOS-Chem describes coupled ozone-NO x -VOCsaerosol-halogen tropospheric (volatile organic compounds;  Wang et al.
1 Tg vs. 164. 6 Tg in the year 2014). These differences mainly reflect the updated regional inventory for China  (Zheng et al. , 2018) , along with the inclusion of regional inventories for DICE-Africa (Diffuse and Inefficient Combustion Emissions in Africa;  Marais and Wiedinmyer, 2016)  and SMoG-India (Speciated Multipollutant Generator;  Venkataraman et al. , 2018) , as well as the updated activity data in CEDSv2 (Fig. S1  in the Supplement). The default GEOS-Chem model includes a monthly threedimensional gridded inventory of aircraft emissions of NO x , CO, and hydrocarbons based on the Aviation Emissions Inventory Code v2. 0 (AEIC) for the year 2005, resulting in 0. 96 TgN of global aircraft NO emissions with no interannual variability. A new study has showed that aircraft activity has exploded in recent decades, with aircraft CO 2 emissions 79.
, 2007; Murray et al. , 2012). Biomass burning emissions in 1995-2017 are from the BB4CMIP (historic global biomass burning emis-sions for CMIP6) inventory as described in van  Marle et al. (2017) , in which the emissions for years after 1997 are the same as the Global Fire Emissions Database version 4 (GFED4; van der  Werf et al. , 2017). Fire plumes can be injected beyond the planetary boundary layer (PBL). We partition 65 % of the biomass burning emissions to the PBL and the remaining 35 % into the free troposphere following Fischer et al. (  2014 ) and  Travis et al. (2016). Model configurations are summarized in Table  2. We spin up the GEOS-Chem model by 10 years to provide an initial field for the atmospheric chemical components on 1 January 1995. The long spin-up time is to properly initialize the lower stratosphere.
Trend estimation We follow  Gaudel et al. (2020)  to determine tropospheric ozone trends from IAGOS and ozonesonde observations using the quantile regression method  (Koenker and Bassett, 1978). The quantile regression method estimates trends based on the rank value of the sample distributions rather than the mean values, which makes no assumptions about the distribution of the data and has better tolerance to outliers  (Koenker and Xiao, 2002; Chang et al. , 2021). These advantages make it a robust tool for estimating trends of time series with many intermittent missing values, such as ozone records from the IAGOS and ozonesonde observations. More details of the method are described in  Koenker and Hallock (2001). There are four indices defining an ensemble member: "r" for realization, "i" for initialization, "p" for physics, and "f" for forcing.
We calculate ozone trends on 15 pressure levels at 50 hPa intervals from 950 to 200 hPa for each IAGOS region and each ozonesonde site. We remove data points with ozone higher than 125 ppbv at altitudes higher than 500 hPa to exclude the influence from episodic stratospheric intrusions and because the effect of these intrusions is diluted in the model  (Zhang et al. , 2014) , based on observed ozone values in fresh stratospheric intrusions and in air pollution plumes  (Nowak et al. , 2004; Cooper et al. , 2005; Archibald et al. , 2020). Following  Gaudel et al. (2020) , we first calculate the monthly mean ozone values to construct the mean seasonal cycle from 1995 to 2017 for each layer and for each IAGOS region or ozonesonde site. The mean seasonal cycle is then used to deseasonalize each ozone record at the same pressure level.
Park et al. (2021)  also reported the ozone underestimation from eight chemical models including GEOS-Chem above South Korea during the Korea-United States Air Quality (KORUS-AQ) campaign in May-June 2016, and  Gaubert et al. (2020)  attributed this to missing CO sources in emission inventories for East Asia. The modeled ozone is biased high in the tropical regions particularly in boreal autumn and winter (Table  S1  in the Supplement). We find that the GEOS-Chem ozone biases are smaller in 2013-2017 when activity data and emission factors are better constrained than in the early period of 1995-1999, smaller in regions where the CEDSv2 emission inventory is scaled to well-developed regional inventories (North America, Europe, East Asia) than in other regions, and larger in the lower troposphere than in the upper troposphere.
In particular, the ozone low biases at the 950-800 hPa layer above tropical Asia and Africa are much larger (11. 3-15. 9 ppbv) in 1995-1999 than in 2013-2017 (about 2. 5 ppbv). A possible reason is that anthropogenic emissions in the early period and in developing tropical regions are biased high, which will also lead to underestimation of tropospheric ozone trends over these regions, as will be discussed later. Figure  4  further compares the simulated vertical distributions of ozone with the ozonesonde measurements. We aggregate the ozonesonde data into six latitudinal bands for comparison, and results for individual sites are shown in Fig. S2. The comparison again shows that GEOS-Chem captures the vertical structure of ozone at these globally distributed ozonesonde stations, but unlike the positive bias relative to IAGOS observations over industrialized areas, GEOS-Chem shows negative ozone biases in the free troposphere relative to ozonesonde measurement, in particular for remote sites in the extratropical regions by up to 20 ppbv.
, 2022). We integrate in Fig. 7a  and Table  4  the annual trends in median ozone for the tropospheric column (950-250 hPa) in 1995-2017 from both IAGOS and ozonesonde observations. This allows us to provide a more complete picture of observed global tropospheric ozone trends than previous studies focusing on the Northern Hemisphere alone  (Gaudel et al. , 2020)  and to check the consistency between the two sources of ozone trend measurements. Both observational datasets consistently reveal widespread ozone increases in the troposphere over the past 2 decades, with larger ozone increases over developing regions in low latitudes than those over mid-latitudes and high latitudes. All 11 IAGOS areas and 19 of 27 ozonesonde sites have measured increases in tropospheric ozone by 2. 9 ± 1. 7 and 1.
The spatial resolution of 4 • × 5 • in our simulations limits the model's ability to capture finerscale ozone trends. We also call for more modeling studies to better understand ozone variability in the lower stratosphere and to quantify its impact on tropospheric ozone trends. Data availability. The ozonesonde data are from https://woudc. org/data. php (World Ozone and Ultraviolet Radiation Data Centre, 2022). The IAGOS dataset is archived at http://www. iagos-data. fr/ (IAGOS, 2022). The updated global anthropogenic emissions data from CEDS are available from https://data. pnnl. gov/dataset/ CEDS-4-21-21  (O'Rourke et al. , 2021). The MERRA-2 reanalysis data are from http://geoschemdata. wustl. edu/ExtData/GEOS_4x5/ MERRA2/ (GMAO, 2022). The CMIP6 model outputs are available on the Earth System Grid Federation (ESGF) website (https: //esgf-data.
Although this biogeographic character does participate to tree estimation to some extent, its influence is minimal. Indeed, as a single character, it generally has little weight compared to DNA and protein datasets that generally consist of thousands of characters. The present section considers extensions of the phylogenetic models that resort to two types of replication: replication over characters, and replication over partitions of characters. Multiple-character partitions typically comprise tree-estimating characters that are grouped together into the same partition assuming that they have similar evolutionary models. Specifically, all characters put together in the same partition are assumed to have identical relative rates (i. e. they share the same R matrix). This grouping of characters into partitions is necessary to avoid having too many parameters in the model.