dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020; Mueller et al. , 2021). However, the bottom-up carbon emissions based on public protocols for cities are prone to large uncertainties  (Gurney et al. , 2021). High-resolution gridded CO 2 emission inventories, e. g. , the Hestia dataset for some US cities  (Gurney et al. , 2019)  or the London Atmospheric Emissions Inventory (LAEI) dataset for Greater London (Greater London Authority, 2021), provide detailed descriptions of emissions from urban domains. This approach relies on a collection of extensive activity data and emission factors and can thus be laborintensive and time-consuming, especially when doing regular updates. Recently, Carbon Monitor Cities has been developed to provide near-real-time city-level CO 2 emissions data for 1500 global cities from 2019 to 2021  (Huo et al.
2  also shows the simulated background CO 2 concentration at the SAC station using the Copernicus Atmosphere Monitoring Service (CAMS) CO 2 dataset as the boundary and initial inputs for WRF-Chem. The atmospheric background CO 2 concentrations have steadily been rising over the past 6 years, which is primarily attributed to global human activities. Generally, the average CO 2 concentrations across the network vary seasonally between 390 and 450 ppm. They are mainly driven by the atmospheric transport, the CO 2 biospheric cycle and their proximity to the urban anthropogenic CO 2 emission sources. The interannual CO 2 variations primarily depend on the year-to-year vari-ations in synoptic weather conditions, air temperature and the associated emissions. For example, the notably high CO 2 concentrations observed near the surface during the 2016/17 winter were caused by stagnant, often stable, atmospheric stratification associated with cold and dry air masses and low-ventilation weather conditions over the north of France  (Bulletin Climatique Météo-France, 2016 , 2017).
, 2021); (ii) the TNO-GHGco inventory at a resolution of 1/60 • ×1/120 • (long × lat) (∼ 1 km × 1 km) for the years 2015, 2017, and 2018  (Dellaert et al. , 2019) ; (iii) the AirParif inventory developed by the local official air-quality agency at a 1 km × 1 km resolution for the years  2005, 2010, 2015, and 2018, (iv)  the city-level CO 2 emissions from the Carbon Monitor Cities dataset (https://cities. carbonmonitor. org/, last access: August 2023,  Huo et al. , 2022)  for the years  2019-2021; and (v)  the study by  Staufer et al. (2016) , who reported a full year's (August 2010-July 2011) estimate of the IdF region fossil fuel CO 2 emissions by assimilating citywide CO 2 measurements from a sparse network of three stations with the inversion methodology. Here, we consider that these multiple emission estimates allow for a cross-validation as they were developed by several research groups using distinct data sources, methods and protocols.
Annual emission estimates Verifying annual emission totals and tracking emission trends over time using multiple methods constitute an indication of the reliability of inverse emissions estimates. Moreover, given the potential impact of atmospheric transport errors on the emission estimates, and assuming that the model errors do not change statistically over time, the emission trends are expected to be less sensitive to model biases than the emissions estimates. The interannual variations are a critical metric to assess the effectiveness of the CO 2 mitigation efforts. Figure  6  shows the multiyear trend in the annual total fossil fuel CO 2 emissions over the IdF region for the 2005-2021 period. The prior and inverse emissions are compared with multiple inventory datasets. The interannual variation in fossil fuel CO 2 emissions indicates a decreasing trend over the IdF region during the 2005-2021 period using the Mann-Kendall (MK) trend test at a 5 % significance level (p values (0.
Conclusions and discussions This study shows the capacity of our city-scale inversion system, with a state-of-the-art inventory and high-precision continuous CO 2 measurements, for a timely and an effec-tive monitoring of urban fossil fuel CO 2 emissions over a long time period from 2016 to 2021. Our results indicate a decreasing trend in the annual CO 2 emissions over the IdF region with an amplitude of ∼ 2 % ± 0. 6 % per year at a 5 % significance level. The comparison of both prior and posterior annual emissions with independent estimates from other inventory datasets shows a less than 10 % difference, which is a satisfying target in terms of emission trend detection and verification for Paris in order to support its emissionsmitigation measures and related policy  (Wu et al. , 2016). In practice, few cities have such a high-resolution near-realtime local inventory like Paris.
The uncertainties in the posterior estimates of CO 2 emissions are caused to a certain extent by errors in the spatiotemporal distribution of emissions at scales finer than the targeted ones. The present inversion system mainly controls the city-scale fossil fuel CO 2 emissions but not the finer resolution in space. We thus further conducted a sensitivity inversion test using the TNO 1 km inventory as a priori as an alternative to the Origins. earth dataset for 2018. The other parameters were kept identical to the reference inversion configuration. The TNO inventory indicates more fossil fuel CO 2 emissions concentrated within inner Paris compared with the Origins. earth dataset (Fig. S12 ). The inversion was able to spatially differentiate between Paris' city center and the greater Paris region (excluding Paris), correcting the respective emissions differently (Fig.
This could be achieved by improving the default VPRM model with a modified gross primary production and ecosystem respiration equations, domain-specific optimized parameters, highresolution input datasets, and high-quality diagnostic phenology  (Gourdji et al. , 2022). In addition, measurements of carbon isotopes ( 14 C, 13 C) and tracers co-emitted with CO 2 (e. g. , CO, NO x , VOCs) could also be used to separate the contributions from fossil fuel and biogenic components to the total CO 2 concentrations, which would be beneficial for the optimization of sectoral CO 2 fluxes. Code and data availability. The hourly-averaged CO 2 data measured at seven in situ stations are available on request from Michel Ramonet (michel. ramonet@lsce. ipsl. fr). The observed ABL height data are available on request from Simone Kotthaus (simone.
kotthaus@ipsl. polytechnique. fr). The Origins. earth CO 2 inventories are available on request from Hervé Utard (herve. utard@origins. earth). The TNO CO 2 inventories are available on request from Hugo Anne Denier van der Gon (hugo. deniervandergon@tno. nl). The ODIAC fossil fuel emission dataset (version: ODIAC2020b) was downloaded from the Center for Global Environmental Research, National Institute for Environmental Studies, https://doi. org/10. 17595/20170411. 001 (Oda and Maksyutov, 2015). The Carbon Monitor Cities dataset was downloaded from https: //cities. carbonmonitor. org/ (Huo et al. , 2022). The eddy covariance measurements were downloaded from the ICOS (https://doi. org/10. 18160/2G60-ZHAK, Warm Winter 2020 team and ICOS Ecosystem Thematic Centre, 2022). Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/acp-23-8823-2023-supplement. Author contributions. JL, TL and PC designed the study. JL did the coding and implementation of the research. JL, TL, PC, FMB, GB, HU, MR and IA contributed to the analysis and interpretation of the results. MR, OL and MC scientifically coordinated the development of the CO 2 measurement stations and ensured the calibration of the dataset. SK and MH processed the observed ABL data and gave sound advice on the model-data evaluation. HU provided the Origins. earth inventory, OS and OP provided the AirParif emission estimates, and HADvdG and SNCD provided the TNO inventory; they all provided valuable feedback and opinions on the multiple dataset comparisons in Sect. 3. 3. JL prepared the paper with contributions and suggestions from all the authors. Competing interests.
The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. The authors would like to thank the SUEZ Group, La Ville de Paris and ICOS Cities for the support of this study. We also thank Marc Jamous at CDS, the IPSL QUALAIR platform team, Cristelle Cailteau-Fischbach (LATMOS/IPSL) at JUS and LSCE/RAMCES technical staff for the maintenance of the CO 2 monitoring network. We acknowledge Pauline Buysse, Jérémie Depuydt, Daniel Berveiller and Nicolas Delpierre for providing the eddy covariance flux measurements at the Grignon and Fontainebleau (Barbeau) ecosystem sites. We also acknowledge Xiaobo Yang and Anna Agusti-Panareda from ECMWF for providing the near-real-time CAMS CO 2 dataset, Elise Potier for her help in processing the TNO inventory, and the technical and IT staff at the SIRTA observatory for operating the ALC.
The authors have received funding from ICOS Cities, a. k. a. the Pilot Applications in Urban Landscapes -Towards integrated city observatories for greenhouse gases (PAUL) project, from the European Union's Horizon 2020 research and innovation program under grant agreement no. 101037319. Thomas Lauvaux was supported by the project CIUDAD (Make Our Planet Great Again program) and the Chair Fellowship CASAL (French Ministry of Research -CNRS).
Financial support. support The authors have received funding from ICOS Cities, a. k. a. the Pilot Applications in Urban Landscapes -Towards integrated city observatories for greenhouse gases (PAUL) project, from the European Union's Horizon 2020 research and innovation program under grant agreement no. 101037319. Thomas Lauvaux was supported by the project CIUDAD (Make Our Planet Great Again program) and the Chair Fellowship CASAL (French Ministry of Research -CNRS).
Here we use two observational data sets, an eddy-permitting reanalysis product and regional high-resolution simulations, to investigate the interannual variability of the Halo and Taylor Cap for the period 2007-2022. Observations include novel hydrographic profiles obtained in the Maud Rise area in January 2022, during the first SO-CHIC cruise. It is demonstrated that the temperature of deep waters around Maud Rise exhibits strong interannual variability within the Halo and Taylor Cap, occasionally to such an extent that the two features become indistinguishable. A warming of deep waters by as much as 0. 8°C is observed in the Taylor Cap during the years preceding the opening of a polynya in 2016 and 2017, starting in 2011. By analyzing regional simulations, we show that most of the observed variability in the Halo is forced remotely by advection of deep waters from the Weddell Gyre into the region surrounding Maud Rise.
Observations We consider the EN4. 2. 2 data set  (Good et al. , 2013) , which compiles hydrographic profiles throughout the global ocean, acquired via various observational systems such as Argo, ASBO, GTSPP, World Ocean  Database 2018, and MEOP (McMahon et al. , 2021). Duplicate profiles are excluded by the processing of the data  (Good et al. , 2013). In this study, we only use profiles that had temperature and salinity available and reached a depth of at least 300 m during the period January 2007-May 2022, and extracted the Maud Rise region (5°W-19°E and 62°S-69°S; Figure  1 , purple box; the positions of the profiles can be found in Figure  S1  in Supporting Information S1). In what follows, data profiles with a salinity lower than 32 g kg -1 or a temperature exceeding 10°C at 10 m depth are excluded.
Data Availability Statement The EN4. 2. 2 data set (Met Office Hadley Centre, 2022) is available from Met Office Hadley Center at https:// www. metoffice. gov. uk/hadobs/en4/. The marine mammal data (Marine Mammals Exploring the Oceans Pole to Pole, 2022) were collected and made freely available by the International MEOP Consortium and the national programs that contribute to it (http://www. meop. net). The GLORYS12V1 data set (Copernicus Marine Environment Monitoring Service, 2022) is available from Copernicus Marine Environment Monitoring Service (CMEMS) at https://doi. org/10. 48670/moi-00021. Model parameters used to generate the model output and computer code to reproduce the plotted figures are available in an open-access repository  (Gülk, 2023). The SO-CHIC CTD data  (Steiger et al.
natural variability, or if it was an indication of the deeper climate changes currently at play (De Lavergne et al. , 2014). comments and suggestions. BG, FR, ANG, AN and GM acknowledge support from European Union's Horizon 2020 research and innovation programme under grant agreement No. 821001 (SO-CHIC). We highly appreciate the work of the scientific personal and the ship crew on the SO-CHIC cruise on SA Agulhas II, especially Brian Ward, Nadine Steiger and Jean-Baptiste Sallée. Further, we are grateful for the stimulating discussions with the SO-CHIC team. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Tetralith partially funded by the Swedish Research Council through Grant Agreement 2018-05973.
, 2013). To avoid damaging the fragile herbarium specimens, different traits were measured for each species within the four growth and reproductive strategies according to feasibility of non-destructive handling. Thus, traits occasionally differ between species for the same growth or reproduction strategy (Table  1 ). Traits were measured directly from the herbarium F I G U R E 1 The study region and study period. (a) Geographical locations of the historical (red dots) and contemporary (yellow dots) specimen collections in the region of Davos, Grisons, SE-Switzerland. (b) Summer (mean of June, July and August) temperature anomalies from the 1981-2010 reference period since 1880 for the Davos weather station. Bars in darker colours represent years when data collections had been madehistorical collections from 1889 to 1985 (orange section of the x-axis, n = 313), contemporary collections from 2008 to 2014 (yellow section of the x-axis, n = 660).
| Climate To estimate the regional climate and its change over time, we downloaded climate data from the nearest weather station in Davos Dorf, covering the period 1864-2014  (Begert et al. , 2005). In the absence of both on-site temperature or precipitation measurements and reliable downscaled climate data for the historical sampling period, the central position of this singular climate station provides a reasonable climate proxy for all historical and contemporary records. Mean summer temperature (June, July, August) and cumulative precipitation from January to August were calculated for each year. We further calculated temperature and precipitation anomalies from the 1981-2010 reference period (Figure  1b ). There is a significant difference in mean summer temperature and precipitation sum between the historical  and contemporary  collection periods (Supplementary Figure  S3 ).
| Statistical analyses Since the accuracy of the written comments from the botanists who collected old herbarium specimens varies, we indexed the herbarium specimens according to the quality and completeness of the accompanying written record, into low (location potentially varying by hundreds of metres, for example, due to geographical terms covering large areas), medium (location potentially varying by tens of metres) and high quality (location very exact). For this study, we ran two preliminary set of models on subsets of the data: (i) one set including only records of the highest quality and (ii) one set including both the medium and high-quality records. Since both sets of models returned similar results we chose to display results from the set of models using most of the data (ii) and report the final results in the paper based on both medium-and high-quality records.
Each model was fitted with a weights argument to account for the total number of individuals collected per species and per sampling elevation. We used R (R version 4. 1. 1) and the ggplot package for data visualisation  (Wickham, 2016). Models were fitted using the lme4 package in R  (Bates et al. , 2015)  and predictions made with the sjplot  (Lüdecke, 2022)  and ggeffects  (Lüdecke, 2018)  package. More information on model outputs is available in the Supplementary Materials.
Sequence libraries were prepared by Génome Québec with the TruSeq® DNA Library Prep Kit (Illumina, San Diego, CA, USA) and quantified using the KAPA Library Quantification Kit for Illumina platforms (Kapa Biosystems). Paired-end sequences were generated on a MiSeq platform PE300 (Illumina Corporation, San Diego, CA, USA) with the MiSeq Reagent Kit v3 600 cycles (Illumina, San Diego, CA, USA). The raw data files are publicly available on the NCBI Sequence Read Archive (PRJNA772886).
16S rRNA Data Processing Illumina sequence data (FASTQ files) were trimmed using Cutadapt (version 2. 8). The 16S rRNA (V3-V4) amplicon sequence variants (ASVs) were generated with the DADA2 pipeline (version 1. 16. 0)  [41]  and subsequently analyzed within the R environment (R version 4. 0. 3)  [42]. RDP (Ribosomal Database Project) 16 classifier database was used for ASV taxonomy assignment  [43]. RDP contains 16S rRNA sequences available from the International Nucleotide Sequence Database Collaboration (INSDC) databases. Some studies have also compared RDP and SILVA  [44, 45]. Archaea and unclassified ASVs at the phylum level (representing 4. 7% of the total ASVs) were removed. The phyloseq, microbiomeSeq, microbiomeMarker and vegan software packages were used to characterize the microbial communities  [46] [47] [48] [49].
Statistical Analysis The alpha diversity index, including Shannon, Pielou's evenness and richness, between the different groups was calculated using the phyloseq R package  [46]. The comparisons of the average values of each group were performed using Wilcoxon rank sum tests. Microbiota composition differences among groups were determined using multivariate analysis of variance with permutation (PERMANOVA) with 9999 permutations followed by pairwise permutation tests. Heatmaps were generated based on the relative abundance and constructed with the 30 most abundant genera. Linear discriminant analysis (LDA) identified the effect size (LEfSe) that differentiates the samples among these taxa. The threshold on the logarithmic score of the LDA analysis was set to 3. 0. Differences in the overall bacterial community composition among specimens were determined based on the UniFrac distance and visualized by principal coordinates analysis (PCoA).
Functional Analysis of the cmDNA Predictive functional analysis from 16S rRNA profiling of cmDNA is routinely used to link the abundance of specific taxa with metabolic profiles  [52] [53] [54]. To further explore the distinctive traits between sedentary and migratory S. trutta, we studied the functional content of the blood microbiome predicted from the KEGG database using the Piphillin tool (http://secondgenome. com/Piphillin)  [53]. This analysis revealed two major differences. The first is the distinct difference in the metabolic profiles of migratory versus sedentary trout (Figure  8 ). We found a clear shift in lipid metabolism, consistent with previous studies in the salmonids  [55]. A similar shift has recently been observed in lipid metabolism in the skin microbiome of S. salar during experimental seawater acclimatization  [8].
Acknowledgments: This research was performed at Kerguelen with the logistic support of the  French Polar Institute Paul- Emile-Victor (IPEV). The authors would like to thank all the personnel from the  IPEV  and the  Terres Australes et Antarctiques Françaises (TAAF)  for their help and hospitality during our stay. The authors would also like to thank  Jean-Pierre Féral  and  Thomas Saucède  and the  PROTEKER team  for their expert advice and technical support and  Dr. Jep Lokesh  for helpful discussions on the manuscript. The author would also like to thank  Marlène Fortier  for her excellent technical help.
Using the XRF data for calibration, the elemental compositions obtained from SEM/EDX mapping could be quantified. This also enabled an approximate estimate of the corrosion depth (d corr ) to be made, considering the cylindrical geometry according to Wersin et al. [18]  :  ( ) d d r r πhρ f = + - ([Fe] -[Fe] ), V V ρ A n z n n b c n c corr CL 1 =1 2 -1 2 m m s (Fe) (FeOOH) Fe (1) where d CL (m) is the corrosion layer (CL) thickness (measured microscopically), V m(Fe) and V m(FeOOH) are the molar volumes (m 3 /mol) of steel and goethite (the main corrosion product), respectively, ρ Fe and ρ b (kg/m 3 ) are the density of steel and bentonite, respectively, A s (m 2 ) is the unit surface of the Fe source (the cylindrical steel surface), n is the number of areas in which the concentration of Fe is increased, corresponding to the measured Fe and background Fe mass fractions [Fe] n and [Fe] c (kg Fe /kg bent ), respectively, r n (m) is the radius of the circle including the radius of the Fe source (r o ) and the distance from the source to the area n, z is the number of areas in which Fe is increased with respect to the pristine bentonite, f c is the calibration factor for converting the Fe measured with EDX to the "true" concentration andh (m) is the unit height of the cylinder.
ACKNOWLEDGEMENTS The authors would like to acknowledge  Urs Mäder ,  Margarita Koroleva  and  Samuel Gilgen  for their help with sampling and laboratory work and the team of the polishing workshop for their cautious preparation of the polished surfaces ( Institute of Geological Sciences, University of Bern). The authors would also thank  Stephan Kaufhold  and  Reiner Dohrmann  from  BGR (Germany ) for providing the large X-ray fluorescence data set on the ABM samples. The entire multinational team involved in ABM and FEBEX is acknowledged for fruitful discussions and the authors are grateful to all the members of the corrosion research team at  Jacobs  and the supporting analysts at  Oxford University  and BGS. The study was financially supported by  Nagra (Switzerland) ,  SKB (Sweden)  and  EURAD.
(2022). For transport rates, Q, depth-integrals were calculated at every instant of T r over the total vertical domain available from ACVP, OBS, and ADV. For bedload net transport rates, instantaneous erosion depths and upper sheet flow limits (based on the 8%-volumetric criterion  (Dohmen-Janssen et al. , 2001)  were used as integration boundaries (both boundaries within the ACVP measuring domain). For suspended load net transport rates, upper sheet flow limits and the upper ends of the domain available from ACVP, OBS, and ADV were used as integration boundaries. Instantaneous transport was time-averaged for most analyses and, for conciseness in the present publication, the "time-averaged" (or sometimes "net") was omitted in most references to transport. Data from multiple tests under the same wave condition were aggregated to provide a cross-shore resolution of detailed measurements around the onshore-and offshore-migrating bars.
Time-Averaged Concentration Figure  5  shows profiles of time-averaged sediment concentration at selected relative cross-shore locations. Near the bed, the ACVP provided profiles of high vertical resolution. Certain tests feature shorter vertical extents because of larger morphological changes in the respective cross-shore location and because the ensemble with smallest vertical extent determined the vertical extent of the ensemble-average (as explained in detail in Grossmann et al. (  2022 )). The single data points at higher elevations originate from OBS measurements, vertically-interpolated as explained in  Grossmann et al. (2022). To analyze processes of sediment suspension, Rouse profiles (black dashed lines) were fitted (nonlinear least squares fit) to the measured data: 𝐶𝐶 ( 𝜁𝜁 ′ ) = 𝐶𝐶0 ( 𝑧𝑧𝑎𝑎 𝜁𝜁 ′ ) 1 𝑚𝑚 (2) where C 0 is the reference concentration measured at reference height z a (0.