dataesr/scientific-paragraphs-categorization huggingface dataset:
8. 0, the CMOR-like reformatting of ICON will include a first implementation to make ICON output fully UGRID-compliant during runtime of ESMValTool. First tests have shown promising results: the adapted ICON data could be successfully regridded with the first-order conservative algorithm provided by iris-esmfregrid. We emphasize that regridding is not a trivial operation in general. ESMValTool's three currently available schemes for unstructured grids are sufficient for many applications; however, this is by no means a complete set of all possible regridding algorithms and does not cover all imaginable applications. For example, variables that describe fractions of quantities within grid cells like land-sea fraction, sea ice concentration, or fractional cloud cover need to be treated with extra care (e.
g. ,  Grundner et al. , 2022). The nearest-neighbor scheme illustrated in Fig. 2  is sufficient for the purpose of monitoring (i. e. , to get a quick overview of simulation results) but should not be used for more sophisticated scientific analyses where precise results are crucial. For the purpose of monitoring, a set of general diagnostics has been added to ESMValTool (see Table  1  for an overview). These diagnostics can be found in the subdirectory diag_scripts/monitor. All of these diagnostics are able to handle arbitrary variables from arbitrary datasets, which makes them versatile and flexible to use. The input for each diagnostic consists of data that have been preprocessed with ESMValTool. In order to configure the output, a number of parameters can be set and customized in the ESMVal-Tool recipe that runs the diagnostic script.
A recipe to reproduce these figures is publicly available on Zenodo  (Schlund, 2022). This recipe showcases the usage of the monitoring diagnostics on native model output and serves as a convenient starting point for users who want to process native model output with ES-MValTool. For a direct comparison with one or multiple reference datasets (e. g. , observations, reanalyses, output from other model versions), Fig. 3  shows simple time series of the global mean near-surface air temperature and precipitation from 1979 to 2014 created by the diagnostic multi_datasets. py for the ESM configuration of ICON (ICON-ESM) and the ERA5 reanalysis  (Hersbach et al. , 2020). The ICON simulation shown here is conducted using a standard Atmospheric Model Intercomparison Project (AMIP) setup at R2B4 resolution (about 160 km).
multi_datasets. py Combines multiple datasets in single plots. One input dataset can be defined as reference, which will be used to plot biases. -Time series [see Fig. Apart from such time series, the monitoring diagnostics can also be used to visualize annual cycles of arbitrary variables. This plot type can be created with the diagnostic monitor. py. Figure  4  shows an example of this using the annual cycle of the global mean near-surface air temperature from CESM2. The simulation shown here also uses a standard AMIP setup as defined by CMIP6 with all forcings (an-thropogenic and natural) from the recent past and prescribed sea surface temperatures and sea ice concentrations. In addition to the time series shown in Fig. 3 , the diagnostic multi_datasets. py also provides vertical profiles for a model and a reference dataset including the difference between the two.
If no reference dataset is provided, a single vertical profile of the model is returned. Figure  5  shows an example of the vertical air temperature profile from EMAC https://doi. org/10. 5194/gmd-16-315-2023 Geosci. Model Dev. , 16, 315-333, 2023 averaged over the years 2005 through 2014. These EMAC results are from the RC2-base-04 simulation, which is a free running simulation following the Chemistry-Climate Model Initiative (CCMI-1) protocol  (Jöckel et al. , 2020). For details about the model setup we refer to  Jöckel et al. (2016). The ERA5 reanalysis is used here as a reference dataset. The top row in the figure shows the vertical profile from EMAC (left) and ERA5 (right), while the bottom row shows the bias (calculated as simple difference) between the two datasets. Moreover, multi_datasets.
py also supports map plots (climatologies). Just like the vertical profiles provided by this diagnostic, these map plots can also be used to visualize differences between model data and a reference dataset. As an example, Fig. 6  shows the global precipitation climatology from EC-Earth3-CC averaged over the years 2005 to 2014 in comparison to the ERA5 reanalysis. The panels are arranged similar to Fig. 5 : the top row shows the climatologies of EC-Earth3-CC (left) and ERA5 (right), and the bottom row the difference between the two. The EC-Earth3-CC simulation shown is an AMIP simulation that has been published as part of the CMIP6 ensemble (ensemble member r1i1p1f1). In contrast to the annual mean climatology given in Fig. 6 , Fig. 7  shows monthly climatologies of the Arctic sea ice concentration for the months March and September averaged over the years 2005 to 2014 as simulated by IPSL-CM6.
In principle, the rich collection of diagnostics provided by ES-MValTool (see the orange box in Fig. 1 ) is now fully available for all supported models. This includes all diagnostics described in the scientific documentation of ESMValTool, e. g. , large-scale diagnostics for a comprehensive evaluation of ESMs  (Eyring et al. , 2020) , diagnostics for emergent constraints and future projections  (Lauer et al. , 2020) , and diagnostics for extreme events, and regional and impact evaluation  (Weigel et al. , 2021). Moreover, many new diagnostics have been added or will be added to ESMValTool, for example, diagnostics and recipes that have been used to compile parts of the latest Assessment Report 6 (AR6) of the Intergovernmental Panel on Climate Change (IPCC; e. g. ,  Eyring et al. , 2021). Since preprocessed output by ESMValTool is fully CMOR-compliant for all input datasets (see Fig.
1 ), no specific changes to these diagnostic scripts are required when dealing with native model output. As an example, Fig. 8  shows the annual mean near-surface air temperature between 1979 and 2014 averaged over the tropical land region (30 • S-30 • N) from the five models described in this paper that have been processed in their native format and an ensemble of (CMORized) CMIP6 models and the ERA5 reanalysis. A similar version of this plot was originally published by  Bock et al. (2020)  to evaluate progress across different CMIP generations (CMIP3, CMIP5, and CMIP6). All datasets show the steady increase of the nearsurface air temperature over the last decades. For all CMIP6 models and the native output of the models CESM2, EC-Earth3-CC, ICON, and IPSL-CM6, this figure shows results of AMIP experiments.
The five models presented here serve as examples and can be seen as a starting point for extending ESMValTool's support for native model output. original extra facets file used to read native EMAC output. These files are project-specific, i. e. , they describe extra facets for all datasets of a given project defined by the name of the extra facets file (here: EMAC). Extra facets files consist of nested dictionaries with four layers. The first layer describes the name of the dataset (here: EMAC). The second and third layer correspond to the name of the MIP table (e. g. , Amon) and the CMOR variable (e. g. , tas), respectively. Finally, the fourth layer lists the facets that will be added to all datasets defined in the ESMValTool recipe that match the description given by the other layers. The key-value pairs given in this fourth layer are model specific.
For example, in the EMAC file given here, possible values are the raw variable name used in the EMAC netCDF files (raw_name), the channel name of the variable (channel), and a post-processing flag that can be used to identify EMAC output files that have already been post-processed by an additional script by the modeler (postproc_flag). For the first three layers, wildcards are accepted, which can be used to conveniently add extra facets for multiple datasets, MIP tables, or variables at once. Code availability. The new extensions described in this paper have been available since ESMValTool v2. 6. 0. ESMValTool v2 is released under the Apache License, version 2. 0. The latest release of ESMValTool v2 is publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3401363 (Andela et al.
, 2022a). The source code of the ESMValCore package, which is installed as a dependency of ESMValTool v2, is also publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3387139 (Andela et al. , 2022b). ESMValTool and ESMValCore are developed on the GitHub repositories available at https://github. com/ESMValGroup (last access: 1 November 2022). An example recipe to get started with processing native model output with ESMValTool is publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 7254312 (Schlund, 2022). This recipe reproduces Figs. 2-7 of this paper. Detailed user instructions on the CMOR-like reformatting of native model output can be found in ESMValTool's documentation at https://docs. esmvaltool. org/en/latest/input. html#datasets-in-native-format (ES-MValTool Development Team, 2022a).
EMAC output (RD2-base-04 simulation, Fig. 5 ) is available from the CERA database at the German Climate Computing Center (DKRZ) under https://doi. org/10. 26050/WDCC/RC2 (Jöckel et al. , 2020). ICON output (Figs. 2, 3, and 8)  used in this study is not publicly available. It is based on an intermediate model version and merely used to evaluate the state of development. Is not considered to contain enough scientific value to merit a data publication on its own; however, it can be provided upon request. Review statement. This paper was edited by Fiona O'Connor and reviewed by two anonymous referees.
Acknowledgements. The development of ESMValTool is supported by the projects " Climate-Carbon Interactions in the Current Century " ( 4C ; grant agreement no. 821003 ), " Earth System Models for the Future " ( ESM2025 ; grant agreement no. 101003536 ), " Infrastructure for the European Network for Earth System Modelling -Phase 3" (IS-ENES3 ; grant agreement no. 824084 ), and the  European Research Council (ERC) Synergy  Grant "Understanding and Modelling the Earth System with  Machine Learning " ( US-MILE ; grant agreement no. 855187 ) under the European Union's  Horizon 2020 Research and Innovation Programme. This study is a contribution to the project  S1   of the Collaborative Research Centre TRR 181  " Energy Transfers in Atmosphere and Ocean " funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  (project no.
We thank the climate modeling groups for producing and making their model output available, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support CMIP and ESGF. We would like to thank  Mattia Righi  ( DLR ) for providing helpful comments about the manuscript. Financial support. This research has been supported by the  H2020 Societal Challenges  (grant nos. 821003  and  101003536 ),  H2020 Excellent Science  (grant nos. 855187 ,  951288 , and  824084 ) programmes, and  Deutsche Forschungsgemeinschaft DFG  (project no. 274762653 ). Brian Medeiros was supported by the  U. S. Department of Energy  (award no. DE-SC0022070 ), the  National Science Foundation (NSF)  ( IA 1947282 ), the  National Center for Atmospheric Research , which is a major facility sponsored by the  NSF  (cooperative agreement no.
As ESMValTool is an opensource community-driven software tool, contributions from other modeling groups are always very welcome. extra facets file. It contains small parts of the https://doi. org/10. 5194/gmd-16-315-2023 Geosci. Model Dev. , 16, 315-333, 2023 Appendix A: Example extra facets file # File emac-mappings-example. yml --- EMAC: # dataset name Amon: # MIP table tas: # CMOR variable raw_name: [temp2_cav, temp2_ave] channel: Amon ta: # defined on plev19 raw_name: [tm1_p19_cav, tm1_p19_ave] channel: Amon CFmon: ta: # defined on hybrid levels raw_name: [tm1_cav, tm1_ave] channel: Amon Omon: tos: raw_name: tsw channel: g3b ' * ': # wildcards also work ' * ': postproc_flag: '' The YAML file above (emac-mappings-example. yml) showcases an example of an.
Trend analysis Observed monthly and annual trends in surface O 3 concentrations at different sites are determined using the non-parametric Mann-Kendall test at the 90 th and 95 th confidence level (CL) and Sen's slope methodology  (Theil, 1950; Sen, 1968 ) (see Text S2). Daily median data are sorted into different months and pre-whitened, due to the presence of autocorrelation, via the 3PW algorithm from Collaud  Coen et al. (2020). Trends using ozonesonde profiles are calculated based on weekly medians for selected pressure levels. For the calculation of relative trends, data are normalized by division with median values and multiplied by 100.
Text S1. Data coverage To ensure proper statistical representation of the data coverage thresholds at least 50% of available data is required for the calculation of monthly and annual trends. Data coverage for the surface sites is shown in Fig. S1. It is also verified that at least one complete year of data is available at the beginning and end of the time series, since incomplete years at the beginning and end of a time series can have a larger influence on trend analysis results  (Collaud Coen et al. , 2020). Esrange has full data coverage, with less than five months having coverage lower than 50%. Pallas, Summit, Tustervatn and Zeppelin have one to three periods of 1-2 years without data that do not preclude long-term trend analysis, although it should be noted that the Summit record only started in June 2000.
These tests require the data to be serially independent and homogeneously distributed. However, the O 3 measurements are significantly lag-1 auto-correlated and exhibit high amounts of seasonality. Therefore, the data is prewhitened to remove the lag-1 autocorrelation. The seasonal Mann-Kendall test is applied to address the seasonality present in the data  (Hirsch et al. , 1982)  using the Matlab version  (Collaud Coen and Vogt, 2021)  of the 3PW algorithm from Collaud  Coen et al. (2020). Present-day model simulations for 2014-2015, including the models contributing to this study, were evaluated first against a limited set of tropospheric Arctic O 3 observations  (Whaley et al. , 2022) , and also in more detail, including tropospheric Arctic O 3 seasonal cycles, in our companion paper  Whaley et al.
Acknowledgements We thank colleagues involved in AMAP (2021), notably  K. von Salzen  ( Env. Canada ),  M. Flanner  ( U. Michigan ). Technicians and logistical support staff are gratefully acknowledged for their work on data collection:  D. Worthy  ( Alert ),  K. Sjöberg  ( Esrange ),  K. Saarnio  ( Pallas ),  B. Jensen ,  C. Christoffersen ,  K. Mortensen  ( Villum ), and  B. Thomas  ( Utqiaġvik/Barrow ). None of the authors declare any real or perceived financial conflicts of interests. Multiple funders are acknowledged for support (see author metadata). Composition Change (NDACC). Data is available via https://ndacc. larc. nasa. gov/index. php/stations. Click on the relevant site location to access the data files. Ozonesonde data for Alert, Resolute and Eureka have been reprocessed according to Tarasick et al.
(2016), available at https://hegiftom. meteo. be/datasets/ozonesondes. All model output files in NetCDF format from the simulations used in this study can be found here: https://open. canada. ca/data/en/dataset/c9a333ea-b81c-4df3-9880-ea7c3daeb76f. Model codes for GISS-E2. 1 are available at: https://www. giss. nasa. gov/tools/modelE/. Open-source codes for the Mann-Kendall test associated with Sen's slope are distributed under the BSD 3-Clause License in dedicated GitHub repositories hosted within the "mannkendall" organization (https://github. com/mannkendall), a Matlab (Collaud Coen and Vogt, 2020, https://doi. org/10. 5281/zenodo. 4134618, https://github. com/mannkendall/Matlab), Python (Vogt, 2020, https://doi. org/10. 5281/zenodo. 4134435, https://github. com/mannkendall/Python), and  R  (Bigi and Vogt, 2020, https://doi.
Acknowledgements We thank colleagues involved in AMAP ( 2021), notably  K. von Salzen  ( Env. Canada ),  M. Flanner  ( U. Michigan ). Technicians and logistical support staff are gratefully acknowledged for their work on data collection:  D. Worthy  ( Alert ),  K. Sjöberg  ( Esrange ),  K. Saarnio  ( Pallas ),  B. Jensen ,  C. Christoffersen ,  K. Mortensen  ( Villum ), and  B. Thomas  ( Utqiaġvik/Barrow ). None of the authors declare any real or perceived financial conflicts of interests. Multiple funders are acknowledged for support (see author metadata). Composition Change (NDACC). Data is available via https://ndacc. larc. nasa. gov/index. php/stations. Click on the relevant site location to access the data files. Ozonesonde data for Alert, Resolute and Eureka have been reprocessed according to Tarasick et al.
Data Availability Statement: Surface O 3 monitoring datasets are provided by EMEP (European Monitoring and Evaluation Program), and Global Atmosphere Watch (GAW) World Data Centre for Reactive Gases. EMEP and GAW O 3 data are available via the EBAS data portal (from end of 1989 to present). CO data at Utqiaġvik/Barrow and Zeppelin are also available via the EBAS data portal: http://ebas. nilu. no. Select the station name, and the component (CO, O 3 ) to access the data files.
for further details. Model Biogenic emissions Forest fire Meteorology name emissions CMAM None CMIP6 Nudged to ERA-Interim reanalysis DEHM MEGANv2 GFAS Nudged to ERA-Interim reanalysis EMEP EMEP scheme (Simpson et FINN (based on Driven by 3-hourly data from MSC-W al. , 2012) Wiedinmyer et al. , the Integrated Forecast System 2011) (IFS) at ECMWF GISS-E2. 1 Isoprene:Guenther et al. CMIP6 Nudged to NCEP reanalysis (2012); Terpenes: ORCHIDEE; Online DMS, Sea-salt and dust MRI-ESM2 Biogenic VOCs emissions CMIP6 Nudged to the Japanese 55-year are taken from Horowitz et Reanalysis (JRA55) al. (2003) UKESM1 Isoprene and monoterpenes Prescribed from Nudged to ERA-Interim interactive with land CMIP6 dataset reanalysis surface vegetation scheme.
Biological data A total of 25,914 yellowfin tuna (Thunnus albacares) were sampled from 1987 to 2019 in the Indian Ocean Tuna canning factory (IOT) in Victoria, Seychelles  (Guillou et al. 2021). All the sampled fish were caught by purse seine vessels in the western IO (details of the sample sizes are provided in Tables  S1  & S2  in Supplement 1). The total weight (W) of the individuals and their fork length (FL) were measured. For each sampled tuna, the fishing vessel and the fishing trip were recorded, but not the specific fishing set from which it was caught. As a consequence, all fishing sets from a trip are a potential catch location for every samples (see statistical analyses section for details on how the uncertainty on location and date was managed). The type of school (either FOBassociated or FSC) was not considered in the main analysis because it was unknown for a large proportion of the sampled fish (around 75 %, Table  S1  in Supplement 1).
As the exact geographic coordinates were not available for most of the sampled fish, a bootstrap process was applied: a dataset was generated by sampling one set of coordinates from all the fishing sets of the trip for each individual and a GAM was then performed. This operation was repeated 1,000 times and for every model built, we selected the most parsimonious explanatory variables based on the Akaike information criterion (AIC), using a stepwise selection procedure and a threshold of 2. The iterated GAM coefficients of the explanatory variables considered as factors (Y, Q and SC) were averaged over the bootstrap replica and their standard deviation was calculated. All analyses were performed using R software v. 4. 0. 3 (R Core.
Acknowledgments The authors sincerely thank  IRD 's Ob7 -"Observatoire des Ecosystèmes Pélagiques Tropicaux Exploités "-in charge of the observer data collection, processing, management, and for sharing the data used in this study;  M. Simier  for her inputs on statistical analyses;  J. D. Filmalter  for his proofreading. The authors acknowledge the  Pôle de Calcul et de Données Marines (PCDM)  for providing DATARMOR storage, data access, computational resources, visualization, web-services, consultation, support services, URL: http://www. ifremer. fr/pcdm. This work was supported by the  MANFAD  project ( France Filière Pêche) , URL: https://manfad-project. com. We thank  ISSF  for its involvement in the overall project. The authors also thank two anonymous reviewers and the handling editor for their insightful remarks.
The Mediterranean Forecasting Systems produces operational analyses, reanalyses and 10-day forecasts for many Essential Ocean Variables (EOVs), from currents, temperature to wind waves and pelagic biogeochemistry. The products are available at a horizontal resolution of 1/24 degrees (approximately 4 km) and 141 unevenly spaced vertical levels. The core of the Mediterranean Forecasting System is constituted by the physical (PHY), the biogeochemical (BIO) and the wave (WAV) components coupled offline, consisting of both numerical models and data assimilation modules. The 3 components together constitute the so-called Mediterranean Monitoring and Forecasting Center (Med-MFC) of the Copernicus Marine Service. Daily 10-day forecasts are produced by the PHY, BIO and WAV components as well as analyses, while reanalyses are produced for the past 30 years about every ~3 years and extended (yearly).
Model initialization, external forcing and boundary conditions The PHY component was initialised in January 2015 using temperature and salinity winter climatological fields from WOA13 V2 (World Ocean Atlas 2013 V2, https://www. nodc. noaa. gov/OC5/woa13/woa13data. html). The atmospheric forcing fields for both NEMO and WW3 models are from 6 hours (3 hours for the first 3 days of forecast), 0. 125° horizontal resolution operational analysis and forecast fields from the European Centre for Medium-Range Weather Forecast (ECMWF); a higher spatial (0. 1° in horizontal) and temporal resolution dataset is used starting from year 2020.
, 1981). The whitecapping dissipation term is based on  (Hasselman, 1974)  whitecapping theory. The wind input and whitecapping dissipation source terms of the present cycle of the wave model are a further development based on Janssen´s quasi-linear theory of wind-wave generation  (Jansen, 1989; Jansen, 1991). The nonlinear transfer term is a parameterization of the exact nonlinear interactions as proposed by  (Komen et al. , 1984) and (Hasselman et al. , 1985). Lastly, the bottom friction term is based on the empirical JONSWAP model of  (Hasselman et al. , 1973). The bathymetric map has been constructed using the GEBCO 30arc-second bathymetric data set for the Mediterranean Sea model and the ETOPO 2 data set (U. S. Department of Commerce, National Oceanic and Atmospheric Administration, National Geophysical Data Centre, 2006.
Model initialization, external forcing and boundary conditions Terrestrial inputs include 39 rivers consistently with the PHY component. Annual nutrients input are about 46500 10 6 molN/y and 881 10 6 molP/y e  (Salon et al. , 2019). Carbon and alkalinity inputs are 9300109 gC/y and 800 109 mol/y, respectively. Estimates are derived considering typical concentrations per freshwater mass in macro coastal areas of the Mediterranean Sea  (Copin-Montegut, 1993; Meybech and Ragu, 1995; Kempe et al. , 1991)  and the river water discharges from the PERSEUS dataset (Deliverable of Perseus, 2012 as before). Annual atmospheric nutrient depositions are 81300 10 6 molN/y and 1194 10 6 molP/y for nitrogen and phosphorus, respectively  (Ribera d'Alcalà et al. , 2003). Atmospheric pCO2 mean annual concentrations are from the ENEA Lampedusa  (Trisolino et al.