dataesr/scientific-paragraphs-categorization huggingface dataset:
The inventory has also a Comments section, describing specific features for each simulation. Completing ESGF simulations. Two main priorities have shaped the C3S CORDEX dataset, both enforced by the requirements of the IPCC AR6 WGI assessment report: 1) the number of simulations for every region should be sufficient to estimate regional climate change uncertainties, and 2) simulations should be publicly available (e. g. on ESGF) before 31 January 2021. In this respect, several domains had very few simulations available on ESGF at the start of this work, such as the Polar (ARC and ANT) and the Asian (CAS, EAS and SEA) CORDEX domains. Therefore, data publication for these domains was prioritized contacting modeling centers to retrieve their simulation data and help them to curate (see quality control section below) and publish the data on ESGF before the IPCC deadline.
Selection of simulations and variables. CORDEX provides simulations at various spatial and temporal resolutions. The standard horizontal grid spacings of about 50, 25 and 12,5 km are typically referred to as 0. 44°, 0. 22º and 0. 11º, or simply 44, 22 and 11, corresponding to the native resolution in degrees of a rotated longitude-latitude geographical projection used by many of the participant models. The standard temporal aggregations provided go from hourly to monthly resolution. The C3S CORDEX dataset was assembled considering a grand ensemble with all available simulations for the standard 0. 44°, the CORDEX-CORE 0. 22°, and 0. 11º at daily temporal aggregation (note that the C3S provides additional temporal frequencies and variables for the European and the Mediterranean domains). For the European domain, only the large 0.
So, all in all, the worldwide C3S CORDEX dataset comprises regional projections from all fourteen CORDEX domains (Figure  1a ) for the fifteen variables listed in Table  1  (plus land-sea mask and elevation), for daily temporal resolution. The evaluation, historical and RCP (Representative Concentration Pathways;  Moss et al. , 2010; van Vuuren et al. , 2011)  scenarios constitute the different standard experiments provided by CORDEX. Evaluation simulations are nested into reanalysis (ERA-Interim reanalysis,  Dee et al. , 2011). This so-called "perfect lateral boundaries" experiment allows for an evaluation of the RCM quality, relative to ERA-Interim. We used the common 30-year period  1979-2008, established  originally in the CORDEX protocol, although the shorter period 1990-2008 was considered in some cases.
Historical simulations, using lateral boundary conditions from CMIP5 simulations under the historical scenario, were run for 1950-2005, except for models covering shorter periods  (e. g. 1970-2005 or 1980-2005). These simulations can be used to evaluate the GCM-RCM pair, and also as a reference for comparison against future scenario runs. Future scenario simulations (2006-2100) follow the boundary conditions from CMIP5 projections using RCP forcing scenarios. The RCP scenarios included in the C3S CORDEX dataset are RCP2. 6, RCP4. 5 and RCP8. 5, as they were, by far, the most downscaled. Models providing no scenario simulations were not included in the dataset since it is intended for climate change assessment. Simulations interpolated to a regular geographic latitude/longitude grid were only considered when the original simulation on the native computational grid projection was not available.
Quality control. A goal of this initiative is to ensure that all CORDEX data, regardless of origin, can be processed with identical workflows. Therefore, all the simulations (both those existing on ESGF and the new simulations gathered from modeling centers) were quality controlled following both the CORDEX archive specifications  (Christensen et al. , 2020)  and the Climate and Forecast (CF) Conventions, using the quality assurance tool for climate data QA-DKRZ 8 (version 0. 6. 7-55). This quality checker looks for non-CF-compliant files, metadata errors or inconsistencies in the global attributes and in the filenames, omissions of required CORDEX metadata, incompatible temporal dimension of the data and plausible ranges. As a result, a number of simulations were discarded due to critical problems with the data (e.
g. value ranges not consistent with the units, etc. ). Some examples are ANT-CORDEX simulations for the CCAM-2008 RCM, due to the incorrect interpolation of sea-ice area fraction (problem reported by the modeling center) and SAM-44 simulations for the ICTP-RegCM4-3_v4 (driven by MPI-ESM-MIR_r1i1p1), due to inconsistencies in the time variable. Other metadata problems were fixed, and changes were annotated in the CORDEX inventory 9 of the GitHub IPCC Atlas repository  (Iturbide et al. , 2021)  to keep track of ESGF and C3S CORDEX differences. 9 https://github. com/IPCC-WG1/Atlas/blob/devel/data-sources/CORDEX_simulations_ATLAS. xlsx   2 ), which are exceeded in many cases by the actual simulation domains. Gray lines in the bottom panel correspond to the IPCC AR6 WGI reference regions  (Iturbide et al.
, 2020)  which are used to create the mosaic (note that CORDEX domains assigned to each region are shown in the table below, following  Gutiérrez et al. , 2021  but including the CAS domain for EEU, WSB, and ESB regions). Note that MED-and MENA-CORDEX domains are not used in the mosaics because they are overlapped by other domains with a higher number of simulations   1. List of surface variables (all at daily resolution) archived in the worldwide C3S CORDEX dataset. Italics are used for spatial static variables (with no temporal axis), which provide information on the grid used by the models. Note that the European (EUR) and Mediterranean (MED) domains provide 26 variables and some of them at sub-daily temporal aggregation.
The C3S CORDEX dataset The dataset resulting from the selection, curation and quality control process was published on the C3S Copernicus Climate Data Store (CDS) under the Catalog "CORDEX regional climate model data on single levels" 10 , which includes a complete dataset description. Moreover, basic diagnostic and simple evaluation indices were computed and made available along with the dataset 11. The entire data volume is 235 TB in size. The C3S CDS provides this worldwide C3S CORDEX dataset with a high operational service level, including dedicated personnel, user support with a help desk, and infrastructures, which build on three dedicated distributed replicas of the dataset. Note that the C3S CORDEX dataset is a "frozen" subset of the CORDEX data archived on ESGF as of Jan 31st, 2021 (ESGF is a live federated repository), with quality-controlled and homogenized metadata.
The CORDEX dataset used in the IPCC AR6 Atlas and Interactive Atlas is a subset of the C3S CORDEX dataset (see Section 2. 4). Table  2  summarizes the information on the number of simulations per domain and the horizontal grid resolutions which form the worldwide C3S CORDEX dataset. Additionally, this table highlights the subset used for the IPCC AR6 (IPCC, 2021), particularly for the Atlas and the Interactive Atlas  (Gutiérrez et al. , 2021). In the Atlas, the per domain mosaic ensembles were built pooling together all available resolutions and interpolating the results to a common 0. 5º regular grid (except for the European domain where only the highest horizontal grid resolution was used); a complete description of the simulations used is available at the official GitHub Atlas repository  (Iturbide et al.
Model documentation The availability of comprehensive model documentation is vital to allow for informed use of the data, considering key factors which may explain different model results (e. g. different parameterization assumptions, use of interactive aerosols, the complexity of the land surface model, or the use of other interactive componentslakes, cities, etc. ). This is a time-consuming task, and the CMIP community has developed technologies and tools to facilitate this through the Earth System Documentation (ES-DOC -https://es-doc. org) project. As a result, documentation of the contributing GCMs is available for the different CMIP versions (e. g. structured metadata is readily available 12 for the CMIP5 global models used as boundary conditions for the CORDEX simulations presented here).
The activities carried out within the framework of the C3S required close contact and exchange with the modeling centers contributing simulations for the different domains. This facilitated gathering common information on the different RCMs contributing to CORDEX regarding the various components and other simulation details. A repository with metadata from the CORDEX RCM ensemble has been compiled as part of the worldwide CORDEX dataset  (Diez-Sierra et al. , 2022) , enabling future updates and backtracking to this publication. This information is not exhaustive and other studies provide more detailed information on particular components for particular CORDEX domains (e. g. Gutiérrez et al. , 2020  on aerosol treatment in EURO-CORDEX). This table extends the information gathered for the IPCC WGI AR6 Annex II on models (IPCC, 2021: Annex II) by including the domains and other useful information (e.
, 2000) , which keep the large-scale RCM circulation close to that of their driving GCM also in the interior of the domain. Others used bias adjusted GCM input  (Bruyère et al. , 2014). While these approaches are valid in general, attention should be paid when comparing simulations with different experimental designs. For instance, when the consistency of GCM and RCM results is evaluated, simulations driven with spectral nudging are likely to be more consistent than un-nudged ones. Similarly, those simulations driven by bias-adjusted GCM fields are also likely to be more skillful in historical model assessments. All these particular details of the simulations can be found in Diez-Sierra et al. (  2022 ) and used as potential explanatory factors in any subsequent evaluation or analyses. Note that this information is provided for all CORDEX simulations available, and not just for those included in the C3S CORDEX dataset (Section 2.
Use of the data in the IPCC AR6 WG1 report and additional resources CORDEX has been extensively used as a new line of evidence in the IPCC AR6 WGI report  (IPCC, 2021) , in particular in the chapters dealing with regional information and the Atlas  (Doblas-Reyes et al. , 2021; Gutiérrez et al. , 2021; Ranasinghe et al. , 2021; Seneviratne et al. , 2021). The main source of information was the worldwide C3S CORDEX dataset and the particular domains and simulations used in different chapters and sections are described in the chapters' supplementary material. In the case of the Atlas and Interactive Atlas  (Gutiérrez et al. , 2021) , the GitHub Atlas repository  (Iturbide et al. , 2021; Iturbide et al. , 2022)  contains detailed provenance information as well as aggregated data for mean temperature and precipitation using the IPCC AR6 reference regions  (Iturbide et al.
Consistency of the climate change signal across CORDEX domains In this section, we assess the consistency of the C3S CORDEX dataset across domains, focusing in particular on model biases and climate change signals in regions where several domains overlap. It is important to identify and account for cases where simulated signals differ across domains in overlapping regions. These apparent conflicts could be due to the specific configurations used in the different domains (e. g. the set of GCM/RCM pairs and model versions), but there could also be other contributing factors, such as domain size or position, which are important to understand. This section explores such overlaps and formulates recommendations on how to interpret any differences. We extend a previous analysis performed over the Mediterranean region (e.
g. Legasa et al. , 2020)  by assessing the consistency of climate change signals for mean temperature and precipitation across all regions where the C3S CORDEX dataset domains overlap. To avoid local gridbox variability we use the new subcontinental reference regions (see Figure  1b ) used in the IPCC AR6  (Iturbide et al. , 2020)  and check the consistency of the spatially aggregated simulations. For every pair of overlapping domains (see Figure  1a ), we use the common sub-ensemble of GCM-RCM pairs -i. e. the same RCM driven by the same GCM-and intercompare their evaluation and historical simulation biases, and climate change signals obtained from different domains. The RCP8. 5 scenario is chosen to maximize data availability (see Tables  2, 3  and 4 ) and the projected climate change signal  (Dosio et al.
, 2020; van Vuuren et al. , 2011). All the analyses were performed at daily aggregation. Similar biases and climate change signals indicate a consistent performance of the GCM-RCM pair, whereas differences indicate inconsistencies that should be analysed more in-depth. Following the procedure used in the IPCC AR6 Atlas, all model results were re-gridded using a first order conservative remapping to a regular half-degree horizontal resolution grid. This is the same grid considered in the bias-adjusted ERA5 reanalysis data (WFDE5;  Cucchi et al. , 2020) , employed as the reference to compute biases. In this way, simulation and observational databases can be directly compared to obtain performance measures. We focused on near-surface air temperature (tas) and precipitation (pr) and considered only those simulations that overlap more than 90% of the grid cells with the regions analysed (with the exception of the simulations over the Africa CORDEX domain for the Mediterranean regions, which had a slightly smaller overlap ~85%).
The resulting aggregated data is available for reproducibility and reusability in the GitHub IPCC Atlas repository  (Iturbide et al. , 2021). Apart from the C3S CORDEX dataset, we also include in the analyses the driving model of the different simulations, that is, ERA-Interim reanalysis data and CMIP5 GCM output. The evaluation period considered is 1986-2005. Mean biases for near-surface air temperature are calculated as the simple difference of the spatial and temporal average between either CORDEX evaluation/historical simulations or the corresponding ERA-Interim/CMIP5 driving data and WFDE5:   =  - 5. Biases for precipitation are calculated as relative differences (%):   = 100]. Climate change signals (deltas) are calculated for the far future 20-year period 2081-2100, relative to the historical period 1986-2005, as differences (K) for temperature and relative differences (%) for precipitation:   =   -  and   = 100 [   -    ]), respectively.
The goal of this section is to provide users with some preliminary information, albeit at a broad spatial scale (IPCC reference regions), to inform dataset selection for their particular applications. Figures  7  and 8  show the climate change signals for temperature and precipitation, respectively. The figures highlight that the grand ensemble might be beneficial for some regions where the 'home' domain (i. e. the domain selected for a specific region to create the mosaic approach) provides few simulations (see Figure  1b ). For example, this is the case for the ECA region, where the home domain (EAS) contributes with 11 simulations, while WAS and EAS domains contribute with 9 and 5 simulations, respectively. Note that there are regions where even the grand ensemble provides relatively small ensembles (e.
g. Dosio et al. , 2020 for WAF) , would be required to understand the discrepancies among ensembles summarized in Figure  8. Note that the results shown in Figure  8  are reasonable considering that future precipitation projections generally show low robustness in the change in most areas of the world for the main reference datasets (CMIP5 and CORDEX). In fact, those analyzed regions where the IPCC WGI AR6 predicts confident changes in the precipitation for the long-term period and the RCP8. 5 scenario (NWN, CAR, NEU, MED, ECA) show very good agreement across the CORDEX grand ensemble, the mosaic ones and the CMIP5 ensembles at this coarse spatial scale. Finally, CORDEX ensemble results obtained for both variables generally exhibit more variability in the mosaic approach, consistent with the use of a smaller number of simulations.
These results support the use of the C3S CORDEX dataset for worldwide studies. The assembly of grand ensembles pooling the data available from different domains for a particular region can be considered in regions where the home domain provides few simulations. However, caution must be taken in regions where local feedback may dominate the projections. In such cases, it is very important to assess the projections using the domain which includes all relevant forcing mechanisms. As an example, experience in the South American Monsoon region, indicates that the Central American domain prevents a proper representation of the large-scale dynamics in the region, which is too close to the domain boundaries and does not allow for sufficient spatial spin up. Therefore, the Central American domain should not be used to study future projections there.
Likewise, other regions near domain boundaries would need detailed analyses before use. The above activities contribute to supporting the CORDEX and ESGF communities and the preparation and documentation of the CORDEX dataset used in the IPCC report  (IPCC, 2021: Annex II). The open resources for data documentation and exploitation, as well as some aggregated datasets developed as part of the IPCC activities are available from the IPCC GitHub Atlas repository  (Iturbide et al. , 2021). Despite this major effort to unearth existing CORDEX simulations, there are still regions in the world covered by a small number of future projections, which poorly explore the uncertainties involved in regional climate simulation. Therefore, one of the next CORDEX challenges to provide regional information globally is to fill this gap by balancing the amount of simulations in the different domains.
Data Availability Statement. The worldwide C3S CORDEX dataset is publicly available through both C3S-CDS (https://cds. climate. copernicus. eu/cdsapp#!/dataset/projections-cordex-domains-single-levels) and ESGF (https://esgf-data. dkrz. de/search/cordex-dkrz/) under the Creative Commons Attribution license CC-BY 4. 0 with the exception of the simulations from the following RCMs: BOUN-RegCM4-3 model (for Central Asia and Middle East and North Africa domains) and RU-CORE-RegCM4-3 model (for South-East Asia domain) which are distributed under CC-BY-NC 4. 0. A complete description of the subset of simulations used for the IPCC AR6 (IPCC, 2021), particularly for the Atlas and the Interactive Atlas (Gutiérrez et al. , 2021), is available at the official GitHub Atlas repository  (Iturbide et al. , 2021)  and in AR6 Annex II (IPCC, 2021: Annex II).
This repository contains aggregated information for different variables and open resources for data exploitation. Common information on the RCM components is available on Zenodo (version 2. 1, as of this publication), enabling future updates and backtracking to this publication (Diez-Sierra et al. , 2022). The code used to produce main results is available at Zenodo (https://doi. org/10. 5281/zenodo. 7010026).
Conclusions This article presents the work done over the past three years in the framework of the Copernicus Climate Change Service (C3S) to assemble a worldwide CORDEX dataset globally consistent and aligned with IPCC AR6 activities and deadlines. This work required close contact and exchange with the modeling centers producing simulations for CORDEX. The protocol followed required: 1) creating an inventory of all available simulations, in coordination with the CORDEX project office, 2) gathering existing simulations available from modeling groups in areas with scarce published data, such as the Polar and the Asian domains, and supporting their curation, standardization and publication on ESGF, 3) assembling and making available on the C3S-CDS a globally-homogeneous quality-controlled dataset for a subset of the 15 most popular variables, and 4) collecting common information on the RCM components (atmosphere, land) and forcings (aerosols, ocean surface) in a summary table, which constitutes the most comprehensive metadata available for the CORDEX ensemble to date.
The C3S CORDEX dataset is available through the C3S-CDS along with detailed documentation (the original simulations are available from ESGF); the RCM summary tables are available from Zenodo, to enable future updates. Additionally, the resulting dataset has been studied in the present paper analyzing the spatial consistency and potential differences arising in areas where domains overlap both for the global CMIP5 and the CORDEX domain results. For these areas, cross-domain consistency was assessed by comparing average model biases and climate change signals for regional averages and mean variables. Note that regional differences would be expected across the CMIP5 and CORDEX datasets when considering climate at smaller scales and/or extreme rather than mean variables at large-scale, where regional models are expected to provide added value; see for example the differences for mountainous and coastal regions  (Giorgi et al.
3. 0)  [40]. ComBat (R package "sva"; v3. 38. 0)  [41]  was used to correct the effect of sequencing depth on calculating the abundance of intestinal microbial functional genes. The non-redundant proteins were assigned to the KEGG database using KofamKOALA (v1. 3. 0)  [42]  for KO annotation. We also aligned the non-redundant proteins into HMD-ARG database (v5)  [43]  using BLASTp (v2. 7. 1+) with "-e 1e-5" for antibiotics resistance gene (ARG) detection. To detect the virulent factor (VF) of each microbe, the nonredundant proteins were aligned against the VF database (VFDB: VFDB_setB_pro. fas)  [44]  using diamond (v0. 9. 21. 122)  [45]. The VF annotation of a gene was using its best hit alignment with the identity greater than 90% and an e value below 1e-5.
Prediction model for discriminating the resistant and susceptible families To evaluate the potential role of the intestinal microbiome and functional genes in discriminating the resistant and susceptible families, we used the microbial community composition and the microbe-associated host DEGs, and their combinations to develop the random forest models using the R package "randomForest "  [46]. To avoid overfitting, we used 70% samples for training and 30% samples for testing. According to MeanDecreaseAccuracy and MeanDecreaseGini of the random forest model, the markers with outstanding performance in individual prediction were selected for a combinatorial marker panel. The links between the combinatorial marker panel and the phenotypic characters (represented by BW and TL) were also investigated, and only the Spearman rank correlations with adjusted p < 0.
Datasets The ERA5 reanalysis  (Hersbach et al. , 2020) , produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), combines observations from across the globe with model output into a globally complete and consistent data set. Output is provided hourly on a 0. 25° grid. We extracted 2-m air temperature, soil moisture, snowmelt, 10-m (longitudinal) u-component, and 10-m (latitudinal) v-component of wind. Each product was re-gridded to a daily temporal resolution with a 1° spatial resolution. Leaf area index (LAI) is an essential climate variable  (Belward et al. , 2016)  which measures amount of foliage as a measure for the photosynthetic active area. LAI is based on the National Oceanic and Atmospheric Administration Climate Data Record (NOAA-CDR) data set version 5  (Vermote, 2019)  and estimated using surface reflectance data from Advanced Very-High-Resolution Radiometer (AVHRR) data aboard eight NOAA polar-orbiting satellites ) using an artificial neural network.
BIC is similar to AIC, however it adds a higher penalty for models with more parameters. If SSR, AIC, and BIC are lower for the unrestricted model, then we can conclude that X Granger-causes Y. This analysis was facilitated by the Python statsmodels package  (Seabold & Perktold, 2010)  and performed by averaging the LAI and soil moisture within a large region (50°N-75°N, 50°E-120°E).