dataesr/scientific-paragraphs-categorization huggingface dataset:
Both a piControl and a historical simulations initially performed on Curie were repeated on the Joliot-Curie supercomputer to ensure the climate statistics were comparable on both supercomputers. The throughput is about 13 and 16 simulated years per day on Curie and Joliot-Curie, respectively, on 960 processors with the full CMIP6 output. Outputs from the IPSL-CM6A-LR model are managed by the XML Input/Output Server (XIOS  Meurdesoif et al. , 2016). For the CMIP6 production, model output variables in native format were kept to the minimum. Instead, and in sharp contrast to CMIP5, the CMIP6-compliant model output has been produced on the fly using XIOS methods from the code. XIOS is driven by XML files describing the whole netCDF file structure  (dimensions, attributes, etc. ). Such XML files were produced using the dr2XML (https:// github.
com/rigoudyg/dr2xml) python library developed by our CNRM-CERFACS collaborators, which translates the CMIP6 Data Request and Controlled Vocabulary (https:// github. com/WCRP-CMIP/CMIP6 CVs) into XML files for XIOS. We use dr2XML to generate XML files for each simulated year of a given CMIP6 experiment and member. The netCDF time series are created and filled by XIOS all along the simulation to avoid concatenation during post-processing. A quality assurance is applied at the end of each CMIP6 model simulation. The simulation is validated from a scientific point of view to make sure there is no critical issue or inconsistencies in the diagnostics (e. g. , incorrect application of a forcing term, wrong sign, recurring patterns, etc). Each file then undergoes several checks against the CMIP6 controlled vocabulary to ensure its conformance with the CMIP6 Data Refer-.
The list of evaluated model variables and datasets against which they are evaluated are presented in Table  1. Thirty-two members have been performed for the historical period. Most of the diagnostics are not qualitatively sensitive to the choice of the member (when looking essentially at the mean state). We thus use only the first member (r1i1p1f1) in the diagnostics and illustrate the spread within the ensemble for some of the diagnostics. We present the most common variables used in climatology (like sea surface temperature,  surface air temperature, precipitation etc. ) and concentrate on variables that are affected by the coupling between LMDZ, NEMO and ORCHIDEE. Thus we do not repeat the evaluation of variables that are close to those presented in LMDZ6A AMIP paper  (Hourdin, Rio, Grandpeix, et al.
The warm bias over the Northern part of Canada has been reduced in the annual mean. There is actually some compensation of a warm bias in summer and a cold bias in winter in IPSL-CM6A-LR that replaces a warm bias all year long in IPSL-CM5A-MR (Figure  2 ). The biases over the Arctic are linked to the position of the sea ice edge and depend to some extent on the member being considered. However, a large warm bias is consistently simulated in winter over the Arctic. Over inland Antarctica, a cold bias can be seen in IPSL-CM6A-LR surface air temperature in all seasons. This cold bias is likely to correspond to a warm bias diagnosed in the reanalysis surface temperature from a comparison with weather station data  (Fréville et al. , 2014; Jones & Lister, 2015)  but the magnitude of this cold bias (up to 8 • C) exceeds the warm bias in ERA (up to 5 • C).
PMP large-scale summary statistics This section provides a general synthetic view of the evolution of the climatology of the atmosphere of the IPSL models between CMIP5 and CMIP6. We have used the PCMDI Metrics Package (PMP) to calculate a set of large-scale performance metrics  (Gleckler et al. , 2008) , also called summary statistics, to summarize the agreement between the climate simulated by the model over the recent period and a set of references (observa-  tions and reanalysis, as listed in Table  1 ). Figure  7  shows the results for the most common atmospheric variables: 2-meter air temperature (tas), surface precipitation (pr), precipitable water (prw), pressure at sea level (psl), upwelling shortwave (rsut) and longwave (rlut) radiation at the top of the atmosphere, cloud radiative effect at top of atmosphere on longwave (rltcre) and shortwave (rstcre) radiation, temperature, zonal and meridional wind at 850 mb (ta850, ua850, va850) and at 200 mb (ta200, ua200, va200), and geopotential height at 500 mb (zg500).
There are however some discrepancies; in particular there is no member that reproduces the observed cooling trends in the Southeastern Pacific and the Southern Ocean to their full extent. The model generally reproduces the land/sea contrast in warming, with an average ratio of 1. 61 (ranging from 1. 52 to 1. 79) between the global temperature over land and ocean over the 1978-2018 period compared to the observed ratio of 1. 67 from the HadCRUT4 dataset  (Morice et al. , 2012)  (the model data, SST over ocean and TAS over land, are regridded onto the observations temporally masked prior to the analysis). The Arctic amplification tends to be overestimated by the model, with an average trend of 0. 88 K per decade over the 70-90 • N region (ranging from 0. 22 to 1. 58 K per decade) relative to the global-mean trend of 0.
26 K per decade (ranging from 0. 16 to 0. 36 K per decade), whereas the trend is about 0. 79 K per decade over the 70-90 • N region and about 0. 19 K per decade for the global mean in the  Cowtan and Way (2014)  dataset. Further work is going on to assess the diversity of historical members of the IPSL-CM6A-LR model and their relevance against observations.
Changes in carbon fluxes The land and oceanic net carbon fluxes for the scenarios are shown on Figure  29. The oceanic carbon uptake is projected to increase or decrease according to the scenario being considered, with a clear saturation occurring at large CO 2 concentrations (e. g. , SSP585 and SSP460) and a decrease in the sink when the atmospheric CO 2 levels off or decreases. The net land carbon uptake peaks at about 3 PgC yr -1 between 2020 and 2060 in all scenarios before returning to near-zero, or even negative, values. The downregulation of the maximum photosynthetic capacity that was implemented to account for the impact of nutrient limitation on the CO 2 fertilization effect (see section 4. -64- This article is protected by copyright. All rights reserved. Table  1. List of evaluated model variables and datasets against which they are evaluated.
manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES) ence Syntax (http://pcmdi. llnl. gov/CMIP6) in terms of time axis and coverage, variable and global required metadata, filename syntax, etc. The data are then published on the Earth System Grid Federation (ESGF), which guarantees a strong and effective data management. The esgprep toolbox (http://esgf. github. io/esgf-prepare) is a piece of software that eases data preparation according to CMIP6 conformance. Once a model simulation is validated and checked, the netCDF files are migrated in the proper CMIP6 directory structure with the esgprep commands in a shared space of the file system. The IPSL hosts an ESGF index with all datasets from the French climate simulations and a data node to disseminate datasets from the IPSL climate simulations.
Cowtan and Way (2014)  andRohde, Muller, Jacobsen, Muller, et al. (2013); Rohde, Muller, Jacobsen, Perlmutter, et al. (2013) observational datasets. The observed GMST time series is within the spread of the ensemble simulations but the model ensemble mean qualitatively departs from observed changes around 1935-1945 and since 2005 (Figure 27b). The departure from observations for the recent period is slightly enhanced if the anomaly -36-This article is protected by copyright. All rights reserved.
All rights reserved. Acknowledgments The authors are grateful to the developers of the OASIS software and the Earth System model components not in the author list. This work was undertaken in the framework of the L-IPSL LABEX and the  IPSL Climate Graduate School EUR. As such it benefited from the  French  state aid managed by the  ANR  under the " Investissements d'avenir " programme with the reference  ANR-11-IDEX-0004 -17-EURE-0006. It also benefited from  Belmont  project  GOTHAM , under grant  ANR-15-JCLI-0004-01 , the  ANR  project  ARISE  under grant  ANR-18-CE01-0012 , and  MOPGA/Investissements d'Avenir project Archange,  under grant  ANR-18-MPGA-0001. The  CMIP6  project at IPSL used the HPC resources of TGCC under the allocations  2016-A0030107732 ,  2017-R0040110492  and  2018-R0040110492  (project  gencmip6 ) provided by  GENCI  ( Grand Équipement National de Calcul Intensif ).
This study benefited from the  ESPRI (Ensemble de Services Pour la Recherche  l'IPSL) computing and data centre (https://mesocentre. ipsl. fr)which is supported by  CNRS ,  Sorbonne Université ,  École Polytechnique  and  CNES  and through national and international grants. Support from the  European Commission 's  Horizon 2020 Framework Programme  is acknowledged, under Grant Agreement number  641816  for the "Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and  Outreach (CRESCENDO) " project ( 11/2015-10/2020 ) and under Grant Agreement number  820829  for the " Constraining uncertainty of multi-decadal climate projections (CONSTRAIN) " project. Peter Gleckler  and colleagues from the  Program for Climate Model Diagnosis and Intercomparison (PCMDI)  are acknowledged for their contribution to the performance metrics package.
Such as least squares method, the estimation method for heteroscedastic models has a low breakdown point of 1/n, meaning that only one outlier in the observed response can have a large effect on the estimation of the mean parameters  [21, 24, 28]. The estimation of the variance parameter is also affected as maximum likelihood methods are very sensitive to outliers  [9, 22]. In practice, outliers are prevalent in environmental dataset. They can be found in both response variable and covariates but, in this work, our interest lies only in outliers that are present in the response variable. In the presence of outliers, robust methods must be applied. They aim to produce reliable estimates that are not seriously affected by outliers, extreme values or small deviations from model assumptions  [13].
In this article, we propose an estimation procedure which adapts the weighted M-estimation method of Carroll et al. [4]  to time series by taking into account temporal correlations, and, chooses the value of the tuning constant by minimizing the variance of the estimators such as the work of Wang et al. [24, 25]. We perform numerical studies to compare the proposed method with other usual regression methods. Finally, we illustrate our methodology with a dataset on fine particle matter pollution (PM 2. 5 ) in the city of Beijing. This dataset is particularly interesting for two reasons. First, variability of PM 2. 5 concentration is not likely to be constant. Second, largest concentrations, which are related to increased coal consumption during cold days, may have a significant impact on the estimation of regression parameters.
Numerical studies In this section, we investigate the performance of our procedure. We compare mean bias and mean squared errors of the estimates obtained by different methods such as least squares (lm function in R), generalized least squares method (gls function from the nlme package),M-estimation with c = 1. 345 (rlm function from the MASS package), the weighted M-estimation (whm function from rlmDataDriven package) with c = 1. 345 and our data-driven method (rlmDD het function from rlmDataDriven package). For one simulation, we generate a multivariate normal dataset (n = 500) using the model (1). In our case, x T i β = β 0 + β 1 x i1 where we fix the value of β 0 and β 1 to 10 and x i1 comes from a uniform distribution on (0, 1). For σ i , we test two functions: the power function σ i = |x T i β| γ with γ = 0.
Regression analysis The hourly data come from a previous study lead by  Liang et al. [16]  (Figure  2 ). The response variable PM 2. 5 is a concentration; therefore a transformation was needed in order to avoid predictions lower than zero with the regression analysis. Two transformations were tested on the dataset: square-root and logarithm transformation (Figure  2 ). For both transformations, mean squared and mean absolute errors were computed after re-transformating fitted values in original scale. Althougth logarithm transformation is the classical transformation for air quality data, we chose the square root transformation as it yielded slightly lower errors (Supporting information, Table  S -3). The model is fitted with least squares method by the lm function from R statistical software  [20].
Robust regression analysis with the proposed method We fitted the same regression model to the data with the proposed method. This method has been implemented in a R function: rlmDD het, available in the package RlmDataDriven. The R code for the presented analysis can be found in the supporting information file. In the literature, we did not find previous papers or indications on how to model variance of PM 2. 5 concentration, consequently, we used common variance functions for the analysis such as power or exponential functions. Hereafter, we present the result obtained by considering σ i = φe γ|x T i β|. First, our method chooses best tuning constant by testing a range of values for the tuning parameter between 0 and 3. As stated earlier, the best value of c will be the value that minimizes the sum of the variance of the regression parameters.
Discussion This method is data-dependent by the optimal choice of the tuning constant and it incorporates temporal correlations by adding lagged terms in the covariates. The numerical study showed that this procedure outperforms the other usual regression methods when data are contaminated by providing more precise estimates for the mean parameters. In the application with the PM 2. 5 concentration dataset, we proved that our method results in estimates with significantly lower variance compared to the ones obtained by least squares estimation, leading to better hypothesis testing. This method is well suited for environmental dataset due to the frequent presence of heterogeneity and outliers. Hyslop et al. [12]  utilized Thiel-Sen robust regression to evaluate long-term trends in aerosol concentrations via the historical PM2.
5 element measurements. Van Donkelaar et al. [23]  used a geographically weighted regression (GWR) statistical model to represent bias of fine PM2. 5 concentrations over North America. Knibbs et al. [15]  utilized the land-use regression (LUR) to estimate PM2. 5 at continental scale and explained the most spatial variability in PM2. 5 in Australia. These three methods did not consider the heterogeneity and autoregressive errors. In the future, the proposed method could be generalized to these three models. Furthermore, the proposed method could be extended to model time-series of counts by using a link function such as the generalized linear model  [17]. Concerning the variance, it would be helpful to consider more flexible approaches for the estimation of the parameters and to provide guidance for choosing the most appropriate variance function for a dataset.
Supplementary Material Online supplementary material includes : -Table  S -1 : Mean biases and associated standard errors of the estimated parameters obtained by several regression methods for the power variance function. -Table  S -2 : Mean Absolute Error and Root mean squared error for different transformations of PM 2. 5 and different models. -Figure S-1 : Mean squared errors of the estimated parameters obtained by several regression methods for the power variance function. -Page 4 : Information on the dataset with downloading link and indications on the different steps to tidy the dataset before analyzing it. -The R script used to obtain the results in the application section. Fig. 3  Regression diagnostic plots for the least squares model (upper panel) and the proposed method (lower panel).
Acknowledgements The work was carried out during the visit of  Aurelien Callens  to the  School of Mathematical Science, Queensland University of Technology, Brisbane, Australia. This research was partially funded by the  Australian Research Council  project ( DP160104292 ). Liya Fu's research was supported by the  National Natural Science Foundation of China  ( 11871390 ), the  Fundamental Research Funds for the Central Universities  (No. xjj2017180 ), the  Natural Science Basic Research Plan in Shaanxi Province of China  ( 2018JQ1006 ).
, 2006; Gerde et al. , 2001; Gøtze et al. , 1994; Ramesh et al. , 2002 Ramesh et al. , , 2001)). Since the measured values were all obtained following exposure to particle-phase PAHs, the calculated bioaccessibility values for only particle-phase SVOC exposure were used for the comparison. Fig. 3  shows that the calculated bioaccessibility values for benzo[a]pyrene and benzo[k]fluoranthene are within the range of the measured values. For the other SVOCs, no measured data in the literature are available that can be used to validate the bioaccessibility values calculated in the present work.
Results and Discussion Microbial Community Diversity of Brothers Volcano Hydrothermal Deposits Reveals Two Distinct Communities on the Caldera Wall. To explore the patterns of microbial diversity associated with hydrothermal deposits from the geologically and geochemically different areas at Brothers volcano, we collected hydrothermal deposits from 16 actively discharging hydrothermal vents and two diffuse flow samples from the UCW, NWC, LC, and UC sites (Fig. 1 , Dataset S1A, and SI Appendix, Fig. S1 ). Wherever possible, hydrothermal fluids from vent orifices were collected to examine possible geochemical drivers of differences in microbial community assemblies (Dataset S1B). Similar to previous analyses of Brothers vent fluids (6, 7), fluids from the NWC and UCW feature chemistry indicative of seawater and rock interactions at high temperatures (∼300 °C).
These vent fluids reveal subcritical phase separation with chloride values above and below background seawater concentrations (540 mmol/kg), with most being greater than seawater. Magnesium and sulfate have been removed from the endmember fluids, and potassium, calcium, trace metals, and sulfide are all elevated (Dataset S1B). In contrast, samples from the cone sites do not show phase separation and indicate a greater degree of magmatic degassing (as compared to water-rock interactions) with large releases of CO 2 , H 2 , and shallow mixing of gases and seawater. While both cones have vent field systems primarily showing magmatic degassing, fluids sampled in our study at the UC were hotter at 200 °C than previously recorded  (7) , compared to the 45 to 60 °C fluids from the LC. The pH in the UC samples was pH 2.
1 ± 0. 1 and higher in the LC samples (pH 5. 4 ± 0. 3). Initially we assessed the microbial community diversity and composition associated with the deposits (and one fluid sample) from the different Brothers volcano sites using 16S ribosomal RNA (rRNA) gene amplicon sequencing (Dataset S1C). The taxonomic composition associated with the magmatically influenced cone sites differed from that of the NWC and UCW sites. Furthermore, there also appeared to be two distinct communities associated with the different caldera wall sites, NWC-A, and NWC-B+UCW (Fig. 2A ; NWC-A versus NWC-B+UCW, analysis of similarities [ANOSIM] R = 0. 896, P = 0. 002). Several of these different communities were almost adjacent sites, for example S145 and S146 (Fig. 1 ). Despite their close proximity, the microbial community diversity of the NWC-A samples clustered somewhat with samples from the cone sites, while those from NWC-B+UCW formed a unique cluster (Fig.
2A ). Based on similarity percentages breakdown (SIMPER) analysis (Dataset S1D), the Epsilonbacteraeota and Deltaproteobacteria* (in NWC-A) and the Gammaproteobacteria, Aquificae, Aigarchaeota/Geothermarchaeota group (SILVA nomenclature Aigarchaeales) and Hydrothermarchaeota (in NWC-B+UCW) are some of the taxa that contribute to the compositional differences seen between these distinct Caldera Wall communities. The shared Epsilonbacteraeota between the cone sites and NWC-A drive some of the similarity in composition between these communities. Thermoacidophiles, like members of Thermoplasmata, are also shared between the cone sites and NWC-A. In addition, members of the Candidate Phyla Radiation (CPR) and the Diapherotrites, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaeota (DPANN) Archaea  (19)  contribute to differences in community structure between the UC and LC and the other sites.
Geochemical Energy Calculations Confirm Importance of Sulfide Oxidation at All Sites. Fluid chemistry could be one potential factor driving the above observed community structure differences. The notable differences between the distribution of the Epsilonbacteraeota and the Gammaproteobacteria at the Brothers sites suggest that these taxa may be examples of ecological indicators of the environmental conditions driving the microbial composition shifts. In general, Epsilonbacteraeota occupy niches of lower oxygen and higher sulfide concentrations, while Gammaproteobacteria thrive in higher oxygen and lower sulfide levels  (20). Unfortunately, due to insufficient geochemical data for the fluids associated with the samples, especially from NWC-A (only one dilute sample for S142) and NWC-B+UCW (four samples; Dataset S1B), we could not determine a statistically relevant correlation between end-member fluid chemistry and the observed microbial community structure.
such as syntrophy, symbiosis, and heterotrophy, all contributing to the overall microbial community composition. To determine to what extent differences in community composition (NWC-A, NWC-B+UCW, UC, and LC) resemble differences in functional complexity, we sequenced the metagenomes of all 18 samples (Dataset S2A) and identified genes coding for proteins involved in energy conservation, mediating key steps of biogeochemical cycles, and indicative of oxygen sensitivity (Fig. 3  A and B, Dataset S2B, and SI Appendix, Fig. S2 A-F ), including the functional processes explored through thermodynamic modeling. cydA (K00425) dfx (K05919) ccoO (K00405) coxA/ctaD (K02274) SOD2 (K04564) porA (K00169) ackA (K00925) aclA (K15230) rbcL/cbbL (K01601) cdhE/acsC (K00197) fwdA/fmdA (K00200) abfD (K14534) ACSS3/prpE (K01908) narG/narZ/nxrA (K00370) napA (K02567) nirS (K15864) norB (K04561) nosZ (K00376) nifH (K02588) nirB (K00362) coxL/cutL (K03520) coxM/cutM (K03519) cooS/acsA (K00198) cooC (K07321) [NiFe] Group 1b hydrogenase [NiFe] Group 1d hydrogenase [NiFe] Group 1g hydrogenase [NiFe] Group 4d hydrogenase S015 -- S010 -- S017 -- S013 -- S016 -- S014 -- S147 -- S011 -- S142 -- S144 -- S145 -- S140 -- S141 -- S146 -- S012 -- S139 -- S143 -- S009 The level of similarity based on the normalized abundance patterns of 123 functional genes/gene categories in the assemblies (Fig.
The shifts in aclA and rbcL/cbbL may mirror the differences in relative abundance of Epsilonbacteraeota and Gammaproteobacteria at these sites, respectively. The Epsilonbacteraeota are typically prevalent in lower-oxygen/higher-sulfide environments where the rTCA cycle for carbon fixation is advantageous  (25) , while the Gammaproteobacteria are typically found in higheroxygen/lower-sulfide environments  (20)  and use the CBB cycle for carbon fixation  (25). Genes for the dicarboxylate/4-hydroxybutyrate and 3-hydroxypropionate/4-hydroxybutyrate cycles (abfD) and the 3-hydroxypropionate bicycle (prpE) were also detected in low relative abundance. Which Taxa Contribute to the Observed Functional Differences between Sites? In order to explore further which taxa were most likely contributing to the functional differences between the different Brothers volcano sites, we used the metagenome assemblies to reconstruct 701 medium-to high-quality draft genomes (MAGs) according to recently established standards  (26)  (Dataset S3A and SI Appendix, Fig.