dataesr/scientific-paragraphs-categorization huggingface dataset:
Transient Holocene Simulations and SST Observation Data The two transient simulations used in this study, Vlr01 and Sr02, are described in  Braconnot et al. (2019)  and were also part of the model-data comparison in  Carré et al. (2021). The IPSL fully coupled model includes the energy, water, and carbon cycle between the atmosphere, ocean, land, and ice. For Vlr01, obtained with the IPSL-CM5 model, the atmosphere has a horizontal resolution of 96 points in longitude and latitude and a vertical resolution of 39 levels. The ocean has a resolution of 182 points in longitude, 149 points in latitude, and 31 vertical levels. The model version is described in  Dufresne et al. (2013). Sr02 is run with a modified model version with a different soil hydrological scheme over land and a dynamical vegetation module  (Braconnot et al.
, 2019). It also has a higher horizontal atmospheric resolution of 144 points in longitude and 143 in latitude. Both simulations are run from 6,000 BP to 0 BP  (1950 CE). The initial state corresponds to a mid-Holocene simulation with the corresponding model version. Earth orbital parameters and trace gases are updated annually  (Braconnot et al. , 2019) , following the experimental protocol proposed by  Otto-Bliesner et al. (2017). In both versions of the IPSL model, the equatorial upwelling extends too far west, eroding the warm pool, and there is a tendency to produce too many spring El Niño events  (Dufresne et al. , 2013; Saint-Lu et al. , 2016). Dynamic and thermodynamic feedbacks are smaller than observed, and ENSO variability is underestimated  (Bellenger et al. , 2014). To compare the model results with observations, we use two historical SST datasets: COBE-SST2  (Hirahara et al.
, 2014)  and ERSSTv5  (Huang et al. , 2017). The temporal coverage of COBE-SST2 extends from 1850 to 2019 and ERSSTv5 from 1854 to 2023. ERSSTv5 is derived from the International Comprehensive Ocean-Atmosphere Dataset (ICOADS). The SST field from COBE-SST2 is reconstructed from in-situ SST and sea ice concentration observations. The common time frame for comparing the SST datasets and simulations is 1854-1950 CE. To have the same comparison framework, COBE-SST2, ERSSTv5, Vlr01, and Sr02 were remapped to a resolution of 1° in both latitude and longitude.
Although estimations of the E and C flavors for COBE-SST2 and ERSSTv5 match almost perfectly for the period 1950 to 2019 (Figure  1c  and Figure  S1  in Supporting Information S1), differences also emerge. Indeed, from 1854 to 1950, the agreement between the two datasets is not as good (Figure  1c ). This can be attributed to the time window  used in  Takahashi et al. (2011)  to calibrate M2 from M1, which is too short to document the range of El Niño event diversity properly (Equations 3 and 4). A calibration for the    between the two periods and datasets can also be attributed to how ship observation biases were estimated before 1950. For ERSSTv5, the nighttime marine air temperature helps correct the ship observations' biases. 140 Empirical Orthogonal Teleconnections (EOT) were also used to reconstruct an SST field from 1854 to the present  (Huang et al.
Carré et al. (2021)  used the ERSSTv5 dataset from 1900 to 2017 to compare the variance ratio of their defined EP and CP boxes with the variance ratio of E 2 and C 2. They matched, albeit with minor differences in magnitude. However, two boxes do not suffice to cover the E and C flavor pattern shifts over time. We could consider M1 to be the most rigorous method due to its ability to properly distinguish unique pattern characteristics between E and C flavors. However, comparing the results from M1 to the boxes defined by the paleo-archives' location is not viable, since these boxes fail to encompass the two patterns resulting from the EOF rotation. Our results imply that different techniques of detecting E or C flavor-like El Niño events in models and observations could lead to incoherent results.
Data Availability Statement The processed data are available at https://doi. org/10. 6084/m9. figshare. 20789203 and the Jupyter Notebook used to make the figures is available at https://github. com/iadicarlo/elnino-flavors-paper (https://doi. org/10. 5281/ zenodo. 7938040). We acknowledge NOAA/OAR/ESRL PSL, USA for providing us the COBE-SST2 and ERSSTv5 data respectively at https://psl. noaa. gov/data/gridded/data. cobe2. html and https://psl. noaa. gov/data/ gridded/data. noaa. ersst. v5. html.
This challenge has led some researchers to conduct a parametric sensitivity study of the hygrothermal models to investigate the influence of parameters on heat and mass transfer  [33] [34] [35] [36]. At the material scale, Benkhaled et al. [33]  conducted a sensitivity analysis to identify the most influential parameters affecting hygrothermal transfer in hemp concrete. The study was based on the probability density distribution of bounded Gaussian law using MATLAB R2020a software. As a result, a simplified HAM model was elaborated by eliminating parameters that had minimal impact on the hygrothermal behavior of the material under investigation.
The weather in Lyon is characterized as temperate, as indicated in Figure  4. To evaluate the hygrothermal performance of the wood-based building, monitoring campaigns were carried out for indoor hygrothermal conditions, as well as within the wood walls at three different positions (3 cm from interior side, at the center of the wall, and 4 cm from the exterior side). These campaigns span a 6-month period, from 20 April 2023, to 20 October 2023. Figure  4  illustrates the daily fluctuations in indoor and outdoor air temperature and relative humidity during this timeframe. While the data were continuously recorded at hourly intervals, daily averages are considered for better data visualization. Since the investigated building is equipped with an HVAC system, the indoor temperature and relative humidity remain quite stable despite the high fluctuations in outdoor conditions.
Numerical Simulation Tool: COMSOL Multiphysics To solve the coupled equations related to the Reduced HAM model, the COMSOL Multiphysics v. 5. 5 software was selected. This software provides a robust platform for modeling and solving a wide range of engineering and research problems. It is especially well-suited for handling Multiphysics problems where several phenomena are studied simultaneously, as is the case of heat and mass transfer. The software employs the finite element method for solving partial differential equations. In COMSOL, the "Mathematics" branch is the optimal choice for solving equationbased models as it supports various formulations (weak form, general form, coefficient form PDE, etc. ) of partial differential equations (PDEs). Within the "PDE interfaces", the preferred equation formulation for entering the equations is the "Coefficient form PDE".
, 2001). The AOD at a wavelength of λ = 675 nm is daily averaged and compared to daily averaged modeled values. Maps of the stations for which the measurements were used are presented in Fig. 2. The detailed names and location of these stations are provided in Tables  A1  and A2. The map of the AERONET stations shows the entire modeled domain. The second map is a zoom on the region that we will study in more detail. Note that the stations closer to the studied fires are Airvault (FR09304), La Tardiere (FR23124), Aytre (FR09008) and Zoodyss (FR09302). Second, and in order to have information on the vertical, Cloud-Aerosol Lidar Pathfinder Satellite Observation (CALIPSO) lidar data are used. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar measurements, on-board the CALIPSO satellite  (Winker et al.
It is true both for the measurements and the model results. In Palaiseau, only the peak of 19 July is visible on the AOD time series. It is the same for the PM 10 surface concentrations in Rambouillet (close to Palaiseau). But in this latter case, model values are underestimated: when the model simulates a peak at ≈ 50 µg m -3 , the measurements show high values at ≈ 110 µg m -3. It means that the plume coming from Landes reaches the Paris area but is simulated too low compared to the measurements. In order to refine the analysis on PM 10 surface concentrations, the modeled aerosol composition is presented in Fig. 7  as size distribution. Depending on the data availability, results are presented here for Arcachon and Paris and for the 19 July at 15:00 UTC. to measurements: nofire and f2all.
, 2010). Vertical lidar profiles are analyzed to obtain an aerosol subtype classification (CALIOP v4. 10 product), developed by  Omar et al. (2010)  and  Burton et al. (2015). This classification is built on thresholds of lidar-derived optical characteristics. Of course, this estimation is uncertain and limitations are quantified in  Tesche et al. (2013). For the CHIMERE model results, a specific development was done in  Menut et al. (2018)  to retrieve the same classification but based on all modeled aerosols. The comparison is presented in Fig. 9  for the dataset named CAL_LID_L2_VFM-ValStage1-V3-41. 2022-07-18T13-51-17ZD. It corresponds to a trajectory quasi-iso-longitude, and the data are presented for the latitude from 10 to 60 • N. The CALIOP data are scarce (white areas are for no data) but show that the aerosol plume is mainly between the surface and 5000 m a.
Acknowledgements. We thank the investigators and staff who maintain and provide the AERONET data (https://aeronet. gsfc. nasa. gov/, last access: 29 June 2023; Holben et al. , 2001). The European  Environmental Agency (EEA)  is acknowledged for their air quality station data that are provided and freely downloadable (https://www. eea. europa. eu/data-and-maps/ data/aqereporting-8, last access: 29 June 2023). Review statement. This paper was edited by  Lea Hildebrandt Ruiz  and reviewed by two anonymous referees.
2 and 3. 3), the intensities I X (E) were corrected for the fragmentation of these attributed species due to their ionization by electron impact. The fragmentation patterns were taken from the NIST database  (Linstrom & Mallard 2022). The resulting intensities were then converted into Xray photodesorption yields Γ X (E), expressed in molecules desorbed per incident photon (simplified to molecules. photon -  in this study), using equation 1, Γ X (E) = k X I X (E) ϕ(E) , (1) where ϕ(E) is the photon flux at E, and k X is a conversion factor associated with the neutral species X. The coefficient k X was calibrated on N 2. k N 2 relates the QMS current to a calibrated number of N 2 molecules desorbed during TPD experiments (see  Basalgète et al. (2022)  for more details of the calibration procedure).
Part of the m/z 44, whose global intensity is doubled in the case of the mixed H 2 O:CH 3 CN ices compared to the mixed 13 CO:CH 3 CN ices, can be attributed to the carbamoyl-ionized radical [H 2 N-CO] + , for example, resulting from the cracking of acetamide CH 3 -CONH 2. Another possibility to explain the m/z 44 signal from the H 2 O-mixed ices would be the desorption and further fragmentation of formamide HCONH 2. Formamide desorption should produce a signal on the m/z 45 that is higher than the m/z 43 and 44 according to the NIST database  (Linstrom & Mallard 2022) , however. As we did not detect a desorption signal on the m/z 45 from the H 2 O-mixed ices, we might exclude formamide desorption even if the large uncertainties prevent us from concluding this definitively. Both acetamide and formamide have been CH 3 CN 4.
Acknowledgements. This work was carried out with financial support from the  Region Ile-de-France DIM-ACAV + program ; the  Sorbonne Université  " Emergence" program ; the  ANR PIXyES project ,  Grant  No. ANR-20-CE30-0018  of the  French "Agence Nationale de la Recherche "; and the  Program National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  cofunded by  CEA  and  CNES. We would like to acknowledge SOLEIL for the provision of synchrotron radiation facilities under Project No. 20210142  and  N. Jaouen ,  H. Popescu , and  R. Gaudemer  for their help on the SEXTANTS beam line.
We found that organic hydroperoxide cations are largely dissociated by loss of OOH. This fingerprint was used for the identification and accurate quantification of the organic peroxides, and it can therefore be used to improve models for autoxidation chemistry. The synthesis method and photoionization dataset for organic hydroperoxides are useful for studying the chemistry of hydroperoxides and the reaction kinetics of the hydroperoxy radicals and for developing and evaluating kinetic models for the atmospheric autoxidation and combustion autoxidation of the organic compounds.
The molar concentration (mol/ml) of ROOHs was confirmed by the iodometric method before the PICS measurement. See SI Appendix, section S3 for details on the PICS measurement of ROOHs (SI Appendix, Fig. S4  and Table  S3 ) and database containing the PICS of the ROOHs (SI Appendix, Tables  S4-S16 ). The uncertainty for the PICS of the ROOH is estimated to be ±12% based on compounding 5% due to the precision of the measurement on the binary mixture, 10% due to the uncertainty of the PICS of the reference molecule, and 5% due to the error bar for the iodometric method. The binary mixture was prepared by weighing each component on an analytical balance with a precision of 0. 1 mg; thus, the uncertainty in the preparation of the binary mixture is negligible. Although the beaker for the binary mixture was well sealed before injection into the syringe pump, there might be volatilization of the binary mixture, as a result of which we estimate 5% as an upper limit of uncertainty of the binary mixture.
Implication for Atmospheric and Combustion Chemistry Hydroperoxide intermediates are widespread in atmospheric autoxidation. The PICS database of ROOHs obtained in this work can be used to identify and quantify the hydroperoxides formed in the autoxidation of hydrocarbons initiated by OH radicals and by the ozonolysis of terpenes. The PICS database is also useful for studying the reaction kinetics of peroxy radicals, such as the reactions with HO 2 and OH, and the reactions of peroxy radicals, where hydroperoxides are also important intermediates. In combustion autoxidation, hydroperoxide intermediates have been observed since the 1980s (  14 ) and have been widely detected by the SVUV-PIMS method since 2010  (4, 15, 41, 54). However, the accurate quantification of the ROOH and KHP intermediates has not been Here, using the PICS database of the organic hydroperoxides measured in this work, we give an accurate quantification for methyl hydroperoxide, ethyl hydroperoxide, pentyl hydroperoxide, and the C5 KHP in Fig.
Summary We have shown that many ROOHs can be synthesized by the mild and environmental-friendly method developed in this work. This allowed the measurement of the PICS of the ROOHs, the elucidation of their photodissociation fingerprint by SVUV-PIMS, and the creation of a database of ROOH PICSs. A chemical titration method was combined with an SVUV-PIMS measurement to get the PICS of 4-hydroperoxy-2-pentanone, which could be adopted to obtain the PICSs of other KHPs in combustion and atmospheric autoxidation. These data are valuable for the quantification of organic hydroperoxide species, for the illustration of the photoionization mechanism, and for the refinement of kinetics models. Furthermore, these PICS data are valuable for developing theoretical methods  (33, 41)  and group additivity methods  (55)  to calculate or estimate the total PICS of organic hydroperoxides.
, 2006). In DigiBog_Congo, root inputs are allocated evenly with depth through the rooting zone. We allow root depth to vary with peat thickness until a maximum rooting depth of 30 cm is reached (Figure  S1 ; Table  1 ;  Sciumbata et al. , 2023). Litter input data available from the study site showed no statistical relationship with water-table depth or temperature (Section 3. 1): We therefore did not vary litter inputs according to water-table depth F I G U R E 2 Schematic of how DigiBog_Congo is set up to simulate the largely intact interfluvial peatlands in the central Congo Basin. A single column made up of layers of peat increases or loses height because of the balance between plant litter addition (above-and belowground) and peat decay. There is no subsurface drainage. Water is added to, or removed from, the simulated peatland as the difference between net rainfall-(P) minus evapotranspiration (E)-and surface water (blue layer) overland flow losses.
| Model parameters In our main model runs, we assume PFT proportions of 50% hardwood tree and 50% palm, but these were varied for the sensitivity analysis. Table  1  shows the model parameters for the different PFTs and their source. Where parameter information was not available from the plot datasets, we searched for it in published studies. To parameterise the litter inputs, we used 2 years of data collected between March 2019 and March 2021 from two 1 ha plots located ~20 km from the CEN-17. 4 core site. The two plots, representative of a hardwood-dominated (EKG-02) and palmdominated swamp forest (EKG-03), respectively, were set up and monitored using Global Ecosystem Monitoring (GEM) protocols  (Malhi et al. , 2021). We used mean annual aboveground leaf litterfall from litter traps for each PFT; mean annual coarse woody inputs collected in ground traps; and root inputs from minirhizotrons  (Sciumbata et al.
, 2023). To parameterise peat decay, we used data from a replicated litterbag experiment, with litterbags filled with litter fractions from the two PFTs, placed at 0 cm (surface) and 50 cm depth and decay tracked for 2 years. The two depths represent primarily the effects of oxic and anoxic decay, respectively. The field experiments did not separate litter fractions into labile and recalcitrant pools. We therefore chose to set the oxic decay rate of recalcitrant material to be the same as the oxic decay rate of labile material-evidence suggests that all litter decays rapidly in oxic conditions  (Hoyos-Santillan et al. , 2015). Because there are currently no data available on the anoxic decay of recalcitrant material from our site or other tropical peatlands, this parameter was estimated from several model runs by comparing the simulated age-depth curve with the core from CEN-17.
yr BP, buffering by surface water delayed ongoing carbon losses until ~4000 cal. yr BP. Furthermore, of the peat lost, some was aged up to ~7000 cal. yr BP, affected by secondary decomposition (i. e. it had accumulated ~3000 years before the drier-climate-driven peat losses began; Figure  3a, d ). This highlights the catastrophic effect on peat carbon stocks of deeper water levels that could be caused by draining the peatlands, such as for agriculture, oil exploration or a future drying climate. Although we did not simulate changes in rainfall seasonality, our results suggest that increases in dry-season intensity or length, as have been documented in some datasets for the central Congo region over the past two decades  (Feng et al. , 2013; Jiang et al. , 2019)  In large domed peatlands such as the one at CEN-17.
Ian Lawson: Funding acquisition; writing -review and editing. Susan Page: Funding acquisition; writing -review and editing. A. Jonay Jovani-Sancho: Writing -review and editing. Enno Schefuß: Resources; writing -original draft; writing -review and editing. Matteo Sciumbata: Investigation; writing -review and editing. Sofie Sjogersten: Funding acquisition; writing -review and editing. Simon L. Lewis: Conceptualization; funding acquisition; project administration; writing -original draft; writing -review and editing. ACK N O WLE D G E M ENTS This work was funded by CongoPeat-a NERC large grant (NE/ R016860/1) to S. L. L. , I. T. L. , S. E. P. , A. B. , A. J. B. , P. J. M. , P. G. and S. S. Eleanor Burke was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101).
, 2009). In the atmosphere, for the simulations presented in this study, global mean concentrations of greenhouse gases are set to their values of the simulated year, with values taken from  Meinshausen et al. (2017). Ozone varies spatially on a monthly timescale. The data set is taken from the input data sets for model intercomparison projects (input4MIPS) with a resolution of 1. 875 • by 2. 5 • (latxlon), and the year 2014 is chosen as default. Aerosols are specified from the climatology of  Kinne (2019) , which provides monthly data on a 1 • grid. Initial conditions for the atmosphere are derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis for the chosen start date. Initial conditions for soil moisture, soil and surface temperatures, and snow cover are also derived from the ECMWF analysis.
The output has mostly been analyzed using Jupyter notebooks and Python, with which efficient ways to analyze the output directly on the unstructured grid have been developed. As part of ICON-Sapphire activities and of the next Generation Earth Modelling Systems (nextGEMS) project, an ebook has been developed as a community effort, providing information on the simulations as well as hints and example scripts. The e-book can be found at https://easy. gems. dkrz. de (last access: 17 January 2023). An important tool for selecting a subset of the output or in general for reformatting and transforming variables is the Climate Data Operators (CDO), which are developed at MPI-M  (Schulzweida, 2022). Calculations are performed in CDO with double precision, whereas the output format of ICON-Sapphire simulations (see next section) is single precision.
to obtain the initial state of G_AO_2. 5km. In all the simulations, sea surface salinity is relaxed towards observed 10 m salinity of the PHC data set with a time constant of 3 months. Through this initialization technique, the ocean is spun up and already entails mesoscale eddies without having to use a multi-decadal spin-up at 2. 5 km, which is computationally too expensive. The conducted long spin-up gives some confidence that the ocean model is working correctly. Comparing the last full month of the spin-up (December 2019) to the Ocean Reanalysis System 5 (ORAS5,  Zuo et al. , 2019) , the global mean SST bias is -0. 53 K. The SST is consistently too cold except along 60 • S, north of 50 • N, and along the western coast of North America. Due to the relaxation, the salinity is well captured except in the Arctic Ocean, with the latter showing a bias of more than 1 g kg -1.
6a  and c ). This bias is linked to SSTs that are too low at the Equator, as shown in  Segura et al. (2022)  in their detailed assessment of the representation of tropical precipitation in G_AO_5km. Over land (Fig. 6b  and d ), in contrast, the seasonal migration of the tropical rain belt is very well reproduced, but the amounts are overestimated. Tropical mean precipitation amounts over land are 3. 6 mm d -1 in G_AO_5km and 3. 11 mm d -1 in observations for the 10-year mean, with no observed yearly value larger than 3. 30 mm d -1. Hence, ICON-Sapphire overestimates precipitation amounts at least over the tropics. This remains true in the global mean, with an overestimation by 0. 4 mm d -1. The overestimation is indicative of a radiative cooling of the atmosphere that is too strong. However, even on the observational side, existing discrepancies between observed net radiation and observed precipitation suggest that precipitation amounts may in reality be larger than what observational precipitation data sets are suggesting  (Stephens et al.