dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2003)  satellite, following the algorithm detailed in  Kim et al. (2018). Besides clouds, dust and polluted dust, the latter defined as the mixture of dust with biomass burning and urban pollution  (Kim et al. , 2018) , are present over the Alps in the CALIPSO overpasses for the two AR events; ➢ In situ ground-based measurements of air temperature, relative humidity, snow depth and surface radiation and heat fluxes available on a 3-hourly basis for the FlexAlps weather station at Col du Lautaret (45. 0413 • N; 6. 4106 • E) in France. This station is located on the western side of the Alps where the effects of the ARs were clearly noticeable, and therefore is used to investigate their surface and near-surface signatures. In addition to the observational datasets, four reanalysis products are considered in this work: ➢ ERA-5 reanalysis data  (Hersbach et al.
, 2020) , available from January 1979 up to five days behind real time, on a 0. 25 • × 0. 25 • grid and an hourly basis. This dataset is used to investigate the largescale atmospheric conditions associated with the AR events. The 1979-2020 climatology is used to compute the anomalies and normalised anomalies. Fields extracted from ERA-5 include the 2-meter temperature and water vapour mixing ratio, surface heat and radiative fluxes, and the integrated water vapour (IWV) and cloud liquid water content (LWC). The surface fluxes are given as mean rates over the previous hour before at a given time stamp (e. g. , the fluxes at 12 UTC are the mean rates between 11 UTC and 12 UTC), and are defined as being positive if downwards into the surface. The IWV and LWC, both in units of kg m - 2  , are defined as follows: IWV = 1 g ∫ 1000 hPa 1 hPa qdp (1) LWC = 1 g ∫ 1000 hPa 1 hPa q c dp ( 2 ) where g is the gravitational acceleration (9.
80665 m s -2 ), q and q c are the water vapour and cloud liquid water mixing ratios (kg kg - 1  ), and p is the pressure, with the integrals extending from 1000 hPa to the top of the atmosphere. ➢ Global AR database available from  Guan and Waliser (2019). For the climatology over 1980-2017, the AR database used is based on integrated water vapour transport (IVT; kg m -1 s -1 ) derived from sixhourly ERA-Interim reanalysis  (Dee et al. , 2011)  fields of specific humidity and vector winds at 17 pressure levels between 1000 and 300 hPa, calculated using Eq. (  3 ) where g is the acceleration due to gravity (m s -2 ), q is specific humidity (kg kg -1 ), u and v are zonal and meridional winds (m s -1 ), respectively, and dp is the pressure difference between adjacent pressure levels. For the February 2021 AR cases, the ERA5 data were used for consistency with other variables examined during those cases.
In addition, AR events (i. e. , AR days) are defined as a day with at least one of the four reanalysis time steps within detected AR shape boundaries over northwest Africa and southwest Europe (25-45 • N, 15 • W-15 • E). ➢ Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2;  Gelaro et al. , 2017)  reanalysis data. Given that the radiative effect of dust is one of the foci of this work, and that MERRA-2 represents aerosols and their interactions with the climate system, this dataset is also employed for analysis. MERRA-2 provides dust-related fields such as AOD and surface mass concentration at 0. 625 • × 0. 5 • spatial and 1-hour temporal resolution. The anomalies and normalised anomalies of the selected fields are extracted using the 1980-2020 climatology. ➢ Goddard Earth Observing System, Version 5 model  (GEOS-5; Yasunari et al.
The monthly values are downloaded from NOAA's Physical Sciences Laboratory website,2and are manipulated as the NAO index to generate yearly indices. Medium-Range Weather Forecast's data portal (https://apps. ecmwf. int/ datasets/). The Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) is available on NASA's EarthData website (https://earthdata. nasa. gov/), while the forecasts of the Goddard Earth Observing System, version 5 model (GEOS-5) are extracted from NASA's Global Modeling and Assimilation Office website (https ://gmao. gsfc. nasa. gov/GEOS_systems/). The FluxAlps weather station data were obtained from Romain Biron, Institut des Geosciences de l'Environnement, France.
Acknowledgments The Spinning Enhanced Visible and Infrared Imager (SEVIRI) satellite images were downloaded from The European Organisation for the Exploitation of Meteorological Satellites website (https://eoportal. eumetsat. int/). The Clouds and Earth's Radiant Energy System (CERES) surface fluxes are available on the  National Oceanic and Atmospheric Organization 's website (https://ceres. larc. nasa. gov/data/),while the vertical profiles of aerosol and cloud properties measured by the  Cloud-Aerosol Lidar and Infrared Pathfinder (CALIOP)  instrument has been downloaded from the  National Aeronautics and Space Administration's (NASA 's) website (https://www-calipso. larc. nasa. gov/products/lidar/browse_images/production/). ERA-5 reanalysis data is freely available on the Copernicus'  Climate Change Service  website (https://c limate.
Analysis of ATR-FTIR Spectra ATR-FTIR spectra were analyzed using custom software written in Wolfram Mathematica and run under version 12. The software analyzed the OH-stretch band by fitting the data using the 'NonlinearModelFit' function, with a model using the sum of three, four, or five Gaussian distributions with floated peak frequencies, amplitudes, and widths. Using this model, fitting a baseline is necessary where real values may extend beyond the available measured dataset or the data may have a residual offset. We found that the best and most reliable fits gave peak frequencies within the error band of values from literature  [22] , which were therefore fixed for all later fitting routines. These values, which we confirmed earlier  [17, 20] , are 3080 cm -1 , 3230 cm -1 , 3400 cm -1 , and 3550 cm -1 , represented by µ 1-4 respectively.
NMR Measurements NMR chemical shifts (d) in ppm were determined using a Bruker Avance 300 spectrometer (operating at 300. 13 MHz for 1 H and 75. 47 MHz for 13 C NMR). Solutions containing 0. 25 mol dm -3 of pyridine-N-oxide in each sample aimed to be characterized, and a solution of tetramethylsilane (TMS) in pure deuterated water (99. 9% D) as an internal standard, were used in NMR tubes adapted with coaxial inserts. The TMS/D 2 O solution was always used as the inner part of the concentric tubes, while each sample was used in the outer part of the NMR tube. Using this approach, it is possible to guarantee that the TMS standard and D 2 O are not in direct contact with the sample, avoiding possible interferences or deviations in the 13 C NMR chemical shifts. The Mnova software (Santiago de Compostela, Spain) was used for data processing.
This study demonstrates the interest of planetary geophysical mission concepts based on seismic remote sensing with balloon platforms, and their interest to complement terrestrial seismic networks. Plain Language Summary After a quake, the surface of our planet vibrates like the surface of a drum. These vibrations generate sound waves at low frequencies that propagate upward in the atmosphere. These signals from two earthquakes have been recorded by barometers on board a network of long duration high altitude balloons deployed by the Strateole-2 experiment. The analysis of these records demonstrates that the amplitude and arrival time of the vibrations are properly predicted by our modeling tools. The oscillations of the balloon/gondola system forced by the acoustic waves are also observed, but the quake distance and magnitude can be estimated only from the data recorded on board the balloon gondolas.
While the dispersion is difficult to observe on balloon 17 due to the interference between various waves at short distance from the source, balloon 16 presents a record consistent with surface wave dispersion (Figure  4b ). The dispersion of the fundamental mode of Rayleigh seismic surface waves is computed for an internal structure model described Table  S1  in Supporting Information S1. This model is taking into account the local crustal structure extracted from CRUST2. 0 model  (Bassin, 2000)  on top of AK135 global seismic model  (Kennett et al. , 1995). Rayleigh wave group velocities are computed from the phase velocity dispersion curves obtained by the Computer Programs in Seismology code  (Herrmann, 2013). As shown in Figure  4b  the predicted arrival times of the fundamental mode of Rayleigh seismic surface wave estimated from prior internal structure models, and distance and origin time estimated only from balloon data, properly reproduce the dispersion features observed on balloon pressure records.
Data Availability Statement The TSEN pressure records and balloon GPS locations described in this study are available through this link at the following doi: https://doi. org/10. 5281/zenodo. 6344454. The seismic records used in this study were collected at IRIS data management center through the Wilber3 web interface: https://ds. iris. edu/wilber3/. The quake parameters were extracted from GlobalCMT data base: https://www. globalcmt. org/CMTsearch. html.
To quantify the global or regional budgets, we integrated the gridded emissions through corresponding land areas. To estimate land N 2 O emissions, we removed anthropogenic emissions from the energy, industry and waste sectors using the PRIMAP-hist historical emission database  [21] , despite this not having a noticeable effect on the general relationship between anomalies of N 2 O emissions and precipitation (supplementary figure  1 ). Keeping vs. removing biomass burning emissions using GFED-v4. 1 s did not change the global pattern between land N 2 O emissions and precipitation anomalies noticeably (supplementary figure  1 ). For the global budget, we reported results that exclude biomass burning emissions. We also analyzed land N 2 O emissions from process-based land models that participated in the NMIP project  [13].
Climate data Gridded climate data used in this study includes aridity, monthly mean land surface air temperature, precipitation and solar radiation. We used the gridded (0. 5 • × 0. 5 • ) CRU time-series (TS) data version 4. 04 (CRU TS4. 04)  [23] , provided by CRU at the University of East Anglia as our climate dataset for temperature, precipitation and solar radiation. CRU TS4. 04 does not include solar radiation, and we therefore used the cloud cover to approximate variations in solar radiation. We also applied the CRU-NCEP dataset (https://rda. ucar. edu/datasets/ds314. 3/) as an alternative for temperature, precipitation and especially for solar radiation. CRU-NCEP combines observations from CRU with the NCEP-NCAR reanalysis data products, and was applied as meteorological forcing data in NMIP.
Solar radiation in CRU-NCEP was corrected to match empirically derived monthly solar radiations based on latitude and sunshine hours, which is correlated with the CRU cloud fraction. To test the robustness of the relationship between precipitation and land N 2 O emissions and to quantify the uncertainty, we included 3 other precipitation datasets that combine different sources of rain gauge stations, satellites, sounding observations and reanalyses. The Global Precipitation Climatology Centre (GPCC) provides quality-controlled monthly data from 7,000-9,000 stations (www. dwd. de/EN/ourservices/gpcc/gpcc. html). GPCC v2018 was obtained from (https://psl. noaa. gov/data/ gridded/data. gpcc. html) (assessed Jan 2021). GPCPv2. We separated the global land into hyperarid, arid, semi-arid, dry subhumid and humid regions based on the aridity index.
Aridity index here is defined as the ratio between precipitation and the reference evapotranspiration, provided by  [24]. Hyperarid region is classified as the land with the smallest aridity index (AI < 0. 03) and covers 7. 5% of the total global land area according to the United Nations Environment Programme  [25]. The class with the second smallest aridity index (AI: 0. 03-0. 2) is the arid region, which covers 12. 1% of the global land, followed by semi-arid (17. 7% of land area, AI: 0. 2-0. 5), dry subhumid (9. 9%, AI: 0. 5-0. 65) and humid regions (52. 8%, AI > 0. 65). All gridded datasets (climate and N 2 O) were regridded to 1 • × 1 • using the nearest neighbourhood method for consistency. Projections of 5 Earth system models (CESM2-WACCM, ACCESS-CM2, GFDL-ESM4, IPSL-CM6A-LR, MIROC6) under SSP5-85, SSP3-70, SSP2-45 and SSP1-26 from the ScenarioMIP  [26]  serve to quantify future changes in precipitation anomalies.
Anomalies, sensitivities and decompositions The growth rate of land N 2 O emissions of a specific month was calculated as the difference between N 2 O emissions from that specific month and the month in the previous year. The monthly growth rates were detrended (linear least-squares fitting) and summed every 12 months to estimate the interannual anomalies (N 2 OEA) similarly as in  [20]. Interannual anomalies of climate variables (PRE: precipitation; TEMP: temperature; RAD: solar radiation) were calculated in a similar way. Current available global datasets of anthropogenic nitrogen inputs are coarse in temporal resolution (yearly), with the signal dominated by the increasing trend in the past several decades. The direct impact of the anomaly of nitrogen inputs alone is less likely to be the major driver of N 2 OEA as the anomaly signal of nitrogen inputs is small after detrending.
We decomposed variations of climate-driven N 2 OEA into contributions from precipitation (N 2 OEA_PRE ), temperature (N 2 OEA_TEMP) and radiation (N 2 OEA_RAD) similarly as in  [27] : N 2 OEA d, t = α PRE d × PRE d,t + α TEMP d × TEMP d,t + α RAD d × RAD d,t + ε d,t (1) N 2 OEA d, t ≈ N 2 OEA_PRE d,t + N 2 OEA_TEMP d,t + N 2 OEA_RAD d,t (2) where α d with the superscript is the sensitivity of N To estimate the uncertainties in α PRE and climate-driven N 2 OEA, we applied the bootstrapping method with 1000 random samples combining different data sources (five precipitation, two radiation and two temperature datasets) for each of the N 2 O datasets. Uncertainties were expressed as the standard deviations of these samples. To understand the spatial patterns and mechanisms that lead to the enhanced influence of precipitation on N 2 OEA, we calculated α PRE and N 2 OEA_PRE STD across spatially aggregated groups.
We included radiation as a climate predictor considering especially its role in vegetation dynamics, which regulates the energy (carbon) and substrate (nitrogen) availability for nitrification and denitrification. With an ensemble of estimates combining the three N 2 O inversions and different datasets of climate variables (Methods; PRE: 5 datasets; TEMP: 2 datasets; RAD: 2 datasets), the overall global α PRE is 2. 50 ± 0. 98 (mean ± standard deviation) Tg N 2 O-N (100 mm) -1 during 1998-2016. That is, a 100 mm rise in precipitation over the global land increases N 2 O emissions by 2. 50 ± 0. 98 (t-test, P < 0. 01) Tg N 2 O-N. To translate sensitivities to contributions to N 2 OEA during 1998-2016, we decomposed N 2 OEA into the contributions from precipitation (N 2 OEA_PRE), temperature (N 2 OEA_TEMP), and radiation (N 2 OEA_RAD) (see Methods, equations (1) and  (2).
Semi-arid regions stand out for their highest N 2 OEA_PRE STD per unit area (3. 33 ± 0. 28 mg N m -2 yr -1 , figure  2(c) ). The dominant contributions of humid (45%-48%) and semi-arid (20%-29%) regions to the global correlation between N 2 OEA and PRE were also verified by calculating the contribution of each region using the additive properties of covariances (supplementary table  1 ). We also found that the global α PRE has increased with time during the study period from 1998 to 2016. Combining three datasets of N 2 O emissions from inversions, α PRE is 0. 50 ± 0. 73 (t-test, P > 0. 3) in the earliest time window (first 96 months, 1998-2005), and reaches 3. 63 ± 1. 22 (t-test, P < 0. 001) Tg N 2 O-N (100 mm) -1 in the recent time period  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014).
Due to the complexity and relatively high computation requirements, these global N 2 O models typically do not simulate nitrifying and denitrifying microbial communities explicitly. Whether the lack of microbial physiology leads to biased simulation of moisture responses remains to be explored. Note there are limited numbers of process-based global N 2 O models  [29] , and a comprehensive assessment on the capacity to model soil moisture responses requires more effort from both modelers and data collectors. The average correlation between satellite derived soil moisture anomalies (ESACCI v05. 2, the combined product) and N 2 OEA from atmospheric inversions is 0. 27. Satellite derived soil moisture datasets normally only capture first top centimetres of soil, which is not adequate in representing biogeochemical active soil zones.
This correlation value reached 0. 42 from model estimated soil moisture (CPC soil moisture, one-layer hydrological model) or 0. 40 from a reanalysis dataset (ERA5, 0-28 cm). The global scale soil moisture datasets are likely to be associated with higher uncertainties than observation-derived precipitation datasets. While water filled pore space (approximated by soil moisture in models)  [5, 13]  is a more direct regulator of soil N 2 O emissions than precipitation, how to realistically capture its dynamics and impacts on large-scale soil N 2 O emissions requires more efforts in the future. The top-down (atmospheric inversion) and process-based modelling estimates are consistent in terms of the decadal global N 2 O budget  [14] , while the climate sensitivities of land N 2 O emissions may need more studies considering the uncertainties in both atmospheric inversions and process-based modelling, and regional scale observations related to this topic are especially in need.
Acknowledgments We acknowledge research groups and institutes provide atmospheric N 2 O inversions, process-based N 2 O modelings, atmospheric N 2 O measurements and climate datasets. We especially thank  Rona L Thompson  for providing publicly available atmospheric inversion datasets for N 2 O. P C, O B and Y H. This work benefited from the  French  state aid managed by the  ANR  under the 'Investissements d'avenir' programme with the reference  ANR-16-CONV-0003. The LMDz modelling results were funded through the  Copernicus Atmosphere Monitoring Service  (https://atmosphere. copernicus. eu/),implemented by ECMWF on behalf of the  European Commission , and were generated using computing resources from LSCE.
assessed Jan 2021) provides 1 • × 1 • global grids of monthly precipitation reconstructed from observations over 17 000 gauge stations. 3 (https://climatedataguide. ucar. edu/climate-data/ gpcp-monthly-global-precipitation-climatology-project, assessed Jan 2021) is the Global Precipitation Climatology Project monthly precipitation dataset from 1979-present that combines observations and satellite precipitation data into 2. 5 • × 2. 5 • global grids. NOAA's Precipitation Reconstruction over Land (NOAA, https://psl. noaa. gov/data/gridded/data. precl. html,.
Funding This research was supported by the  Region Pays de la Loire (Lancom  project) and Ph. D. grant for A. P. was co-funded by  Region Pays de la Loire  ( Lancom  project) and  Le Mans Universite. M. F. benefited from the  Horizon 2020 Research and Innovation Program GHaNA  (The  Genus Haslea, New marine resources for blue biotechnology and Aquaculture , grant agreement No [ 734708/GHANA/H2020-MSCA-RISE-2016 ] J. -L. M. ).
Despite efforts to expand the CyABA, the number of people involved in the organization remains small. At the time of this writing, there are six active professionals certified as BCBA and one as BCaBA, who work privately by offering services to families and their children with disabilities. According to data retrieved from the BACB online Certificant Registry on April 28, 2021, there are an additional seven professionals whose status either expired or is inactive. The future of ABA in Cyprus remains uncertain but promising. Future efforts should include two main goals: (1) creating an VCS (verified by ABAI) with local universities to increase the number of ABA professionals on the island; and (2) pushing forward to establishing the profession of behavior analysis under Cyprus's existing legislation framework.
Spain As of 2021, there are 35 BACB certificants in Spain (31 BCBAs, 1 BCaBA, 3 RBTs), most of them certified within in the last few years, which suggests that the number of certificants is finally gaining momentum. Twenty-six out of the 33 BCBA/BCaBAs (81%) in Spain are graduates from ABA España's program. The current rate at which new candidates are becoming certified has increased by a factor of 2. 5 over the last 5 years. The rate of certification lapses calculated as the number of inactive BCBA and BCaBA certificants divided by the number of active and inactive certificants is 3. 4%, which suggests that certificants see value in preserving their certification after it has been acquired. In addition, Spanish nationals and residents have had a high pass rate in certification exams. The data generated through a survey conducted in 2019 by the Spanish ABAI chapter ABA España suggests that certified and uncertified professionals do not differ in their training and sociodemographic characteristics (Tables  1  and 2 ).
Section 2 describes the CNRM-CM6-1 AOSCM, which is referred to as CNRM-CM6-1D hereafter. Based on the CINDY2011/DYNAMO field campaign and other related data, Sect. 3 develops the forcing appropriate for an AOSCM case study. It also introduces the data used as a reference for analysis. Section 4 discusses the representation of the SST diurnal cycle in either atmospheric or oceanic stand-alone versions of the AOSCM, while Sect. 5 fully makes use of the coupled AOSCM to assess its ability to represent the SST diurnal cycle and how this representation depends on the AOSCM configuration. Section 6 concludes this work.
g. , vertical velocity, horizontal advection, sub-array diabatic terms) at the scale of two large-scale arrays (∼ 800 km) over the tropical Indian Ocean. This dataset was used in  Abdel-Lathif et al. (2018)  to force a previous version of the ARPEGE-SURFEX SCM. In contrast, available ocean data mostly come from the R/V Revelle, which was used as a fixed station at 0 • N, 80. 5 • E for periods of several months. These local data are not sufficient to derive a consistent large-scale forcing of the AOSCM ocean component. We therefore decided to develop a more local AOSCM forcing based on the R/V Revelle data. This case study has been developed in the context of the French research project COCOA and has been used in several studies  (Brilouet et al. , 2021). The ship's location at the Equator implies possible strong large-scale advection, which needs to be quantified for forcing the AOSCM.
Reference datasets To assess the model simulations, we use local observation data from the R/V Revelle. SSTs taken are obtained from the ship's intake thermosalinograph at 5 m depth corrected to represent the skin temperature based on the Sea Snake floating thermistor at 5 cm depth  (Edson et al. , 2016; de Szoeke et al. , 2015). In the following the SST used as a reference will be the SST skin temperature. Reference turbulent fluxes are obtained from the NOAA PSD (Physical Science Division) and provided by Chris Fairall and Ludovic Bariteau at hourly frequency. Two products are derived from high-frequency field measurements, either computed using eddy covariance (EC) or inertiodissipative (ID) methods. We also use both conductivitytemperature-depth (CTD) casts and the Oregon State University Chameleon profiler to get ocean temperature and salinity profiles, as well as acoustic Doppler current profiler (ADCP) records to get current profiles  (Moum, 2016).