dataesr/scientific-paragraphs-categorization huggingface dataset:
Alexey V. Eliseev  and  Igor I. Mokhov  were supported by the  Russian Foundation for Basic Research. Tomohiro Hajima  and  Kaoru Tachiiri  acknowledge support from " Integrated Research Program for Advancing Climate Models " by the  Ministry of Education, Culture, Sports, Science, and Technology of Japan. Philip B. Holden  was funded by  LC3M , a  Leverhulme Trust Research Centre Award. Aurich Jeltsch-Thömmes  acknowledges support from the  Oeschger Centre for Climate Change Research. Gary Shaffer  was supported by  FONDECYT. Laurie Menviel  acknowledges support from the  Australian Research Council. Jerry Tjiputra  and  Jörg Schwinger  acknowledge  Research Council of Norway  funded projects KeyClim and IMPOSE. Andrew H. MacDougall  is thankful to Compute Canada. GFDL ESM2M simulations were performed at the  Swiss National Supercomputing Centre (CSCS).
Victor Brovkin  is grateful to  Thomas Raddatz  and  Veronika Gayler  for their assistance with the MPI-ESM experimental setup. Roland Séférian  and  Martine Michou  acknowledge supercomputing time provided by the  Météo-France/DSI  supercomputing centre. The authors thank all participants of the expert workshop on carbon budgets, co-organized with the support of the  Global Carbon Project , the  CRESCENDO  project, the  University of Melbourne , and  Simon Fraser University , for discussions that initiated the authors' thinking about ZECMIP. We thank  Ric Williams  and an anonymous reviewer for their helpful comments. Financial support. This research has been supported by the  Natural Sciences and Engineering Research Council of Canada , the  Swiss National Science Foundation  (grant nos.
e. MPAs where some fishing is allowed or some illegal fishing may occur due to weak enforcement; n = 22; Figure  S1 ). However, because our multi-scale analyses rely on pairwise comparisons between rarefaction curves, we chose to use only protected and fished categories to simplify the presentation of results. Our samples from fished and protected sites were not matched spatially (i. e. we do not have samples from inside and outside protected areas at all locations; Figure  1 ). To make inferences of protection effects as robust as possible, we gathered additional data to adjust for variation in the environment and other human impacts. Habitat complexity was measured in situ along each transect as substrate rugosity (see  Guidetti et al. , 2014) , and environmental covariates (e. g. temperature, chlorophyll A concentration, etc.
) were extracted from Bio-ORACLE  (Tyberghein et al. , 2011 , see appendix A for details). For a proxy of human pressure, we used the cumulative impact layer that integrates 22 anthropogenic drivers (e. g. various types of fishing, invasive species, climate change, nutrient input) for the Mediterranean from  Micheli et al. (2013). These data were included as covariates in our α-scale analyses, and we used permutation tests to examine for systematic differences between fished and protected areas across all sites. All covariates were mean centred and standardized by dividing by one standard deviation prior to all analyses. PERMANOVA on a Euclidean distance matrix did not reveal strong evidence for systematic differences between fished and protected sites (F = 2. 2, p =. 08; Table  S1 ), and the variance of the covariates did not differ between fished and protected sites (F = 2.
A. B. thanks the Marine Ecology & Biodiversity laboratory at TAU, the Biodiversity Synthesis group at iDiv, the editor and reviewers for constructive feedback at various stages of this project. This work was partially supported by an Israel Science Foundation (ISF) grant number 1356/15 to J. B. J. M. C. , T. M. K. , F. M. and S. A. B. gratefully acknowledge the support of the German Centre of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (funded by the German Research Foundation; FZT 118); T. M. K. was also supported by the Alexander von Humboldt Foundation; F. M. acknowledges support from the Stanford Woods Institute for the Environment.
DATA AVA I L A B I L I T Y S TAT E M E N T Data available via the Dryad Digital Repository https ://doi. org/10. 5061/dryad. v9s4m w6r1  (Blowes et al. , 2019b). Code to reproduce results https ://doi. org/10. 5281/zenodo. 3545807 (Blowes et al. , 2019a).
S. A. B. thanks the Marine Ecology & Biodiversity laboratory at TAU, the Biodiversity Synthesis group at iDiv, the editor and reviewers for constructive feedback at various stages of this project. This work was partially supported by an Israel Science Foundation (ISF) grant number 1356/15 to J. B. J. M. C. , T. M. K. , F. M. and S. A. B. gratefully acknowledge the support of the German Centre of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (funded by the German Research Foundation; FZT 118); T. M. K. was also supported by the Alexander von Humboldt Foundation; F. M. acknowledges support from the Stanford Woods Institute for the Environment.
Surveys We used data from the National Dietary Survey (NDS) and the Household Budget Survey (HBS), both conducted in 2008 and 2009 by the Brazilian Institute for Geography and Statistics. NDS was simultaneously collected in a random subsample of ~25% of the HBS. A twostage sampling process was adopted: in the first stage, census tracts were randomly selected; and, in the second stage, households were randomly selected within census tracts. Census tracts (n = 12,800) were grouped into 550 household strata with geographical and socioeconomic homogeneity, and the number of tracts in each stratum was proportional to the number of households in the stratum. The samples included 55,970 households (HBS) and 13,569 households (NDS). Household visits in each stratum were uniformly distributed throughout the 12-month period to encompass seasonal variations in both food intakes and prices.
Model inputs Dietary intakes. Dietary intake based on the NDS was collected from two non-consecutive food records (97% response rate for the second food record) filled by 32,746 individuals �10 years old (pregnant and breastfeeding women excluded; n = 1,254). It was reported 305 different food items, most of them were aggregated into a single food, for example, different types of banana into banana, or different preparation of red meat (boiled, roasted, grilled, etc. ) into red meat. The aggregation resulted in a list of 102 foods. Food prices. Food prices were extracted from the HBS database, where all the household members registered the amount purchased and expenditure with each food product for home consumption over a one-week period. The purchase records (about 850. 000) were registered in a specially designed booklet.
Data referring to prices were indirectly inferred: individuals reported expenditures and amounts, and prices were calculated using the division of expenditure per item in relation to its respective amount, and then converted into prices per 100g of edible portion. The information on expenditures is collected using both self-reported information and receipts presented by the individuals interviewed, which are checked by the interviewer in order to ensure its reliability. Food product subtypes were clustered (e. g. , different types of orange into 'orange') which resulted in the same 102 foods as described above. The final price for each food was estimated as the mean price of the food subtypes weighted by the frequency of reporting in the budget survey. We matched each food price to its corresponding food reported in the dietary intake survey according to the GES, thus, the price variation over the GES was preserved.
We calculated the nutrient content of the observed and optimized diets using the Brazilian Food Composition database (TBCA-USP)  [22]. Nutrient composition of food subtypes clustered into foods (e. g. different types of rice into 'rice') was estimated as the mean composition of the foods weighted by the frequency of reporting in the dietary survey. The food consumptions estimated in each GES were used as the starting point for the optimized diets in the linear programming models.
The simulated aerosols from BCC-ESM1 are quite coherent with CMIP5-recommended data and in-situ measurements from surface networks (such as IMPROVE in the U. S. and EMEP in Europe). A comparison of the aerosol optical depth (AOD) at 550 nm for all aerosols with the satellite AOD observations retrieved from MODIS and MISR and surface AOD observations from AERONET shows reasonable agreement between simulated and observed AOD. However, BCC-ESM1 seems to show weaker upward transport of aerosols from the surface to the middle and upper troposphere, likely reflecting the deficiency of representing deep convective transport of chemical species in BCC-ESM1. With an overall good agreement between BCC-ESM1 simulated and observed aerosol properties, it demonstrates a success of the implementation of interactive aerosol chemistry in BCC-ESM1.
2016) , including the Aerosol Chemistry Model Intercomparison Project (AerChemMIP,  Collins et al. , 2017)  and the Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP,  Jones et al. 2016). The purpose of this paper is to evaluate the performance of BCC-ESM1 to simulate aerosols and their optical properties in the 20th century. The description of BCC-ESM1 is presented in Section 2. The experimental protocol is shown in Section 3. Section 4 presents evaluations of aerosol simulations with comparisons to CMIP5-recommended data and data from both global surface networks and satellite observations. The regional and global characteristics compared to observations and estimates from other studies are analyzed. Simulations of aerosol optical properties in the 20th century are also analyzed in Section 4.
Conclusions and discussions are summarized in Section 5. Information about code and data availability is shown in Section 6. Wu et al. , 2019). The horizontal resolution of BCC-AGCM3-Chem is T42 (approximately 2. 8125ºx2. 8125º transformed spectral grid). The model has 26 levels in a hybrid sigma/pressure vertical coordinate system with the top level at 2. 914 hPa. The land component BCC-AVIM2. 0 is described in details in  Li et al. (2019). It includes biophysical, physiological, and soil carbon-nitrogen dynamical processes, and the terrestrial carbon cycle operates through a series of biochemical and physiological processes on photosynthesis and respiration of vegetation. Biogenic emissions from vegetation are computed online in BCC-AVIM2. 0 following the algorithm of the Model of Emissions of Gases and Aerosols from Nature version 2.
Aerosols of organic carbon and black carbon BCC-AGCM3-Chem treats two types of organic carbon (OC), i. e. water-insoluble tracer OC1 and water-soluble tracer OC2, and two types of black carbon (BC), i. e. water-insoluble tracer BC1 and water-soluble tracer BC2. As shown in Table  2 , hydrophobic BC1 and OC1 can be converted to hydrophilic BC2 and OC2 with a constant rate of 7. 1×10 -6 s -1  (Cooke and Wilson, 1996). The 4 tracers of organic carbon and black carbon are mainly from emissions of anthropogenic activities including both fossil fuel and biomass burning, and are from the CMIP6 data package (https://esgf-node. llnl. gov/search/input4mips/). Beside anthropogenic emissions, hydrophilic organic carbon OC2 can also come directly from natural biogenic volatile organic compound (VOC) emissions.
Global aerosols budgets We further evaluate global aerosols budgets by comparing a 10-year average of BCC-ESM results from 1990 to 2000 with various observational data for sulfate, BC, OC, sea salt, and dust. Their annual total emissions, average atmospheric mass loading, and mean lifetimes are listed in Tables  5  and 6. It is worth emphasizing that the global mean total source and sink for each type of aerosols in BCC-ESM1 are almost balanced. The global DMS emission from the ocean is 27. 4 Tg S yr -1 in BCC-ESM. It is higher than the value reported in  Liu et al (2005) , largely due to stronger wind speed near the sea surface. This high emission in BCC-ESM is nearly balanced by the gas-phase oxidation of DMS to form SO 2. The DMS burden is 0. 06 Tg S with a lifetime of 0. 78 days, which is within the range of other models reported in the literature.
(  2001 ) but slightly higher than the CMIP5 data. The average lifetime for dust particles is 3. 23 days that is shorter than the AeroCom mean (4. 14 days). The simulated sea salt emission is 4667. 2 Tg yr -1 , slightly lower than the  As show in Figure  9 , dust concentrations in the atmosphere show largest values over strong source regions such as Northern Africa, Southwest and Central Asia, and Australia, and over their outflow regions such as the Atlantic and the western Pacific. In DJF, the CMIP5 data shows centers of high concentrations over East Asia and Central North America, but both centers are missing in BCC-ESM1. We think, however, that these two high-value centers in the CMIP5 data may not be true, since frozen soils in these areas in winter lead to unfavorable conditions for soil erosion by wind.
With the CMIP6 anthropogenic emissions of SO 2 , OC, BC from 1850 to 2014 and their natural emissions implemented in BCC-ESM1, the model simulated SO 4 2-, BC, and OC aerosols in the atmosphere are highly correlated with the CMIP5-recommended data. The long-term trends of CMIP5 aerosols from 1850 to 2000 are also well simulated by BCC-ESM1. Global budgets of aerosols were evaluated through comparisons of BCC-ESM1 results for 1990-2000 with various observational data at present day for sulfate, BC, OC, sea salt, and dust. Their annual total emissions, global atmospheric mass loading, and mean lifetimes are all within the range of values reported in relevant literatures. Evaluations of the spatial and vertical distributions of BCC-ESM1 simulated present-, BC, Dust, and Sea Salt aerosol concentrations against the CMIP5 datasets and in-situ measurements of surface networks (IMPROVE in the U.
CMIP6-recommended emissions of black carbon and organic carbon aerosols assume 80% in hydrophobic form and 20% in hydrophilic form following Chin et al. (2002). Seven tracers in Table 1 belong to biogenic volatile organic carbons (VOCs), i. e. ISOP, ACET (CH 3 COCH 3 ), C 2 H 4 , C 2 H 6 , C 3 H 10 , Terpenes (C 10 H 16 ), and OC 2. As shown in Table 4, (2) yearly global gridded land-use forcing data sets, and (3) solar forcing. All these datasets were downloaded from https://esgf-node. llnl. gov/search/input4mips/. The principal GHGs for radiation calculation in BCC-AGCM-Chem1 include H 2 O, O 3 , CH 4 , N 2 O, CO 2 , CFC11 and CFC12. Only one GHG, O 3 , is a prognostic variable at each model step and interacts with radiations. 3. 1 Surface emissionsSurface emissions of chemical species from different sources are summarized in Table4.
We thus assumed that the BA collected from the soil cores were likely produced by several species and that these had different ages at the time of sampling. In the process of analysis, we included all the BA into the same pool for NIR readings. As a result, the sample for NIR spectrophotometer is a mixture of biogenic structures produced by different species of varying age. Only when an individual is found in the soil core we suppose that some of the aggregates were produced by that specific species, but even in this case uncertainty prevails. The field NIR spectral signals were grouped together in the vicinity of lab models and are not widely scattered in areas where no model projections exist in the factorial plane. Here we provided data that constitute a spectra baseline that can be used for further assessment of the functionality of the species found in the area.
Particulate matter concentrations The particulate matter (PM) measurements are from EMEP (covering Europe), and IMPROVE (for North America). The PM data have been made available either via the EBAS database infrastructure (http://ebas. nilu. no), or in the original IMPROVE data to be found in the VIEWS database (http://views. cira. colostate. edu/). Both PM 10 and PM 2. 5 (with unit µg. m -3 ) are used in this study. Note that the PM 10 size fraction of particles below 10 µm encompasses the PM 2. 5 aerosol mass below 2. 5 µm. The first PM measurements in EMEP started in 1996 and the number of sites increased steadily in the following decade  (Tørseth et al. , 2012). Most of the sites use the gravimetric method for both size fractions, though some used automated monitors, i. e. TEOM FDMS or b-attenuation.
SO 4 air concentration The sulphate aerosol concentration dataset is a subset of the global data presented in  Aas et al. (2019)  and is based on measurements obtained in different regional networks as described in Table  1. The sulfate aerosol concentrations are obtained from analysis of aerosol filters. In the EMEP, CASTNET, CAPMON and EANET networks these are either sampled with a PM 10 inlet or a total aerosol inlet, with no specific size cut off effective, using a filterpack sampler In the IMPROVE network sulfate measurements are done using a filterpack sampler with a PM 2. 5 inlet. The filters are typically analysed by ion chromatography after water extraction of the aerosol filter. The data have been screened to be regionally representative and of satisfactory quality. Urban sites are not included, nor are sites where the surroundings have changed considerably in the period in question.
Scattering and absorption coefficients Due to the scarcity of stations available for long-term trend analysis (only 28), the presence of regionally non-representative stations (e. g. , stations located near roads, in cities), difficult to capture by global models, can have large effects on the computation of the regional average time series. The urban stations have therefore been removed from this analysis. The level 2 data (quality controlled, hourly averaged, reported at standard temperature and pressure conditions) were used for two parameters measured by different instruments: -Scattering coefficients (σ sp , in Mm -1 ), were measured by integrating nephelometers. For better consistency in the model comparisons (model data for these parameters are reported for RH=0%), only the measurement data obtained when the relative humidity in the instrument was lower than 40% were utilized  (Pandolfi et al.
CAMS-Reanalysis The CAMS reanalysis, which is the successor to the MACC reanalysis (Monitoring Atmospheric Composition and Climate), is the latest global reanalysis dataset of atmospheric composition produced by the Copernicus Atmosphere Monitoring Service  (Inness et al. , 2019). It is produced using 4DVar data assimilation in the CY42R1 model cycle of the ECMWF (European Centre for Medium-Range Weather Forecasts) Integrated Forecast System (IFS), with 60 hybrid sigma/pressure vertical levels. The model used in the CAMS reanalysis includes several updates to the aerosol and chemistry modules on top of the standard CY42R1 release. The IFS model assimilates several satellite products, from aerosols (AOD) to greenhouse gases (CO2, CH4)  Inness et al. (2019) , where most relevant for aerosol trends are data from both MODIS sensors and AATSR/ATSR2.
-AERONET is the most important network in terms of number of instruments. More than 1000 observation points, with more or less long time series, are found across the globe. The highest density of instruments is in Europe and in the central part of North America (US). The lowest densities are found in southern Africa and Australia. -Particulate Matter: 212 instruments are used in this study and are spread mostly over Europe and North America. -SO 4 : Altogether 346 instruments have been operating, mostly in North America and Europe. A few stations are also located in Asia and North Africa. σ sp and σ ap : Combined for both parameters circa 50 stations are spread over North America, Europe, North Africa and Asia. Due to time coverage issues (2005 is the first year where in situ optical data are available in the European time series), the data from 2000 up to the year 2018 were used to compute the regional time series of these two parameters.
Time series aggregation requirements The regional time series are computed by combining, for each month, the valid data of all the stations in the corresponding region. In order to construct consistent and robust regional time series, some additional criteria are required to be met to provide a valid point (a station with valid measurements) going into the regional time series. Stations having operated very shortly (e. g AERONET DRAGON campaign stations) are eliminated by requiring a minimum of 300 valid daily measurements in the whole period from 2000 to 2014, which reduces, as an illustration, the number of AERONET stations from 1010 to 437. A minimum of three valid points (daily or monthly depending on the available resolution) is required per month to be present in the overall regional time series.
Those differences are then converted into a score (%) by using a normal distribution f described by a mean µ = 0 and a standard deviation of σ = 0. 5. The choice of these parameters leads to a representativity score of 100% when there is no difference in the trends of a reference and an experiment dataset, while a difference of 0. 5%/yr would indicate a representativity score of 50%. For a given parameter p and a region r, the representativity Rep(p, r) is calculated as following Rep space,time (p, r) = f tExpspace,time(p,r) -tRefspace,time(p,r) (2 ) where t is the relative trend of the corresponding dataset. Finally, the total score is computed as the mean of the time and the space representativities. An example of the calculation is presented in Figure  4  for AOD in Europe and North America.
In both regions, the Ref time dataset, corresponding to the available observations, reveals strong seasonal cycles when considering the number of points used to compute the regional time-series. These cycles are observed with most of the sun photometer datasets since the instruments only operate during daytime and cloud free conditions, and the amount of daylight and clouds varies with the season. Together with this seasonal cycle, one observes an increase in the number of points with time, which reflects the increasing number of stations over these two regions. The trends in Europe show similar values for the time study, which means that the trend is not greatly affected by the variation of the available measurements in time. The difference is larger when considering all the grid-boxes of the domain, but the overall difference of the two studies corresponds to a representativity of 69%.
Evaluation of the models trends against observations In order to evaluate the trends from the models, the regional time series have been computed with the model output collocated in space and time to the available observations at the station level. The model trends are computed in a similar manner to the trends for the observation datasets. However, for the few models providing output every 5 years (in addition to 2014), the minimum required number of points has been reduced from 7 to 4, so the trends can be computed using the years 2000, 2005, 2010 and 2014. The results, shown in Figure  6 , reveal different performances of the various models, for the reproduction of the observed trends, depending on the parameters and the regions. -AOD: the models show trends in the same direction as the observations over all the regions except in Asia, where the associated uncertainties are, however, usually larger than the trend values.