dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on this study, around 259 kt N yr -1 of TN, 9. 6 kt P yr -1 of TP, 304 kt Si yr -1 of TSi, and 145 kt C yr -1 of TOC were exported to the French Atlantic Ocean through these seven estuaries. They accounted for about 80% of the total water discharge from all the estuaries on the French Atlantic coast (based on long-term analysis of runoff data from French national databases) and 83% of the total watershed areas on the French Atlantic coast, and hence a similar proportion of the nutrient fluxes. Note: DIN=NH4+NO3, TN=DIN+(Phy (in C)+TOC)/5. 7 (the Redfield-Brzezinkski ratio,  Brzezinski, 1985; Redfield et al. , 1963)  DIP=PO4, TP=DIP+(Phy (in C)+TOC)/41 (the Redfield-Brzezinkski ratio,  Brzezinski, 1985; Redfield et al. , 1963)  TSi=DSi+Phy (in C)/2. 8 (the Redfield-Brzezinkski ratio,  Brzezinski, 1985; Redfield et al.
Consequently, the results depend on the robustness of the ECO-MARS3D predictions, which is better for hydrodynamic variables (elevation, temperature, salinity, currents) than for biogeochemical variables. Riverine boundary conditions comprise the observed data, extracted at the station closest to the model's upper boundary, which might still be located several kilometers upstream or downstream of the model's upstream boundary. Observed data are usually measured at monthly or bimonthly intervals. Some variables were sampled more frequently, two or three times per month, while Chl-a concentrations were usually only available from March to September. Also, DSi, TOC, and Chl-a are usually less available than other variables. Instantaneous SPM and water quality sampling data were linearly interpolated to obtain the daily values between the adjacent measurements.
https://doi. org/10. 5194/bg-2021-164 Preprint. Discussion started: 28 June 2021 c Author(s) 2021. CC BY 4. 0 License. dissolved) organic carbon (TOC), and chlorophyll a (Chl-a), as an indicator of phytoplankton biomass. Daily discharge data were obtained from the national Banque Hydro database (http://www. hydro. eaufrance. fr/). Water quality variables were collected from the national database: (1) the French Water Agencies through the NAIADES portal (http://naiades. eaufrance. fr/) for the physicochemical parameters of surface waters; (2) the REPHY (REPHY, 2021) database (https://www. seanoe. org/data/00361/47248/) specifically for the monitoring of phytoplankton for the estuarine and marine parts; and (3) the SOMLIT database (https://www. somlit. fr/) for coastal and estuarine water quality parameters.
Acknowledgments. Financial support. This study (Prest' Eaux  project) was supported by the  Office Français de la Biodiversité (OFB) , greatly acknowledged for the project follow-up. Xi Wei 's post-doc was funded by this project. Josette Garnier , Research Director at https://doi. org/10. 5194/bg-2021-164Preprint. Discussion started: 28 June 2021 c Author(s)  2021. CC BY 4. 0 License.
Both m and vC hence summarize complex processes that are not modeled mechanistically. A schematic diagram, which illustrates the structures and processes controlling the individual-and community-level dynamics of a forest in the TROLL model, can be found in Maréchaux and Chave (2017) (Appendix S5, Figure  S1 ). Necessary inputs for a run of TROLL include (i) climate forcing data for the simulated location, (ii) species-specific parameters of plant traits for the simulated forest, and (iii) species-independent parameters. The source code of TROLL (v2. 5) is written in C++ and is available at https://github. com/troll-code/troll. On a computing cluster, each simulation of 200 × 200 m and 500 years uses around 15 min of CPU time.
Global climate forcing The TROLL model requires the following climate forcing variables: monthly mean values of daytime and nighttime mean temperature, cumulated rainfall, mean wind speed, daytime mean irradiance, and daytime mean vapor pressure deficit (VPD), as well as average normalized daily variations of temperature, irradiance and VPD. We used the CRU-NCEP reanalysis as a standardized climate forcing (version 8; version 7 archived at https://rda. ucar. edu/datasets/ds 314. 3/)  (Viovy, 2018). The CRU-NCEP data set is a global gridded (0. 5 • × 0. 5 • ) sub-daily (6-hourly) climate product spanning the 1901-2016 period. It provides eight climatic variables: temperature, precipitation, wind speed in two orthogonal components, downward longwave and shortwave radiations, air specific humidity, and atmospheric pressure, resulting from the combination of observation-based CRU TS 3.
For the Fushan site, we implemented 94 species for which a complete set of measured trait values were available: this represents ca. 90% of the trees. The methodology of data collection is detailed in Appendix B. Climatic data were extracted from the CRU-NCEP dataset at both sites. We also used local climate data to force the model simulations. At Nouragues, semi-hourly meteorological data are available from 2013 to 2019, recorded 400 m away from the plot (4 • 05 ′ N, 52 • 41 ′ W). At Fushan, daily meteorological data are available from 1991 to 2012, with hourly data from 2013 to 2016, recorded at a meteorological station km east of the forest plot (24 • 45 ′ N, 121 • 35 ′ E). A comparison of the local versus gridded climatic conditions is provided in Appendix C. For all simulations, we simulated forest regeneration from bare soil for a reference plot area of 4 hectares (200 m × 200 m) for a duration of 500 years (6000 monthly timesteps): based on trial simulation, after years, the forest has reached a steady state.
We performed 500 calibration runs for both study sites. For each simulation, three parameters (φ, vC and m) were randomly drawn from the uniform prior distribution, and the two crown allometry parameters (CR a and CR b ) were drawn as a pair from the correlated Beta prior distributions. Goodness of fit was assessed using four summary metrics: stem density (DBH ≥ 10 cm; N 10 , trees ha -1 ), large stem density (DBH ≥ 30 cm; N 30 , trees ha -1 ), aboveground biomass (AGB, Mg ha -1 ), and gross primary productivity (GPP, MgC ha -1 yr -1 ). These metrics summarize both forest structure and functioning and overall constrain the model well. Empirical values for these metrics were obtained from census data for N 10 , N 30 and AGB, and from a global gridded database for GPP  (Madani and Parazoo, 2020).
Forest response to climatic conditions: a virtual experiment To study the dependence of forest structure and dynamics on temperature, irradiance and VPD, we performed the following simulated experiment. In the CRU-NCEP dataset, we selected a subset of points corresponding to lowland light-limited rain forest within the 35 • N-35 • S latitude range, based on elevation (< 1000 m), climate (annual precipitation > 2000 mm yr -1 ;  Guan et al. , 2015; Wagner et al. , 2016) , and land cover (ESA 'forest' CCI Land Cover classes: 50, 60, 70, 80, and 90). At both study sites, we then performed 500 simulations, each time using the three climatic variables at a randomly sampled point within the selected subset, and using "optimal parameter values", the general parameter values of the one simulation that provided the best overall fit during calibration with the initial climatic condition (Table  2 ).
Simulated temporal dynamics of canopy height were then compared with remote sensing data to determine the succession status of each site, which was then used to generate Amazon-wide estimation of other forest attributes such as biomass and productivity. Yet, these studies assigned trees to a small number of plant functional types that relied on empirical parameterization. Our study, although smaller in scope, is a proof of concept demonstrating that trait-based IBM upscaling is achievable with minimal calibration and is therefore realistic in the tropics, provided that trait measurements exist and tree floristic composition is available at the focal site. Moreover, since model output contains detailed information about forest composition, TROLL could also help answer how plant diversity responds to environmental changes.
With every forest model, assumptions are made about which parameters are species-dependent and which are not. The model described here, TROLL, is designed with the aim to contain as much speciesspecific information that is currently available. For an individualbased model, this choice does not necessarily incur higher computational burden than the plant functional type approach, since in both cases every individual tree is simulated. However, supplying models with species-specific information requires considerably more parameterization effort. With the ongoing collection effort of plant traits in permanent plots around the world, the assembly of global trait databases  (Anderson-Teixeira et al. , 2015; Chave et al. , 2009; Kattge et al. , 2020)  and development of techniques to measure new plant traits, we expect that it will be easier to generalize this approach to many sites.
Climate impact on forests using IBMs Another important part of assessing transferability of forest IBMs consists in evaluating how the model responds to environmental forcing, an important step in understanding how forests respond to climate change  (Shugart et al. , 2018). We here examined the effect of climate forcing without the need of re-calibration  (Fauset et al. , 2019; Shugart et al. , 2018). Many forest IBMs prescribe climatic conditions based on locally measured data  (Ma et al. , 2017; Shuman et al. , 2015) , yet it is important to provide a consistent climate forcing condition even at places where local measurements do not exist, and to ensure comparability among sites. The integration of the gridded CRU-NCEP climate dataset as model input fulfills this condition, and thus further simplifies large-scale implementation.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. area (LA, cm 2 ) was quantified with the software ImageJ  (Schneider et al. , 2012). The leaf samples were subsequently oven-dried at 80 • C for 72-96 h, until reaching constant dry weight. Leaf mass per area (LMA, g m -2 ) was then calculated as dry weight divided by fresh leaf area  (Pérez-Harguindeguy et al. , 2013). Nitrogen and phosphorus content (N mass and P mass , g g -1 ) were determined by the microplate method  (Huang et al. , 2011; Iida et al. , 2014). Wood density (WD, g cm -3 ) was measured following the ForestGEO wood density measurement protocol  (Condit, 2008; Iida et al.
Acknowledgements Simulations were run on the OLYMPE cluster at CALMIP, Toulouse. We thank  Dr. Shiang-Yue Lu  and  Hui-Hsueh Huang  at  TFRI  ( Taiwan Forestry Research Institute ) for providing us with the local climate data at the Fushan meteorological station. This work was supported by the " Investissement d'Avenir " grants managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ;  TULIP, ref. ANR-10-LABX-0041 ;  ANAEE-France :  ANR-11-INBS-0001 ).
The plumes were transported very long distances and dominated the stratospheric aerosol layer of the whole Southern Hemisphere for several months, producing the most important impact on the climate system linked to stratospheric aerosol perturbations since the eruption of Pinatubo in 1991  [3]. This paper provides a description of the AEROS5P algorithm backbone, and all additional datasets used in this work (Section 2). Section 3 shows comparisons of AEROS5P retrievals with respect to observations of the AOD from AERONET network stations, its horizontal distribution from MODIS and VIIRS (Visible Infrared Imaging Radiometer Suite) and vertical profiles derived from the CALIOP spaceborne lidar. The following section describes the 3D distribution of smoke plumes and their injection heights for the period of analysis, and the link with respect to the wildfire activity and meteorological conditions.
They are calculated at a high spectral resolution of 0. 01 nm and then convolved by the instrument response function for deriving effective cross sections at the RT coarser spectral resolution. This technique significantly speeds up computations while keeping sufficient accuracy, as done for ozone retrievals by  [36, 37]. Sensitivity tests based on the retrieval of one day of measurements over Australia (20 December 2019) suggest that retrievals of the mean altitude of the aerosol layers using effective oxygen cross sections are highly correlated (correlation coefficient R of 0. 96) with those based on high spectral resolution radiative transfer calculations. A mean bias of -270 m (lower altitudes with effective cross sections) and a root-mean-squared (RMS) difference of 560 m was also seen between the two datasets.
For each wavelength λ of the RT calculations, it is derived as a linear combination of high spectral resolution empirical orthogonal functions (EOF) according to the following equation: ω 0 (λ) = n ∑ i=1 c i. EOF i (λ) (1) the EOFs are derived from 400 to 800 nm at 1 nm resolution from a principal component analysis of a large database of high spectral resolution measurements of land reflectance for several surface types (vegetation, soils, man-made and ocean from  [58] ) and the United States Geological Survey (USGS) digital spectral library (version splib06a,  [59] ). We consider six and three spectrally resolved EOFs respectively over land and ocean. The a priori values of the coefficients c i are calculated using the TROPOMI-derived DLER database mentioned in Table  1  for a given location and pointing geometry.
The latter database is provided for 19 specific wavelengths. As done by  [58] , the coefficients are obtained by solving the linear system of Equation (  1 ) evaluated at the 19 wavelengths of the DLER data. This is performed with the following operation: C a priori = A T A -1 A T Ω DLER (2) where C a priori is a vector with the a priori values of the coefficients c i , A is a matrix where each column is a EOF interpolated at the 19 wavelengths and the vector Ω DLER is the DLER values from the TROPOMI-derived database. The values of the coefficients c i are also jointly adjusted with the vertical profile of aerosols for better fitting TROPOMI reflectance spectra during the AEROS5P inversion process. Once the reflectance spectra are simulated by VLIDORT, they are convolved by the instrument spectral response function (or slit function) of TROPOMI for enhancing the resemblance with measurements.
For a better horizontal coverage, we used the Multi-angle Implementation of Atmospheric Correction (MAIAC), which includes data from two MODIS sensors, as well as VIIRS using Dark Target. We considered the MYD04_L2 product from MODIS onboard the Aqua platform, MCD19A2 combining MODIS/Aqua+Terra (MAIAC), and the AERDB_L2 product from VIIRS onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. We under-sampled these products at the AEROS5P grid resolution. For comparison of the horizontal distribution of AOD, we used MODIS/MAIAC data as they offer an enhanced horizontal coverage compared to the MODIS approaches that use one sensor. Co-localized comparisons of AOD were done with respect to MODIS Aqua, as the overpass time is similar to that of TROPOMI. Aerosol vertical profiles and mean heights retrieved from AEROS5P were validated against those derived from co-located measurements from CALIOP spaceborne lidar.
It is worth noting that the operational TROPOMI product of aerosol layer height is not included in the comparison due to limited spatial coverage. Indeed, almost no data are available in coincidence with the transects of CALIOP and AEROS5P observations analyzed in this paper. In addition to aerosol products, we analyzed the results derived from AEROS5P in terms of the 3D distribution of BB aerosols with other datasets describing the fire activity and the atmospheric conditions. The distribution of carbon monoxide (CO) was examined with the TROPOMI operational product  [67, 68]. Fire activity was surveyed using fire detections and estimates of Fire Radiative Power (FRP) from merged MODIS data onboard Aqua and Terra (MYD14 and MOD14 respectively) products  [69]  gridded to 0. 2 × 0. 2 • resolution.
6-0. 7 (see Figure  S2  on 21 December 2019). The last case of a mismatch between the AOD of AERONET and that of AEROS5P was on 22 December at the station Lake Lefroy. The AEROS5P AOD was then around 0. 6 while ~0. 1 was measured by the ground-based station. This difference was certainly caused by cloud contamination unfiltered by the VIIRS cloud mask used within AEROS5P. This is consistent with the fact that most nearby pixels were screened out and only isolated pixels near Lake Lefroy showed high AODs (Figure  S2 ). A better cloud filtering mask may improve the retrieval quality in the pixels neighboring cloudy situations. A more systematic comparison of all co-located retrievals of AOD from AER MODIS/Aqua and VIIRS is shown in Figure  4. It covers the period of 20-24 Dec 2019. A good overall agreement was found between the three datasets, whereas the was better between AEROS5P and VIIRS.
The range of values of comparison for VIIRS is greater (AOD up to 5) than for the comparison with MODIS, explaining partly the A more systematic comparison of all co-located retrievals of AOD from AEROS5P, MODIS/Aqua and VIIRS is shown in Figure  4. It covers the period of 20-24 December 2019. A good overall agreement was found between the three datasets, whereas the match was better between AEROS5P and VIIRS. This is shown by the fact that 65% of AEROS5P/VIIRS co-located pixels showed differences smaller than 0. 2, a correlation coefficient of 0. 87 and RMS differences of 0. 3. These values are roughly similar to those found from the comparisons of AOD between MODIS and AEROIASI from the desert dust over the Sahara, with a correlation coefficient of 0. 90 and an RMS difference of 0. 2  [12, 28].
Aerosol Extinction Profiles A comparison of two transects of aerosol extinction profiles derived from CALIOP and AEROS5P on 21 and 22 December 2019 is shown in Figure  5. The vertical distribution of aerosols by the two datasets is in fairly good agreement. On 21 December, both retrievals showed a denser aerosol layer extending from 1. 0 to about 2. 5 km in the southern part, approaching the surface at 28-30 • S and progressively increasing in altitude up to 1-3 km around 24 • S. The transect of the following day showed in both cases a rather homogenous aerosol layer from the surface up to ~4 km. The aerosol layer top gradually varied from 4 km of altitude at the southern part up to 2 km of altitude near 16 • S. Quantitatively, values of aerosol extinction profiles were quite similar between the CALIOP and AEROS5P transects.
This particularly stood out on 22 December 2019 (Figure  5b, d ), when the vertically integrated AOD for AEROS5P and CALIOP was very similar to that derived from MODIS (Figure  5f ). The transect of 21 December 2019 showed moderately larger values of extinction for AEROS5P than those derived from the inversion of CALIOP measurements, while the AOD from AEROS5P was in better agreement with MODIS than that from CALIOP (it underestimated the AOD south of 28 • S in Figure  5e ). Some other differences may have partly come from the presence of clouds and the difference in overpass times of CALIOP and TROPOMI of about 1. 5 h. The mean altitude of the aerosol layers is depicted as dashed lines in the figure. There is a good agreement between the two datasets. Considering all coincidences in the two transects, they show a correlation of 0.
These datasets present correlations of up to 0. 87 and root-mean-squared differences of 0. 3. Transects of vertical profiles of aerosol extinction derived by AEROS5P show similar structures of aerosol plumes to those derived from measurements of the CALIOP spaceborne lidar. Mean altitudes of aerosol layers derived by AEROS5P and CALIOP are also in good agreement. These two datasets show a small mean bias (185 m), root-mean-squared differences of 500 m and a correlation coefficient of 0. 83, for the mean plume altitude, for this event. Moreover, AEROS5P provides an unprecedented daily description of the 3D structure of the vertical injection of smoke plumes. The 3D distribution of these plumes is consistent with fire radiative power hotspots detected by MODIS and atmospheric stability described by a buoyancy frequency diagnostic.
C. ; Project administration, partial funding acquisition, C. D. ; software writing, methodology, M. E. ; Project administration, writing-review, G. D. ; providing datasets for analysis, G. S. ; writing-review, analysis P. S. ; writing-review, providing surface albedo principal components, X. L. and P. Z. ; writing-review, analysis, fire power data providing, S. T. ; Oxygen A-band cross section calculation, D. T. ; providing swath dependent soft correction for TROPOMI and writing-review, R. L. and D. L. All authors have read and agreed to the published version of the manuscript.
Acknowledgments: This work is part of the  Make Our Planet Great Again (MAPGA)  initiative through the project  Make Air Quality Great Again (MAQGA ) lead by  R. Subramanian  within the  EFLUVE OSU (Observatoire de Sciences de l'Univers)  and it is supported by  Region Ile-de-France/DIM QI 2  ,  PNTS  ( AEROMETOP  project),  CNES  ( SURVEYPOLLUTION/TOSCA  project),  ARIA Technologies  and the  ANR ASTuS. We acknowledge the free use of the TROPOMI surface DLER database provided through the Sentinel-5p+  Innovation  project of the  European Space Agency (ESA). The TROPOMI surface DLER database was created by the  Royal Netherlands Meteorological Institute (KNMI)  and  GRASP SAS  (https://www. grasp-sas. com). We are grateful for the free use of the TROPOMI L1b database. We thank  Maarten Sneep  and  Veefkind Pepijn  from  KNMI  for providing help for TROPOMI slit function usage.
Funding: This work is funded by the  Region Ile-de-France  in the framework of the  Domaine d'Intérêt Majeur Réseau de recherche Qualité de l'air en Ile-de-France  ( DIM QI 2  ) through the  programme Paris Region PhD (PRPHD), ARIA Technologies , the  Programme National de Télédétection Spatiale  (PNTS, http://programmes. insu. cnrs. fr/pnts(accessed on  20 April 2022 ); grant no. PNTS-2016-02 , project " AEROMETOP "), and the  Centre National des Etudes Spatiales (CNES)  through the  SURVEYPOLLUTION  project from  TOSCA (Terre Ocean Surface Continental et Atmosphère ), and supported by the  Centre National de Recherche Scientifique-Institute National de Sciences de l'Univers (CNRS-INSU)  and the  Université Paris Est Créteil (UPEC).
Data Availability Statement: The data used in this study was from the following platforms: MODIS AOD data (1 September 2021), MODIS Fire Power (4 October 2021), VIIRS (19 September 2021) data from EarthData portal from NASA (https://search. earthdata. nasa. gov/); CALIOP measurements (13 September 2021) from the AERIS/ICARE datacentre (https://www. aeris-data. fr/ and http: //www. icare. univ-lille1. fr/); TROPOMI L1B data (13 July 2020) from the Sentinel 5P hub (https://s5 phub. copernicus. eu); TROPOMI Instrument Spectral Response Function (20 May 2020) from: http:// www. tropomi. eu; AERONET AOD measurements (16 March 2021) from the AERONET portal (https: //aeronet. gsfc. nasa. gov); TROPOMI surface DLER database from Sentinel 5p+ Innovation project (https://www. grasp-sas. com/projects/aod-brdf_sentinel-5p-innovation/); ERA5 meteorological reanalyses (21 September 2021) from the ESPRI IPSL mesocentre (https://mesocentre.