dataesr/scientific-paragraphs-categorization huggingface dataset:
Accounting for CO The definition of CO 2 -LULUCF emissions by global carbon cycle models, as used here and in  Canadell et al. (2021b) , differs from IPCC definitions  (IPCC, 2006)  applied in national greenhouse gas inventories (NGHGI) for reporting unhttps://doi. org/10. 5194/essd-13-5213-2021 Earth Syst. Sci. Data, 13, 5213-5252, 2021 der the climate convention and, similarly, from FAO estimates of carbon fluxes on forest land  (Tubiello et al. , 2021). Concretely, this means that NGHGI data include natural terrestrial fluxes caused by changes in environmental conditions, e. g. effects of rising atmospheric CO 2 ("CO 2 fertilization"), climate change, and nitrogen deposition -sometimes called "indirect effects" as opposed to the direct anthropogenic effects of land-use change and management  (Houghton et al.
e. effects of changes in environmental conditions from effects of land-use change and land management. By contrast, the NGHGI approach separates fluxes based on areas, with all those occurring on managed land being declared anthropogenic. Given that observational data of carbon stocks or fluxes cannot distinguish between the co-occurring effects of environmental changes and land-use activities, an area-based approach that does not require this distinction can more consistently be implemented across countries. These conceptual differences between global models' and NGHGI approaches have been acknowledged  (Canadell et al. , 2021a; Petrescu et al. , 2020a) , and approaches have been developed to map the two definitions to each other  (Grassi et al. , 2018 (Grassi et al. , , 2021)). For non-CO 2 GHGs, drivers and areas coincide, such that FAOSTAT data for CH 4 and N 2 O are complementary to bookkeeping CO 2 -LULUCF emissions.
EDGAR CO 2 -FFI emissions as used in there track at the top of the range as shown in Fig. 1. This is partly due to the comprehensive system boundaries of EDGAR but also due to the assumption of 100 % oxidation of combusted fuels as per IPCC default assumptions. Once system boundaries are harmonized, EDGAR continues to track at the uphttps://doi. org/10. 5194/essd-13-5213-2021 Earth Syst. Sci. Data, 13, 5213-5252, 2021 per end of the range but no longer at the top. EDGAR CO 2 -FFI estimates are further well-aligned with emissions inventories submitted by Annex I countries to the UNFCCCeven though some variation can occur for individual countries such as Kazakhstan, Ukraine, or Estonia, in general, or for certain years (see Fig. S4 ). Differences in FFI-CO 2 emissions across different versions of the EDGAR dataset are shown in the Supplement (see Fig.
Differences across different versions of the EDGAR dataset are shown in the Supplement (Fig. S1 ). The US-EPA shows the lowest estimates, probably due to missing estimates from a significant number of countries not reporting to the UN-FCCC (US-EPA2020 includes estimates from only 195 countries) and incomplete sectoral coverage. EDGARv6 estimates of anthropogenic CH 4 emissions, as used here, are in the upper range of the different inventories across most anthropogenic sources. However, none of these inventories covers CH 4 emissions from forest and grassland burning, which amount to about 10-12 Mt yr -1 globally. Saunois et al. (2020)  provide estimates of CH 4 sources and sinks based on BU and TD approaches associated with an uncertainty range based on the minimum and maximum values of available studies (because for many individual source and sink estimates the number of studies is often relatively small).
, 2019; O'Neill et al. , 2016). In recent years, the use of the very high forcing scenarios -particularly SSP5-8. 5 -has been debated in the scientific community (e. g. Hausfather and Peters, 2020b, a; Pedersen et al. , 2020; Schwalm et al. , 2020). Historical GHG emissions from our database are consistent with the levels and trends in the scenario data, despite the scenarios being calibrated on older data sources  (Gidden et al. , 2019)  -mainly CEDS  (Hoesly et al. , 2018). The observed differences are larger for the GHGs with the highest uncertainty, notably CO 2 -LULUCF, N 2 O, and F-gas emissions  (Sect. 3. 2, 3. 4, and 3. 5). Across the different GHGs, historical emissions track on aggregate with the higher forcing scenarios such as the SSP3-7. 0 and SSP5-8. 5 markers, in terms of both levels and growth rates.
CO 2 -FFI emissions Figure  6. Levels of and changes in GHG emissions by country. Aggregate GHG emissions are reported in GtCO 2 eq. based on global warming potentials with a 100-year time horizon (GWP-100) from IPCC AR6  (Forster et al. , 2021). Panel (a) shows per capita GHG emissions levels (tCO 2 eq. yr -1 ) for the year 2018 using UN population data for normalization  (World Bank, 2021). Panel (b) shows average annual changes (in %) in GHG emissions by countries for 2009-2018. Panel (c) shows average annual changes (in %) in GHG emissions by countries for 1970-2018. Note that this excludes CO 2 -LULUCF, as there is currently low confidence in national-level estimates. still tend towards the higher end of the scenario range shown here, but there are signs that CO 2 -FFI emissions are slowing to more moderate forcing levels (e.
, 2018 (Grassi et al. , , 2021)) , the attribution of environmental and anthropogenic changes differs between methods. Further, it should also be mentioned that system boundaries partly differ across datasets, and FAOSTAT data  (Conchedda and Tubiello, 2020)  are currently limited to CO 2 fluxes related to forests and emissions https://doi. org/10. 5194/essd-13-5213-2021 Earth Syst. Sci. Data, 13, 5213-5252, 2021 from drainage of organic soils under cropland/grassland, excluding other managed land or agricultural conversions. In principle, bookkeeping and DGVMs include all fluxes but are often coarse in their description of management, which observation-based approaches capture  (Arneth et al. , 2017). Several authors  (Grassi et al. , 2018; Obermeier et al. , 2021; Pongratz et al. , 2014)  have shown the strong dependence of CO 2 -LULUCF emissions estimates on the time a certain land-use change event happened to occur if environmental changes are represented transiently over time, as is the case for typical simulations with dynamic global vegetation models.
Measurements have been collected by radar altimeters on-board 13 different satellites that have been regularly intercompared and intercalibrated (in particular, during dedicated intercalibration phases when a satellite altimeter flies right behind its successor, a few seconds apart, to enable a close comparison of the measurements). Satellite altimeters have also been regularly validated against in-situ data from tide gauge records either locally in precisely monitored calibration sites like in Corsica  7  , Bass straight 8 or California  9  or at global scale with hundreds of tide gauge records  10. These campaigns of satellite altimeters' calibration and validation have given invaluable insights on the precision of the sea level measurements from space. They form the basis for the current estimations of uncertainties in sea level data.
inter-mission bias errors. In general, local sea level is estimated from an objective analysis of all altimetry data collected by all available radar instruments flying on different platforms, over different orbits, and different time periods. For example, CMEMS products 35 use all available altimetry measurements and C3S product 6 use at each time step the measurement from two different missions (to remain homogeneous along the record). Measurements from different satellites are combined through an optimal interpolation scheme after precise cross-calibration  18  which ensures that secondary missions are tied to the reference mission (namely TOPEX, Jason 1,2 and 3). In the gridded products, switches from one reference mission to another are critical for the long term record stability  36.
This dataset focuses on long-term stability for climate applications and provides an homogeneous processing from 1993 to present. Daily SLA maps at 1/4° spatial resolution are available from https://cds. climate. copernicus. eu. The time span considered here is limited to 1993 to 2019 to consider only complete years. C3S sea level grids are corrected for a time-varying TOPEX-A drift following  37  and for local geocentric SL changes due to GIA. Here We focus on large scale sea level patterns (both in space and time) which respond to climate fluctuations. Individual meso-scale eddies can generate substantial variability in sea level and mask the underlying climate fluctuations. To remove this noise from eddies we perform a spatio-temporal smoothing of the original grids. A 3° wide gaussian spatial filter is applied to remove high frequency spatial features and yearly averages are computed from the daily grids.
, 1999)  Arakawa C-grid with 40 vertical sigma levels  (Mikolajczak et al. , 2020). The bipolar grid presents a horizontal resolution between 300 and 500 m over the shelf and gradually decreases to several kilometres towards the south of the domain along the Algerian coast. This configuration allows us to have more than half of the total points of the grid over the shelf while keeping the open boundaries far from the study area, the Gulf of Lion. River runoffs were considered using measured daily values for French rivers (Banque Hydro database, http://www. hydro. eaufrance. fr/, last access: 12 July 2018) and the Ebro (SAIH Ebro database, http://www. saihebro. com/saihebro/ index. php, last access: 12 July 2018) and mean annual values for the others. The implementation of rivers in the model was described in  Estournel et al.
The model allows a potential multi-nutrient limitation of the phytoplankton growth, as it has been observed that phosphorus and nitrogen can both play a controlling role in the Mediterranean Sea  (Diaz et al. , 2001; Marty et al. , 2002). The internal composition, i. e. the stoichiometry, is considered to be flexible for phytoplankton and constant for heterotrophic organisms. A description of the biogeochemical model and its coupling with the hydrodynamical model is given in the Supplement. Rhône River nutrient inputs (nitrate, ammonium, phosphate, silicate, and dissolved organic carbon) were determined using in situ daily data (Mistrals-Sedoo database, http://mistrals. sedoo. fr/MOOSE/, last access: 12 July 2018). Concentrations of dissolved organic phosphorus and nitrogen and particulate organic carbon were estimated from this dataset and the relations found in the literature  (Moutin et al.
, 1998; Sempéré et al. , 2000). The Orb, Aude, and Hérault river monthly data were extracted from the Naïades database (Agence de l'eau, http://www. naiades. eaufrance. fr/, last access: 12 July 2018) and were interpolated on the period of the simulation with a daily resolution. At the other river (Tech, Têt, Agly) mouths, climatological values were used according to  Ludwig et al. (2010). The deposition of organic and inorganic matter from the atmosphere was based on the low estimations of Ribera d'  Alcala et al. (2003). Finally, the pelagic-benthic coupling of inorganic nutrients was made using the meta-model described in  Soetaert et al. (2001). An adjustment of this model was made according to  Pastor et al. (2011). As for the hydrodynamic model, the initial state and the open boundary conditions were generated by a "parent" simulation (Eco3M-S) that encompassed the whole Mediterranean Sea.
Satellite data Spatial maps of daily chlorophyll-a concentrations, with a 1 km resolution, were obtained using products from the Moderate Resolution Imaging Spectroradiometer (MODIS) (https://oceancolor. gsfc. nasa. gov/, last access: 19 December 2018). Products, analysis, and calibrations used were provided by IFREMER Nausicaa services and OC5 IFREMER algorithms for chl-a concentrations from  Gohin (2011). We then estimated the daily spatial median surface chlorophylla concentration (in µg L -1 ) using a filter to discard images with more than 50 % occupied by clouds over the GoL and discarding surface chlorophyll-a data for depths lower than 20 m since these data could be affected by residual contamination from turbidity despite dedicated treatment.
Conclusion and future works A 3D coupled hydrodynamic-biogeochemical model was used to estimate the POC dynamics in the Gulf of Lion shelf over the period 2011-2016. The validation of the model was performed using existing data from a multi-platform observation system. A good agreement between model results and observations at different space scales and timescales was shown. The model represents the high dynamical character of the Gulf of Lion shelf in terms of hydrodynamic (stratification and winter mixing) and biogeochemical conditions (nutrients, chlorophyll a, and POC dynamics). Spatial, seasonal, and inter-annual changes in the different POC input and output terms were identified. Model results highlight the high NPP occurring in the GoL shelf (198. 9 × 10 4 t C yr -1 ), with maximum values in spring and in the outer shelf, in particular in the eastern region.
Acknowledgements. Numerical simulations were performed using the SYMPHONIE model, developed by the  SIROCCO group  (https: //sirocco. obs-mip. fr/,last access: 21 September 2021) and computed on HPC resources of CALMIP ( CALcul en MIdi-Pyrénées , projects  1331 ,  1325  and  09115 ) and  GENCI  (Grand Equipement.
0 DN, greatly exceeding this amplitude. Detection of the Charon-light illuminated terrain could only be done by stacking the complete P_DEEPIM data set to beat down the shot-noise contribution. By averaging all 360 images, the error in the mean level would decrease to 0. 47 DN in a single pixel; however, we scaled the simulated noise level up by 2 to 0. 66 DN to account for the error in the scattered-light model, which was constructed from 360 images with the same background 13 Charon is fully below the horizon on Pluto south of latitude -88°. 0. 14 This is the distance to a point halfway between the center of Pluto and the sub-Charon point on its surface to represent the typical distance to Charon over the illuminated hemisphere. 15 In passing, we note that the calculated R shows that the illumination on Pluto provided by Charon light at any phase is about half of the moonlight flux on Earth provided by the Moon at the same phase.
In short, the much larger solid angle of Charon as seen from Pluto and its higher albedo compared to the Moon nearly counter the strongly diminished solar flux at Pluto. For the present P_DEEPIM sequence, the light provided by Charon is about that provided by the Moon a day or so prior to first quarter. level. The right panel in Figure  3  shows the result of adding this level of noise to the binned image on the left. While nearly all the fine-scale features were lost in the noise, large-scale albedo contrasts, such as between the bright surface of Sputnik Planitia and dark zone of Cthulhu Macula present in the MVIC input image, remain evident in the simulated P_DEEPIM stack. The strongest contrast is the sharp delineation of the bright limb of Pluto against the dark background, but unfortunately the strong ring of scattered light from haze in the atmosphere overwhelmed the Charon-light illuminated limb in the real data set.
The spacecraft observed Pluto with the top of the LORRI field rolled to PA 243°. 3; the orientation in the figure reflects that of the observations. As the Sun was only 14°away from Pluto during the sequence, scattered sunlight strongly contaminated the images. The modeling and subtraction of sunlight in the Pluto images is a major task of the present analysis, which will be discussed in detail in Section 3. In anticipation of this problem, however, the P_DEEPIM program included an additional set of 360 scattered-light images offset from Pluto (MET 0299232248 to 029923341), but with the same Sun-spacecraft geometry to provide for modeling and subtraction of the scattered light present in the Pluto images. As with the Pluto images, the first exposure for the first 180 scattered-light images was 0.
Throughout the experiments, the SIP signal was recorded at frequencies of 0. 1-10 000 Hz. The impedance and phase shift data were used to calculate the real and imaginary conductivity. Additional information regarding the soil composition and column packing is in Sect. S1 in the Supplement. Prior to each experiment, a 200 mg L -1 NaCl background solution was injected into the column. The background solution injection proceeded until equilibrium between the inlet and the outlet EC was obtained. Subsequently to the EC stabilization, the inflow background solution was replaced by either 360 mg L -1 CaCl 2 solution or 360 mg L -1 ZnCl 2 solution. The replacement of the background solution by the CaCl 2 or ZnCl 2 solutions marked the beginning of the experiments (t = 0).
Modeling A simple solute transport model was constructed based on the HYDRUS 1D platform  (Simunek et al. , 1998). HYDRUS 1D is a computer program for the numerical modeling of water flow and solute transport in porous media. The inverse tool of HYDRUS 1D was applied to fit model parameters to experimental results, based on the Levenberg-Marquardt nonlinear minimization method  (Marquardt, 1963). The appropriate mass balance equation for this application is given by the advection-dispersion equation (ADE) ∂c ∂t + ρ b θ ∂ ĉ ∂t = ∂ ∂l D h ∂c ∂l - ∂ ∂l (νc), (6) where c (g cm -3 ) is the dissolved species concentration t (h) is time, l (cm) is the vertical coordinate, v (cm h -1 ) is the wa-ter velocity, D h = αν+D is the hydrodynamic dispersion coefficient (cm 2 h -1 ), α (cm) is the dispersivity, D (cm 2 h -1 ) is the diffusion coefficient, ρ b is the soil bulk density (g cm -3 ) and ĉ is adsorbed concentration (g g -1 ) that can be expressed in terms of c using an adsorption isotherm.
Acknowledgements. This research was funded in part by the  German-Israeli Water Technology Cooperation Program  (project number  WT1601/2689 ), the  German Federal Ministry of Education and Research (BMBF)  and the  Israeli Ministry of Science, Technology and Space (MOST). This research was also supported in part by the  Ministry of Science and Technology, Israel, and Ministero degli Affari Esteri e della Cooperazione Internazionale, Italy , as well as the  Israel Science Foundation  (grant no. 2130/20 ). The authors would like to thank the anonymous reviewers and the editor  Christine Stumpp  for the constructive comments that helped to improve this paper. Financial support. This research has been supported by the  German-Israeli Water Technology Cooperation Program  (grant no. WT1601/2689 ), the  Israel Science Foundation  (grant no.
Code and data availability. Data and code used for this paper are available at https://doi. org/10. 17632/zd8c7t7vfz. 1 (Ben Moshe  et al. , 2021)  or upon request from the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-25-3041-2021-supplement.
2 ), deciphering the taphonomic processes is essential for palaeontological and ecological studies, especially for taxa without extant representatives. Consequently, the aim of this study is to review soft tissue taphonomy in the Fezouata Shale based on a multidisciplinary approach combining data across palaeontology, sedimentology, geochemistry and mineralogy. This in-depth reconstruction starts at the life of an organism in its environment and ends at its discovery in surface sediments passing through biostratinomy, diagenesis, metamorphism, and modern weathering (Fig. 2 )  (Sansom et al. , 2010; Bath Enright, 2018; Parry et al. , 2018; Purnell et al. , 2018). To do so, we will answer the five following questions: • Question 1: What is the stratigraphic distribution of exceptional preservation in the Fezouata Shale? • Question 2: What were the sedimentary surface processes affecting organisms? • Question 3: Under what conditions did decay and diagenetic mineralization take place? • Question 4: What were the post-diagenetic processes? • Question 5: What is the fidelity of preservation in the Fezouata Shale? Answering these questions allows for comparison of the Fezouata Shale communities and their preservation with both the Burgess Shale and the Chengjiang Biota.
4A )  (Martin et al. , 2016; Vaucher et al. , 2016 Vaucher et al. , , 2017;; Saleh et al. , 2020a). These storms were erosive, and their deposits are characterized by the presence of normally graded beds (Fig. 4A, C )  (Saleh et al. , 2020a) , and by oscillatory ripples that were increasing in wavelength from distal (null to millimetric wavelength) to proximal settings (metric to plurimetric wavelength) (Fig. 4B, D )  (Vaucher et al. , 2016 (Vaucher et al. , , 2017)). Storm-wave deposits are discontinuous in the Fezouata Shale. In proximal settings, a tidal modulation of storm waves occurred and is recorded as a repeated stack of larger (low tide) to smaller (high tide) oscillatory structures within fine-grained sandstones (Fig. 4E )  (Vaucher et al. , 2017). A statistical correlation between sedimentological data from cores and palaeontological data from outcrops showed that exceptional preservation is associated with one of the most distal facies of the Fezouata Shale (Fig.
, 2020d). If the Fezouata Biota had a similar taxonomic composition to the Walcott Quarry and the Chengjiang Biota, the total diversity of this site is most probably underestimated by at least 10% (entirely cellular organisms being completely absent). Since this taphonomic bias can be identified and accounted for, the in-situ preservation in the Fezouata Shale becomes an asset for sophisticated ecological studies that examine the evolution of community structure during the Palaeozoic at the transition between the Cambrian Explosion and the Ordovician Radiation. Cambrian ecosystems contrast with Ordovician ones (niche vs dispersal assembled)  (Na and Kiessling, 2015; Rasmussen et al. , 2019; Stigall et al. , 2019). The Fezouata Shale is the only exceptionally preserved biota with hundreds of fossiliferous levels within two stratigraphic intervals (Tremadocian and Floian in age), providing a database that can be used to describe the continuum of ecological change between major Palaeozoic evolutionary events and to decipher if the transition from niche to dispersal-built ecosystems was abrupt or gradual  (Harper and Servais, 2018; Rasmussen et al.
découvertes de gisements à préservation exceptionnelle dans l'Ordovicien du Maroc' (2017) and the TelluS-INTERRVIE projects 'Mécanismes de préservation exceptionnelle dans la Formation des Fezouata' (2018), and 'Géochimie d'un Lagerstätte de l'Ordovicien inférieur du Maroc' (2019) all funded by the INSU (Institut National des Sciences de l'Univers, France), CNRS. This paper is as well a contribution to the International Geoscience Programme (IGCP) Project 653 -The onset of the Great Ordovician Biodiversification Event. This paper is supported by Grant no. 205321_179084 from the Swiss National Science Foundation, awarded to ACD as Principal investigator. The authors are particularly grateful to Yves Candela, Lukáš Laibl, Pierre Gueriau, Martina Nohejlovà, Eric Monceret, Stephen Pates, Daniel Vizcaïno, Francesc Pérez-Peris, and Lorenzo Lustri who helped in the field.
Evaluation of seismic reflection data has identified the presence of fluid escape structures cross-cutting overburden stratigraphy within sedimentary basins globally. Seismically-imaged chimneys/pipes are considered to be possible pathways for fluid flow, which may hydraulically connect deeper strata to the seabed. The properties of fluid migration pathways through the overburden must be constrained to enable secure, long-term subsurface carbon dioxide (CO 2 ) storage. We have investigated a site of natural active fluid escape in the North Sea, the Scanner pockmark complex, to determine the physical characteristics of focused fluid conduits, and how they control fluid flow. Here we show that a multi-scale, multi-disciplinary experimental approach is required for complete characterisation of fluid escape structures.