dataesr/scientific-paragraphs-categorization huggingface dataset:
ODIAC fossil fuel CO2 emission map We use the high-spatial resolution (30" × 30" ≈ 1 km × 1 km) global annual fossil fuel CO 2 emission map for the year 2016 from the Open Source Data Inventory of Anthropogenic CO 2 Emission (ODIAC, version 2017)  (Oda and Maksyutov, 2011; Oda et al. , 2018)  for calculating clumps. To our knowledge, it is the only emission map with global coverage and a spatial resolution high enough to match the pixel size of ≈ 1 km of atmospheric XCO 2 imagers. We chose the year 2016 assuming that the emission spatial distributions do not change significantly from year to year. The ODIAC dataset provides emissions from power plants based on the CARMA database (Carbon Monitoring and Action, http://carma. org). Emissions from these point sources were spatially allocated to the exact locations from CARMA.
, 2013). In TIMES, the hourly profiles were provided as 24 scaling factors for each hour of the day that can be multiplied by daily average emissions to derive hourly emissions. Hourly scaling factors of TIMES were derived for residential, commercial, industrial, electricity production and mobile on-road sectors from the bottom-up model of fossil fuel CO 2 emissions Vulcan v2. 0 over the US  (Gurney et al. , 2009)  with mobile non-road, cement manufacture and aircraft assumed temporally constant. The TIMES dataset also gives hourly scaling factors for other 19 high-emitting countries. These profiles were weighted by the emissions fraction in each sector from EDGAR to determine hourly profiles of total CO 2 emissions. The US and other 19 high-emitting countries are called proxy countries. Other countries in the world were assigned the same profiles than one of the proxy countries, accounting for standard international time zones, local socio-demographic patterns (e.
Calculation of emission clumps The emission clumps from point sources and intense area sources in ODIAC are separated in this study. In ODIAC, the point sources only refer to power plants in the CARMA database. Before clumps are calculated, Fig. 1  illustrates the ranked distribution of emission rates during morning hours from point sources (red) and other grid cells (blue). Excluding emissions from point sources, the maximum emission rate of emitting grid cells from area sources is 20. 7 g C m -2 hr -1 and most grid cells including point sources have much larger emission rates than this value. In total, 35% of the global total emissions are from 12433 30"×30" grid cells encompassing at least one point source. Figure  1  Cumulative distribution of mean emission rates during morning hours in ODIAC for power plants (red) and area sources (blue).
This is illustrated in Fig. 3b  by the red grid cell labeled as 1 and 2. There are 6226 grid cells in ODIAC2017 who encompass at least one power plant and whose emission rates are above threshold-1, which account for >99. 99% of total emissions of all CARMA power plants globally. Secondly, emissions clumps from area sources are calculated. We combine two data streams to calculate area clumps: 1) the administrative division of major urban areas; and 2) two thresholds (threshold-1 and threshold-2 detailed below) applied to the grid cells of ODIAC. We assume that a group of emitting pixels encompassing some adjacent high emitting pixels (forming a core of the emission clump) and their surroundings will generate an individual plume in XCO 2. The urban area and the high threshold (threshold-1) define the cores of each emission clump, while threshold-2 defines the lower limit of surrounding emitting pixels to be potentially included in the clumps.
4e ), and because the emission rates per capita is also high in China  (Janssens-Maenhout et al. , 2017). As a result, our algorithm finds: 1) more cores (of non-ESRI clumps) in China than other regions; and 2) larger area with emission rates larger than threshold-2. Figure  5  shows the locations and annual emissions of the clumps. The densities of emission clumps are high in Europe, the East Coast of US, the East Coast of China and India. Fig. 6  shows the fractions of total emissions allocated to different clump categories. Globally, 27% of the clumps are calculated as non-ESRI, but the total emission from these clumps is less than 13% of the total emissions. Point sources form 55% of the total number of clumps and 44% of the total emissions. In China, however, point sources contribute only 21% of the total number of clumps and 39% of the total emissions, which may be explained by the fact that the power plants in China considered in CARMA dataset (and thus in ODIAC) are limited to the few larger power plants.
EDGAR v4. 3. 2, developed by the Joint Research Center under the European Commission's service, has more realistic emission estimates in Europe. Therefore, we focus the clump consistency analysis between ODIAC and EDGAR v4. 3. 2 for Europe, between ODIAC, PKU-CO 2 -v2 and MEIC v1. 2 for China, and between ODIAC and VULCAN v2. 2 for the US. Syst. Sci. Data Discuss. , https://doi. org/10. 5194/essd-2018-131.
Data availability The ODIAC2017 data product is available from a website can be downloaded from the website http://db. cger. nies. go. jp/dataset/ODIAC/ (or https://doi. org/10. 17595/20170411. 001). The TIMES data product can be downloaded from http://cdiac. ess-dive. lbl. gov/ftp/Nassar_Emissions_Scale_Factors/. The clump map can be downloaded from https://doi. org/10. 6084/m9. figshare. 7217726. v1. X air =Psruf/g/M air (2) where Psurf (=1. 013×10 5 Pa) represents the surface pressure, g (=9. 8 m s -2 ) represents the acceleration of gravity, M air (=29×10 -3 kg mol -1 ) represents the average molar mass of air. Thus, the minimum emissions F* that would generate a 0. 5 ppm excess of XCO 2 is computed: F*=0. 36 g m -2 hr -1.
EDGAR v4. 3. 2, developed by the Joint Research Center under the European Commission's service, has more realistic emission estimates in Europe. Therefore, we focus the clump consistency analysis between ODIAC and EDGAR v4. 3. 2 for Europe, between ODIAC, PKU-CO 2 -v2 and MEIC v1. 2 for China, and between ODIAC and VULCAN v2. 2 for the US. Syst. Sci. Data Discuss. , https://doi. org/10. 5194/essd-2018-131 Earth Open Access Earth System Science Data DiscussionsManuscript under review for journal Earth Syst. Sci. Data Discussion started: 30 November 2018 c Author(s) 2018. CC BY 4. 0 License.
UMUKCA-UCAM only include HCHO and SOCOL3 only include HCHO and C5H8. Other models include multiple primary NMVOC species and more complex VOC chemistry. The anthropogenic emissions recommended for the two CCMI reference simulations are the MACCity inventory  (Granier et al. , 2011 ) for 1960 -2000. After 2000 , the REF-C1 experiment continued to use the MACCity inventory, while the REF-C2 used the RCP6. 0 inventory  (Masui et al. , 2011). The CMAM model did not follow this procedure and used the ACCMIP historical database of emissions  (Lamarque et al. , 2010)  until 2000 followed by RCP8. 5 emissions  (Riahi et al. , 2011)  instead. CCMI model simulations also include natural emissions from lightning, soil and biogenic sources. Lightning NOx emission are calculated based on meteorological data such as cloud top height  (Price and Rind, 1994; Grewe et al.
, 2015). Chemical sinks of CH4 The LMDz model has been applied in various studies focusing on long-lived gases such as CH4, CO2 and MCF  (Bousquet et al. , 2005; Pison et al. , 2009; Lin et al. , 2018). It has also been used in model intercomparison projects such as the TransCom experiment  (Patra et al. , 2011)  with the simplified chemistry module SACS  (Pison et al. , 2009)  and CCMI  (Morgenstern et al. , 2017)  but only with the stratospheric chemistry model PEPROBUS  (Jourdain et al. , 2008). The CH4 emissions input to LMDz simulations are provided by the Global Carbon Project (GCP) methane and include anthropogenic and biofuel emissions from EDGARv4. 3. 2 (Janssens-Maenhout et al. , 2017), the mean wetland emissions from  Poulter et al. , (2017) , fire emissions from the Global Fire Emissions Database Version 4.
Acknowledgements This work has been funded by the  Gordon and Betty Moore Foundation  through Grant  GBMF5439  ' Advancing Understanding of the Global Methane Cycle ' and takes benefit of the expertise of the  Global Carbon Project methane initiative. We acknowledge the modelling groups for making their simulations available for this analysis, the joint  WCRP SPARC/IGAC Chemistry-Climate Model Initiative  ( CCMI ) for organising and coordinating the model data analysis activity, and the  British Atmospheric Data Centre (BADC)  for collecting Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2019-281Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 1 April 2019 c Author(s)  2019. CC BY 4. 0 License.
The second method reproduces present-day ice sheet geometry and velocity well but does not capture past climate evolution and current trends of ice mass, due to inconsistencies between datasets  (Seroussi et al. , 2011) , also impacting the ice sheet response to perturbations. To combine the best of these two approaches, models using long transient spin-ups have integrated simple inverse methods to match present ice sheet geometry  (Pollard and DeConto, 2012a) , while models using data assimilation have run short-term relaxation periods to limit the initial shock caused by inconsistent datasets  (Gillet-Chaulet et al. , 2012). These additions are widening the spectrum of initialization methods (see also  Goelzer et al. , 2018). Since ice sheets have a slow response time, their initial conditions influence their evolution for centuries to millennia.
, 2013; Rignot et al. , 2013). The melt rate anomaly is the average between these two datasets and averaged over ice shelves in each of the 20 ice sheet basins defined, so that a different mean melt rate anomaly is specified for each of the 20 ice sheet basins, with a spatially uniform anomaly within each basin (Fig. 1b ). Thus, this melt rate anomaly represents a doubling of present-day estimates of melting. The anomaly is applied under all floating ice, including ice that ungrounds during the experiment. www. the-cryosphere. net/13/1441/2019/ The Cryosphere, 13, 1441-1471, 2019 For the asmb and abmb experiments, anomalies in SMB and sub-shelf melt rates are applied in addition to the forcings used in the init and ctrl experiments. The anomalies are applied as time-dependent functions, increasing stepwise each year over the first 40 simulation years and remaining constant over the last 60 years: EX(t) = EX ctrl + EX anom × [t] 40 ; for 0<t<40 years EX(t) = EX ctrl + EX anom ; for t>40 years, where EX(t) is the forcing at time t, EX ctrl the forcing used in the ctrl experiment, EX anom the applied anomaly (Fig.
Model setup Ice sheet models are free to use whatever initialization procedure is deemed appropriate, given model characteristics and requirements. Submitted simulations rely on long paleoclimate spin-ups, steady states, data assimilation, or a combination of these methods. There is no constraint or suggestion on forcing datasets (including SMB and sub-shelf melt rates) or on specific physical processes and parameterizations (e. g. , basal sliding laws, ice rheology, and stress balance approximation). The initialization time varies among models but is near the beginning of the 21st century. Previous multi-model ice sheet studies  (Bindschadler et al. , 2013; Nowicki et al. , 2013a, b)  showed the difficulty of separating the effects of initial conditions, physical processes, and external forcings.
Four models are based on a steady-state equilibrium in which the model is run for an extended period of time, until the ice sheet becomes close to a steady-state equilibrium, with two models also including present-day geometry as a target  (Pollard and De-Conto, 2012a). The remaining seven initializations are based on data assimilation, with three models also including a short relaxation period after the data assimilation to limit the impact of inconsistent datasets  (Seroussi et al. , 2011; Gillet-Chaulet et al. , 2012). For the external SMB forcing, models use output from RACMO2  (Lenaerts et al. , 2012 ), RACMO2. 3 (van Wessem et al. , 2014 ), RACMO2. 3p2 (van Wessem et al. , 2018) , MAR  (Agosta et al. , 2019) , ERA Interim  (Dee et al. , 2011) , or  Arthern et al. (2006). Five simulations use a positive degreeday scheme (PDD;  Reeh, 1991).
3a ). The RMSE bewww. the-cryosphere. net/13/1441/2019/ The Cryosphere, 13, 1441-1471, 2019 tween observed  (Rignot et al. , 2011a)  and modeled surface velocity (Fig. 3b ) also has a large spread among models, varying from 47. 5 to 308 m yr -1. These values are significantly affected by the inclusion of observed surface velocities during the initialization procedure: the RMSE in surface speed varies from 47. 5 to 94. 5 m yr -1 for models including data assimilation of surface velocities and from 116 to 308 m yr -1 for the other models. Most of these errors are caused by large discrepancies in ice shelves and a few fast-flowing ice streams: the RMSE for the logarithm of the speed, which emphasizes the slower-moving regions, varies only between 0. 62 and 1. 51 (Fig. 3c ), or 3 times less than the RMSE of the speed.
These errors are in part affected by the exact year of the initialization procedure, as observations of velocity and thickness are not acquired at the same time. However, the temporal variability of observed thickness and Table  2. List of initMIP-Antarctica simulations and main model characteristics. Numerics rely on the finite-difference (FD), finite-element (FE), or finite-volume (FV) method. Initialization methods are as follows: spin-up (SP), spin-up with target values for the ice thickness (SP+; see  Pollard and DeConto, 2012a) , data assimilation (DA), data assimilation with short relaxation (DA+), data assimilation of ice geometry (DA * ), equilibrium state (Eq), and equilibrium state with target values for the ice thickness (Eq+). Initial SMB is derived from the following: RACMO2 (RA2;  Lenaerts et al.
Discussion The initMIP-Antarctica experiments are designed to analyze the impact of ice sheet model initial conditions on the evolution of the Antarctic ice sheet and its response to simple climate forcings. For this exercise, 16 groups submitted 25 simulations, more than 4 times the number of Antarctic simulations submitted for the SeaRISE project  (Bindschadler et al. , 2013) , highlighting the importance and the fast evolution of this research field  (Pattyn et al. , 2017). The simulations represent a large diversity of initialization methods, forcing datasets, and model parameters, and the results show a large spread in the mass balance and dynamic evolution of this ice sheet in century-scale simulations. The initial ice volume above floatation varies from 1. 8 to 2. 5 × 10 7 Gt, or almost 32 %, which is much larger than the spread of about 8 % in SeaRISE  (Nowicki et al.
H. Seroussi et al. : An ice sheet model initialization experiment Appendix A: Outputs and output format initMIP-Antarctica participants are required to provide output variables according to the data request plan. Three types of 2-D fields are reported by modeling groups at 5-year intervals: state variables, flux variables, and constants. Also, scalar outputs (e. g. , total ice mass, ice mass above floatation, SMB, basal melt) are reported every simulation year. Table  A1  provides the complete list of requested variables. In addition to model output results, a README file describing model characteristics and details of the initialization procedure was requested from modeling groups for each simulation.
B2 AWI_PISM The simulations are performed with PISM version 0. 7. 3. For the 220 ka-long spin-up simulations with paleoclimatic forcing (PISM1Pal), time slice anomalies for the Last Interglacial (LIG) and the Last Glacial Maximum (LGM) from the Earth System Model COSMOS  (Pfeiffer and Lohmann, 2016; Zhang et al. , 2014)  are used in addition to datasets for present-day (PD) Antarctic climate (RACMO2. 3, van Wessem et al. , 2014; WOA09, Locarnini et al. , 2010). Timedependent and spatially variable climate anomaly fields are interpolated during the PISM run between LIG, LGM, and PD climate time slices with a glacial index method  (Sutter et al. , 2016) , where the glacial index is derived from Dome C deuterium depletion  (Jouzel et al. , 2007). For the SMB, PISM's positive degree-day (PDD) scheme is used.
The model is first initialized for 100 kyr using the average 1979-2014 SMB and surface ice temperature from  RACMO2. 3 (van Wessem et al. , 2014)  and mapped with OBLIMAP  (Reerink et al. , 2010 (Reerink et al. , , 2016)). Bedrock elevation is fixed in time with data taken from the Bedmap2 dataset  (Fretwell et al. , 2013) , and geothermal heat flux data are from  Shapiro and Ritz-woller (2004)  and mapped with OBLIMAP  (Reerink et al. , 2010 (Reerink et al. , , 2016)). We then run for 30 kyr with constant ice temperature from the first run to get to a dynamic steady state, which is our initial condition.
B9 JPL_ISSM Model setup, as follows, is adapted from  Schlegel et al. (2018). The model domain covers the present-day Antarctic ice sheet, and its geometry is interpolated from the Bedmap2 dataset  (Fretwell et al. , 2013) , with additional refinement in the Amundsen Sea sector, Recovery Ice Stream, and Totten Glacier, from  Morlighem et al. (2011)  and  Rignot et al. (2014). The forward simulations rely on a 2-D Shelfy-Stream Approximation  (MacAyeal, 1989)  for stress balance, with a mesh resolution varying between 1 km at the domain boundary and within the shear margins and 50 km in the interior and a resolution of 8 km or finer within the boundary of all initial ice shelves. To estimate land ice viscosity, we compute the ice temperature based on a thermal steady state with 15 vertical layers  (Seroussi et al.
, 2011; Millan et al. , 2017). The thermal model is constrained by surface temperatures from  Comiso (2000)  and the geothermal heat flux from  Shapiro and Ritzwoller (2004) , both included in the SeaRISE dataset  (Nowicki et al. , 2013b). The SMB used in the control experiment is from  RACMO2. 3 (van Wessem et al. , 2014).
Acknowledgements. We acknowledge the  Climate and Cryosphere (CliC)  project and the  World Climate Research Programme (WCRP)  for their guidance, support, and sponsorship. We thank the CMIP6 panel members for their continuous leadership of the CMIP6 effort and the  Working Group on Coupled Modeling (WGCM) Infrastructure Panel (WIP)  for overseeing the CMIP6 and ISMIP6 infrastructure and data request. Research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). Hélène Seroussi ,  Nicole-Jeanne Schlegel ,  Eric Larour ,  Sophie Nowicki , and  Erika Simon  are supported by grants from the  NASA Cryospheric Science ,  Sea Level Change Team , and  Modeling Analysis  and  Prediction Program.
Material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under Cooperative Agreement no. 1852977. Computing and data storage resources for CISM simulations, including the Cheyenne supercomputer (https://doi. org/10. 5065/D6RX99HX),were provided by the  Computational and Information Systems Laboratory  (CISL) at  NCAR. Ralf Greve  was supported by the  Japan Society for the Promotion of Science (JSPS) KAKENHI  grant numbers  JP16H02224 ,  JP17H06104 , and  JP17H06323. Christian Rodehacke  (DMI) has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7) for research, and Theme 6 Environment  as part of the  NACLIM (North Atlantic Climate)  project (grant agreement  308299 ), as well as the  Nordic Centers of Excellence eSTICC (eScience Tool for Investigating Climate Change at High Northern Latitudes ) funded by  Nordforsk  (grant  57001 ).
Support for  Matthew J. Hoffman ,  Stephen F. Price , and  Tong Zhang  was provided through the  Scientific Discovery through Advanced Computing (SciDAC)  program funded by the  US Department of Energy Office of Science, Biological and Environmental Research and Advanced Scientific Computing Research programs. This research used resources of the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the US Department of Energy  under contract no. DE-AC02-05CH11231. The  Ministry of Education, Culture and Science (OCW) , in the Netherlands, provided financial support for this study via the program of the  Netherlands Earth System Science Centre (NESSC). The work of  Thomas Kleiner  and  Angelika Humbert  has been conducted in the framework of the  PalMod  project (FKZ:  01LP1511B ), supported by the  German Federal Ministry of Education and Research (BMBF) as Research for Sustainability initiative (FONA).
Aurélien Quiquet  acknowledges funding from the  European Research Council  grant ACCLIMATE no. 339108. Fabien Gillet-Chaulet  and  Julien Brondex  (IGE) have received funding from the  French National Research Agency (ANR)  under the  TROIS-AS  project. IGE-ELMER simu-lations were performed using HPC resources from  GENCI-CINES  (grant  2017-016066 ) and using the Froggy platform of the  CI-MENT infrastructure , which is supported by the  Rhone-Alpes region  (grant  CPER07_13   CIRA ), the OSUG@2020 laBex (reference  ANR10 LABX56 ), and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ). Torsten Albrecht  was supported by the  Deutsche Forschungsgemeinschaft (DFG)  in the framework of the priority program " Antarctic Research with comparative investigations in Arctic ice areas " by grant  LE1448/6-1  and  LE1448/7-1.
Sainan Sun  was supported by the  FRS-FNRS   MEDRISM  project and the  BELSPO   MIMO  project ( Stereo III ). Philippe Huybrechts and  Jonas Van Breedam  acknowledge support from the  iceMOD  project funded by the  Research Foundation -Flanders (FWO-Vlaanderen ). Daniel Lowry acknowledges support from the  Antarctica New Zealand Doctoral Scholarship program  and the  New Zealand Ministry of Business, Innovation and Employment  (grant  15-VUW-131 ). Review statement. This paper was edited by  Kenichi Matsuoka  and reviewed by  Jesse Johnson  and one anonymous referee.
Data availability. The model output from the simulations described in this paper and forcing data sets will be made publicly available with a digital object identifier https://doi. org/10. 5281/ zenodo. 2651652. In order to document CMIP6's scientific impact and enable ongoing support of CMIP, users are asked to acknowledge CMIP6, ISMIP6, and the participating modeling groups. www. the-cryosphere. net/13/1441/2019/ The Cryosphere, 13, 1441-1471, 2019.
Biological sampling and data collection The material used in this study belongs to the collections of the Natural History Museum (BMNH) in London (UK), the Museum für Naturkunde (MfN) in Berlin (Germany), the Muséum National d'Histoire Naturelle (MNHN) in Paris (France), the Royal Museum for Central Africa (KMMA/RMAC) in Tervuren (Belgium), the American Museum of Natural History (AMNH) in New York (NY, USA) and the National Museum of Natural History (USNM) in Washington (DC, USA). Our dataset is the result of the landmarking of 243 specimens from the eight extant species of pangolins (Fig. 1 ): P. tricuspis (Fig. 2 ; white-bellied pangolin, N = 97), Phataginus tetradactyla (Linnaeus, 1766) (black-bellied pangolin, N = 23), Smutsia gigantea (Illinger, 1815) (giant ground pangolin, N = 16), Smutsia temminckii (Smuts, 1832) (Cape pangolin, N = 21), Manis crassicaudata Geoffroy, 1803 (Indian pangolin, N = 10), M.