dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019) , the global GEOS-Chem (Goddard Earth Observing System -Chemistry) model, and the E3SM (Energy Exascale Earth System Model) Atmosphere Model (EAM). Measured variables which are compared include the aerosol layer top/bottom boundaries (as determined from lidar and in situ), aerosol extinction (lidar and in situ), black carbon and organic aerosol mass concentrations (SP2 and AMS), carbon monoxide, scattering and absorption Ångström exponents, and the single-scattering albedo (in situ). The new datasets allow an extension beyond previous assessments emphasizing the aerosol layer boundaries only  (Das et al. , 2017; Koffi et al. , 2012). Not all of the models include all of these variables, and no effort is made to standardize model features such as the emissions databases and frequency of initialization.
Illustration of remote-sensing test bed As alluded to in Sect. 2. 1, a subordinate, yet important objective for ORACLES was the acquisition of data that can be used for the refinement and testing of retrieval capabilities for instrument concepts that have a potential for deployment to space. Among the remote-sensing instruments participating in ORACLES, the following have a link to space-based instrument concepts: HSRL-2 (future HSRL in space), APR-3 (CloudSat's CPR, GPM's DPR, RainCube's radar, Earth-CARE's CPR, and candidate radars for future missions targeting clouds, convection, and precipitation), RSP (APS on Glory), AirMSPI (MAIA), and eMAS (MODIS). Examples of algorithm developments using ORACLES data include  Xu et al. (2018 ), Segal-Rozenhaimer et al. (2018 ), and Miller et al. (2020) , who created new cloud retrieval algorithms for the AirMSPI and RSP instruments.
20 ). The resulting dataset will be used in the testing of instrument and algorithm concepts for future satellite missions, such as the NASA Aerosol, Clouds-Convection and Precipitation (ACCP) mission, recommended by the 2018 Decadal Survey for Earth Observations from Space (National Academies of Sciences, Engineering, and Medicine, 2018) and the NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission  (Werdell et al. , 2019) , due for launch in 2023.
The discrimination between cloud adaptations to the aerosol shortwave absorption, cloud-nucleating properties, and variability in the large-scale circulation is inherently complex. While this work will assuredly require focused modeling activities that can control more easily for cause-effect relationships, ongoing analysis of ORACLES datasets will frame the modeling activities. In one example, the multiple routine flights along 5 • E, conducted in both August 2017 and October 2018, are being analyzed to determine the dominant thermodynamic, dynamic, and aerosol features. In another example, remote-sensing data from the ER-2 platform in September 2016 on the aerosol vertical structure and cloud properties are being constrained by sea surface temperature and location to assess liquid water path responses to aerosol loading and vertical structure.
Conclusions and future work The ORACLES project is a highly successful NASA EVS-2 project that had well surpassed its Level-1 baseline science requirements upon the conclusion of its last field deployment in October 2018. We conducted a total of three flight deployments totaling 350. 6 science flight hours with the NASA P-3 aircraft and one flight deployment with the NASA ER-2 totaling 97. 3 science flight hours, surveying, probing, and exploring the various features of the SE Atlantic aerosolcloud-climate system. The ORACLES dataset permits the study of aerosol radiative and cloud-nucleating properties, their vertical distribution relative to clouds, the locations and degree of aerosol mixing into clouds, and cloud changes in response to such mixing. Here, we have only touched upon some of the key findings, leaving the detailed exploration of the various factors determining aerosol-cloud-climate interactions in the SE Atlantic to the individual investigations being conducted as part of, or spawned by, the ORACLES project.
, 2019; Cochrane et al. , 2020) , if less to the range in total heating rates; there was ample evidence for aerosol-induced modifications of Sc cloud properties (Fig. 18 ) and those of midlevel clouds  (Adebiyi et al. , 2020) ; a new cloud/rain dataset produced by a joint multi-wavelength cloud radar and multi-angle/multiwavelength polarimeter will allow for further investigation for the suppression of drizzle by aerosol  (Dzambo et al. , 2019 (Dzambo et al. , , 2020)) ; chemical transport models and climate models exhibit a fairly systematic underestimation of aerosol loadings in the SE Atlantic  (Shinozuka et al. , 2020). An analogous summary of lessons learned for logistics and planning of large field campaigns to address aerosol-cloudclimate interactions includes the following: the ORACLES science objectives and questions provided crucial guidance for flight execution in each of the deployment years; progress towards achieving science objectives was facilitated by the bookkeeping of flight maneuvers that relate directly to the various detailed science objectives, although such bookkeeping proved challenging in the field; the joint efforts in developing flight plans by scientists with nominally different flight objectives (e.
g. , cloud microphysics and radiation) revealed that relatively minor adjustments to flight maneuvers often resulted in datasets that are conducive to addressing a broad range of science questions, well beyond the benefits of flight plans that were developed by any one focused group in isolation; the scientific connection to international deployment efforts conducted by the CLARIFY and AEROCLO-sA teams in the same region and timeframe as ORACLES-2017 proved a worthy investment of time -while the connecting science is ongoing, there are already measurable outcomes from the international scientific collaborations in terms of the geographic extension of datasets and related publications (e. g. ,  Mallet et al. , 2019; Formenti et al. , 2019; Haywood et al. , 2020) ; the routine sampling strategy for about half of the OR-ACLES flights allowed for a statistical assessment of climate and chemical transport models, unprecedented for suborbital efforts in this field of study; the involvement of the modeling community in the conception and development of the ORACLES project in general, and its flight planning specifically, proved to be invaluable for collecting a dataset that can be used for addressing model deficiencies that hamper our ability to accurately simulate aerosol-cloud-climate interactions.
In this paper, we describe a sea-level database compiled using published Last Interglacial, Marine Isotopic Stage 5 (MIS 5), geological sea-level proxies within Eastern Africa and the Western Indian Ocean (EAWIO). Encompassing vast tropical coastlines and coralline islands, this region has many occurrences of well preserved last interglacial stratigraphies. Most notably, islands almost entirely composed by Pleistocene reefs (such as Aldabra, the Seychelles) have provided reliable paleo relative sea-level indicators and well-preserved samples for U-series chronology. Other sea-level proxies include uplifted marine terraces in the north of Somalia and tidal notches in luminescence limited aeolian deposits in Mozambique. Our database has been compiled using the World Atlas of Last Interglacial Shorelines (WALIS) interface and contains 57 sea-level indicators and 2 terrestrial limiting data points.
For these two points, it can be only determined that sea level was, at the time of their formation, below the measured elevation of the landform. Very few studies within the EAWIO have the express intent to establish detailed surveys of Last Interglacial (LIG) sea-level proxies. This is especially true with respect to elevation measurements. Most surveys conducted during the latter half to the 20 th century do not report a methodology used in measuring elevations. It is not until the advent of Global Navigation Satellite Systems (GNSS) and Total Stations that surveys on many of these remote shorelines could be accurately documented. The elevation measurement techniques used in the studies we compiled in the database are shown in Table  1. When no accuracy was given for an elevation measurement in the original study, the typical accuracy was used.
Relative Sea Level Indicators In total, our database counts 57 sea-level indicators and 2 terrestrial limiting points (Figure  2 ). All sea-level indicators are used to gather an associated paleo relative sea level (PRSL) based on the indicative meaning associated with the landform. The indicative meaning was either calculated based on modern analog data described by the original authors, or from IMCalc  (Lorscheid and Rovere, 2019). As the EAWIO is a wide geographic area, we describe our data points based on a country by country rationale. We start in the north with Somalia down to Mozambique in the south. We then move offshore to the islands along the Mozambique channel, including Madagascar. Finally, we review sea-level histories of the Seychelles, Mauritius, and other small minor islands.
(2010)  are the first to provide semi-reliable vertical positioning data for samples taken. Additionally,  Accordi et al. (2010)  undertook the most extensive dating of the emerged Kenyan reef sequence. Here, Tridacna gigas were sampled in situ and analyzed using U-Series alpha-spectrometry. Due to variability of the calculated initial 234 U/ 238 U activity ratio of their samples (compared to the seawater value over the last 120 ka),  Accordi et al. (2010)  considered their samples as diagenetically altered, and used the open system model of  Scholz et al. (2004)  to recalculate ages. Therefore, ages are treated with caution in WALIS, as mollusks (i. e. T. gigas) are susceptible to inconsistent U-series uptake and loss rates when compared to coral specimens  (Ayling et al. , 2017). The samples were split into three groups by  Accordi et al.
Within the database, however, we treat each sample independently in order to separate interpretation from the raw data. Finally, calculated PRSL uses the indicative range described by  Accordi et al. (2010)  for T. gigas along the modern Kenyan coast, 3 to 10 m below MSL. In many instances there are poorly preserved massive or branching corals in growth position within this same facies. Therefore, we identify this as a coral reef terrace within the database and not a marine limiting point.
8 ± 1. 2 m above MLWS with an age between 125. 5 ± 1. 8 and 136. 9 ± 2. 3 ka (ST18-003-001 and ST18-004-001, WALIS ID# 159, Table  4 ). These ages mirror the younger age of Baie des Dunes (130 ± 40 ka) by  Battistini (1977)  and are significantly younger than the older 160 ± 30 ka age. The elevation reported by  Stephenson et al. (2019)  is also higher than that described by  Battistini (1977) , 6. 8 m vs. 2 m respectively. As with other datasets, the differences in analytical capability between the 1970s and late 2010s can certainly be a leading cause of these discrepancies in age and in elevation. This translates to a PRSL of + 5. 3 ± 1. 56 m. Finally, further south on the shore of Irdo Bay, the coral reef terrace is observed again, this time at an elevation of +2. 8 ± 1. 2 m MLWS and an age of between 126.
Main Islands The third-largest island of the granitic Seychelles archipelago, La Digue, has been the subject of several geomorphological investigations (e. g. (Montaggioni and Hoang, 1988; Israelson and Wohlfarth, 1999; Wallinga and Cunningham, 2015). Dutton et al. (2015)  describe coral colonies attached to granitic bedrock, similar to those observed within the present subtidal zone. From this, a total of five RSL indicators are included in the database. These are accompanied by 25 U-Series ages determined using MC-ICPMS  (Dutton et al. , 2015; Chutcharavan, in prep. ). All PRSL elevations for this area in WALIS are determined using the modern analog scheme described by  Vyverberg et al. (2018). At Inland (WALIS ID# 570, Table  4 ), indicator elevation was measured at +6. 7 ± 0. 2 m above MLWS and a PRSL was calculated to be +7.
Mauritius Dominating the central Indian Ocean, the island nation of Mauritius consists of two main island groups: the Chagos and the Mascarene Archipelagos. Here,  Montaggioni (1972)  and  Montaggioni (1976)  describe emergent reefs along the majority of the Mauritius coastline as well on the two small islets to the north of the main island: Île Plate and Îlot Gabriel. The reefs are generally characterized as a framestone with large in-situ Acropora, Pocilloporidae, and Faviidae corals (Figure  7 ). The morphological description of the reefs was accompanied by one U-Series Alpha-Spectrometry age from  Veeh (1966). Therefore, we consider the dated facies on Îlot Gabriel and neighboring Île Plate for use in the database. This index stands between +1. 5 and +2 m MSL, representing a PRSL of 3.
(2013). Similar to Rocher Sud, the slightly larger Rocher Vert sits roughly 3 km to the northeast of the island emerging from the modern reef flat. Two samples were taken from Rocher Vert (GU13-007-001 and GU13-008-001, WALIS ID# 174; Table  4 ), however both samples were rejected as overestimating age based on especially high initial 234 U/ 238 ratios. The maximum elevation of the reef facies from Rocher Vert were none the less used within the database and an PRSL of +6. 3 ± 0. 5 m was calculated. To the far eastern extent of the island group, Ile du Lys is home to the island group's most extensive elevated reef flat exposure. Unfortunately, only one sample, GU13-014-001, passed the calcite screening process and has an age of 124 ± 6. 4 ka (WALIS ID# 175, Table  4 ). Two different lithologies separated by a disconformity are described.
Acknowledgements. The data used in this study were compiled in WALIS, a sea-level database interface developed by the  ERC Starting Grant "WARMCOASTS" (ERC-StG-802414 ), in collaboration with PALSEA (PAGES / INQUA) working group. The database structure was designed by  A. Rovere ,  D. Ryan ,  T. Lorscheid ,  A. Dutton ,  P. Chutcharavan ,  D. Brill ,  N. Jankowski ,  D. Mueller ,  M. Bartz ,  E. Gowan  and  K. Cohen. The data points used in this study were contributed to WALIS by  Patrick Boyden , with  P. Chutcharavan  providing assistance with some U-Series data entry. Support for PB was provided by the  DFG (Deutsche Forschungsgemeinschaft ) Excellence Cluster "EXC 2077: The  Ocean Floor -Earth's Uncharted Interface " (Project number:  390741603 ). JWA post-doctoral fellowship was funded by the  French National Institute for Sustainable Development (IRD).
Data availability The East Africa and the Western Indian Ocean database is available at: https://doi. org/10. 5281/zenodo. 4043366 (Version 1. 02)  (Boyden et al. , 2020). The description of the database fields can be found at:https://doi. org/10. 5281/zenodo. 3961543 (Rovere et al. , 2020). Author contributions. PB compiled the database with extensive help from JWA on translating and assessing older French publications into English. AR is the main developer of WALIS. PB wrote the initial manuscript, with significant input from JWA and AR. Further input on the manuscript was provided by PD and DO. All authors revised the final text and agree with its contents.
To track progress towards keeping global warming well below 2 • C or even 1. 5 • C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970-2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO 2 emissions, CH 4 emissions, N 2 O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF 6 , NF 3 ) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO 2 emissions from land use, land-use change, and forestry (LULUCF).
There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article  (Minx et al. , 2021)  can be found at https://doi. org/10. 5281/zenodo. 5566761.
As a result, many still lack a well-developed statistical infrastructure to provide detailed reports  (Janssens-Maenhout et al. , 2019). Here we describe a new, comprehensive, and synthetic dataset for global, regional, and national GHG emissions by sector for 1970-2018 with a fast-track extension to 2019. Our focus is on GHG emissions from anthropogenic activities only. We build the dataset from recent releases of the Emissions Database for Global Atmospheric Research version 6 (EDGARv6) for CO 2 emissions from fossil fuel combustion and industry (FFI), CH 4 emissions, N 2 O emissions, and fluorinated gases (F-gases: HFCs, PFCs, SF 6 , and NF 3 )  (Crippa et al. , 2021). For completeness we add net CO 2 emissions from land use, land-use change, and forestry (CO 2 -LULUCF) from three bookkeeping models  (Gasser et al.
Overview Our dataset provides a comprehensive, synthetic set of estimates for global GHG emissions disaggregated by 27 economic sectors and 228 countries and territories. Our focus is on anthropogenic GHG emissions: natural sources and sinks are not included. We distinguish between five groups of gases: (1) CO 2 emissions from fossil fuel combustion and industry (CO 2 -FFI); (2) CO 2 emissions from land use, land-use change, and forestry (CO 2 -LULUCF); (3) methane emissions (CH 4 ); (4) nitrous oxide emissions (N 2 O); (5) fluorinated gases (F-gases) comprising hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF 6 ) as well as nitrogen trifluoride (NF 3 ). F-gases that are internationally regulated as ozone-depleting substances under the Montreal Protocol, such as chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs), are not included.
This affects the growth rate by about 0. 3 % yr -1 (1/366) and causes calculated growth rates to go up by approximately 0. 3 % if the first year is a leap year and down by 0. 3 % if the second year is a leap year. We calculate the relative growth rate in percent per year for multi-year periods (e. g. a decade) by fitting a linear trend to the logarithmic transformation of E across time (see  Friedlingstein et al. , 2020). We compile our dataset from four sources: (1) the full EDGARv6 release for CO 2 -FFI as well as non-CO 2 GHGs covering the time period 1970-2018  (Crippa et al. , 2021) ; (2) EDGARv6 fast-track data for CO 2 -FFI providing preliminary estimates for 2019 (and 2020)  (Crippa et al. , 2021) ; (3) CO 2 -LULUCF as the average of three bookkeeping models, consistent with the approach of the global carbon project  (Friedlingstein et al.
, 2020) ; (4) 2019 non-CO 2 emissions based on  Olivier and Peters (2020). As shown in Table  2 , sectoral detail is organized along five major economic sectors as commonly used in IPCC reports on climate change mitigation  (IPCC, 2014) : energy supply, buildings, transport, industry, as well as Agriculture, Forestry and Other Land-Use Changes (AFOLU). We devise a classification for assigning our 228 countries and territories to regions, combining the standard Annex I/non-Annex I distinction with geographical location. We provide other common regional classifications from the UN and the World Bank as part of the Supplement. The dataset including the sector and region classification can be found at https://doi. org/10. 5281/zenodo. 5566761 (Minx et al. , 2021).
The Emissions Database for Global Atmospheric Research (EDGAR) EDGAR emissions estimates included in our dataset are derived from the full version 6 release which includes CO 2 and non-CO 2 GHG emissions estimates from 1970 to 2018 computed from stable international statistics and fast-track estimates of fossil CO 2 emissions up to the year 2020  (Crippa et al. , 2021). This general EDGAR methodological description is largely taken from Janssens-Maenhout et al. (2019). The EDGAR bottom-up emissions inventory estimates are calculated from international activity data and emissions factors following the 2006 IPCC Guidelines for National Greenhouse Gas Inventories  (IPCC, 2006)  -updated according to the latest scientific knowledge. Emissions (EMs) from a given sector i in a country C accumulated during a year t for a chemical compound x are calculated with the countryspecific activity data (AD), quantifying the activity in sector i, with the mix of j technologies (TECH) and with the mix of k (end-of-pipe) abatement measures (EOP) installed with the share k for each technology j , the emission rate with an uncontrolled emissions factor (EF) for each sector i and technology j and relative reduction (RED) by abatement measure k, as summarized in the following formula: EM i (C, t, x) = j,k [AD i (C, t) • TECH i,j (C, t) • EOP i,j,k (C, t) • EF i,j (C, t, x) • 1 -RED i,j,k (C, t, x) ].
We apply these fast-track data by  Olivier and Peters (2020)  to our dataset by calculating the country-and sector-specific emissions growth between 2018 and 2019 and multiplying it by the 2018 values in our data. We consider all fluxes of CO 2 from land use, land-use change, and forestry. This includes CO 2 fluxes from the clearing of forests and other natural vegetation (by anthropogenic fire and/or clear-cut), afforestation, logging and forest degradation (including harvest activity), shifting cultivation (cycles of forest clearing for agriculture and then abandonment), regrowth of forests and other natural vegetation following wood harvest or abandonment of agriculture, and emissions from peat burning and drainage. Some of these activities lead to emissions of CO 2 to the atmosphere, while others lead to CO 2 sinks.
CO 2 -LULUCF therefore is the net sum of emissions and removals from all human-induced land-use changes and land management. Note that CO 2 -LULUCF is referred to as (net) land-use change emissions, E LUC , in the context of the Global Carbon Budget  (Friedlingstein et al. , 2020). Agriculture per se, apart from conversions between different agricultural types, does not lead to substan-   , 2006, 2019)  as well as relevant GHGs. Note that EDGAR v6 distinguishes between biogenic CO 2 and CH 4 sources with a "bio" label, with all other sectors "fossil" by default, even if that source is not related to fossil fuel activities. The fossil/bio label is hence not descriptive in nature. Two HCFC gases (denoted with * ) are included in the dataset, despite being neither PFCs nor HFCs (and hence regulated under Montreal).
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 un-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. , 2012)  -through adoption of the IPCC socalled land-use proxy approach when they occur in areas that countries declare to be managed.