dataesr/scientific-paragraphs-categorization huggingface dataset:
It is important to note that spectral power at frequencies of ∼40 kyr -1 and ∼100 kyr -1 is not evidence for a direct forcing by the obliquity and eccentricity of Earth's orbit (with dominant periodicities at these values, respectively). Furthermore, it is instructive to also evaluate the timing of cycles in the IRD MAR data relative to cycles in orbital parameters. In figure  4b-c , we show that while the spectral power is dominated by ∼40 kyr -1 frequencies (pre-and post-MPT) there is a close coupling between high IRD MAR and low obliquity. A mechanistic relationship here is supported by our climate model results (Extended Data 6) which indicate that the low obliquity 27ka experiment yields cooler SO SST and increased sea-ice extent relative to LGM. Indeed, a relationship between obliquity and SO seaice  15, 91  , as well as stronger westerlies (related to the increased latitudinal insolation gradient  45, 92  ) has been previously demonstrated; both would act to increase iceberg survivability and transport into and across the SAZ (Extended Data 7).
Extended Data Figure Captions Extended Data Figure  1 : Schematic representation of the 'Southern Escape' mechanism described in this study. a. Shows an idealized representation of the Southern Ocean during Interglacial conditions. The front panel gives meridional-averaged overturning circulation (following  93  ), with circulation divided into an Upper Cell and Lower Cell. Arrow width represents the relative strength of each circulation cell (wider being stronger). The top panel represents the ocean surface, with icebergs calving from the Antarctic Ice Sheet (AIS) and following a northward then eastward trajectory. The blue shading represents iceberg meltwater being entrained into the Lower Cell as icebergs melt south of the main Antarctic Circumpolar Current (ACC) belt. Orange wavy arrows represent brine rejection from sea-ice formation.
c. Same as a. and b. , but for full glacial conditions (occurring after b. ). Here, the Southern Escape of meltwater has successfully perturbed the Upper Cell, which is now weaker and has contracted upwards. The now-dominant Lower Cell has thus increased in volume. Extended Data Figure  2 : AP comp Age model and composite record construction. a. The appending of U1475 to the bottom of MD02-2588 using δ  18  O benthic with the new AP comp composite depth scale on the right-hand axis. b. Age-depth tie points (circles) with the median and 95% confidence bounds for the deterministic age model. Depth is given on the AP comp scale. c. The δ  18  O benthic from this study (purple) and the tuning target, the global delo stack  53  (light grey), and the implied linear sedimentation rates (black; dashed line) and calculated mass accumulation rate (green; solid line) from the final age model in the lower subplot.
In purple are observed modern iceberg trajectories from the BYU Giant Iceberg database  95  (QSCAT); the modelled PI trajectories appear substantially longer than the observed likely because icebergs are tracked in Pyberg even after then become too small to be identified and hence tracked by modern observational techniques. b. , d. , and f. Zonally averaged meltwater estimates for PI (b), LGM (d), and LGM27ka (f) experiments. The average is taken for each latitude between 0 and 50 • E; in other words, this shows the latitudinal distribution of meltwater across the Indian-Atlantic Ocean Gateway.
PI LGM 27ka Extended Data Figure  6 : LGM27ka Iceberg Meltwater (Gt) Meltwater Distribution (Gt) Meltwater Distribution (Gt) PI Iceberg Meltwater (Gt) LGM Iceberg Meltwater (Gt) Meltwater Distribution (Gt) LGM Iceberg Meltwater (Gt) Extended Data Figure  7:.
Acknowledgements This research used samples and/or data provided by the  International Ocean Discovery Program (IODP). Funding for this research was provided by  The Natural Environmental Research Council GW4+ Doctoral Training Partnership (AS)  and  NERC  Grant  NE/P000037/1  (IRH). AS acknowledges further funding through the  Antarctic Science International Bursary. XZ  acknowledges funding from  Lanzhou University  (Nr. 225000-830006 ) and  National Key R& D program of China  (Nr. 2018YFA0606403). FJJE acknowledges funding through  Spanish Ministry of Science and Innovation  (grant  CTM2017-89711-C2-1-P ), co-funded by the  European Union  through  FEDER funds. L. Owen ,  S. Slater ,  A. Nedebragt  and  D. Muir  are thanked for laboratory assistance.
METHODS We built an SIR-type compartmental model for the COVID-19 data provided by the Johns Hopkins University (https:// github. com/ CSSEGISandData/ COVID-19/ tree/ master/ csse_ covid_ 19_ data/ csse_ covid_ 19_ time_ series). 3 he model is a parametric model whose parameters change from country to country to reflect differences in dynamics. A classic SIR model considers three types of individuals: susceptible individuals (S), infected individuals (I) and recovered individuals (R). 4  Many extensions to this model have been proposed, in particular by adding a.
Once the model is fully defined, its parameters can be estimated for each country through the maximum likelihood estimation method and using precisely that country's data. It should be noted that the uncertainty related to the estimation of the parameters has not been evaluated. This could be done, for instance, by means of parametric bootstrap. The model, the parameter estimation algorithm, the method for model selection as well as several plotting routines have been implemented in the R package covidix. This package is available on Github (https:// github. com/ MarcLavielle/ covidix). An example of how to use covidix can be found here: (http:// webpopix. org/ covidixExample. html). Of interest is the fact that an interactive, easy-to-use web application (Shiny V. 1. 4. 0. 2, 2020) also allows to visualise the data and the fitted model for several countries (http:// shiny.
Topography: data and analytical tools Our study relies on topographic data obtained from the ALOS World 3D -30 m (AW3D30) digital elevation model (DEM) provided by the Japanese Aerospace Exploration Agency. It has been shown that this DEM has the highest precision when compared to other DEMs with equivalent resolution so that it is well suited to the analysis of river profiles in high-relief regions  (Schwanghart and Scherler, 2017). For our analysis, we use the MATLABbased function library TopoToolbox, which provides essential tools for large-scale geomorphological approaches, including drainage network and watershed extraction, or computation of usual river or topography metrics  (Schwanghart and Scherler, 2014). For additional information, in particular on the various algorithms of the library used hereafter, the reader is referred to the TopoToolbox documentation (https://topotoolbox.
Finally, because the drainage network is not stable with ubiquitous divide migrations in the hinterland, the previous use of denudation rates as a proxy for tectonic uplift rates in Bhutan (Le  Roux-Mallouf et al. , 2015; Adams et al. , 2016)  may be problematic, and results should be re-evaluated. Our work therefore emphasizes the need for a precise investigation of landscape dynamics and disequilibrium over various spatial scales as a first essential step in morpho-tectonic studies of active landscapes. Data availability. This study relies on topographic data obtained from the ALOS World 3D -30 m (AW3D30) digital elevation model (DEM) provided by the Japanese Aerospace Exploration Agency, accessible at: https://www. eorc. jaxa. jp/ALOS/en/aw3d30/ index. htm (last access: July 2021) (ALOS, 2021).
The MATLABbased function library TopoToolbox used in this study for morphometric analyses is accessible at: https://topotoolbox. wordpress. com (last access: July 2021) (TopoToolbox, 2021). Satellite images were taken from the Google Earth database. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esurf-9-895-2021-supplement. Author contributions. MS designed and conceptualized the research objectives of this study. She also wrote the main draft of the manuscript with contributions from all co-authors. TSS carried the presented geomorphic analyses and prepared all associated figures. RC gathered all the necessary financial support that led to the work presented here. All co-authors participated in fieldwork, scientific discussions and the co-editing of the manuscript.
S4). Satellite observations are taken from the Google Earth database. (a) © Google Earth view (image from CNES -Airbus 2020) of the relief and elevation contrasts across the western drainage divide of the Bumthang surface. White arrows locate observed beheaded channels (wind gaps) and indicate the direction of deduced drainage divide migration. (b) © Google Earth view (image from CNES -Airbus 2020 and Landsat -Copernicus) of the relief and elevation contrasts across the eastern drainage divide of the Bumthang surface. White arrows locate observed beheaded channels and indicate the direction of deduced drainage divide migration. (c) Field picture of the Sengor area (southeastern part of the Bumthang surface), where a filled valley is surrounded by higher relief rims. The filled valley is connected to the main river network by a tributary of the Kuri Chhu.
Data and definitions In this paper we use data from the Survey of Health, Ageing and Retirement in Europe (SHARE). 10 SHARE is a multidisciplinary and cross-national panel database collecting micro data on health, socio-economic status and social and family networks. Collected every two years since 2004, the objective of the survey is to better understand the ageing process and, in particular, to examine the different ways in which people aged 50 and older live in Europe. The main advantage of using SHARE is that it contains a broad range of variables referring to physical and mental health, which makes it a unique dataset for studying health-related questions, including socio-economic inequalities in health (O'Donnell, 2009). The analysis is based on data from Wave 5 (collected in 2013) and focused on individuals living in Luxembourg.
14 n what follows, the distinction between different sensibilities of the social planner will be made by imposing different values to α: α = 10, 30, 50, 70 are related to a moderate risk sensibility, whereas α = 200 being the extreme risk sensibility. A code is accessible in the GitHub platform, which allows to select different values for the parameter ν, different levels of risk sensibility of the social planner, and bootstrap confidence intervals. 15.
Conclusion This paper contributes to the existing literature on behavioral OR approaches and proposes a two-parameter family of socio-economic health inequality indices, which depends on different health dimensions and on one risk factor. These indices allow to capture the risk sensibility and the inequality aversion of the social planner in order to provide a non-ambiguous ranking of multidimensional health distributions. In other words, the social planner behind the veil of ignorance is endowed with a bi-dimensional view, which is necessary to apprehend the impact of different risk factors (beyond the individuals' control) on the level of inequality in a society. Based on SHARE data for Luxembourg, we investigate four different variables representing childhood circumstances to illustrate how the two-parameters family of health indicators links one risk factor and a set of health dimensions: parental education, longevity of parents, nationality of parents, and financial situation during childhood.
This research is part of the  HEADYNAP  project supported by the  National Research Fund, Luxembourg  (contract  FNR C12/SC/3977324/HEADYNAP ) and by core funding for  LISER  from the  Ministry of Higher Education and Research of Luxembourg. This paper uses data from SHARE Wave 5 (DOI:  10. 6103/SHARE. w5. 100 ). The SHARE data collection has been funded by the  European Commission  through  FP5  ( QLK6-CT-2001-00360 ),  FP6  ( SHARE-I3 :  RII-CT-2006-062193 , COMPARE:  CIT5-CT-2005-028857 , SHARELIFE:  CIT4-CT-2006-028812 ),  FP7  ( SHARE-PREP: GA №211909 , SHARE-LEAP:  GA №227822 ,  SHARE M4: GA №261982 ) and  Horizon 2020  ( SHARE-DEV3 :  GA №676536 , SERISS:  GA №654221 ) and by  DG Employment, Social Affairs & Inclusion. Additional funding from the  German Ministry of Education and Research , the  Max Planck Society for the Advancement of Science , the  U.
To the authors' knowledge, this is the first analysis of the diurnal evolution of ammonia from both total column and surface measurements, in close relation with particle-phase measurements. Section 2 provides information concerning the instruments from the OASIS and SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) sites, as well as other datasets (see Sect. 2. 1, 2. 4 and 2. 5) used for this study. We also provide a brief description of the new retrieval of ammonia from OASIS  (Tournadre et al. , 2020). In the third section, we present and discuss the analysis of these datasets. First, we describe the regional conditions of the Paris pollution event in late March 2012 using meteorological analysis, a chemistry-transport model and satellite data (Sect. 3. 1). Then, we analyze the diurnal evolution of surface and total columns of ammonia and particulate matter as well as meteorological variables over the Paris region (Sect.
Description of ground-based sites and platforms An original aspect of this work is the analysis of the diurnal evolution of total column observations of ammonia derived from OASIS. This remote sensing observatory is located in Créteil (OASIS; 48. 79 • N, 2. 44 • E; 56 m a. s. l. , above sea level), in the southeastern suburbs of Paris, on the rooftop of the Université Paris-Est Créteil (UPEC;  Chelin et al. , 2014). It is an urban site mainly affected by background levels of pollution (Fig. 1 ). Measurements from other sites over the Paris region are also used in the current study (Fig. 1 ). Meteorological and detailed atmospheric composition data at the surface level are measured at the "Site Instrumental de Recherche par Télédétection Atmosphérique" supersite near Palaiseau (SIRTA; 48. 72 • N, 2.
, 2008)  and jointly adjusted with that of NH 3. We also use climatological concentrations for minor interfering gases (i. e. , nitric acid, HNO 3 ; sulfur hexafluoride, SF 6 ; ethane, C 2 H 4 ; and chlorofluorocarbons -e. g. , CFC-12) that may essentially impact the baseline of the spectra. The spectral signatures of absorption of infrared radiation by ammonia are clearly seen in individual spectra measured by OASIS, such as those recorded during a pollution event in March 2012 (as compared to the atlas from  Meier et al. , 2004 ; see Fig. 2 ). Atmospheric columns of ammonia derived from the 9-year database of OASIS range from 0. 0005 × 10 16 to 9×10 16 molec. cm -2 (molecules per square centimeter), and their retrieval error is estimated to be 20 %-35 %  (Tournadre et al. , 2020) , dominated by the systematic errors that are the combination of uncertainties in the spectroscopic parameters of ammonia and the interfering species (the dominating term), radiometric noise, instrumental parameters, and forward model uncertainties.
Regional conditions from satellite data and models For characterizing the pollution event during March 2012, we use a suite of satellite and model datasets concerning both the pollutant distributions at regional and continental scales and meteorological conditions. Aerosol optical depth (AOD) data derived from satellite and groundbased measurements are used for analyzing the spatial and temporal evolution of total particle abundance integrated over the atmospheric column. The horizontal distribution of AOD over western Europe is described using MODIS (Moderate Resolution Imaging Spectroradiometer;  Remer et al. , 2005)  data aboard the Terra (MOD04L2) satellite with overpasses at 10:30 LT (from the NASA Worldview website https://worldview. earthdata. nasa. gov/, last access; 27 February 2019;  Levy and Hsu, 2015).
nasa. gov/ (last access: 27 February 2019) (EOSDIS, 2019) and AErosol RObotic NETwork https://aeronet. gsfc. nasa. gov/cgi-bin/webtool_aod_v3 (last access: 9 June 2019)  (NASA, 2019). Meteorological data are available from Pierre-Simon Laplace Mésocentre https://mesocentre. ipsl. fr (last access: 6 March 2019) (ESPRI, 2019) and https://www. ecmwf. int/ en/forecasts/datasets/reanalysis-datasets/era-interim (last access: 6 March 2019)  (ECMWF, 2019). ERA-Interim reanalysis data are accessible via the ClimServ platform, https://climserv. ipsl. polytechnique. fr/ (last access: 24 April 2020)  (ClimServ, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-12091-2021-supplement. Author contributions. RDK is the main author of the paper; they wrote the text, made most of the figures and analyzed the data.
JC, PC, JEP and MB contributed to the article writing, discussions and analysis of the figures. JC made one of the figures of the paper. JEP provided data and carried out the ISORROPIA II calculations. PC, MR and XL operated the OASIS observatory. BT and PC made an initial analysis of OASIS data. JCD provided observational data, and AR provided the ESMERALDA and CHIMERE outputs. FH and JO provided support for the analysis and the PROF-FIT code for processing the OASIS dataset and deriving NH 3 data. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. The authors from LISA acknowledge support from  CNES  ( Centre National des Etudes Spatiales ) and the  INSU/CNRS (Institut National des Sciences de l'Univers/Centre National de Recherche Scientifique)  in the framework of the projects  IASI-TOSCA (Terre Ocean Surface Continental Atmosphère ) and LEFE-CHAT as well as the  OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie)  and the  Université Paris-Est Créteil  for the routine operation of the OASIS observatory. The research was also funded by  DIM Qi2 (Paris region). A particular acknowledgement shall be given to the collaborator from  KIT, Karlsruhe, Germany , for their continuous support and involvement. Work at IMK has been funded by the  ATMO program  of the  Helmholtz Association of Germany Research Centres.
Finally, the authors want to acknowledge and thank  AERONET  for the provision of the sun photometer data. Financial support. This research has been supported by the  Centre National des Etudes Spatiales (IASI-TOSCA  ( Terre Ocean Surface Continental Atmosphère )), the  Institut national des sciences de l'Univers (LEFE-CHAT) , the  Helmholtz Association of Germany Research Centres (ATMO Program) , and the  Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie , the  Université Paris-Est Créteil  and the  DIM Qi2 (Paris region)  (recurrent funding). Review statement. This paper was edited by  Rolf Müller  and reviewed by two anonymous referees.
Code availability. Details and access to PROFFIT code is provided by Frank Hase (frank. hase@kit. edu) at the KIT, Karlsruhe, Germany. Further information is given within  Hase et al. (2004). Data availability. In the following, URLs are provided for data platforms, which were used within this study. Surface data from the SIRTA site are available via SIRTA/IPSL, http://sirta. ipsl. fr, last access 29 January 2019 (SIRTA, 2019). Maps from AirParif can be demanded via Interface de programmation applicative | Airparif. OASIS data can be obtained by contacting Pascale Chelin (pascale. chelin@lisa. ipsl. fr) at the LISA, Créteil CEDEX, France. https://doi. org/10. 5194/acp-21-12091-2021 Atmos. Chem. Phys. , 21, 12091-12111, 2021.
, 2018). Verification of the chimney-like behaviour suggested by model results requires detailed analysis of high-resolution airborne measurements. The StratoClim (Stratospheric and upper tropospheric processes for better climate predictions; http://www. stratoclim. org, last access: 21 January 2021) campaign using the stratospheric research aircraft M55 Geophysica is the first airborne campaign to provide data suitable for such verification. Based out of Kathmandu, Nepal, Stra-toClim conducted eight research flights in the central region of the ASM anticyclone in summer 2017. In this work, we use airborne in situ measurements of carbon monoxide (CO), ozone (O 3 ) and nitrous oxide (N 2 O) collected both outside and inside the ASM anticyclone, and here particularly in the vicinity of the chimney regions.
Field campaigns Two airborne field campaigns utilizing the high-altitude aircraft M55 Geophysica were carried out under the umbrella of the EU project StratoClim. Three flights were conducted from Kalamata, Greece, between 30 August and 6 September 2016, and eight flights were conducted from Kathmandu, Nepal, between 27 July and 10 August 2017. Our study mainly adopts a statistical approach to analyse the entire observational data set comprising measurements from all flights. Figure  1  shows all flight tracks and their positions relative to the typical ASM anticyclone area for the two respective years determined using the criterion defined below in Sect. 2. 3. 2. The Kalamata base in 2016 was located outside the ASM anticyclone, and air inside or exported from the ASM anticyclone was only probed sporadically.
The Kathmandu base in 2017 was located inside the ASM anticyclone region, and according to the criterion used (Sect. 2. 3. 2. ), the bulk of the observations were made within the ASM anticyclone. A comprehensive campaign overview and descriptions of the purpose and specific meteorological situation for each flight are given by  Stroh et al. (2021). Full instrumental details will be given in a forthcoming paper  (Kloss et al. , 2021). CO mixing ratios were retrieved from observed infrared (IR) spectra using a transition at 2050. 90 cm -1 with line parameters taken from the HITRAN 2012 database  (Rothman et al. , 2013)  and no further calibration parameters. The accuracy was cross-checked for a range of mixing ratios (30-5000 ppb) prepared from a 5 ± 0. 05 ppm CO standard (Air-Products) by dilution with nitrogen or argon (argon was used at the lowest mixing ratios, because the nitrogen gas bottle contained a CO impurity at a concentration of ∼ 30 ppb).
Nitrous oxide (N 2 O) N 2 O was measured at 90 s time resolution with an average precision of 0. 5 % and average accuracy of about 0. 6 % by the High Altitude Gas AnalyzeR (HAGAR). The instrument, operated by the University of Wuppertal, comprises a two-channel gas chromatograph with an electron capture detector (ECD) measuring a suite of long-lived tracers (N 2 O,  CFC-11, CFC-12, Halon-1211, CH 4  , SF 6 , H 2 ) and a nondispersive IR absorption sensor for fast CO 2 measurements  (Homan et al. , 2010). The instrument is calibrated every 7. 5 min during flight with either of two standard gases, which are inter-calibrated in the laboratory with standards provided by NOAA GML (National Oceanic and Atmospheric Administration Global Monitoring Laboratory). N 2 O data are available for all flights of the campaign (note that the first flight on 30 August 2016 in Kalamata suffers from a comparatively poor N 2 O precision of ∼ 2 %).
Geolocation and meteorological data Temperature was measured at 10 Hz resolution with an accuracy of 0. 5 K and a precision of 0. 1 K by the commercial Rosemount probe instrument Thermodynamic Complex (TDC). Pressure and geolocation data were obtained at 1 Hz resolution from Geophysica's avionic system. Potential temperature along the flight track was calculated from these data. Physical properties on larger scales were derived from ECMWF ERA-Interim data  (Dee et al. , 2011) , including wind speed and direction, potential vorticity (PV), vertical velocities and radiative heating, and local temperature profiles to determine the heights of the lapse rate tropopause (LRT) and cold point tropopause (CPT). The vertical resolution of ERA-Interim data in the tropopause region is about 1 km, with relevant levels at  132.