dataesr/scientific-paragraphs-categorization huggingface dataset:
Thereafter, the diurnal cycle was calculated by taking the mean of each hour, every day. In cases where data from the same month existed at both sites, a mean diurnal cycle was calculated from the two obtained diurnal cycles. Sufficient quantities of pCO 2 were only available for eight months of the year. While no apparent diurnal variability was evident in February (Fig. 6 ), distinct diurnal variability was observed in the remaining months (April, May, June, July, August, September and October). The amplitudes of the calculated diurnal cycles were much smaller than the short-term variability seen in the raw data from the two sites. Thus, the calculated monthly diurnal cycles did not capture the potentially much larger variability that may occur. Therefore, one month with a dense data coverage of measured surface pCO 2 was also examined within the modelling framework.
7km) and Denmark (5. 6km × 5. 6km). The time step of the DEHM model meets the criteria for stability, and thus depends on both grid size and wind velocity, typically being in the range of 3-20 min. The meteorological forcing used in DEHM was simulated by the Weather Research and Forecast Model (WRF)  (Skamarock et al. , 2005) , which makes use of the same nests as the DEHM model, and uses the ERA-Interim meteorological data as the outer boundaries  (Dee et al. , 2011). Surface fluxes of CO 2 from NOAAs ESRL Carbon Tracker system, version CT2013b  (Peters et al. , 2007) , were applied in the DEHM model for anthropogenic emissions, wild fire emissions and biospheric fluxes. Here, the optimised biosphere fluxes were used. Their resolution is 1 • × 1 • , with updated values every three hours. However, for Europe, hourly anthropogenic emissions on a 10 km × 10 km grid, from the Institute of Energy Economics and the rational Use of Energy (IER) (Pregger et al.
Experimental design. In order to investigate the influence of the temporal resolution of pCO 2 on the air-sea CO 2 exchange, various model simulations were conducted, where only the temporal resolutions of pCO 2 in water and atmosphere were changed (Table  1 ). The temporal resolution of pCO 2 in simulations with the BSC was either monthly or hourly, where the hourly resolution of the pCO 2 fields was obtained by imposing the average monthly diurnal cycles onto the corresponding monthly surface field. The diurnal cycles followed the local hour in the DEHM model grids. Due to a lack of data, it was not possible to estimate average diurnal cycles representing the conditions during January, March, November and December, so values from months representing corresponding seasons were applied. Thus January, November and December used the diurnal variability from February, while data from April was used for March.
Acknowledgements Our utmost gratitude to  Joachim Kuss , of the  Leibniz Institute for Baltic Sea Research , for sharing the surface water pCO 2 continuous measurements from the  Arkona Sea platform. Christian Rödenbeck  is appreciatively acknowledged for providing daily global maps of surface water pCO 2 , for which we also extend our acknowledgement to the SOCAT data base. NOAA ERSL CarbonTracker, version  CT2013B , has greatly contributed to this work, as have GLOBALVIEW-CO2, 2013, and EDGAR,  IER  and  Aarhus University  for the emissions inventories for fossil fuel emissions. Peter Staehr  and  Cordula Göke  at  Department of Bioscience, Aarhus University , are gratefully thanked for their assistance in creating the near-coastal Danish climatology of surface water pCO 2. We are thankful for the very constructive comments from two anonymous reviewers that helped to improve and clarify the manuscript.
, 2010)  and in a maximum observed median draft of the drifting ice over the 2003 to 2012 period  (Ryan and Münchow, 2017). The following question arises: would a tendency towards shorter lifespan of ice bridges and associated increased ice export through straits be the conse- quence of a thinning and/or a mechanical weakening of the ice cover? In a recent study,  Gimbert et al. (2012b)  analyzed in the frequency domain the drift of Arctic sea ice over the period 1979-2008 as estimated from the International Arctic Buoy Data Programme dataset and identified an increase of the inertial motion of the ice cover. With the use of a simple dynamically coupled ice-ocean boundary-layer model,  Gimbert et al. (2012a)  were able to relate this evolution to a genuine mechanical weakening of the ice pack in the Arctic and peripheral zone over the period 2002-2008 relative to the previous 22 years, that is, a weakening that is independent of a concomitant sea ice thinning and could be due to an evolution towards a more fractured ice cover and smaller ice floes.
Data availability. The Maxwell-EB model is entirely developed within the C++ finite element environment RHEOLEF, which is available freely at https://www-ljk. imag. fr/membres/Pierre. Saramito/rheolef/(Saramito, 2013a). The system of equations and the exact numerical scheme employed are presented in Appendix A. This study did not use any other data. www. the-cryosphere. net/11/2033/2017/TheCryosphere, 11, 2033Cryosphere, 11,  -2058Cryosphere, 11,  , 2017.
Empirical formulations of the scattering and loading effects are available in the literature and permit the correction of Aethalometer data for these artefacts (W2003;  Arnott et al. , 2005; Schmid et al. , 2006; Virkkula et al. , 2007; C2010). The correction of the multiple scattering effect instead requires the knowledge of a correction factor C ref , which needs to be directly estimated by comparison of Aethalometer data against reference absorption measurements (W2003; C2010). Currently data for C ref are available for soot particles (C ref = 2. 1-2. 2 at 660 nm, W2003), internally and externally mixed soot particles and organic material (C ref = 2. 3-3. 9, W2003), and ambient aerosols collected in Europe and Amazonia (C ref = 2. 6-4. 8, C2010; C ref  =4. 9-6. 3, Saturno et al. , 2016)  and in the Arctic (C ref = 3.
Their length, varying between 0. 3 and 0. 7 m, was adjusted based on the flow rate of each instrument to ensure an equivalent particle loss, so that the same aerosol size distribution was input to the different instruments. Particular care was given to ensure the same aerosol size at the input of the Aethalometer and the MAAP. To this end, as illustrated in Fig. 1 , the two instruments sampled air from the same manifold exit line, and the same sampling flow rate was also set for the two instruments (8 L min -1 ). Particle loss calculations were performed with Particle Loss Calculator (PLC) software  (von der Weiden et al. , 2009). Aerosols were generated in three ways: -Mineral dust was generated by mechanical shaking as described and validated in Di  Biagio et al. (2014 Biagio et al. ( , 2017)).
Number size distribution and effective fine and coarse diameter The number size distribution was measured by a combination of SMPS and OPC observations. For the SMPS, corrections for particle loss by diffusion in the instrument tubing and the contribution of multiple-charged particles were performed using the SMPS software. The electrical mobility diameter measured by the SMPS can be converted to a geometrical diameter (D g ) by taking into account the particle dynamic shape factor (χ ; D g = D m /χ ). In this study, the SMPS showed good agreement with OPC data for a shape factor χ = 1, which corresponds to spherical particles. The OPC optical-equivalent nominal diameters were converted into sphere-equivalent geometrical diameters (D g ) by taking into account the aerosol complex refractive index.
After conversion, the OPC diameter range became 0. 28-18. 0 µm for dust (taking into account the particle cut at ∼ 20 µm due to the use of the impactor), and 0. 27-58. 0 µm for kaolinite and 0. 28-65. 1 µm for ambient air (the impactor was not used in these cases). The uncertainty was < 15 % at all diameters. The aerosol effective fine (D eff,fine ) and coarse (D eff,coarse ) diameter were estimated from OPC data as D eff = D 2 D 1 D 3 g dN d log D g d log D g D 2 D 1 D 2 g dN d log D g d log D g , (12) with D 1 = 0. 3 µm and D 2 = 1 µm for the fine mode and D 1 = 1 µm and D 2 = 10 µm for the coarse mode.
Results The time series of observations for all the experiments are shown in Fig. 2  as 2 min averages. Seven experiments were performed on mineral dust issued from six different areas in the Sahel (Niger), eastern Asia (China), North America (Arizona), northern Africa (Tunisia), Australia, and southern Africa (Namibia), and on a kaolinite powder. Experiments were performed between the 3 and the 9 November 2016 and lasted between 1 and 2 h each. The experiments on Niger dust (labelled as Niger 1 and Niger 2) were duplicated to test the repeatability of the obtained C ref. Ambient air data were collected between the 8 and the 14 November 2016 for a total of 7 h of measurements. Eight different periods characterized by little variation and different levels of SSA were selected in the whole set of ambient air measurements.
This trend is statistically significant (correlation coefficient of R 2 = 0. 85). The relationship between C ref and SSA is also investigated in Fig. 6  (bottom panel) for all aerosol samples. Globally, Fig. 6  suggests a decrease of C ref for increasing SSA, in particular at 450 nm, albeit with a poorer statistical significance at both Mineral dust 2. 09 ± 0. 22 1. 92 ± 0. 17 Kaolinite 2. 49 ± 0. 02 2. 31 ± 0. 02 Ambient air 3. 31 ± 0. 75 2. 32 ± 0. 35 wavelengths (R 2 = 0. 35 and 0. 59). Data are also compared to those reported in W2003 and C2010 at 660 nm for different aerosol types. Diesel soot and soot mixed with ammonium sulfate were investigated in W2003, while C2010 reported data for ambient aerosols sampled at different locations in Europe and in Amazonia. W2003 also reported the C ref for soot particles at 450 nm (not shown in Fig.
Acknowledgements. The  RED-DUST  project was supported by the  French national programme LEFE/INSU , by the  Institut Pierre Simon Laplace (IPSL) , and by  OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie)  through dedicated research funding. Claudia Di Biagio  was supported by the  CNRS  via the Labex L-IPSL, which is funded by the  ANR  (grant no. ANR-10-LABX-0018 ). This work has also received funding from the  European Union's Horizon 2020  ( H2020 ) research and innovation programme through the  EUROCHAMP-2020 Infrastructure Activity  under grant agreement No  730997. The authors thank  Konrad Kandler ,  Dave Seibert , and the  LISA  staff who collected the soil samples used in this study,  Emilie Journet  who provided the kaolinite sample,  Andreas Petzold  for helpful discussions on the Aethalometer multiple scattering effects, and  Benjamin Tamime-Roussel  for logistic help with.
Acknowledgements. The authors wish to thank  Stephen Cornford  and  Bill Budd  for useful discussions about the simulations. The authors wish to acknowledge  CSC -IT Centre for Science, Finland , for computational resources. This research utilised the  NCI National Facility in Canberra, Australia , which is supported by the  Australian Commonwealth Government. Rupert Gladstone  was funded from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  299035. This research was supported in part by  Academy of Finland  grant number  286587. Ralf Greve  was supported by a  UTAS  ( University of Tasmania ) Visiting Fellowship (September-December 2014), and by a  JSPS (Japan Society for the Promotion of Science)   Grant-in-Aid for Scientific Research  A (no.
Discussion Our marine geophysical data provide new insights into the late Quaternary landscape evolution of the Dover Strait with implications for understanding its history of breaching. In particular, the new data show that at least two significant erosional episodes shaped the opening of the Strait. While it is widely held that initial opening of the Strait was a consequence of spillover of a proglacial lake that existed in the southern North Sea basin during MIS12 (refs 2-7), direct evidence for this spillover process has up to now been lacking. The presence of large bedrock-eroded with landforms associated with erosion by high-magnitude floods in the central and eastern English Channel has been proposed as evidence of catastrophic lake spillover  5, 7. Our detailed mapping of the geometry and distribution of the Fosses Dangeard depressions show that the Fosses represent plunge pool depressions formed at the base of a chalk escarpment.
Methods Marine geophysical data. Our study uses marine geophysical data sets collected in the Dover Straits between 2002 and 2012. The locations of the individual survey areas are shown in Supplementary Fig. 2. Seismic reflection data. The sub-bottom data were collected during four separate field campaigns onboard RV Sepia II and RV Belgica. Both vessels were equipped with dGPS, giving horizontal location accuracy better than 10 m. Single-channel seismic reflection data. During the 2002 Seismic survey (Lille-RCMG) cruise (Dangeard 1 cruise), B200 km of seismic lines were acquired using a 300J Centipede sparker system triggered every 1 s, and a single-channel surface streamer consisting of 10 hydrophones. Both seismic and positioning data were logged with Delph iXBlue software. In total 20 parallel lines were collected with a B10 km length and B200 m spacing, together with one cross-profile.
During the RV Belgica cruises 2010/09 and 2012/03 (2010-2012 ROB-RCMG seismic surveys) B315 km of single-channel high-resolution seismic-reflection profiles were collected with a 500J Centipede sparker system triggered every 0. 75 s, and a single-channel surface streamer of 10 hydrophones. The data were recorded through a Delph IXSEA seismic acquisition software system. Sampling frequency was set at 10 kHz and record length at 400 ms TWT. The two surveys consisted of 48 seismic-reflection profiles spaced 1. 5 km apart acquired to the southeast and northwest of the 2002 seismic survey. The single channel seismic data were processed together using the open-source package Seismic Unix (SU)  50  in conjunction with a python interface to SU (supy) developed at the Royal Observatory of Belgium. Processing consisted of: (1) conversion from SEG-Y to SU format, (2) swell filter, (3) navigation correction, (4) predictive error filtering, (5) band-pass filtering and time-varying gain, (6) muting, (7) tide correction and (8) conversion from SU to SEG-Y format.
The vertical resolution of the single-channel data is 1-3 m. Multi-channel seismic-reflection data. The multichannel seismic survey (2012 Seismic survey) was acquired during RV Belgica cruise 2012/25 and was carried out by means of a 24-channel streamer composed of 10 hydrophones per channel (channel spacing: 10. 5 m), which was provided by the Royal Netherlands Institute for Sea Research (NIOZ). A GI-gun was used as source, fired at B100 bars of pressure every 7 s. Lines used either a 1 ms sample rate per 5 s record length or 0. 5 ms sample rate per 2 s record length. In total, 12 profiles were collected sub-parallel to the English and French coasts with lengths ranging between 13 and 20 km and spaced between 2 and 5 km to one another. Four control cross-profiles were also acquired. The processing of the multichannel seismic-reflection data set was performed at the Renard Centre of Marine Geology (RCMG) of Ghent University using the processing software RadExPro (www.
radexpro. com) by applying the following processing flow: (1) trace editing, (2) swell filtering, (3) spherical divergence corrections, (4) trace balancing, (5) amplitude decay, (6) band pass filter (30-325 Hz), (  7 ) mild F-K filter on all offsets, (8) harsh F-K filter on far offset traces, (9) 2:1 channel interpolation, (10) pre-stack multiple removal in the channel domain, (11) velocity analysis every 50 CMP, (12) NMO correction, (13) stack, (  14 ) post-stack multiple removal, (15) post-stack random noise suppression and (  16 ) post-stack time varying amplitude scaling. Seismic data analysis. The processed seismic data were imported into the interpretation software Kingdom Suite (TKS) and Opendtect (http://www. opendtect. org). The Fosse Dangeard basal erosional surface and internal erosional surfaces (when possible) were correlated from one profile to another by using the three-dimensional software Opendtect.
All grids used a UTM Zone 31 Coordinate System. Multi-beam echo sounding. The 2006/2007 MCA survey data set was collected under contract to the Maritime and Coastguard Agency (MCA) as part of their 'Civil Hydrographic Program' to IHO order 1 standards. The data were collected in 2006 and 2007 by a total of four vessels using a variety of sonar systems. The majority of the survey was collected from MV Victor Hensen with a Kongsberg EM710. This equipment operates with frequencies ranging from 60 to 100 kHz. Later infill lines were collected from MV Meridien (Reson SeaBat 8101ER and the SeaBat 7125 with operating frequencies of 240 and 400 kHz, respectively); MV Geniusbank (Kongsberg EM3002D with operating frequency of 300 kHz) and MV Jetstream (also equipped with a Kongsberg EM3002D). The merged data set was provided to the Imperial College of London pre-processed and tide-corrected in a GSF file format.
The data were gridded in IVS 3D DMagic with a 1. 5 m cell size. The 2010 ROB-RCMG survey data set was collected in April of 2010 on board of the RV Belgica with a hull-mounted Kongsberg EM3002 multi-beam echo-sounder operating at 300 kHz. Sound absorption coefficient was calculated in the survey area from the temperature, salinity and pH of the water given by an ODAS-II system. Based on the estimated sound absorption coefficient and a sound-velocity profile that was taken with the SV-plus probe on 15. 04. 2010 at 51. 025°N, 1. 51°E, an average sound velocity of 1,483 m s À 1 was selected for the water layer. These data were subjected to statistical spike removal, manual cleaning of artefacts and tide correction. The tide correction was performed by applying information provided by the Hydrographic and Oceanographic Centre of the French Marine (SHOM) on the water elevation at the different times and locations surveyed.
No special filtering was necessary due to the good quality of the raw data. The corrected bathymetry set was gridded at 5 m cell size at the Renard Centre of Marine Geology using the Sonar Scope software. Single-beam echo sounding. Historical single-beam echo soundings of the UK sector of the English Channel collected by the Maritime and Coastguard Agency (MCA) between 1988 and 2004 and archived at the UK Hydrographic Office (UKHO) were provided to Imperial College London. The soundings were gridded using the software QPS Fledermaus DMagic by G. Potter in November 2009 with a 30 m cell size. Historical single-beam echo soundings of the French sector of the English Channel (La Manche) collected by the Service Hydrographique et Oceanographique de la Marine (SHOM) between 1973 and 2000 were provided to the University of Lille 1 under contract 205/2011.
The data were gridded at the University Lille 1, France, using QPS Fledermaus DMagic in July 2012. The density of the soundings in the data set was highly variable so two grids were produced, one with a 80 m cell size and a second with a 40 m cell size. Bathymetry merge. The final bathymetric map was produced using QPS Fledermaus DMagic and Global Mapper 14. Individual Arc Grid files (. asc) from the previously gridded. sd files were used for the 'scene file merge'. The Arc Grid files were loaded into Global Mapper in a single workspace and overlapped in order to display the data set at the highest resolution in each area. The workspace comprising all the different grids was then saved into a single Arc Grid file respecting the different cell size of each of the grids composing the map. Data availability.
The data that support the findings of this study are available from the corresponding author upon reasonable request. Details on Dangeard-1cruise data that support the findings of this study are available from Sismer through the following link http://dx. doi. org/10. 17600/2440010. SHOM bathymetric data at 20 m resolution is available at http://diffusion. shom. fr/produits/bathymetrie/mntcotier-pas-de-calais. html UKHO data is available as a B400 m DEM at http://portal. emodnet-bathymetry. eu/?menu=0310000_000000.
Acknowledgements The UK bathymetry surveys were gathered under the  UK Civil Hydrography Programme  managed by the  Maritime and Coastguard Agency (MCA)  and the  United Kingdom Hydrography Office (UKHO). We thank  A. Talbot  ( UKHO ) for assistance. Shiptime on RV Belgica  for bathymetric and seismic surveys was provided by the  Belgian Science Policy Office  and the  Management Unit of the North Sea Mathematical Models (MUMM). We thank the  Captains and Crews of CNRS/INSU RV Sepia II  and  RV Belgica  for their assistance in data acquisition. We acknowledge the support of  FWO-Flanders  and the  Special Research Fund of Ghent University. Service Hydrographique et Oceanographique de la Marine (SHOM)  provided single-beam data for French waters (Contract No  205/2011 ). We thank Eurotunnel for permission to use geophysical data from the 1986 and 1988 Channel Tunnel seismic studies.
Sequence analyses After high-throughput sequencing, we removed the chimeric and low-quality sequences using the FASTX-ToolKit (http: //hannonlab. cshl. edu/fastx_toolkit) and UCHIME algorithm  (Edge et al. , 2011 ) before statistical analysis. The remaining high-quality sequences were normalized to 7973 reads to compare the different samples effectively. They were then clustered into operational taxonomic units (OTUs) at a 97 % similarity cutoff using USEARCH software (Version 7. 1, http://drive5. com/uparse/). We used the OTUs as the basis for estimating the alpha diversity and beta diversity. Alpha diversity estimators, including Chao1, Simpson's index, and Shannon's index, were performed by the Quantitative Insights into Microbial Ecology software (Version 1. 8. 0, http://qiime. org/scripts/assign_taxonomy.
html; Kuczynski et al. , 2011). The taxonomy of ITS sequences was analyzed by RDP Classifier against the UNITE database (release 7. 0, http://unite. ut. ee/index. php; Koljalg et al. , 2013 ) using a confidence threshold of 70 %. RDP Classifier was used to determine the taxonomic composition at the phylum, class, order, family, genus, and species levels  (Koiv et al. , 2015; Miettinen et al. , 2015). The raw reads were deposited into the NCBI Sequence Read Archive database under accession number SRR5146156.
Statistical analyses To determine the differences in the fungal community variations among different size fractions, meta-analyses based on the permutation t-test were conducted using Mothur software (version 1. 35. 1). The program Metastats can produce a tab-delimited table to display the mean relative abundance of the mean, variance, and standard error, together with the p values and q values. Values were considered significant if p ≤ 0. 05 and q ≤ 0. 05. The Kruskal-Wallis rank sum test was used to evaluate the seasonal variation of the microbial community. Boxplots and q values have been provided for illustration. The relationship between the ambient microbial community and environmental factors, including PM concentrations and chemical compositions, was assessed with nonparametric Spearman's rank correlation coefficients by SPSS 16.
Acknowledgements. This work was supported by the  National Natural Science Foundation of China  (no. 41375126 ,  21527814 ),  Taishan Scholar Grant  (grant number  ts20120552 ),  Cyrus Tang Foundation  (no. CTF-FD2014001 ), the  Ministry of Science and Technology of China  (no. 2016YFC0202701 ,  2014BAC22B01 ), and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 690958  ( MARSU  project). Edited by: Tong Zhu Reviewed by: four anonymous referees.