dataesr/scientific-paragraphs-categorization huggingface dataset:
Space-borne observations The Spinning Enhanced Visible and Infra-Red Imager (SE-VIRI), onboard Meteosat Second Generation (MSG), measures aerosol optical depth (AOD) with spatial and temporal resolutions of 10 km and 15 min, respectively  (Bennouna et al. , 2009). We use the operational version 1. 04 of the AOD product at 550 nm, downloaded from the ICARE data service center (http://www. icare. univ-lille1. fr/, last access: 16 August 2018). The Moderate Resolution Imaging Spectroradiometer  (MODIS, Salmonson et al. , 1989; King et al. , 1992)  flies aboard polar-orbiting platforms Aqua and Terra. Terra crosses the Equator from north to south in the morning (∼ 10:30 local time), whereas Aqua crosses from south to north during the afternoon (∼ 13:30 local time). They provide a complete coverage of the Earth's surface in 1 to 2 days with a resolution between 250 and 1000 m, depending on the spectral band.
The chemistry and aerosol initial and boundary conditions for the 2 km CHIMERE simulation are provided by the 10 km simulation (one-way nesting). The representation of the atmospheric dynamics in the 2 km simulation was verified against dynamical and thermodynamical observations from both aircraft (Fig. S1  in the Supplement) and the DACCIWA radiosounding network from Accra and Cotonou (Fig. S2 ), yielding satisfactory results. For each aircraft and sounding data point, the corre-sponding WRF grid cell value is extracted. A bilinear interpolation is performed horizontally to exactly match the horizontal position of the balloon or aircraft. Linear interpolations are also performed vertically between two WRF levels as well as temporally between two consecutive model outputs to match the altitude of the balloon or aircraft at the time the pressure, temperature, humidity, and wind observations are made.
We also show that the overturning cells are robust features of the atmospheric circulation over the Gulf of Guinea in July 2016. To the best of the authors' knowledge such features have not been documented in the literature to date. Still, one should keep in mind that the mechanisms described in detail are based on a unique dataset. Even though we have highlighted the fact that some of the key dynamical features are persistent during July 2016, and hence not just representative of 2 July, caution should be exercised when drawing more general conclusions regarding the role of observed circulation in aerosol redistribution in this region. Further research will be dedicated to enhancing our understanding of the complex interactions between the monsoon flow and the orography north of major coastal cities as well as the land-sea and land-lake breezes, and their impact on the dispersion of pollution emissions from major coastal cities in SWA.
Future research will also be conducted to assess the long-term impact of the land-sea surface temperature gradient (and related shallow overturning circulation) on the distribution of aerosols over the northern Gulf of Guinea. Data availability. The aircraft and radiosonde data used here can be accessed using the DACCIWA database at http://baobab. sedoo. fr/DACCIWA/  (Brissebrat et al. , 2017). The tracer simulations discussed in this paper are also available in the database. An embargo period of 2 years after the upload applies. After that, external users can access the data in the same way as DACCIWA participants before that time. Before the end of the embargo period, external users can request the release of individual datasets. It is planned for DAC-CIWA data to get DOIs, but this has not been realized for all datasets yet.
JB, CD, PR, RD, TB and AS contributed to the acquisition and the processing of the aerosol data gathered on the ATR42 during the flight on 2 July 2016. AC, PD, JD and MR contributed to the acquisition and the processing of the gas-phase chemistry data gathered on the ATR42 during the flight. All co-authors contributed to the data analysis. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "Results of the project "Dynamics-aerosol-chemistry-cloud interactions in West Africa" (DACCIWA) (ACP/AMT inter-journal SI)". It is not associated with a conference.
Acknowledgements. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement no. 603502. The European Facility for Airborne Research (EUFAR, http://www. eufar. net/,last access:  16 August 2018 ) also supported the project through the funding of  Transnational Activity  project  OLACTA. The  Centre National d'Etudes Spatiales (CNES)  provided financial support for the operation of the ULICE lidar. The personnel of the  Service des Avions Français Instrumentés pour la Recherche en Environnement  (SAFIRE, a joint entity of  CNRS ,  Météo-France , and  CNES , and operator of the  ATR 42 ) are thanked for their support. Marco Gaetani  has been supported by the  LABEX  project funded by the  Agence Nationale de la Recherche  ( French National Research Agency , grant  ANR-10-LABX-18-01 ).
The principal or the most dominant variance found by SSA in the coral proxy records is the long-term secular trend (Fig. 2 ), which was removed (detrended) for subsequent analyses. To estimate the power spectral density and significance of each record, MTM spectral analysis  69  was completed on the detrended proxy records (Supplementary Fig. 11 ). Wavelet and cross-wavelet coherence analysis  70, 71  (Fig. 7  and Supplementary Fig. 12 ) was also completed to examine the relationships and coherences between the proxy records and assessed by Monte Carlo methods (ρ = 0. 05)  71  for probability distribution. Change point analysis was performed on the individual D. heliopora isotope records using the Bayesian change point algorithm  72  in MATLAB to determine the exact timing of regime boundaries or significant changes in each proxy's secular trend.
Acknowledgements Primary funding for this research was supported by a fellowship to H. C. W. with funding to D. D. and A. T. from the  Institute Pierre Simon Laplace (Labex L-IPSL ) under the Work Packages 4 and 5, IMPACTS: Project  16 -Impacts of climate change  (ocean acidification and warming) on corals, which is provided by The  French National Research Agency, ANR  ( Grant  no:  ANR-10-LABX-0018 ). Additional funding support was provided by The  ARTICLE NATURE COMMUNICATIONS | DOI: 10. 1038/s41467-018-04922-1 12 NATURE COMMUNICATIONS | (2018) 9:2543 | DOI: 10. 1038/s41467-018-04922-1  | www. nature. com/naturecommunications French National Research Agency ,  ANR  Project  CARBORIC  (Grant no:  ANR-13-BS06-0013-04 ) to E. D. , D. B. and by an internal research project support funded by the IPSL to C.
In particular the aerosol emissions remain the same. Hourly measured ozone and temperature data are taken from 23 CASTNET (Clean Air Status and Trends Network) stations with a nearly continuous data record during the period from 1992 to 2013 for the months of June, July and August (92 days each summer). In addition, to enhance the data record, we included two additional stations (Beaufort, NC, and Lassen Volcanic, CA) at which the first 2 or 3 years of data were missing, respectively. See Fig. 2  for station locations. CASTNET sites are situated to sample regional ozone concentrations so as to minimize the more local impact of urban areas. We supplement the CASTNET data with temperature and ozone measurements from the Environmental Protection Agency (EPA) Air Quality System (AQS) Data Mart for the years 1992-2010.
This gives an additional 124 stations with nearly complete ozone and temperature data (see Supplement). Ozone data from the first model level provide a good estimate of 10 m ozone concentrations as measured by  CASTNET (Brown-Steiner et al. , 2015). The maximum daily 2 m temperature is used in both the CASTNET measurements and the simulations. To render the data approximately stationary on both an interannual and seasonal basis, we adopt the procedures in  Phalitnonkiat et al. (2016). Formally, let x y,d represent the data on day d in year y, where x refers to either daily maximum temperature or MDA8 ozone. Since there are 91 days included in each summer period and 20 years (19 years for REFC1SD), d = 1 refers to 1 June and d = 91 refers to 30 August. To minimize year-to-year variability so as to minimize any ozone trends while still keeping extreme data relevant, for each year y, we take the average of the data over that year but omit a number (a) of the highest values.
Acknowledgements. We would like to thank the three anonymous referees whose careful reading considerably improved this paper. This research was made possible by  EPA  award  RD-83520501  and  NSF  award number  1608775. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the  U. S. EPA. The  CESM  project is supported by the  National Science Foundation  and the  Office of Science (BER)  of the  U. S. Department of Energy. The  National Center for Atmospheric Research  is funded by the  National Science Foundation. In addition, we acknowledge the joint  WCRP SPARC/IGAC Chemistry-Climate Model Initiative  ( CCMI ) for organizing and coordinating the model data analysis activity, and the  British Atmospheric Data Centre (BADC)  for collecting and archiving the CCMI model output.
Results show that the PUY station is located in BL with frequencies ranging from 50% during the winter, up to 97% during the summer. Then, the classification is applied to a year-long dataset (2015) of particle size distribution data to study the differences in particle physical characteristics (size distribution) and black carbon (BC) concentrations between the FT and the BL. Although BC, Aitken, and the accumulation mode particles concentrations were higher in the BL than in the FT in winter and autumn, they were measured to be higher in the FT compared to BL in spring. No significant difference between the BL and the FT concentrations was observed for the nucleation mode particles for all seasons, suggesting a continuous additional source of nucleation mode particles in the FT during winter and autumn.
Back-trajectories have been computed using LACYTRAJ, a three-dimensional kinematic trajectory code  [48]. We use 3D wind fields produced by the ERA-Interim reanalyses with a horizontal resolution of 1 • in latitude and longitude, and 37 vertical levels for the trajectory calculations. Each grid point is advected using a bilinear interpolation for horizontal wind fields and time and a log-linear interpolation on pressure level for the vertical wind field. A cluster of 100 trajectories starting from a box between 45. 6 and 46 • N and 2. 7 and 3. 1 • E near 1465 m a. s. l is calculated, with a time resolution of 15 min, during 204 h. The altitude of each trajectory is compared with the calculated height of the BL, interpolated in time and space from ECMWF ERA-Interim, along all trajectory points, in order to estimate the interaction of the air mass with the BL, that is, the time spent of the sampled air mass in the FT since last contact with the BL.
Segregating between Boundary Layer (BL)/Aerosol Layer (AL) and FT Air Masses Four different criteria were investigated in the present work to determine when the PUY station is sampling in the BL/AL or in the FT, including the analysis of LIDAR profiles, radon concentration, the ratio of NO x to CO concentrations and ECMWF outputs. In the present paper, as in previous studies (example: De Wekker et al. (2015)  [4] ), we assimilate the ALH to the convective BLH during the daytime, and to the RLH during nighttime. In order to harmonize the different datasets over the same time resolution, data were averaged over a time step of 3 h. The dataset was further harmonized by only selecting data when all methods are available simultaneously; resulting in 1429 values (Figure  1 ). Specifically, this implies that only clear sky conditions will be discussed hereafter since the presence of clouds prevents the estimation of the ALH from LIDAR.
Atmosphere 2018, 9, x FOR PEER REVIEW 6 of 25 the daytime, and to the RLH during nighttime. In order to harmonize the different datasets over the same time resolution, data were averaged over a time step of 3 h. The dataset was further harmonized by only selecting data when all methods are available simultaneously; resulting in 1429 values (Figure  1 ). Specifically, this implies that only clear sky conditions will be discussed hereafter since the presence of clouds prevents the estimation of the ALH from LIDAR. In the following sections, we will compare these four different criteria in order to segregate FT from "BL/AL" air masses. June, Summer: 20 June to 21 September, and Autumn: 22 September to 21 December) using data from 2015. The ALHLIDAR does not show strong variations with the time of the day.
Comparison between the Aerosol Layer Height from LIDAR Profiles and Boundary Layer Height Simulated with ECMWF Figure  2a  shows the 3-h average diurnal variation of the ALH retrieved from the LIDAR profiles (ALH LIDAR ) for the four seasons separately (Winter: 1 January to 19 March, Spring: 20 March to 19 June, Summer: 20 June to 21 September, and Autumn: 22 September to 21 December) using data from 2015. The ALH LIDAR does not show strong variations with the time of the day. The maximum of the ALH LIDAR is reached during summer and spring (average 2000 m a. s. l. , green and black) while in winter and autumn (red and blue), the average ALH LIDAR never exceeds 1600 m. Lowest ALH LIDAR are observed during night time, before 9 UTC, when the WCT most likely retrieves the height of the RL. These typical ALH diurnal and seasonal variations are similar to the ones previously reported for the PUY station (Figure  2  in  Chauvigné et al.
SH = H (1 -Fr) (3) H is the height difference between the PUY station and the foot of the mountain and Fr is the Froude number  [14, 49]. When ALH/BLH are higher than the altitude of PUY, the station is always considered to be in the BL/AL. This is especially the case for unstable conditions (Fr > 1) when the BLH always reaches the PUY station after the air mass encounters the obstacle. For stable conditions (Fr < 1), two different situations are identified depending on SH. If the BLH/ALH is lower than SH, the stream will drift away from the obstacle, and the station will be in free tropospheric conditions. In contrast, for a BLH/ALH higher than SH, the air mass will be uplifted along the mountain slope, and the station will, hence, lay in the BL/AL (Table  1 ). SH data was calculated every three hours for 2015.
When ALH/BLH are higher than the altitude of PUY, the station is always considered to be in the BL/AL. This is especially the case for unstable conditions (Fr > 1) when the BLH always reaches the PUY station after the air mass encounters the obstacle. For stable conditions (Fr < 1), two different situations are identified depending on SH. If the BLH/ALH is lower than SH, the stream will drift away from the obstacle, and the station will be in free tropospheric conditions. In contrast, for a BLH/ALH higher than SH, the air mass will be uplifted along the mountain slope, and the station will, hence, lay in the BL/AL (Table  1 ). SH data was calculated every three hours for 2015. Atmosphere 2018, 9, x FOR PEER REVIEW 8 of 25  NOy/CO is an indicator of the "photochemical age" of an air mass that can be used to identify fresh pollution transported to high altitude sites from lower atmospheric layers  [3, 10].
We found that both criteria are correlated at the 95% significance level (Pr = 0. 54) with the following equation determined over the one-year dataset: BLH ECMWF = 1204 + 1. 3 × 1 NO x /CO (4) This equation allowed us to determine a threshold value of NO x /CO = 0. 005 above which the PUY station is considered to be in the FT (that is, for BLH ECMWF > 1465 m). However, we observe that the diurnal amplitude of the NO x /CO ratio varies according to the season (Figure  2c ). As a consequence, using a yearly threshold, the site is considered to be in the AL during the day (between 6 and 21 UTC) and in the FT in the night (from 21 to 6 UTC) for all seasons.
Comparisons of the Free Troposphere and Boundary Layer Aerosol Properties In this section, we use the classification proposed by the combination of the four criteria of FT/(BL/RL) segregation in order to investigate the differences of thermodynamical variables and aerosol properties when sampled in the BL/RL or in the FT at the PUY station. The values obtained for all criteria when the PUY station is at the interface between both layers represent a smaller database that is not statistically relevant and will not be discussed here (Table  A1 ). In order to assess whether the different parameters show statistically significant differences in FT/(BL/RL), the Wilcoxon-Mann-Whitney test  [59]  was applied to the median values (Temperature (T), Relative Humidity (RH), Pressure (P), Black Carbon (BC), and for the nucleation (Nuc), Aitken (Ait), accumulation (Acc) and coarse (Coa) aerosol particles size mode concentrations) (Figure  7 ).
This nonparametric test evaluates the difference in medians between two similarly shaped populations. This test showed that the FT/(BL/RL) difference significant for some parameters (for example, T, RH, and P), however, for certain parameters during certain seasons (for example, the BC and Acc mode particles in summer and for Nuc mode particles during all seasons) no differences between BL/RL and FT properties were detected. Atmosphere 2018, 9, x FOR PEER REVIEW 13 of 25 In this section, we use the classification proposed by the combination of the four criteria of FT/(BL/RL) segregation in order to investigate the differences of thermodynamical variables and aerosol properties when sampled in the BL/RL or in the FT at the PUY station. The values obtained for all criteria when the PUY station is at the interface between both layers represent a smaller database that is not statistically relevant and will not be discussed here (Table  A1 ).
(2011)  [27]  reports the highest new particle formation frequency during spring and autumn, but the intensity of the new particle formation events (in term of nucleation rate and further growth) is the highest during summer. The accumulation mode particle concentrations have a similar behavior to the BC concentrations previously discussed, and we assume that they have similar sources for winter and autumn. Higher concentrations of the Aitken mode particles during springtime may be partly due to the growth of nucleation mode particles that were observed to occur more frequently during spring  [24, 26]  and preferentially at higher altitudes  [27] , as also witnessed by higher concentrations during the spring season for the present dataset, and partly due to a contribution from biomass burning aerosols due to the active convection transport from BL/RL to FT.
(2015)   [3]  during the summer season. In our study, the observed concentrations are slightly higher then what was found by  Venzac et al. (2009)   [14]. This would confirm the tendency observed by  Venzac et al. (2009)   [14]  of the aerosol concentration increasing over the 6 years of data but would need a careful trend analysis which is beyond the scope of the present study. For coarse mode particles, no data is available for winter and autumn, and the Wilcoxon-Mann-Whitney test is not significant for spring (medians are equal for FT and BL/RL) (Figure  9f ). However, for summer, high median concentrations of the coarse mode particles were measured in FT conditions, compared to the BL/RL conditions. This difference is likely due to a more efficient long-range transport of dust and marine aerosols at high altitudes than at lower altitudes.
Conclusions A one-year dataset of multiple measurements acquired at the PUY station has been used in this work to determine the specific physical characteristics of FT aerosols and hence of their sources and transport pathways from the BL. In order to achieve this goal, a combination of four criteria was used to identify whether the PUY In aged air masses, the high concentrations of the Aitken and accumulation mode particles coincide with the high concentrations of BC, originated from continental air masses. These high BC, Aitken, and accumulation concentrations can, thus, partly be due to biomass burning emissions. Another source of these high concentrations of Aitken mode particles is the growth of nucleation mode particles originated from the Atlantic and Mediterranean air masses. Within aged air masses, coarse mode aerosol concentrations are also higher in Mediterranean and Atlantic air masses compared to continental air masses, likely a result of transported sea salt and dust particles.
Conclusions A one-year dataset of multiple measurements acquired at the PUY station has been used in this work to determine the specific physical characteristics of FT aerosols and hence of their sources and transport pathways from the BL. In order to achieve this goal, a combination of four criteria was used to identify whether the PUY station lies within the FT or within the BL. BLH was determined using two independent methods, firstly the ECMWF model, using the "global method of Richardson" to calculate the BLH ECMWF, and secondly, the WCT method to calculate the ALH WCT from LIDAR measurements. In addition, NO x /CO and radon-222 tracers were used to determine the degree of influence from the AL NOx/CO and AL Rn , respectively. Using these tracers, we observe that the PUY station is considered to be in the FT up to 50% during the winter months.
Figure2ashows the 3-h average diurnal variation of the ALH retrieved from the LIDAR profiles (ALHLIDAR) for the four seasons separately (Winter: 1 January to 19 March, Spring: 20 March to 19 June, Summer: 20 June to 21 September, and Autumn: 22 September to 21 December) using data from 2015. The ALHLIDAR does not show strong variations with the time of the day. The maximum of the ALHLIDAR is reached during summer and spring (average 2000 m a. s. l. , green and black) while in winter and autumn (red and blue), the average ALHLIDAR never exceeds 1600 m. Lowest ALHLIDAR are observed during night time, before 9 UTC, when the WCT most likely retrieves the height of the RL. These typical ALH diurnal and seasonal variations are similar to the ones previously reported for the PUY station (Figure2in Chauvigné et al.
3. 1. 1. 11 Figure2ashows the 3-h average diurnal variation of the ALH retrieved from the LIDAR profiles (ALHLIDAR) for the four seasons separately (Winter: 1 January to 19 March, Spring: 20 March to 19 June, Summer: 20 June to 21 September, and Autumn: 22 September to 21 December) using data from 2015. The ALHLIDAR does not show strong variations with the time of the day. The maximum of the ALHLIDAR is reached during summer and spring (average 2000 m a. s. l. , green and black) while in winter and autumn (red and blue), the average ALHLIDAR never exceeds 1600 m. Lowest ALHLIDAR are observed during night time, before 9 UTC, when the WCT most likely retrieves the height of the RL. These typical ALH diurnal and seasonal variations are similar to the ones previously reported for the PUY station (Figure2in Chauvigné et al.
The performance of remote sensing instruments on-board has been evaluated through intercomparison with instruments in reference networks (i. e. AERONET and EARLINET), showing that the system is capable of providing high quality data. This also illustrates the application of such system for instrument intercomparison field campaigns. Applications of the 25 mobile system have been exemplified through two case studies in northern France. MODIS AOD data was compared to ground-based mobile sun-photometer data. A good correlation was observed with R 2 of 0. 76, showing the usefulness of the mobile system for validation of satellite-derived products. The performance of BSC-DREAM8b dust model has been tested by comparison of results from simulations to the lidar-sun-photometer derived extinction coefficient and mass concentration profiles.