dataesr/scientific-paragraphs-categorization huggingface dataset:
(2016a). Practically, the vertical background stratification is corrected using the CTD profiles of the MOOSE 2012 oceanographic cruise. Estournel et al. (2016a)  show that this correction is necessary to properly simulate the preconditioning phase and the triggering of the convective phase. In this study, to also improve the sea surface temperature initial state, the surface temperature is restored toward the SST satellite data (Buongiorno  Nardelli et al. , 2013)  during the 14 days preceding the beginning of the simulations using a restoring timescale of 3 days. At the end of this process, we obtain a surface temperature that closely matches the satellite observation of 16 August 2012. During the rest of the simulation, the coupled model evolves freely, without further data assimilation and without any restoring or nudging procedure.
Acknowledgements. This work is a contribution to the  MIS-TRALS/HyMeX  programme through the  ASICS-MED  ( ANR-12-BS06-0003 ) project funded by the  French National Agency for Research (ANR). Data were obtained from the  HyMeX  programme, sponsored by grants from  MISTRALS/HyMeX and Météo-France. The authors acknowledge the international ARGO programme, the  LEFE/GMMC  programme and the  French NAOS  project for supporting the deployment of profilers. We acknowledge the crews of  R/V Suroit  and  Tethys II , and the scientists involved in the different cruises mentioned in this paper. Numerical simulations were performed using HPC resources from  CALMIP (CALcul en MIdi-Pyrénées , projects  1247 ,  09115  and  1325 ) and  GENCI  ( Grand Equipement National de Calcul Intensif , project  010569 ).
Data availability. The Argo data were collected and made freely available by the CORIOLIS project (https://doi. org/10. 17882/42182). The CTD MOOSE data were collected and made freely available by the MOOSE network (https://doi. org/10. 17600/12020030). The CTD DOWEX, DEWEX leg 1 and DEWEX leg 2 data were collected and made freely available by the MERMEX project (DOWEX: https://doi. org/10. 17600/12450170, DEWEX leg 1: https://doi. org/10. 17600/13020010 and DEWEX leg 2: https://doi. org/10. 17600/13020030). The LION mooring data were collected and made freely available by the MOOSE network (https://doi. org/10. 17882/44411).
Meteorological Data and Backward Trajectory Plots Meteorological variables (pressure, temperature, relative humidity and wind speed) were monitored continuously by the ground based meteorological stations at Opme and puy de Dôme. Details on these stations can be found at http://wwwobs. univ-bpclermont. fr/SO/mesures/index. php. The variables are measured routinely and included in ACTRIS-FR, the French component of the distributed research infrastructure ACTRIS (Aerosol Cloud and Trace gases Research Infrastructure). Vertical profiles of 4 parameters (relative humidity, cloud cover, cloud liquid water content and cloud ice water content) have also been extracted from ECMWF ERA-Interim re-analyses  [36]  and used for statistical analyses. The nearest grid point from Opme has been taken from the horizontal resolution 0.
125 • output, on 23 vertical levels between the ground and 12 km altitude and with a temporal resolution of 6 h (00, 06, 12 and 18UT). The 4 profiles have been plotted for each day of precipitation sampling, and the plots are presented in the Supplementary Materials (Supplementary Materials zipfile 1). In order to determine the dynamical origin of the precipitating clouds, we calculated back-trajectories with the LACYTRAJ model  [37]. LACYTRAJ is a 3D kinematic trajectory code using ECMWF ERA-Interim re-analyses. In this work, initialization wind fields have a horizontal resolution of 1 • in latitude and longitude, and 37 vertical levels. Each trajectory point is advected using a bilinear interpolation for horizontal wind fields and time and a log-linear interpolation for vertical wind field.
Data Analyses Statistical analyses were performed using Paleontological Statistics Software Package for Education and Data Analysis (PAST) v. 3. 07  [43]  and SigmaPlot 13. 0. For statistical comparisons of datasets, normality was verified by Shapiro-Wilk test and parametric (F or t-test, Pearson correlation) or non-parametric (Mann-Whitney, Kruskal-Wallis, Spearman correlation, ANOVA on ranks) tests were used accordingly. Correlation tests including qualitative environmental variables (season and geographical origin) were performed by assigning a natural number arbitrary starting from 1 to each category; all the different assignment possibilities were included in the tests.
Main Characteristics of the Samples During the period between 30 September 2015 and 11 June 2017 (620 days), a total of 421 days were investigated; 138 of these had precipitation events, and 121 of them were analyzed for INPs. The dataset investigated is summarized in Table  1 , and presented in its entirety in the Supplementary Materials (Table  S1 ); the cumulative freezing profiles are synthesized in Figure  1 , and the corresponding individual freezing profiles are shown in Figure  S1. Atmosphere 2017, 8, 229 4 of 17.
Main Characteristics of the Samples During the period between 30 September 2015 and 11 June 2017 (620 days), a total of 421 days were investigated; 138 of these had precipitation events, and 121 of them were analyzed for INPs. The dataset investigated is summarized in Table  1 , and presented in its entirety in the Supplementary Materials (Table  S1 ); the cumulative freezing profiles are synthesized in Figure  1 , and the corresponding individual freezing profiles are shown in Figure  S1. a) Inferred from the volume of water collected; (b) over the duration of the sampling period; (c) fraction of water vapor lost from air mass prior this precipitation event; derived from water isotope ratio. INP concentration increased exponentially with decreasing temperature, as usually observed (e. g. ,  [6, 20] ); values spanned in general over two orders of magnitude at each temperature depending on the sample, with ranges from <0.
5 • C, and rather than being more diluted by the larger volume of water, the sample had one order of magnitude high concentration of INP -7. Similarly, the two samples with the most extreme high and low freezing characteristics were both collected in spring 2017, on the 19 and 31 May. While the first sample started freezing at -7. 5 • C, so no INP -7 was detected, the second froze at -3 • C and had ≥8. 15 INP mL -1 at -5. 5 • C. Between these contrasting examples, notably the origin of air masses had changed from Western to Eastern, and pH had risen closer to neutrality. In further statistical analyses, we explored the whole dataset in order to detect general trends that could best explain the variability of INP concentration observed in precipitation. Multivariate analysis (PCA) was carried out first, considering meteorological and temporal variables, and air mass characteristics when available, along with freezing parameters (INP -7 and onset freezing temperature).
About 40% of the variance of the dataset was explained by the two first components, related mainly to temporal (season) and local meteorological (temperature and pressure) variables for the first one, and with the influence of sources and atmospheric processes for the second (pH, fraction of water lost previously from the air mass) (Figure  3 ). Freezing parameters themselves were related in particular to these latter variables, which does neither imply strict correlations with them, nor exclude specific influence of other parameters. Atmosphere 2017, 8, 229 7 of 17 ≥8. 15 INP mL -1 at -5. 5 °C. Between these contrasting examples, notably the origin of air masses had changed from Western to Eastern, and pH had risen closer to neutrality. In further statistical analyses, we explored the whole dataset in order to detect general trends that could best explain the variability of INP concentration observed in precipitation.
Multivariate analysis (PCA) was carried out first, considering meteorological and temporal variables, and air mass characteristics when available, along with freezing parameters (INP-7 and onset freezing temperature). About 40% of the variance of the dataset was explained by the two first components, related mainly to temporal (season) and local meteorological (temperature and pressure) variables for the first one, and with the influence of sources and atmospheric processes for the second (pH, fraction of water lost previously from the air mass) (Figure  3 ). Freezing parameters themselves were related in particular to these latter variables, which does neither imply strict correlations with them, nor exclude specific influence of other parameters. S1 ). The correlation between each individual variable was further examined by a non-parametric Spearman Rank correlation test.
05) from both the number of consecutive days with precipitation and from the daily precipitation rate (Table  S3 ). Conversely, microbial cells were diluted in heavy rainfalls compared with slight precipitation events (p = 0. 01 with the daily precipitation rate; n = 62; Table  S3 ). This indicated a notable contribution of wash-out processes to the pool of microbial cells observed in precipitation, as proposed earlier [52], but no or negligible influence on INPs. In our dataset, 81 precipitating events were associated with air masses originating from the West (Atlantic Ocean), 23 from the South (Mediterranean Sea) and 17 from the East (Continent). The freezing characteristics were not statistically different between these geographical origins (ANOVA, Kruskal-Wallis' test, p > 0. 05). Major ion concentrations were in the range ~0.
s. l. ), in clouds and aerosols in the frame of other studies  [6, 30, 32]. In addition, INP concentration measurements were carried out on aerosols collected on a weekly basis in 2012 on quartz filter at the same site to account for different sampling methods. In order to compare the freezing profiles of precipitation with those of cloud water and aerosols while avoiding the influence of dilution, INP concentration data at the temperature T (INP T ) were normalized by the concentration measured in the same sample at -8 • C (INP -8 ), i. e. , the warmest temperature at which INP concentration was within the quantification range for most samples in all the datasets. The average value of INP -8 was not different between precipitation and clouds, as mentioned above (Figure  S3 ). The normalization of INP data allows revealing the relative contribution of different categories of INPs to the raw corresponding freezing profiles.
In any case, this fact could add to the "rain suppression" effect caused by anthropogenic aerosols  [68]  and lead to even larger decreases of rain occurrence in some areas of the globe impacted by pollution. The pH was strongly correlated with the freezing characteristics of the samples; as this is easily measurable, it could probably be used as a proxy for rapid investigation of the freezing properties of atmospheric waters, or it should at least be measured simultaneously to verify this relationship. In turn, the water isotope ratio might not be as informative as expected  [19]  for investigating INPs distribution and processing in precipitation when collected well below cloud base. The proximity with puy de Dôme Meteorological station, where many studies concerning the chemistry and biology of the atmosphere are conducted, gave us the possibility to compare datasets on aerosols, clouds, and precipitation, and investigate the atmospheric fate of INPs.
Acknowledgments: We warmly thank  Emiliano Stopelli  for the isotope ratio measurements. Opme and puy de Dôme sites are routinely operated by  OPGC/LaMP  staff, and supported by  University Clermont-Auvergne ,  CNRS  and  CNES. We acknowledge  ECMWF  for providing ERA-Interim re-analysis, and  IRSN  for part of the meteorological data presented, and the  Fédération de Recherche en Environnement FR 3467 UCA/CNRS/INRA  for partial funding.
The paper is organized as follows: In section 2, the datasets utilized in this study are introduced. Section 3 gives an overview of the dust sources in Iraq. In Section 4 we show evidence of the link between strong Shamal winds and low surface visibility using a 6year dataset of SYNOP stations in Iraq and Iran. Section 5 details the case study and give an estimate of the dust loads associated with Shamal-induced dust emissions based on space-borne observations and meso-NH numerical simulation. Section 6 provides a summary and conclusions.
Space-borne observations The distribution of dust aerosols at regional scale (mobilization and transport) is described using three satellite products. The Moderate Resolution Imaging Spectroradiometer (MODIS) fields of AOD are obtained from the MODIS/AQUA MYD08 (collection D3. 051) daily level 3 products. Two AOD products at 550 nm are used: the AOD processed with deep blue algorithm  (Hsu et al. , 2004)  over desert surfaces and the standard product processed over dark surfaces. Both are available from the Giovanni Web portal (http://disc. sci. gsfc. nasa. gov/giovanni). We use the level 3, gridded (1°Â 1°) daily products, representative of the aerosol load around 1:30am/pm equator crossing time. Sayer et al. (2013)  and  Banks et al. (2013)  have shown that AODs derived from Deep Blue AQUA collection 6 data compared well with AERONET (generally within 20-25%) over the Sahara and the Arabian Peninsula.
, 2000) , the Rapid Radiative Transfer Model parameterization  (Mlawer et al. , 1997 ) that takes into account the radiative effect of dust and a dust scheme  (Grini et al. , 2006). The later included the Dust Entrainment and Deposition (DEAD) scheme  (Zender et al. , 2003)  that calculates dust fluxes from wind friction speeds and the ORganic and Inorganic Lognormal Aerosols Model (ORILAM:  Tulet et al. (2005) ) in which transport, dry and wet deposition, and radiative properties of the distribution of three dust particle size are parameterized. The meso-NH model has been extensively validated over north and west Africa where dust uplift associated with LLJ dynamics are observed on a regular basis all year round. It has been validated using many ''aerosol related" datasets acquired from groundbased platforms  (Tulet et al.
, 2008; Todd et al. , 2008; Mallet et al. , 2009) , airborne platforms (Bou  Karam et al. , 2008 Karam et al. , , 2009b;; Crumeyrolle et al. , 2011; Schepanski et al. , 2012)  and spaceborne platforms (Bou  Karam et al. , 2009a Karam et al. , , 2010 Karam et al. , , 2014;; Chaboureau et al. , 2011) ). The fact that the processes of dust emission and transport over Iraq are similar to those observed in North Africa gives us confidence that the simulation of these processes by the meso-NH should be accurate even though we only have a limited dataset to compare it to.
The maximum AODs from MODIS are observed in June (Fig. 3f ). We hypothesize that this is related to the number of Shamal events occurring during a given month in the summer (winter Shamal events being far less numerous than summer Shamal events) as well as the presence of a nocturnal LLJ over Iraq during the summer Shamal period. This is now investigated using the 6-year dataset from the synoptic stations over Iraq. Fig. 4  shows the monthly distribution of the number of events for which the wind direction was from the 270°-360°sector, the wind speed was larger than 6 m s À1 and the visibility below 3. 5 km, in Mosul, Balad, Tallil and Abadan, for the period 2005-2010, as a function of the time of day (UTC, local time in Baghdad is UTC + 3 h). Also shown is the time of sunrise and sunset in Baghdad.
The estimation of the dust load associated with the Shamal summer wind was computed from three independent datasets, i. e. using MODIS and SEVIRI derived AODs and directly from the simulations. The three datasets gave roughly the same estimate. In general, dust emission by Shamal winds over Iraq was well reproduced by the model. However, a better representation in dust models of dust sources in Syria, Jordan and the Sinai Peninsula especially alluvial dust sources is suggested for future studies given the nature of the dust sources in this region.
Acknowledgements The  European Centre for Medium-Range Weather Forecasts (ECMWF)  is acknowledged for making the meteorological analyses available from their data server. The CALIPSO data were obtained from the  ICARE Thematic Center. The CALIPSO data were obtained from the  NASA Langley Research Center Atmospheric Science Data Center. MODIS AODs  used in this study were produced with the Giovanni online data system, developed and maintained by the  NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The  Research Institute of NYU Abu Dhabi  is thanked for supporting the research activity at the  Center for Global Sea Level Change (CSLC)  through grant  G1204.
6. This figure shows the reactive nature of flocculation in the natural Mekong River estuarine fluid mud determined in this study compared to a large range of other mud settings. Before comparing the data, it must be kept in mind that the various techniques used in prior studies can generate inherently different results (along the Y axis), as suggested by  Ross (1988). Furthermore, most prior experiments focused on the flocculation regime (experiments 1 to 18) and only five studies covered the full range of settling regimes, up to the hindered regime (experiments 19 to 23). However, the comparison shows that the floc characteristics of the Mekong Estuary (present study, No. 23, circles) have several specificities. For SSC 200 mgÁL À1 , the characteristics of flocculi in the Mekong River are not influenced by sediment concentration; the settling velocity seems to remain constant, which is unique for the 23 available datasets (Fig.
Acknowledgments J. Ne  ´mery and  T. Phong  are fully acknowledged for their kind help during the preliminary experiments; we also would like to acknowledge all colleagues involved in the field survey. This project was partially funded by the  ''Institut de recherche pour le developpement '',  GFZ German Research Centre for Geoscience , as well as  ANR SCAF JCJC ANR-12-JS06-0006  project and  LMDCZ (EU-AFD-SIWRR ) projects. A scholarship funding by the  German Academic Exchange Service DAAD  ( NAWAM program , No. 91587027 ) of the PhD work of  Tran Tuan Anh  is gratefully acknowledged. We would like to thank the two reviewers,  K. Strom  and  R. de Wit  (invited associated editor), as well as the associated editor  I. Manighetti  for their fruitful and constructive remarks. M. Marchesiello  is acknowledged for her support to improve the English.
Surface water pCO 2 Several surface maps of pCO 2 , with varying resolution in time and space, were used in the current study, in order to investigate the importance of both the spatial and temporal variability of surface water pCO 2 on the air-sea CO 2 exchange. Lansø et al. (2015)  created a monthly surface water pCO 2 climatology for the Baltic Sea and Danish inner waters, based on a combination of observed values of pCO 2 and a number of water chemistry parameters for this area. The Baltic Sea climatology (BSC) has been further developed to also include improved maps of near-coastal pCO 2 for the Danish inner waters (fjord systems and straits). The coastal region of the Danish inner waters has been defined as reaching up to 10 km offshore from any Danish coastline. These coastal pCO 2 values were calculated from discrete monthly samples of alkalinity, pH, water temperature ( • C) and salinity (psu), using data from the Danish National Aquatic Monitoring and Assessment Program (DNAMAP,  Kaas and Markager (1998) ), for the period of 2000 to 2011.
Calculations of pCO 2 were performed using the speciation programme, CO2SYS  (Lewis and Wallac, 1998) , where the seawater scaled was used for pH. Cole et al. (1994)  showed that the pCO 2 values, calculated using this approach, have a strong and unbiased relationship with direct measurements of pCO 2 (r 2 = 0. 88, N = 330). Samples from 1 m depth were used to measure alkalinity using the Danish Standards or Gran titration  (Mackereth et al. , 1978; Markager and Fossing, 2015. Likewise, pH were measured on samples collected at a depth of 1 m using a bleeding electrode calibrated to either pH 4 og 7 according to technical guidelines and procedures for the DNAMAP database  (Kaas and Markager, 1998). Data on salinity and water temperature at 1 m were extracted from water column conductivity, temperature and depth profiles.
Depending on the region, 12 to 46 samples were taken per year. For each region, data were aggregated to create monthly average values. Six subdivisions of the Danish coastal inner water were constructed in this study. Five of these subdivisions cover individual fjords, where water chemistry has been measured, and the last subdivision is a combination of the Danish inner waters, the near-coastal area and the remaining Danish fjords (Fig. 1 ). Even though variations can be found within these six areas, this division was made in order to have sufficient amount of observational data to construct the climatology for the individual subdivisions. Like the original pCO 2 climatology, the calculated near-coastal Danish pCO 2 has been normalised to the year 2000, assuming an annual growth rate of 1.
Hereafter, a cost function minimises the difference between the observed data points of pCO 2 and the modelled data points corresponding, in space and time, to the observations. Lastly, interpolation is used for areas and times with no observational data coverage. The pCO 2 observations used to constrain the modelled pCO 2 have been obtained from the Surface Ocean CO 2 Atlas (SOCAT), version 2  (Bakker et al. , 2014). For the study area, the same tendency for the seasonal cycle of pCO 2 was observed for the BSC and JENA maps (Fig. 4 , note: monthly means of pCO 2 for the JENA data product have been conducted for 2011), with the highest pCO 2 surface values during winter and lower values in summer. However, the seasonal amplitude of the Baltic climatology was 50-100 µatm larger than JENA. Diurnal variability of surface water pCO 2 in the Baltic Sea and in the Danish waters was assessed by using the few available, continuous time series of measured surface water pCO 2 , recorded at stationary sites within the Baltic Sea.
It was important that the surface data was obtained from stationary sites, in order to only account for the temporal variability. Of the two sites, one is located in the Arkona Sea on a platform and the other in the Baltic Sea proper, on a small island (Östergarnsholm), off the coast of Gotland. From May 2003 to September 2004, a SAMI-CO 2 sensor was deployed on a floating monitoring platform in the central Arkona Sea (54 • 53 N, 13 • 52 E) at a depth of 7 m  (Kuss et al. , 2006). Water depth in this region is 45 m, with surface water typified by an outflow of brackish water from the Baltic Sea, while deeper water is more saline, originating from the North Sea  (Weiss et al. , 2007). Due to instrumental breakdown, a few data gaps are present in the time series. The Östergarnsholm site (57 • 27 N, 18 • 59 E) has been in operation since 1995.
This may possibly have been due to a combination of two things: inter- annual variability, as the measurements at the two sites were from different years, and the site representation of open water vs. coastal areas, as mentioned above. Both diurnal and meso-scale variability were present in the observed time series of surface water pCO 2. These may be linked to changes in both atmospheric and marine system and processes. The focus, in this study, has been placed on the diurnal variability, with the construction of diurnal cycles of pCO 2 on a monthly basis, i. e. one diurnal cycle was calculated for each month, where data existed at either of the two sites. To calculate the mean diurnal cycles, the approach as in  Leinweber et al. (2009)  was applied. First the mean monthly diurnal anomaly was obtained by removing a central 24 h running mean from the pCO 2 time series in the month under investigation.