dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1997). A particular focus is placed on internal variability, which has been shown to explain much of the model spread of simulated Arctic sea-ice trends (e. g. Swart et al. , 2015; Notz, 2015)  and at least some of the spread between Antarctic sea-ice trends  (Mahlstein et al. , 2013; Gagné et al. , 2015). While a rough analysis of the quantitative model spread and of internal variability has already been possible with the limited set of sea-ice-related variables stored in CMIP5, the SIMIP data request will allow us to more specifically understand differences in the drivers of internal variability across different models. This is because the new variables requested by SIMIP allow us to better differentiate between atmosphere vs. ocean-driven variations in sea-ice coverage, as well as thermodynamic vs.
These groups are explained in more detail in this section. They are (1) sea-ice state variables, (2) tendencies of sea-ice mass, (3) heat and freshwater fluxes, (4) sea-ice dynamics, and (5) integrated quantities. For each variable, we specify a priority that describes how crucial knowledge of this variable is for our understanding of sea ice. As a guiding principle in defining the priorities, we roughly grouped variables relative to the number of researchers most likely to use a given variable. Priority 1 variables are those variables needed to quantify the large-scale evolution of sea ice or to understand the forcing of sea ice on either the ocean or the atmosphere. These variables will be used by many scientists, even those without a specific interest in sea ice per se. We hence strongly recommend that they are stored in any large-scale model simulation carried out within CMIP6 independent of the SIMIP data request, as they provide at least a basic description of the large-scale sea-ice state with a fairly light requirement on data storage.
Sea-ice dynamics While the variables requested so far allow researchers to analyse in great detail the change of sea-ice mass in different models and in any given grid cell, the movement of sea ice is also of central importance to understand the evolution of Arctic sea ice as simulated by large-scale climate models. Hence, SIMIP requests variables that describe this movement and that allow researchers to understand its driving forces in CMIP6 simulations. At the highest priority 1, the SIMIP data request includes for sea-ice dynamics the xand ycomponents of sea-ice velocity and the sea-ice speed. Because of the importance of these three variables in examining the movement of sea ice, for example in response to strong cyclones, they are requested as daily and monthly means. The specific request of sea-ice speed allows researchers to analyse the highresolution movement of sea ice, for example in comparison to the sub-daily speed reported by sea-ice drift buoys, without the error associated with vector averaging.
Observations As outlined, SIMIP uses model simulations to understand the sea-ice evolution of the real world. This is done by linking the model simulations to the real world through observations. Hence, SIMIP would not be possible without a reliable, wide range of observational records that allow one to understand if CMIP6 model simulations capture the most important aspects of sea-ice evolution as it also occurs in reality. For this reason, SIMIP sees itself not only as a pure modelintercomparison exercise, but also as a forum for identifying the best possible use of observations for the evaluation and improvement of model simulations. For this purpose, SIMIP works closely with the National Snow and Ice Data Center (NSIDC), with other data centres and with the observational community to maintain a detailed, up-to-date list of sea-ice-related observational records that can be used by any researcher to analyse the performance of sea-ice model simulations.
Data availability All model output requested by SIMIP will be distributed through the Earth System Grid Federation (ESGF) with digital object identifiers (DOIs) assigned. As in CMIP5, the model output will be freely accessible through data portals after registration. In order to document CMIP6's scientific impact and enable ongoing support of CMIP, users are obligated to acknowledge CMIP6, the participating modelling groups, and the ESGF centres (see details on the CMIP Panel website at http://www. wcrp-climate. org/index. php/wgcm-cmip/about-cmip). Further information about the infrastructure supporting CMIP6, the different CMIP6 MIPs, metadata describing the model output, model documentation, and the terms governing its use are provided by the WGCM Infrastructure Panel (WIP) in their contribution to this special issue.
from individual CMIP6 models. For example, an assessment of the model simulations based on the use of thickness distributions or diagnostic salinity can provide insights into whether models that include such advanced properties generally perform better than models that do not. Basic seaice model documentation was requested in past CMIPs, and SIMIP is contributing additional requests to fill gaps and include requests related to the SIMIP data request for CMIP6 (personal communication, Bryan Lawrence, March 2016). We encourage all modelling groups to provide the requested model documentation information with as much detail as possible, and all scientists working with CMIP6 seaice output to make use of the model documentation to enhance the analysis of the sea-ice simulations. The specific documentation requirements will be distributed through the Earth System Documentation activity (ES-DOC; see http: //es-doc.
2. 1, SIMIP is also interested in assessing the tuning used in the CMIP6 models. In the model documentation request, the following questions will be asked related to the tuning that was done prior to the model code freeze for CMIP6: -Which tuning knobs were used in tuning the sea-ice model? -In what kind of simulations was the tuning done (control, transient 20th and 21st century, all of these)? -What were the sea ice targets in the tuning effort? Appendix C: Some general remarks, including averaging In the following appendices, we list all variables of the SIMIP data request version 1. 0, grouped into the following five categories: (1) sea-ice state variables, (2) tendencies of sea-ice mass, (3) heat and freshwater fluxes, (4) sea-ice dynamics, and (5) integrated quantities. Regarding the spatial storage of variables, for simplicity we request all variables to be stored on the model grid on which they actually are used during a model simulation.
, 2016). Fractional coverages are evaluated either relative to the entire grid cell (e. g. for sea-ice concentration) or relative to the sea-ice covered part of the grid cell (e. g. melt-pond area fraction). This is specified in detail in the following request. As a general rule, we have tried to follow the most widespread standard by which individual fractional coverages are usually represented in sea-ice models. Temporal averages are requested for almost all variables, except for a few variables related to sea-ice dynamics that must be stored as instantaneous values at some point during the averaging period. The standard averaging period for all variables is 1 month. For the most important variables, additional daily averages are requested, as identified in the following detailed data request.
Acknowledgements. We thank the international sea-ice community for their input and guidance during the design of SIMIP, most notably during the  Sea Ice  and  Climate Modelling  workshop in Reading on 26th September 2014. We are very grateful to  Martin Juckes  for his tremendous help and support throughout our work on the design of SIMIP, and to  David Bailey ,  Helmuth Haak , and  Alex West  for their helpful comments and feedback. We thank  WCRP CliC  for their logistical and financial support, which was essential for SIMIP. The article processing charges for this open-access publication were covered by the  Max Planck Society. Edited by: S. Valcke Reviewed by:  D. Bailey  and one anonymous referee.
Additionally, some of these species have empirical data available on their grouping behaviour (table  2 ), which we used to increase the level of species-specificity in the models. We examined the following group parameters: (i) number of groups in the population, (ii) average group size (i. e. the average number of individuals per each group calculated from the total simulated population), (iii) the polarity (i. e. degree to which all individuals in the population are facing in the same direction), (iv) the average nearest neighbour distance between all individuals in the population, (v) speed at which the group stabilized and (vi) the internal structural stability of the group. A group was defined as a subset of individuals that interact with at least one other member within this subset.
In this respect, the United States, especially NOAA, has considerable experience with balloons. Thus, the paper by  Businger et al. (2006)  reviews numerous technical and scientific innovations that helped to advance the state-of-the-art lower-atmosphere drifting balloons. The characteristics of the CNES PBL balloons are presented in the first part of this paper. Various scientific missions and data usages are then presented to illustrate the capabilities of the CNES low-level constant-volume balloons. At this stage, the in-flight behavior of the balloons will be discussed. The last part of this paper discusses an original application of these balloons from an NWP perspective. Additional aspects of the setup and the proceedings of the HyMeX field campaign are provided in the supplemental material of this article (more information can be found online at http:// dx.
1987). The limited range of this communication system prevented long-distance flights, especially in proximity to the ground. To avoid this limitation, use of the Argos system was implemented for INDOEX  (Ethé et al. 2002)  and in 2012 was replaced by the Iridium constellation (see Table  2 ), which offers better global coverage. In the latest version of BLPBs, the data are sampled twice every minute and stored on board. The meteorological data acquired by the external gondola are sent to the housekeeping gondola through a UHF link (see Fig. 2 ). Since telecommunication is an important part of the Flight range and end of flight. Table  4  lists the duration of every flight operated during AMMA and VASCO 2007. The longest flight lasted for more than 1 month. However, most of the flights were shorter, especially in AMMA.
A technical advance would be to implement double-envelope (air and helium) devices to allow for (limited) control of the altitude. For instance, the flight level of BLPBs depends on the buoyancy, which depends on the initial air-helium mixture. It is therefore impossible to change the flight level after the launch. Such a possibility would, however, be useful in the case of persistent change in the flying mass, when there are multiple scientific missions in a single flight, or when one wants to avoid losing the balloon (e. g. , hitting the ground). Another important condition to guarantee Lagrangian behavior is the balloon itself not influencing the air parcel with which it travels. This last condition is important, especially in terms of temperature and relative humidity measurements. It is widely accepted that in situ observing systems are easily influenced by solar radiations (even radiosounding temperature data are corrected for radiative biases).
This instrument is able to measure the size distribution of aerosols in a range from a few tenths to some tens of micrometers as well as to infer the nature of the aerosol from the light observed at two scattering angles  (Renard et al. 2016 ). The LOAC measurements revealed an unexpected persistence of coarse particles inside several desert dust plumes with diameters of between 15 µm and several tens of micrometers  (Renard et al. 2015). Thirteen BLPBs (4 in ADRIMED, 9 in SAF-MED) were fitted with an ECC ozonesonde (Gheusi et al. 2015, manuscript submitted to Atmos. Meas. Tech. ). The ECC ozonesonde, which has previously been placed on drifting balloons  (Bénech et al. 2008) , has been modified so as to last longer, due to the intermittent data collection mode. The ChArMEx campaigns succeeded in capturing the first Lagrangian observation of ozone photo production in the free troposphere  (Gheusi et al.
TRAQA campaign: An example of pollutant tracking. In the TRAQA campaign, five BLPBs were deployed from Sausset-les-Pins (France) in two cases of weakening mistral (clear sky, moderate wind). The purpose was to study the time evolution (ageing) of pollutants within drifting air parcels offshore. The source of the pollutants was the Fos-Berre industrial area, in the vicinity of the launch site. The balloons were deployed as tracers to identify the air parcels as described in  Businger et al. (1996)  and to follow them as they left the continent and traveled above the Mediterranean Sea. The balloons collected pressure, temperature, and humidity as well as ozone data. The SAFIRE ATR42 research aircraft (www. safire. fr) flew several times during the BLPB flights to collect additional data  (Gheusi et al.
2015 , manuscript submitted to Atmos. Meas. Tech. ). The aircraft used the balloons as targets with which to reach the air parcels to sample. Figure  7  shows the flight trajectories in the two cases studied in TRAQA. ATR42 flights are shown as red and green lines with colored dots that indicate the aircraft position every minute when the f light level is lower than 800 m. The color shows the time of day and overlapping trajectories with similar colors signifying collocation. The mean flight level of the balloons was The 17 BLPBs were launched from Goa (southeastern India) in northeasterly wind flow  (Ethé et al. 2002). Most of these balloons reached the Arabian Sea (and even ITCZ) in approximately 7 days, drifting at low levels (950-800 hPa). Temperature, pressure, humidity data, and position (including extra parameters with which to manage the balloon's flight) were collected every 30 min, but no aerosol data were collected.
OTHER SCIENTIFIC APPLICATIONS. AMMA: Space-time variability in West Africa. During AMMA  (Redelsperger et al. 2006 ), a wide range of instruments was deployed throughout West Africa. Among them, 15 BLPBs were launched from Cotonou (Benin) in the PBL between mid-June and mid-July 2006, allowing Lagrangian measurements of wind, temperature, and humidity. Three BLPBs reached the ITD, separating the southwesterly monsoon flow from the northeasterly Harmattan. The data collected by the BLPBs were used to investigate the multiscale variability of the monsoon's circulation and in particular the diurnal cycle of West African monsoon dynamics. They were also used to validate meteorological analyses. Figure  8a  displays the trajectory of BLPB 8, and Fig. 8b  shows the time series of its flight altitude.
This was a case where there was low tramontana. The balloon was launched by chance in a volume of very dry air that stayed intact for several hours above the Mediterranean before the mixing succeeded in breaking the isolation of the air parcel. Individual flight data and graphs can be found online (at www. lmd. polytechnique. fr/BAMED /BAMED_BP_Flight_SOP2. php). In HyMeX SOP2, dynamical processes driving the circulation of the mistral and tramontana, and particularly the wake formation downstream of the Massif Central, were investigated using the BLPBs. Little is known about the finescale structure, especially over the Mediterranean Sea, where highly resolved observations in time and space are lacking. Despite the waves not being isopycnic, the absence of flow recirculation (or wave breaking; see Schär and Smith 1993) and the moderate amplitude of the wakes allowed the BLPBS to collect a unique dataset in order to analyze in depth the finescale offshore dynamics of the mistral and tramontana (wake, convergence area, gravity waves, and convection).
2009; Jansa et al. 2011) , which allowed data from additional RS to be collected on demand from the HyMeX Operation Center. The chief forecasters at the HOC requested daily the extra RSs for the next day. The RS selection was made through the DTS web interface with the help of model sensitivity information, updated daily  (Campins and Navascuès 2016). The deployment of BLPBs as means of adaptive observation was not included in the integrated procedures of the DTS, but the HyMeX Operation Center requested the launch of BLPBs when appropriate. The main difficulty stemmed from the balloon's nature to drift that limited our ability to place the observing platform at the right location and at the right time. With a single launch site, the only degrees of freedom in terms of the control of the balloon's trajectories concerned the date and time of the launch and the flight level.
Example of dataset in AROME-WMED data assimilation. High-frequency data (2 data packets per minute) from the BLPBs during HyMeX SOP1 and SOP2 were filtered to produce NWP-compatible data (1 data packet every 15 minutes) that were assimilated by the French operational NWP models ARPEGE  (Courtier et al. 1991) , AROME-France  (Seity et al. 2011) , and AROME-WMED  (Fourrié et al. 2015). This was a novelty, as low-level drifting balloon data had never been assimilated into AROME before HyMeX. The procedure, called the BAMOBS, produced 828 reports during SOP1, about 700 of which entered the data assimilation procedures. During SOP2, 868 reports were issued. The HyMeX balloons' dataset is available through the HyMeX database (http://mistrals. sedoo. fr/HyMeX/). The assimilated dataset is unprecedented, as very few in situ data exist at low levels above the sea.
The only lower-level aircraft data in the balloons' flight domain are close to the Balearic airports of Ibiza and Majorca. Most of the HyMeX radiosoundings at sea were made by the vessel Le Provence that remained close to the Gulf of Lion moored buoy and only during short periods of the SOP1. These repeated soundings produce the vertical gray stripe at 4. 5°E longitude in Fig. 12  (right panel) and overlap the BLPBs dataset. The balloons, however, never flew in the vicinity of these soundings. Figure  12  shows that BLPBs have been able to collect data at low level, in the middle of the northwestern Mediterranean basin, a crucial region for the prediction of heavyprecipitation events  (Duffourg and Ducrocq 2011 ) and where very few other observations were available for data assimilation and NWP.
CONCLUSIONS. CNES is experienced at operating low-level drifting balloons, as it has been doing so for more than three decades. Most campaigns took place overseas, INDOEX, VASCO-Cirene, and then HyMeX and ChArMEx, but also above land, as with AMMA. The years 2012 and 2013 with HyMeX and ChArMEx were highlighted for balloon activity in the Mediterranean. There were 66 constant-volume balloons deployed in 14 months in a variety of weather conditions and various scientific missions. These missions ranged from Lagrangian studies, for example, chemistry (ozone) and radiation (aerosols), to process studies such as humidity uptake above the Mediterranean and observation for real-time data assimilation. BLPBs are almost ideal observational platforms for Lagrangian studies of chemicals and dust transport, especially as the scientific approach requires calm and dry weather conditions.
With this taken into consideration, BLPB is thought to be a reliable platform, bearing in mind that it is simply a drifting balloon! Despite the fact that all the aforementioned flight conditions are depreciative for strict Lagrangian studies, BLPBs succeeded in collecting valuable datasets. In AMMA, BLPBs succeeded in investigating the diurnal cycle in the Sahel. Flights in HyMeX-SOP2 allowed the study of the wind structure and sampling of the marine humidity uptake. Finally, considering adaptive observation for data assimilation, HyMeX SOP1 f lights allowed the collecting of valuable observations in active weather areas. From experience during recent campaigns, a few improvements in the design of the BLPBs would be welcomed. These include access to renewable energy sources, in-f light control of the buoyancy (even limited), fully recoverable sea landing, and enhanced observing capabilities.
For example, measurement of in situ precipitation along flights over sea is nearly at hand with BLPBs; this would be of incomparable value. ACKNOWLEDGMENTS. The authors would like to acknowledge the importance of the MISTRALS/ChArMEx and MISTRALS/HyMeX programs as well as the support of LEFE and TOSCA and the funding by ANR under Contract IODA-MED ANR-11-BS56-0005. The BLPB field campaigns in the Mediterranean would not have been possible without the motivation of local people who hosted the launch sites during HyMeX and ChArMEx as well as the willingness of air traffic control agents. The scientific community collected unique datasets during these campaigns, thanks to the CNES, its balloons department, and the very professional and motivated staff. A special thank you is extended to Jean-Noël Valdivia for his support concerning technology in the gondolas and to Naomi Riviere and Alan Hally for their support concerning the language.
Differential staining of extracellular and total 13. Discard the primary antibody solution and wash four times with 100 μl of DPBS supplemented with 1% serum albumin. and Hoechst 33342 (ex/em 405/450). 23 fields, corresponding roughly to 6000 cells, are acquired per well using a 10x air objective (numerical aperture: 0. 4). Recorded images (Figure  4 ) are transferred to a database for storage, management and analysis. 18. The number of intracellular bacteria per cell was measured using Columbus analyzing building block routines (see Note 14). 19. Statistical analysis of the results is performed using the strictly standardized mean difference (SSMD) statistical tests for quality control (QC), hit selection and validation  [22]. 20. Classify each hit by cross-examination of the two screening results, i.
13. PFA is toxic and must be handled in an appropriate fume hood. After cell fixation PFA should be disposed of correctly in a sealed container. 14. For this manuscript, we used the Columbus database (PerkinElmer Technologies) for image storage, management and analysis. However, any storage device or database including the Open Microscopy Environment repository OMERO (http://www. openmicroscopy. org/site/products/omero) can be used instead. For image analysis, open resources including ImageJ ((http://imagej. nih. gov/ij/), FIJI (http://fiji. sc/Fiji), Icy (http://icy. bioimageanalysis. org/) and CellProfiler (http://cellprofiler. org/) are also excellent solutions. 15. PFA-fixed cells get permeabilized after 24h. For this reason, the differential immuno-staining of total versus extracellular bacteria should be performed before 24h post PFA fixation.
Remove the DPBS and add 30 μl of 10 μM nuclei dye Draq5. Incubate during 30 min in the dark. 16. Discard the Draq5 solution and wash with 150 μl DPBS. 17. Eliminate the DPBS and leave samples in 100 μl of new DPBS. 18. Acquisition of fixed samples is performed using the automated spinning disk confocal microscope Opera QEHS (PerkinElmer Technologies). The following sequence is used: firstly, CCF4 (excitation/emission [ex/em] 405/535 and 405/450 nm on two separate cameras) and secondly, Draq5 (ex/em 640/690). 23 fields, correspond roughly to 6000 cells, are acquired per well using a 10x air objective (numerical aperture: 0. 4). Recorded images (Figure 3) are transferred to a Columbus database (PerkinElmer Technologies) for storage, management and analysis (see Note 14). 19. Feature extraction was performed for each individual cell by using integrated Columbus building block routines (see Note 14).