dataesr/scientific-paragraphs-categorization huggingface dataset:
and P. rubescens abundances estimated by microscopic cell counting and that corresponding to the read numbers in our sequence dataset. This suggests that, in agreement with some previous studies (e. g. , for cyanobacteria  [41]  and other bacteria  [42] ), high-throughput sequencing allows the evaluation of the relative abundance of various species, at least when they belong to the same phylum. The analysis of our data suggests that several factors and processes have an influence on the relative abundances of the two dominant cyanobacteria, Microcystis sp. and P. rubescens. Among them, variation in the depth of the lake could play a major role, particularly during the mixing period, as suggested by our PCA analysis. Two studies on the collapse of gas vesicles in Microcystis cells have shown that critical pressures leading to this collapse occurred at a depth of 40 m  [43, 44].
The first two are EXCEL spreadsheets for thermobarometric    calculations applied to metapelites and based on calibrations by different authors. The last two  (Berman, 1991; Holland and Powell, 1998)  are based on multi-reaction calculations using consistent thermodynamic data. The THERMOCALC software  (Holland and Powell, 1998)  made the calculation of all possible equilibria for the assemblage Pl-Opx-Sil-Grt-Bt-Kfs-Rt-Ilm-Qtz of the sapphirine-bearing granulite samples possible.
SAMPLES SOFTWARES/ METHODS Kohn and Spear (1999)  Reche and Martinez (1996)  TWEEQU  Berman (1991)  Grt-Bt  The estimated pressure and temperature data calculated with THERMOCALC (8. 4-11. 1 kbar and 900-1000 °C, Table  7 ) retrieve the metamorphic peak at ultrahigh-temperature conditions. For the temperature interval of 900-1000 °C, the different calibrations of the geobarometer Opx-Grt-Pl-Qz  (Bhattacharya et al. , 1991)  lead to pressure that are compatible with those obtained by THERMOCALC. The geobarometer Pl-Grt-Sil-Qtz  (Kohn and Spear, 1999; Reche and Martinez, 1996)  gave widely different results (6-12 kbar) (Table  7 ). Common geothermometers, based on Fe-Mg exchange reactions, such as Grt-Opx  (Sen and Bhattacharya, 1984; Harley, 1984; Lee and Ganguly, 1988; Kohn and Spear, 1999; Reche and Martinez, 1996; Berman, 1991)  and Grt-Bt  (Thompson, 1976; Holdaway and Lee, 1977; Perchuk and Lavrent'eva, 1983; Reche and Martinez, 1996; Berman, 1991) , yield lower temperature ranges, 710-810 °C for the first pair and 500-720 °C for the second.
In the decades following Allen's sampling, there were only sporadic records of red tide events until regular biological sampling at the SIO Pier resumed in 1983. The modern sampling program includes twice-weekly measurements of chlorophyll-a. Weekly cell count measures are also available for some years but sadly these tend to be less consistent reflecting the focus of the funding available at the time. Although different from the direct cell-count measurements, chlorophyll-a measurements are thought to be a good proxy for measuring red tides, and as described in the Results to follow, the vast majority of samples during large algal blooms are dominated by dinoflagellates and specifically those considered HAB species. Thus, these data can be used to address the more specific problems of dinoflagellate blooms and HABs.
Moreover, the modern sampling not only exceeds Allen's original observations in duration, but exists alongside concurrent measurements of phytoplankton cell counts, dissolved nutrient concentrations, water temperature, salinity, rainfall, and wind, as well as derived quantities, such as surface density. These data comprise a long-term record of hydrography, weather, ocean chemistry, and algal dynamics-information that could be used to study blooms as nonlinear events. The blooms at the SIO Pier are representative of the southern California coast in general. McGowan et al. (1998)  found considerable regional coherence in the physical variability observed at the SIO Pier and other monitoring stations along the southern California coast. A preliminary study of chlorophyll and cells counts across the 5 HAB monitoring stations in the Southern California Ocean Observing System (SCCOOS) indicates blooms are correlated over distances of 100 to 180 km, and show higher agreement among species specific comparisons vs.
Data Chlorophyll-a, cell-count, and nutrient time series are compiled from measurements collected at SIO Pier (32°52. 02 0 N, 117°15. 42 0 W) as part of the SCCOOS Harmful Algal Bloom Monitoring Program. Sampling methods are described in  Hatch et al. (2013). Chlorophyll-a and nutrient measurements are typically collected twice weekly on Monday and Thursday. To create time series with fixed time steps, we filter the data to observations that were 3-4 d apart (~1/2 week intervals) or 6-8 d apart (~1 week intervals), and restrict the data to contiguous segments of at least 20 points. Temperature (°C) and salinity (PSU) are taken at the 0 m (sea surface) and 5 m depth as part of the Shore Stations Program (http://shorestation. ucsd. edu/data/in dex_data. html). Kim et al. (2009)  outline these measurement procedures.
Density (kg/m 3 ) at both depths is calculated from temperature and salinity using the equation of state for seawater. Previous work defined a bottom temperature anomaly (T Ã b ) to indicate cold-water run-ups as: T Ã b ¼ T b À T s À 0:8 where T b is the observed water temperature at 5 m depth and T s is the mean year-day surface temperature from 1927-1994. Positive values indicate an absence of coldwater run-up and are therefore ignored by fixing the value at 0. Finally, wind and temperature data from the offshore buoy (station 46025) are taken from NOAA's National Data Buoy Center online database (http:// www. ndbc. noaa. gov/station_page. php?station=46025). Except in the case of the anomaly defined above, SIO Pier measurements for temperature, salinity, and density are averaged across the two depths (0, 5 m), and all environmental variables are converted to the running mean over the previous 7 d.
Studying the SIO pier data with a nonlinear perspective, however, provides a path for understanding the mechanisms involved by identifying causal variables and their meaningful combinations. In particular, the results are consistent with the hypothesis that blooms in this region arise in conditions that give dinoflagellates a competitive edge over other phytoplanktonnamely, when the water column is stable and surface nutrients are depleted. These conditions generally favor motile dinoflagellates, which can assimilate nutrients at depth, over non-motile phytoplankton, such as diatoms. For example, the three example models selected include either water density or wind speed (Fig. 7 ). Both are indirect indicators of water column stability  (Holmes et al. 1967 , Horner et al. 1997). Additionally, Fig.
ACKNOWLEDGMENTS  Patricia Walker  and  Connie Fey  were responsible for data collection at the  SIO Pier. Funding  for data collection was provided by the  Southern California Coastal Ocean Observing System, Harmful Algal Bloom Monitoring Program, NOAA  Grant #  NA11NOS0120029 , the  MacArthur Foundation , and  Cal-COFI. This analysis was supported by  Department of Defense Strategic Environmental Research and Development Program   15 RC-2509 ,  Lenfest Ocean Program award 00028335, EPA-STAR Fellowship Program ,  National Science Foundation GRFP, National Science Foundation  Grant  DEB-1020372 ,  Sea Grant Population Dynamics Fellowship   E/PD-9 , the  Sugihara Family Trust , the  Deutsche Bank-Jameson Complexity Studies Fund , the  Leslie  and  John McQuown Gift  and the  McQuown Chair in Natural Sciences, University of California, San Diego.
g. ,  [40] ), and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) (e. g. ,  [41, 42] ). However, space-borne observations are limited by cloud-coverage and by the satellite overpass time so that ground-based observations are still useful to better characterize the aerosol optical properties over Russia, particularly using a Raman lidar that allows a proper determination of the intensive property that is the extinction-to-backscatter ratio (so-called lidar ratio (LR)). In June 2013, we performed the first road transect from Paris to Ulan-Ude, near Lake Baikal, profiling aerosols all the way from Europe to Siberia with a N 2 -Raman lidar embedded on a van. The general analysis of the data recorded during this journey and the case studies corresponding to forest fire and desert dust outbreaks have already been presented  [43].
Instrumentation The van was equipped with a lidar instrument similar to the one previously described in Royer et al. [48] , based on a 355 nm laser with a 16 mJ pulse energy and a 20 Hz repetition frequency. For the three acquisition channels (elastic, perpendicularly polarized and N 2 -Raman vibrational backscatter), the signals were recorded with an initial resolution of 0. 75 m and 25 s (500 laser shot average), in both analog and photon-counting mode. During daytime, only the analog mode was exploited (the photodetectors being saturated by the sky background light) but, during nighttime, the analog and photon-counting signals were merged to optimize both the dynamic range and the signal-to-noise ratio. After correction for the platform inclination (measured using a MTi-G GPS/inclinometer (Xsens, Enschede, The Netherlands) attached to the optical head) and after cloud screening, data were averaged over 7.
Paris On Tuesday 4 June 2013, the van departed shortly before 07:00 UTC (09:00 LT) from Palaiseau, a town located 19 km south-southwest from Paris; then the van headed to the southeast to exit the agglomeration. The day was sunny and a light wind was blowing from the north-northeast, bringing Paris morning pollution plume over the lidar. All data recorded in the densely-urbanized area surrounding Paris (up to 30 km from Paris center) were gathered into a 1-h average profile covering the end of the morning traffic peak (07:00-08:00 UTC). For this case, only a single-layer constrained Klett inversion was possible due to: (i) the limited range of the Raman channel (these are daytime observations, recorded ~3 h after sunrise); and (ii) the large variations of the aerosol optical properties above the Raman channel maximal range.
Berlin On Thursday 6 June 2013, the van travelled through Eastern Germany. It passed near Leipzig around 08:30 UTC and entered Berlin agglomeration from the southwest at 10:30 UTC. The van left Berlin area from the southeast at 15:10 UTC and headed towards the Polish border. The LR of Berlin aerosols is determined using a 30-min average profile gathering the data recorded when crossing southwest Berlin (10:30-11:00 UTC). For the same reasons as in Paris, only a single-layer constrained Klett inversion was possible to retrieve the lidar ratio over Berlin. In this case (observation recorded close to solar noon), the Raman channel range was actually so limited that the columnar LR had to be constrained using the AOT derived from MODIS Terra (0. 26 at 550 nm, satellite overpass at 10:25 UTC). In addition, the Ångström exponent used to retrieve the cumulative AOT on this peculiar day was 1.
8, following the daily mean value retrieved by the AERONET sun-photometer in Leipzig. Indeed, back-trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) show that both cities were overflown by the same air mass on this day, which came from northwestern Poland and the Baltic Sea (Figure  S1 ). Data recorded over southeast Berlin were not used because boundary layer cumulus clouds had formed at this time, making the aerosol profile too heterogeneous for the constrained Klett procedure to work properly. The LR value retrieved using the morning data was 68 ± 21 sr. Figure  5  presents the AOT, ABC and PDR from the 5-min average profiles, inverted using the LR value retrieved from the lidar/MODIS synergy. Several aerosol layers were present in the free troposphere coming from Eastern Europe, but only the local aerosols trapped in the PBL are considered here.
Moscow The van passed near Moscow on the early morning of Monday 17 June 2013. It entered the agglomeration from the south at 06:15 LT (02:15 UTC), bypassed the inner city using the third ring road (~14 km from the city center) and exited the agglomeration towards the east at 07:45 LT (03:45 UTC). The wind was very light on this morning, blowing from the north-northwest direction so that the van's ride was downwind of Moscow city center. To determine the LR, all data recorded in Moscow agglomeration were gathered into a single 90-min average profile. The sun was lower than when the van crossed Paris or Berlin so the multi-layer constrained Klett inversion could be used up to 1. 5 km a. m. s. l. The resulting average profiles of ABC, LR and PDR are presented on Figure  6a. According to HYSPLIT back-trajectories (Figure  S2 ), the upper layer of low LR (46 ± 8 sr) and relatively high PDR (up to 9%) was part of the dust event described in Dieudonné et al.
The wind was blowing from the southeast, both near the ground and in the free troposphere, so that the lidar was downwind from both the city center and the oil fields. On the contrary, Ufa major industrial sites were in the north-northeast direction and did not a priori impact the lidar. The aerosol optical properties were first determined from a 3. 3-h average profile grouping the night data (00:40-04:00 LT, i. e. , 19:40-23:00 UTC). The time-averaged ABC, LR and PDR profiles are presented on Figure  6b. Due to the very low aerosol load, the ABC was noisy, the LR profile exhibited fluctuations and the inversion gave unrealistically high LR values below 0. 8 km a. m. s. l. The lowest layer, between 0. 8 and 1. 5 km a. m. s. l. , was the residual layer with an average LR of 96 ± 9 sr and an average PDR of 4.
The left part of Figure  8  shows a low ABC background extending up to 3-4 km a. m. s. l. that already appeared on the nocturnal average profile (Figure  6b ). Between 01:20 and 02:15 UTC, a highly depolarizing layer passed over the lidar, with an average PDR of 20 ± 7% and a maximal value reaching 24%. To ensure these high PDR values did not result from an inappropriate LR, the 55-min average profile The aerosol optical properties were first determined from a 3. 3-h average profile grouping the night data (00:40-04:00 LT, i. e. , 19:40-23:00 UTC). The time-averaged ABC, LR and PDR profiles are presented on Figure  6b. Due to the very low aerosol load, the ABC was noisy, the LR profile exhibited fluctuations and the inversion gave unrealistically high LR values below 0. 8 km a. m. s. l. The lowest layer, between 0.
4 and 1. 3 km a. m. s. l. as constraint. The resulting LR value (96 ± 7 sr) was almost identical to the 96 ± 9 sr observed in the residual layer during the night and used in the representative profile, confirming the PDR values presented for this layer on Figure  8  were trustworthy, although we lack information about the local wind field and particle sources to identify the nature of these aerosols. Remote Sens. 2017, 9, 978 14 of 29 grouping data recorded during the layer's overpass were inverted using the single-layer constrained Klett inversion method, using the Raman partial AOT between 0. 4 and 1. 3 km a. m. s. l. as constraint. The resulting LR value (96 ± 7 sr) was almost identical to the 96 ± 9 sr observed in the residual layer during the night and used in the representative profile, confirming the PDR values presented for this layer on Figure  8  were trustworthy, although we lack information about the local wind field and particle sources to identify the nature of these aerosols.
The LR in the afternoon urban PBL was determined from a 4-h average profile (15:35-19:40 LT/07:35-11:40 UTC). The Klett procedure was constrained by the Raman partial AOT over a single layer extending from the complete overlap to the PBL top (1. 6 km a. g. l. ). The resulting LR value was 50 ± 4 sr, which is compatible with the 58 ± 12 sr reported for Central European pollution by Mattis et al. [60]  though it is lower than observations in Western Europe or Moscow at 355 nm (this paper,  [18, 48] ). A second average-profile was computed from nighttime data (23:10-03:15 LT/15:10-19:15 UTC) and inverted using the multi-layer Raman constrained Klett procedure. The resulting ABC, LR and PDR profiles are presented on Figure  6c. The lowest layer (0. 6-0. 8 km a. m. s. l. ) corresponds to the upper limit of the shallow nocturnal inversion layer and exhibits a LR of 80 ± 6 sr.
Supplementary Materials: The following are available online at www. mdpi. com/2072-4292/9/10/978/s1. Figure  S1 : HYSPLIT 3-day back-trajectories ending over Berlin (page 1) and Leipzig (page 2) at the time when the data used to retrieve the LR of Berlin aerosols were recorded (6 June 2013 at 11:00 UTC) and in the middle boundary layer (750 m a. m. s. l. ). This figure was produced using the online HYSPLIT model (http://ready. arl. noaa. gov/) [78, 79] , selecting the GDAS reanalysis at 0. 5 • and 3-h resolution as wind dataset;.
Acknowledgments: The authors would like to thank  Frederik Paulsen ,  Honorary Consul  for the  Russian Federation in the canton of Vaud, Switzerland , both for his financial support and for obtaining the permission to operate in Russia. The authors are also very grateful to  Alexander Ayurzhanaev , from the  Siberian Branch of the Russian Academy of Sciences, Baikal Institute of Nature Management ,  Ulan-Ude , for his vital help with the logistics of the journey while he was aboard the van. We also thank  Yoann Chazette  for his help during the trip. Finally, the authors thank  Cyril Moulin , head of the  Laboratoire des Sciences du Climat et de l'Environnement , for his support and assistance in the administrative part of the project. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and/or READY website (http://www.
When known, the contrail-causing aircraft and ambient conditions are characterized. The individual datasets are briefly described, including a few new analyses performed for this study, and compiled together to form a "contrail library" (COLI). The data are compared with results of the Contrail Cirrus Prediction model CoCiP. The observations confirm that the number of ice particles in contrails is controlled by the engine exhaust and the formation process in the jet phase, with some particle losses in the wake vortex phase, followed later by weak decreases with time. Contrail cross-sections grow more quickly than expected from exhaust dilution. The cross-section integrated extinction follows an algebraic approximation. The ratio of volume to effective mean radius decreases with time. The ice water content increases with increasing temperature, similar to non-contrail cirrus, while the equivalent relative humidity over ice saturation of the contrail ice mass increases at lower temperatures in the data.
In addition to the question of when contrails form, one needs to understand the entire lifecycle of contrails, from initiation until final sublimation or precipitation  (Schumann and Heymsfield, 2016). 5 The critical testing of models requires reliable quantitative data, not only on the contrail and plume properties, but also on the contrail age and the generating aircraft, on the atmosphere conditions in which the contrail formed, and on the observational methods. This paper considers individual contrails with known ages, because these should provide more specific model tests. For this study, we have collected data on individual contrails from various measurements reported in previous 10 publications, partly with additional information collected from the experimenters and some from re-analysis of the data, and compare them to CoCiP results.
Contrail-library parameters The contrail library, COLI, is mainly an Excel table (see supplement) that contains a list of contrail observations. For each observation, we collected available information on the aircraft and engines used, fuel (here kerosene in all jet cases), 25 atmosphere, instrumentation, projects and references, and mean contrail properties. Tables 1 to 6 list and define the corresponding parameters. The list of parameters should be extended when more data (e. g. , for alternative fuels) become available. Because of the variety of sources, the information available for individual observations covers subsets of the set of all parameters. Here we collect data from the investigations listed in Table  7 , sorted by year of observation. We also include older data, discussing instrument aspects, because the number of observations with sufficient information is small and we try 30 to cover a wide range of ages and atmospheric conditions.
03 m s -1 , stronger horizontal than vertical). The PMS FSSP-100 and OAP-2D-C probes detected ice particles larger than 3 m with concentrations of 0. 1 cm -3 and IWC of ~40 mg m -3. The Helgoland radiosonde indicated near-ice saturation at 8. 5-10 km altitude  (Hennings et al. , 1990). 5 Higher ice particle concentrations (up to 1. 5 cm -3 ) were derived for another contrail, observed over the North Sea during ICE 210 on 13 October 1989. In-situ measurements were obtained with a PMS FSSP-100 and a PMS 2D-C probe onboard the MERLIN aircraft of the Centre d'Aviation Météorologique, Meteo France  (Gayet et al. , 1993) , and with an upward looking lidar ALEX-F  (Mörl et al. , 1981)  on a DO-228 aircraft operated by DLR  (Gayet et al. , 1996a). Gayet et al. (1996b)  analyzed both the FSSP-100 and 2D-C probes and listed data for n ice , r eff and IWC in addition to T and altitude for four 10 contrail samples.
The data showed that the SAC threshold temperature is slightly higher for finite overall propulsion efficiency. These observations were of importance in understanding the impact of fuel sulfur on aerosol and contrail formation, and were used for comparisons to model studies in several follow-on papers  (Kärcher et al. , 1996; Schumann, 5 1996; Brown et al. , 1997; Andronache and Chameides, 1998; Kärcher et al. , 2015). SULFUR 2 continued the investigation of sulfur impact on aerosol and contrails using airborne in-situ measurements from the DLR Falcon behind the ATTAS aircraft for fuels having different FSC (170 and 5500 g g -1 ). The single-flight experiment was performed over Southern Germany, 22 March 1995, for temperatures at and below the contrail threshold. Here, we include the W and D data at about 3 km distance (18-s age) behind the aircraft.
5194/acp-2016 -773, 2016  Manuscript under review for journal Atmos. Chem. Phys. Published: 15 September 2016 c Author(s) 2016. CC-BY 3. 0 License. particles were measured with a PMS FSSP-300. Other optical spectrometers, a Hallett-type replicator, and a novel polar nephelometer  (Gayet et al. , 1998)  were used to characterize the size, concentration, shape and scattering phase function of larger ice particles. The paper lists the mean properties of the contrails and cirrus clouds measured in terms of temperature, n ice , r vol and r eff (actually diameters were reported). The given humidity values mostly suggest subsaturation, but the large IWC detected in the contrails suggests humidity exceeding ice saturation. The pressure values were obtained from  Schumann 5 (2012). COLI includes information and a few additional data from the campaigns CIRRUS'92 and CIRRUS'94  (Strauss et al.