dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and methodology The international research program known as the African Monsoon Multidisciplinary analysis (hereafter referred to as AMMA) deployed rainfall gauges across three well-known mesoscale sites in West Africa with the aim of better understanding the links between the hydrological cycle and climate variability at this scale  (Lebel et al. , 2010 , see their Fig. 3 ). The sites studied are: the AMMA-CATCH Niger site located in the Sahel region of Niger (1. 6-3. 2°E; 13-14°N), the Malian Gourma site located in Mali (2-1°W; 15-17°N) and the AMMA-CATCH Benin site-formerly called the Hydro-meteorological Observatory of the Upper Ouémé Valley (hereafter OHHVO)-located in the Soudanian zone in North Benin (9-10. 4°N; 1. 5-3°E). Only the data from the AMMA-CATCH Niger and Benin meso-sites, which include well-instrumented supersites, were used in this study.
Description of the study areas and data The AMMA-CATCH Niger area is approximately 16,000 km 2 and is located between 1. 40°E and 3°E longitude and 13°N and 14°N latitude (Fig. 1 ). The rain gauge network in this region extends into the neighbouring region of Niamey. Originally, the network was set up during the EPSAT (Etudes des Précipitations par SATellite)-Niger experiment, which aimed to characterize the variation in precipitation in the Sahel at a relatively wide range of spatial and temporal scales, including the convective cell scale, to contribute to the development and validation of satellite-based rainfall estimation algorithms  (Lebel et al. , 1992). The AMMA-CATCH Niger study area is located in the core of the Sahel region and is dominated by two seasons, the dry season, which runs from October to May, and the rainy season, which runs from June to September.
). The purpose of such equipment was to study the life cycle of precipitating systems at the mesoscale, and their impact on watersheds at the local scale. Across these study areas, rainfall measurements were taken from tipping bucket gauges with a 0. 5 mm capacity, which provided a suitable temporal description of rainfall rates in the regions  (Depraetere et al. , 2009). This study was conducted using precipitation data from 2000 to 2010 recorded by each station in the EPSAT area (Niger) and from 1998 to 2010 in the OHHVO zone (Benin). The number of data points varied from year to year, with an average of forty (40) stations contributing to the EPSAT dataset and forty two (42) stations contributing to the OHHVO dataset, as outlined in Table  1. The available raw data used in this paper included 5-min rainfall records gathered from the two mesoscale networks for all stations and all years considered in this study.
In this study, we analysed the data from a hydrological point of view because the data were primarily acquired from rain gauges. In this context, the definition of each rainfall event depended on whether we relied on a single gauge ('point aspect') or an ensemble of gauges such as the dense networks across the EPSAT and OHHVO areas ('spatial aspect') to identify the event. Given data from a single station,  Smith and Schreiber (1973)  and  Bouvier (1986)  indicate that a rain event can be characterized by two parameters: (i) a time criterion associated with the minimum duration between rainfall occurrences that can be used to differentiate a prior rain event from the next, and (ii) a quantitative criterion associated with a rain depth threshold, below which the event is considered null. To reflect the dynamics of precipitating systems, it is important to consider the 'spatial' aspect of convective systems, particularly because the same event may be detected by many gauges at different times.
Differentiation of rainfall events and calculation of return periods The hyetographs for previously determined individual rainfall events at each gauge were revisited using sixteen sub-hourly, hourly and daily sampling time intervals: 5 min,  10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60  (1 h), 120 (2 h), 360 (6 h), 720 (12 h), 1440 min (24 h or 1 d). Maximum rainfall per event was then extracted from each gauge for the relevant durations. By considering independent rainfall events for all years using the BMA method, we obtained significantly large datasets per sampling duration. A statistical analysis of these different maximum rainfall samples for durations between 5 min and 24 h was performed to determine the reliability of the extreme nature of these maximum values. We also aimed to detect specific or systematic anomalies in the data series.
Radar is a useful tool to illustrate the spatial organization of convective systems and evaluate the reliability of the rain gaugebased classification across the OHHVO study area. Indeed, the analysis of radar images provides an overview of the organized or disorganized character of various convective systems. During the intensive AMMA (African Monsoon Multidisciplinary Analysis) measuring campaigns in 2006 and 2007, an X-band polarimetric radar (Xport) system was used to sample northern Benin and characterize the spatial and temporal variability of rainfall on the ground, which had been insufficiently described by the additional OHHVO rain gauge network. Further details related to the configuration, operating parameters, and data processing techniques related to the radar data are provided in Gosset et al.
(  2010 ) and  Koffi et al. (2014). Koffi et al. (2014)  explored the AMMA radar dataset to show that the oneparameter polarimetric algorithm ( ) R K DP performs well in retrieving rainfall rates above 30 mm/h relative to other estimators. In the present study, this algorithm is also used to derive rainfall fields from radar data. In Fig. 14 , we show the spatial organization of two rainy systems included in Group II R based on the radar reflectivity field identified at a time when these systems affected the stations recording extreme rainfall levels (Fig. 14a, c ). This figure also shows the cumulative rainfall patterns mid-and one-third of the way through the life cycles of these two events, respectively (Fig. 14b, d ). Both events were highlighted as extreme event instances via a rain gauge data analysis.
Conclusions Using data from two dense rain gauge networks at mesoscale sites located in the Sahel and Soudanian regions of West Africa, this study sampled rainfall events to analyse the extreme characteristics of rain across both areas. For each rainfall event, the maximum amount of rainfall for durations between 5 min and 24 h were sampled. We used the Gumbel extreme values distribution, IDF curves derived from observed maximum rainfall frequency distributions, and the theoretical IDF model outlined in  Casas et al. (2004)  to estimate the threshold for maximum rainfall rates; and we classified extreme rainfall instances according to their common characteristics. Across the Sahelian zone, a total of 32 extreme rainfall events were counted, while 90 events were identified across the Soudanian zone.
Acknowledgments Based on a French initiative, AMMA was built by year international scientific group and is currently funded by a large number of agencies, especially from France,  UK ,  US  and  Africa. It  has been the beneficiary of a major financial contribution from the  European Community 's  Sixth Framework Research Program. Detailed information on scientific coordination and funding is available on the AMMA International web site http://www. amma-international. org. The authors thank the reviewers for their valuable contribution to improve the quality of this paper. The authors are indebted to the geophysical station of  LAMTO (Côte d'Ivoire ) for funding the publication fees of the article.
2 ). Observation of ice rings for Lake Hovsgol in this respect is very important, as it shows that this phenomenon is not limited to Lake Baikal and that there may be similar physical phenomena leading to ice ring formation elsewhere. Our inventory shows that ice rings are also observed in regions that are shallower than depth of occurrence of gas hydrates (such as Maloye More in Lake Baikal-300-400 m or Lake Hovsgol-200-250 m) or in regions without known gas emission sources. This rules out gas release as an universal explanation of ice ring formation. Due to the higher frequency of observations (daily for MODIS vs. every 18 d or 16 d for Landsat) many more rings have been identified after 2002 (start of our MODIS dataset). Moreover, in many cases not all Landsat images are available in the archive.
Thus, prior to winter 2002/2003 many ice rings are probably still not identified, and those identified have often been observed only once per winter (but apparently they existed longer). As a result we can not draw any conclusions about the temporal evolution (interannual variability) of frequency of ice ring appearance, and datasets before and after winter 2002/2003 should be treated separately. There are also cases (such as the whole winter 2005/ 2006) when despite daily image availability from MODIS and the moderate presence of clouds, no ice rings were observed. To assess uncertainties related with ring date detection, for rings with daily availability of MODIS images (Lake Baikal, since winter 2002/2003), we provide number of days between last ring-free scene and first ring observation, as well as number of days between last ring observation and first ring-free scene (for both parameters the median value is 3 d).
The duration of ring observations in our dataset varies from 1 d (due to availability of satellite images and/or cloud cover) to 126 d; the typical duration is 5-10 d. Most frequently ice rings are observed in the second half of April (when snow cover is absent or minimal) before the ice break-up, but they have been observed as early as 31 January and as late as 26 May. The inventory presented in Table  1  is the most complete existing dataset on the location, size, timing of the appearance and disappearance and other parameters of ice rings in lakes Baikal and Hovsgol. However, it is not an exhaustive list, and further exploration using SPOT, AVHRR and other satellite archives will potentially provide new observations of this phenomenon.
018C) and conductivity (accuracy 0. 25% 6 5 lS/ cm, resolution 1 lS/cm). Since 2014, we have also performed semi-quantitative estimations of currents by defining direction and current strength (weak, moderate, or strong) from CTD cable inclination. The existing dataset contains more than 250 stations located along the tracks of radar altimetry missions  (Kouraev et al. 2015) , in the regions of known ice ring observations and other regions with interesting features. For the region near the Nizhneye Izgolovye Cape our sampling strategy is to do a transect(s) covering the region of ice ring locations known from the previous years, and for other regions of known ice rings observations to do at least one or several stations when possible. The winter of 2013/2014 was warmer than usual, the ice was thinner and already on 01 April 2014 an ice ring had been clearly identified on the MODIS image prior our field survey.
Knowing the exact location of the ring we were thus able to perform 2 d later (03 April 2014-04 April 2014) a dedicated study with high spatial sampling. Water structure and ice surface characteristics associated with ice rings As a result we have now four datasets of field hydrographic measurements in the regions where ice rings has been observed (Table  2 ). For the region of the Nizhneye Izgolovye Cape, measures in 2012 and 2014 represent situations when an ice ring was already visible on satellite images. These datasets (Fig. 4 ) are the most detailed and provide two transects across each of the ring structures. Observations in 2013 (Cape Shartlay, Lake Baikal) and 2015 (Lake Hovsgol) (Fig. 5 ) are less detailed but still reveal water structure prior (20-35 d) to ice ring appearance. All these hydrographical sections (Figs.
(2015)  found less mineralised water in the center than at the periphery of the ring (this is similar to our observations), but from the depth of 40 m downward their observations show no increase of salinity. This observation apparently led the authors to conclude that this anomalous structure results from upwelling of deep waters (with lower mineralisation and higher temperature) through the thermocline, and is potentially related to the presence of gas hydrates in bottom sediments. However, our observations of specific conductance for the four datasets (Figs. 4, 5 ) show no such feature; water in the eddy center always has higher mineralisation than water at lower depth and we observe upward and downward extension of water with specific conductance typical for the eddy core (same as for water temperature).
Our datasets for 2013 and 2015 (Fig. 5 ) representing situations 20-35 d before ice ring manifestation are too coarse to draw definite conclusions on ice thickness spatial distribution inside and outside of ice rings. However our observations (Fig. 4 ) for 2012 (when ice ring has been faintly seen) and especially 2014 (when an ice ring was well manifested) clearly show the spatial distribution of ice thickness. For all four datasets ice was black and crystalline (with some ice crust and/or snow on the surface, see Table  2 ) and without impurities. No water was observed on ice and no gas bubbles were presented in the upper water column. In 2012, the ice was thicker (80-91 cm) outside the ring, while in the ring and in its center it was already thinner (65-74 cm). In 2014, the ice was thick outside (61-73 cm) and less thick in the center of the ring (49-65 cm), while in the ring itself it was much thinner (32-61 cm), especially at the two stations of the northern part of the transect G-H (32 cm for station 401 and 34 cm for station 402).
Acknowledgments We would like to thank  A. Beketov  ( Ust' Barguzin National Park, Ust' Barguzin, Russia ),  T. Tivikova  ( Turka Forestry Administration, Turka, Russia ),  S. Sushkeev  ( Tunka National Park, Russia), Hurga and Bimba (Hovsgol National Park, Mongolia ),  A. Laletin  and many others who participated directly or helped to organise field surveys. A. Kouraev ,  A. Suknev  and  A. Laletin  are LEAD International fellows. We would like to thank  N. Granin  ( LIN SB RAS, Irkutsk, Russia ),  E. Berthier  ( LEGOS-OMP, Toulouse, France ) and many other colleagues for interesting and helpful discussions. This research is supported by the  French CNES TOSCA "Lakes," CNRS PICS "BaLaLaICA ,"  RFBR 13-05-91051 ,  ERA. NET RUS Plus S&T  # 226  " ERALECC," FZP 1. 5, ANR "CLASSIQUE," IDEX Transversalit e 2013 InHERA , and  FP7 MONARCH-A projects , as well as by GDRI " CAR-WET-SIB " and  French-Siberian Centre for Education and Research.
In the model, horizontal advection is calculated using the Van Leer second-order scheme and boundary layer turbulence is represented as a diffusion phenomenon, following  Troen and Mahrt (1986). Vertical winds are diagnosed through a bottom-up mass balance scheme. Dry deposition is coded as in  Wesely (1989)  and photolytic rates are attenuated using liquid water or relative humidity. Finally, the numerical time solver uses the TWOSTEP method  (Verwer, 1994). The 2006 annual anthropogenic emissions from the EMEP database  (Vestreng et al. , 2005)  at a resolution of 0. 5 • × 0. 5 • have been used (http://www. emep. int). They include CO, NH 3 , NMVOC, NO x , SO x and PM emissions for the 10 anthropogenic activity sectors of the Selected Nomenclature for Air Pollution (SNAP). The emission values are disaggregated into individual chemical species and at an hourly time step according to IER recommendations, and are spatially distributed over our simulation domain using a kilometric land use database (http://www.
glcf. umiacs. umd. edu). Biogenic emissions have been computed with the MEGAN model  (Guenther et al. , 2006)  using a land use database of 1 km resolution and hourly meteorological parameters from the MM5 model (see below) for the calculation of the various biogenic volatile organic compounds (VOC) emission flux intensity and temporal evolution (http://lar. wsu. edu/megan/). Climatologic LMDZ  (Hauglustaine et al. , 2004 ) model output data were used for boundary conditions. Finally, the mesoscale model MM5  (Dudhia, 1993)  was used to generate hourly meteorological fields for CHIMERE over a European domain covering our simulation domain, with a horizontal resolution of 54 km and using 32 levels in the vertical direction from the surface to 10 hPa. A European domain extending from 6 • W to 20 • E in longitude and from 38 • to 54 • N in latitude (see Fig.
However, due to low amounts of anthropogenic semivolatile organic compounds (SVOC) from the oxidation of classic precursors (Toluene, Xylene, Trimethylbenzene, etc. ) over Europe, the production of anthropogenic oligomer could be negligible  (Aksoyoglu et al. , 2011). To transcribe this approach in the model, we have allocated a first-order oligomer production kinetics to all the hydrophobic and hydrophilic surrogate species (AnA0-1-2D and BiA0-1-2D, AnBlP, AnBmP, BiBmP, ISOPA1 and ISOPA2) of the CHIMERE aerosol module. Preliminary simulations with CHIMERE confirmed the precedent findings, i. e. , a very low budget of oligomers of anthropogenic origin (concentrations reach 10 -3 ng m -3 at the maximum over the domain) compared with biogenic oligomers (which concentrations reach a few µg m -3 for oligomers over many continental areas).
Module implementation As mentioned in the previous section, isoprene oxidation products could not be considered as oligomer precursors in the pH-dependent approach, due to the absence of structural information on these species in the gaseous chemical scheme. A refined chemical scheme for isoprene oxidation in CHIMERE is under development  (Couvidat and Seigneur, 2011)  and will be included later in the model. Pending this future model development, we focus here on a comparative evaluation of oligomerization of monoterpene oxidation products using the two parameterizations described above. Nevertheless, the absence of molecular structure allocation for ISOPA1 and ISOPA2 (model oxidation products of isoprene) is not a limiting factor for the kinetic approach. Thus, considerations for the relative importance of kinetic oligomer production from monoterpenes and isoprene will be presented in Sect.
To check that the mutants were not affected during the differentiation step, we took benefit of the GFP-tag used to construct the mutants to analyze by confocal microscopy the aspect of the bacteroids. As observed for the WT strain, the two mutants were found perfectly differentiated into elongated bacteroids in A. afraspera and spherical bacteroids in A. indica (Fig 4Q  -4V ). Taken together, these data indicate that the absence of the O-antigen do not interfere with the ability of the ORS285 strain to establish symbiosis with Aeschynomene plants irrespective of the symbiotic process used. We considered also the possibility that the NFs synthetized by ORS285 strain, even if they are dispensable for the establishment of the symbiosis with A. indica, could interfere during the interaction of the bacteria with the plant and mask the role played by the O-antigen.
Methods Data sets. We focused on three separate approaches to assess the response of wheat yield to climate change in China: process-based crop models (two types: local agronomical models and generic crop models used for global applications-GGCMs), statistical models and field warming experiments. To integrate results derived from different studies, we used a common measure of temperature sensitivity of wheat yield (S Y,T , yield % change per °C). A literature search was performed on wheat crop yield in China through Web of Science, Google Scholar and China National Knowledge Infrastructure (CNKI; http://www. cnki. net). We considered all peer-reviewed studies published between January 1990 and February 2014 from which S Y,T values could be calculated. For local process-based models, S Y,T is usually derived from the difference between a simulation with an arbitrarily increased temperature (for example, þ 2 °C) and a reference scenario.
Using this criterion, we selected a total of 148 S Y,T samples in this study. Site descriptions (latitude, longitude, growing-season temperature (T GS ), growingseason precipitation (P GS ) and so on) for each study are given in the Supplementary Tables  2 3 4. T GS, P GS and growing-season diurnal temperature range (D GS ; the difference between daytime and nighttime temperature) in the tables are from 0. 1°monthly gridded data from Chinese Academy of Sciences of China Meteorological Forcing Dataset (CMFD)  38  since lots of publications did not report local climate conditions. For regional-scale studies, CMFD climatic data are weighted according to the spatial distribution of wheat cultivation area with 0. 5°s patial resolution  39. For the studies that reported T GS and P GS , we found T GS and P GS from the CMFD fit well with the corresponding observation data provided in the papers (Supplementary Fig.
S Y,T , S Y,P and S Y,R represent temperature sensitivity, precipitation sensitivity and radiation sensitivity of wheat yield, respectively. b 0 and e t are the intercept and error term, respectively. Data analysis. To investigate whether the distribution of modelling or experimental sites are representative of the country, we made a plot delineating the climate space of wheat-growing areas in China (Supplementary Fig. 1 ). We found the field-scale experimental and modelling sites we collected well cover the main wheat-growing area in China, though field-scale modelling sites do not cover areas with P GS of above 400 mm (12. 5% of wheat-growing area in China). To explore the regional variations of S Y,T , the six main production regions are divided with summary information shown in Supplementary Table  1.
To analyse the climate effects on the spatial variations of S Y,T for field warming experiments, ordinary least squares models were applied to derive the relationships between S Y,T and the independent climate variables (background growing-season temperature (T GS ), water supply (W GS ; precipitation þ irrigation), growing-season diurnal temperature range (D GS ) and daylight hours (L GS )). W GS used in this study only represents water input from rainfall and irrigation rather than available soil moisture to plants. Daylight hours (L GS ) were computed based on the latitude and solar declination at each site  41. The ordinary least squares models were then used to map the spatial distribution of S Y,T in observed area where rainfed wheat grows in China  42  based on gridded CMFD baseline climate data (1981-2010).
To achieve a good lateral resolution and to avoid EM coupling effect, the dipole-dipole array was applied to obtain the apparent resistivity and chargeability pseudosections. A Syscal R1+ (from IRIS Instruments©, Orleans, France) has been used in this study. Measurements were performed continuously every two days. The second profile, parallel to the barrier, was a temporary profile. It consists in 48 electrodes separated by 1 m. Measurements were performed every two months with an Elrec Pro (IRIS Instruments, Orleans, France). After performing time-lapse ER and IP measurements, the electrical data were analysed in three stages: (i) Initial anomalous data were filtered by a process of elimination: (a) current  < 100 mA; (b) potential  < 0. 1 mV; (c) apparent chargeability  < 0; (d) error  > 5% (resistivity measurements) and  > 2% (chargeability measurements); (ii) The quadrupoles in common to all the data sets were selected; (iii) Data processing was performed with RES2DINV software  [44] , in time-lapse mode.