dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarities among the individual datasets can also be reasonably well explained by their data category. While ET simulations from the ISIMIP global water and biomes models are very similar, crop models show substantial differences to all other realizations (the majority of the crop model simulations are members of the same cluster; cluster 3 in figure  3 ), reflecting the missing (or only rudimentary) representation of crops in the water and biomes models. E2O simulations also share most of their space-time variability (all but one of the E2O simulations fall into the same cluster; cluster 4 in figure  2  and cluster 5 in figure  3 ). This could be due to stronger similarities among the models participating in this project. Diagnostic datasets mostly show similarities in the spatial variability of the time averages (for ET tot in figure  A3 , all but two diagnostic datasets are associated with the same cluster).
It is also noticeable that ET diagnostics from the LandFlux project (i. e. PM-MU LANDFLUX, PT-FI LANDFLUX and SEBS LANDFLUX) form their own cluster in the spatio-temporal dendrogram (most pronounced in cluster 7, figure  2 ). For ET tot , we also note For each dataset, the coloured or symbolized rows on top of the matrix indicate the associated data category, the meteorological forcing, the number of soil layers, the ET scheme and the time-average of global land mean ET of each dataset. White boxes or missing symbols indicate that an association is not possible. The dendrogram on the top displays the similarity among the datasets. Colours and numbers indicate clusters (ordered by their size) of datasets with particularly small differences among its members. that the LFE products are quite similar to most of the diagnostic datasets (sub-tree of cluster 1 in figure  2 ), adding support that LFE can indeed be used as a reference.
The other two parameters displayed in the cluster diagrams are the (binned) number of soil layers (water and crop models only) and the ET parametrization scheme. These parameters apparently only play a minor role for the clustering. While there are still a couple of instances where the ET scheme seems to contribute to small differences among the datasets (e. g. sub-trees of clusters 1, 2 and 4 in figure  2  indicate that datasets using the Penman-Monteith formulation show particularly little differences), there is no apparent influence of the number of soil layers. It must also be noted that there are many cases where both the ET scheme and the model are the same (e. g. cluster 5 in figure  2 ), suggesting that (in these cases at least) the associated small differences are due to the identity of the model.
Also shown is the median of LFE ALL. The rightmost column shows area averages of individual diagnostic (black, purple, blue and green) and reanalysis datasets (brown), and the associated IQR (grey shading). The long-term average ET estimate from GETA 2. 0 is shown as a horizontal purple line. Drought events in the different regions are marked by vertical red lines. 5 , but for fractional ET for non-irrigated maize crops, also including crop models from the ISIMIP2a agriculture sector. The last year of the global land estimates is excluded here, as it also contains ET from maize crops within the Southern Hemisphere, where the respective growing seasons usually end in the next calendar year (see figure  A2 ). We now have a good overview of the structural differences among the assessed ensemble of datasets.
However, the respective sub-ensemble for ET maize (cluster 4 in figure  6 ) is well embedded in the overall spread for all regions considered. We also observe that crop models from the ISIMIP agriculture sector tend to disagree more on ET maize than datasets from the other clusters, although their median is well in agreement with the medians of the other sub-ensembles (cluster 3 in figure  6 ; note that this ensemble consists of only one model prior to 1979). Except from this, the medians of the clusters agree reasonably well, both in terms of their absolute magnitude (which is close to the median of LFE ALL) and their temporal variability. The prominent jump in ET tot from 1978-1979 apparent for cluster 1 within the Central European and global domains (figure  5 ) is due to the switch in the forcing data from WATCH to WFDEI at this time (when considering a sub-ensemble of only WFDEIforced simulations, this discontinuity becomes even more pronounced, not shown).
This artificial change signal is caused by differences in the reanalysis product used to create these datasets (ERA-40 for WFD, ERA-Interim for WFDEI), and is mainly due to the revision in the average aerosol loadings in North Africa and Europe affecting the short-wave incoming solar radiation in these regions  (Weedon et al 2014). A particularly important aspect to assess in greater detail is the response of the different datasets and subensembles to drought events in the study areas (drought years indicated in rows 2-4 in figures 5 and figure 6; in addition, monthly time series for each event year are shown in figure  7 ). Irrespective of the region and event considered, month-to-month changes in ET anomalies in the displayed sub-ensembles are similar to the changes in the ensemble mean of the diagnostic estimates.
In the case of western Russia, negative ET anomalies accompanying the 2010 heat wave are found in most of the diagnostic datasets, in the reanalyses, and most strikingly in the (model-dominated) clusters, which is in line with the ET signal discussed in Hauser et al  (2016). For the European heatwave in 2003, the diagnostic estimates show a noticeable disagreement from the models and reanalyses. However, the month-tomonth variations in ET anomalies from most of the datasets and simulations (figure  7 ) still resemble the event. The patterns agree both with theory Seneviratne (2012) and catchment-level observations Teuling et al (2013), as anomalously dry weather (accompanied by high net radiation and anomalously warm temperatures) commonly leads to an initial increase in ET, followed by a decrease later in the season (when soil moisture reaches critical levels).
e. spatio-temporal) differences in ET tot. This underlines that besides further model improvements it is at least equally important to further reduce uncertainties in the forcing datasets. Mean uncertainties among the assessed subensembles are lowest for GSWP3-forced ISIMIP simulations (ET tot ) and for EartH2Observe simulations (both ET tot and ET maize ; see table  3 ). However, this does not imply unbiasedness of the corresponding ensemble averages. Irrespective of the substantial uncertainties, the global and regional averages of the cluster-based sub-ensembles are in reasonable agreement to each other. This also holds for crop-specific ET, where the investigated crop models have a similar median tendency but larger inter-model spread. Considerable differences to the reference ET from LandFlux-EVAL and also to the other sub-ensembles have only been found for the subset of ISIMIP models driven by PGMFD v.
2 (cluster number 2, mainly emerging in the global land average). We have further assessed the representation of selected droughts and heat waves in the identified subensembles. We could demonstrate that most of the assessed datasets show the anticipated signal (i. e. negative ET anomalies that emerge after an initial surplus in ET), although the magnitude of these anomalies is usually of the same order as the magnitude of the spread among the estimates. It is tempting to use our results to assign weights to individual datasets using e. g. their bias with respect to parameters describing the central tendency of the entire ensemble. However, as we cannot ensure the unbiasedness of the ensemble mean, we suggest not to do this. As a recommendation, it seems suitable to rather choose the whole ensemble or a representative sub-ensemble.
Dole et al (2011)7 • N-53 • N and 1 • W-20 • E, representing the European land area with the highest JJA temperature anomalies in summer 2003 according toSchär et al (2004)) and (3) western Russia (50 • N-60 • N and 35 • E-55 • E, according toDole et al (2011)). These regions have been selected, as they contain large areas of agricultural land that has been affected by severe heat waves and droughts during the time period covered by most of the assessed datasets. The spatial extent of the study areas is also indicated in figures A2 and A1. For each of the regions, area averages of ET were derived by weighting each grid cell by its land area (based on ISLSCP II Global Population of the World, Balk et al 2010). -2005) using all datasets except from GETA 2. 0, MODIS Global ET and WECANN (which are not of sufficient length).
Acknowledgments This research was funded by the  European Research Council DROUGHT-HEAT  project (contract  617518 ). PEGASUS simulations were carried out by  D. Deryng  on the High Performance Computing Cluster supported by the  Research and Specialist Computing Support service at the University of East Anglia. GL was supported by the  Office of Science of the US Department of Energy  as part of the  Integrated Assessment Research Program. CLM water sector simulations were performed using PNNL Institutional Computing at  Pacific Northwest National Laboratory. PNNL is operated by  Battelle Memorial Institute  for the  US DOE  under contract  DE-AC05-76RLO1830. GPW was supported by the  Joint DECC and Defra Integrated Climate Program -DECC/Defra  ( GA01101 ). J L was supported by the  National Natural Science Foundation of China  ( 41625001 ,  41571022 ) and partly supported by the  Southern University of Science and Technology  (Grant no.
All crop model simulations use fixed fer- tilizer application rates except for LPJmL and LPJ-GUESS, as fertilizer input is irrelevant for this version of these models. As previously mentioned a ET estimates from crop model simulations participating in the ISIMIP2a agriculture sector are performed as pure crop runs only reporting growing season ET without the fallow period for a specific crop type and irrigation scenario (see more details in section 2. 1). bWe distinguish here between four data subsets: (1) diagnostic datasets only (denoted as LFE DIAGNOSTIC), (2) land surface model (LSM) simulations (LFE LSM), (3) reanalysis datasets (LFE REANALYSES), and (4) all datasets combined (LFE ALL). c As naturalized runs are not available for these biomes models, we have used varsoc simulations instead (i.
If any of the grid cells (from any dataset and time step) contain missing data, values corresponding to those grid cells are removed from all datasets for all time steps. From the m = 75 (m = 89 for ET maize ) datasets remaining, we then proceed as follows: ): (1) the Great datasets focusing on (a) a combination of spatial and temporal variability (replicating the method described and applied in Sanderson and Knutti (2012) andSanderson et al (2015)for the univariate case) and (b) spatial variability alone (following Mueller et al 2011). For both approaches, we first create a subset only containing data from the period of temporal overlap(1989.
3D  and E ). Starting from 1 hour of recovery, both glutathione and ascorbate increased significantly in Lp2-6, and continued to increase compared to controls (pre-sub) up to 3 to 5 hours after de-submergence. Although ascorbate levels increased in Bay-0, it was delayed compared to Lp2-6 (from 1 day of recovery onwards). Additionally, we looked for candidate accession-specific genes in the Ribo-seq dataset that could explain higher ROS production in Bay-0. We identified the plasma membrane bound NADPH oxidase RESPIRATORY BURST OXIDASE HOMOLOGUE (RBOHD; At5g46910) that catalyzes ROS production. Ribosome-associated transcript abundance of RBOHD increased during submergence in Bay-0, and recovery conditions further elevated RBOHD transcript abundance compared to a moderate induction in Lp2-6 (Fig.
SAG113 and ORE1 The Ribo-seq data revealed accession-specific genes in the "ethylene-activated signaling pathway" (Fig. 2C ). To further investigate the role of ethylene in the differential submergence recovery responses of the two accessions, whole plant ethylene emission was measured. Ethylene production was significantly higher in Bay-0 than in Lp2-6 after 5 days of submergence (0 hours of desubmergence), and this trend persisted 1 hour and 1 day after de-submergence (Fig. 6A ). Ethylene production in Lp2-6 was almost half that in Bay-0. To investigate whether this ethylene was causal to the stomatal response and plant performance, as reflected in higher chlorophyll loss in Bay-0 during recovery, ethylene action was blocked using 1-methylcyclopropene (1-MCP). Treatment of Bay-0 plants with 1-MCP following de-submergence strongly reduced the number of open stomata (Fig.
6B ) and decline in chlorophyll content (Fig. 6C ). We next explored the Ribo-seq dataset for genes that might mediate the ethylene effect on stomatal behavior and chlorophyll loss during recovery. Amongst the accession-specific genes, we identified two previously confirmed targets of the transcription factor EIN3, a positive regulator of ethylene signaling  (31, 32) : SENESCENCE ASSOCIATED GENE 113 (SAG113; At5g59220) and the transcription factor NAC DOMAIN CONTAINING PROTEIN 6/ORE1/ORESARA 1 (ANAC092/NAC2/NAC6; At5g39610). Both SAG113 and ORE1 were identified as accession-specific genes with increased ribosome-associated transcript abundance in Bay-0 during submergence and 3 hours of recovery, whereas Lp2-6 showed low induction of these transcripts. This trend was confirmed using qRT-PCR in an independent experiment assessing total SAG113 and ORE1 transcript abundance (Fig.
Acknowledgments At Utrecht University, we thank  Rob Welschen  for managing the growth facilities,  Emilie Reinen  and  Zeguang Liu  for genotyping mutant lines, and  Yorrit van de Kaa  for seed harvesting. We appreciate  Timo Staffel  at the  University of Kiel  for assisting with plant growth for EPR measurements. We thank  Thomas Girke  of  University of California, Riverside  for guidance on the RiboSeq workflow in R/Bioconductor. This work was supported by grants to R. S. from the  Netherlands Organisation for Scientific Research  ( NWO 016. VIDI. 171. 006 , and  NWO-VENI 863. 12. 013  and grants to J. B. -S. from the  US. National Science Foundation  ( MCB-1021969 ) and the  US Department of Agriculture National Institute of Food and Agriculture Hatch program. E. Y. was supported by a PhD scholarship from  Utrecht University.
6 g and 540. 3 g. We analysed the sagittal otolith morphology and morphometry to determine a relation with the fish ontogeny. Fish age was determined from the sagittal otoliths to identify growth structures based on digitally processed otolith images aided by the TNPC software. Results. No significant difference between the two otoliths (left and right) was detected (ANCOVA, P ˃ 0. 05). The correlation between each biometric parameter of the otolith (length and width) and fish length (TL) was significant (ANCOVA, P < 0. 05). The evolution of marginal increment analysis (MI) showed that the annual periodicity of the growth ring was between July and December. The growth parameters of the von Bertalanffy model were estimated for each sex separately. In females they assumed the following values: TL ∞ = 35.
9 t in 2014 (data acquired from an unpublished database of the Algerian Ministry of Fisheries). In the Algerian fisheries, L. mormyrus is caught by a multi-gear fishing, mainly with trammel nets and secondary with bottom trawling. These two kinds of fishing gears accounted respectively for 80% and 20% of the total catch of striped seabream in the region between 2012 and 2014 (database of the Algerian Ministry of Fisheries). Despite the FAO catch statistics, which indicate a stable population over the last ten years in the Eastern Mediterranean Sea, there are some signs indicating that the population of L. mormyrus has been affected by overfishing and may also indicate future decline, thus, the species has a 'least concern' status in the IUCN (The International Union for Conservation of Nature) Red List of Threatened Species  (Russel et al.
Otoliths were scanned in reflected light by placing the concave side of the otolith against the glass surface. The scanning process was performed in the dark, with an open scanner cover to get a black background. Each otolith was then analysed using the TNPC software (on digital processing of calcified structures, developed by IFREMER in collaboration with the company VSG, Visualization Sciences Group formerly Noesis). To limit interpretation errors, two different readers examined each otolith. Otolith length (OL) and width (OWi) were measured to the nearest 0. 001 mm. The otolith length was defined as the longest axis between the anterior and the posterior otolith edges and width as the distance from dorsal to ventral edges taken perpendicular to the length through the otolith focus. Analysis of covariance (ANCOVA) was used to evaluate the differences between the fitted total length/otolith parameters (Ow after log transformation, OL, and OWi) relations according to the left and right side of the inner ear.
2 ). The minimal value of marginal increment was used to indicate the month of growth increment formation. Growth analysis. Growth analysis was measured by the von Bertalanffy model (von Bertalanffy 1938) according to the equation ( ) 0 -K (t -t ) t TL = TL 1 -e ∞ ⋅ ⋅ where TL t is the total length at age t, TL ∞ is the total asymptotic length, K is the growth coefficient, and t 0 is the theoretical age at which length equals zero (fitted parameters). Models per sex and for both were generated using the FSA package * in the open source R program. To research potential differences in growth parameters between males and females, maximum likelihood ratio tests  (Kimura 1980)  were calculated using the fishmethods package * * in R program. The phi-prime test (ϕ′)  (Munro and Pauly 1983)  was used to compare the values of the growth parameters given in the present paper with those reported by other authors for the same species where TW is the fish total weight [g] and TL is the total length  [cm].
tions to the temperature retrieval, but no systematic attempts are made to fully establish this conclusion. These authors also explore the possible influences of tides, lidar-satellite co-incidence criteria, satellite vertical averaging kernels, and satellite temperature accuracy as possible contributing factors. The work of this paper is to evaluate the suitability of lidars as a reference data set and to address the problem of systematic errors due to initialization of the lidar algorithm. The subsequent comparison of the improved lidar temperatures to satellite measurements is conducted in the companion paper  (Wing et al. , 2018). This work follows three main goals: (i) the introduction of the long-term data set and the instrumental changes, (ii) treatment of this heterogeneous data set for use in the accompanying paper, and (iii) improvement of the temperature algorithm and reduction of the warm bias compared to satellite soundings.
These goals cannot be completely separated from each other, but goal (i) is broadly addressed in Sects. 2. 1-3. 2 and 3. 4; goal (ii) is addressed in  and goal (iii)  is addressed in Sect. 5. Section 2 of this paper describes the current experimental setup, the specifications of two Observatoire de Haute-Provence (OHP) lidars, and the measurement cadence of two key NDACC (Network for the Detection of Atmospheric Composition Change) lidar systems. Section 3 of this paper outlines techniques for minimizing the magnitude of the aforementioned lidar-satellite temperature bias by systematically detailing a rigorous procedure for the treatment and selection of raw lidar data and will propose improvements to the standard NDACC lidar temperature algorithm for the UMLT (upper mesosphere and lower thermosphere) region.
Section 4 of this paper gives the net results of the temperature modifications and system improvements in the Lidar Température et Aérosols (LTA) lidar at OHP. Section 5 of this paper compares the lidar temperatures produced by an NDACC-certified temperature lidar at 532 nm with temperatures produced by the non-absorbing 355 nm line of a co-located NDACC-certified ozone DIAL (dIfferential absorption lidar) system. This comparison is conducted using a large database of two co-located lidar systems with the goal of providing confidence in the long-term stability of the lidar technique at both wavelengths. There are currently 10 certified temperature lidars, six of which are current in their data submission and have temperature profiles freely accessible online. Similarly, there are 12 certified stratospheric ozone DIAL systems, of which five systems are current with data submission and are available through the NDACC website.
We hope that this work will encourage sites with outstanding data obligations to submit their measurements and for DIAL ozone sites to seek validation for their temperature data products for inclusion in the NDACC database  (CPC Team, 2018). As an ancillary goal we will show that temperatures produced by the Rayleigh lidar tech-nique are accurate, precise, and stable over multiple decades and as such are the ideal type of measurement for use in future ground-based validation of satellite temperatures. The result of this demonstration will be used in the companion paper  (Wing et al. , 2018)  as justification for validating satellite data with lidar temperatures.
Therefore, it is necessary to address this problem using software during the analysis. It is particularly challenging to separate small-amplitude spikes when the signal-to-noise ratio approaches 1. It is therefore necessary to establish a consistent criterion to determine which data points belong to the the population of real lidar returns and which points are likely contamination spikes. We have chosen to employ a straightforward Tukey quartile test  (Tukey, 1949)  on the difference between consecutively binned lidar returns as this statistic is relatively insensitive to signal drift during the course of the night. The quartile technique is equally useful in regions of high signal returns and in background regions, and it shows stability and consistency in identifying outliers. Figure  4  is a plot of photon count rate as a function of binned arrival time and shows an example of several photon count acquisitions plotted as a stack plot with the black line representing the 2σ limit on the population of lidar returns.
In general, N counted < N received due to effects of the system dead time. This dead-time correction can be calculated based on factory specification of the counting electronics or a theoretically derived dead time, or it can be measured directly using a low-gain lidar channel. The OHP lidars measure the dead time directly and correct for saturation in the high-gain channels with information from the low-gain channels. If the low-gain channel is not available, a theoretical correction of 7 ns is applied to pre-2013 data and 4 ns is applied to more recent data following the installation of a Licel digital recorder. In order to measure the dead time experimentally, we assume that the low-gain channel, because it has low photon count rates, will always operate in the linear response regime and will never suffer from dead-time effects.
As we iterate downward, the influence of our choice of a priori pressure becomes less significant and the calculated temperature profile becomes entirely data driven. X i = ρ(z i )g(z i ) z P (z i ) + z 2 (8) T (z i ) = Mg(z i ) R log(1 + X i ) z (9) In order to calculate a single temperature profile from 5 to above 80 km, we meld the photon counts from the high-and low-gain Rayleigh channels together with the counts from the N 2 Raman channel. The slope of the logarithm of each of the three photon counts profiles is compared to a synthetic lidar counts profile generated based on the nightly average MSIS-90 density profile. The comparison gives us a first estimation of the linearity and alignment of the lidar data. We then select a clear linear region of each profile to use in calculating a MSIS derived scaling factor for each profile.