dataesr/scientific-paragraphs-categorization huggingface dataset:
icare. univ-lille1. fr/ (last access: 9 May 2019). The first one is the CS-TRACK product, which is the collocated processed A-Train product on the CloudSat track. This product gives the possibility to work on lidar and radar data on the same resolution grid of 1. 1 km horizontally and 60 m vertically. From these profiles of active instruments data, a technique for the classification of hydrometeors (called DARDAR-MASK) has been developed. This technique is used to select the lidar-radar range bins (or pixels) where ice cloud property retrievals (DARDAR-CLOUD) can be performed  (Ceccaldi et al. , 2013). It is important that the classification is as accurate as possible since including liquid water pixels or noisy pixels in our retrieval could compromise the results. Indeed retrieval techniques are different for liquid droplets and for ice crystals, and a specific analysis should be applied to mixed-phase clouds  (Hogan et al.
The microphysical model The microphysical model is based on three main parameterizations: the normalized PSD, the a priori of N 0 , and the mass-diameter relationship. For DARDAR-CLOUD V 2 , the parameterizations of the PSD and the a priori of N 0 were determined using the in situ dataset described by  Delanoë et al. (2005). The main caveat of this study is that it did not use direct measurements of IWC, which may question the reliability of the validation of the microphysical model. The idea here is to assess and refine these parameterizations, using a more comprehensive and accurate dataset of ice cloud in situ measurements. Delanoë et al. (2014)  present a large in situ dataset collected during several ground-based and airborne campaigns between 2000 and 2007. During those campaigns, direct measurements of IWC were performed with a Counterflow Virtual Impactor or a Cloud Spectrometer and Impactor (CVI/CSI).
Such instruments provide valid measurements in the range from 0. 01 to 2 g m -3. For a better quality control of the measured PSD, the shattering effect was also considered in this study. Using the same in situ dataset, a series of M(D) relationships have been derived by  Heymsfield et al. (2010)  for specific cloud conditions. Delanoë et al. (2014)  compared the measured bulk IWC to the retrieved IWC obtained by the combination of the measured PSD and one of those power laws, which allowed the M(D) relationship giving the best match to the measured IWC to be selected for each campaign. A description of the selected M(D) is given in Delanoë et al. (  2014 ) (Table  3 ). The general mass-size parameterization, specific to this dataset and made of different power laws as a function of the measurement campaign, will be referred to as the "RETRIEVED" parameterization.
The BFM mass-size relationship used in DARDAR-CLOUD V 2 was validated on direct measurements of IWC, using a total water content probe combined with a fluorescence water vapour sensor. However, those measurements were restricted to a couple of flights performed in April 1992 over the North Sea and to the south-west of the UK, providing a dataset of fewer than 3000 points recorded at temperatures between -30 and -20 • C. Other relationships are described in the literature, for specific types of clouds, crystal habits, or temperature ranges (see  Heymsfield et al. , 2010, and Erfani and Mitchell, 2016). To account for the dependency of the relationship between D and M on temperature and particle size,  Erfani and Mitchell (2016)  propose to use a δ coefficient depending on temperature. However, temperature is not the only parameter that matters for the determination of M(D).
In order to accurately fit this relationship to each and every cloud situation, we would need more information on cloud type and particle size, which are not straightforward to derive from the CloudSat-CALIPSO synergy. In addition, it is difficult to change M(D) in the retrieval scheme upon the cloud type and the meteorological conditions without risking bringing discontinuity into the retrievals. As a result, in the case of the DARDAR-CLOUD product, we decided to focus on statistical results and assume a single M(D) relationship which can work for most of the situations. In the following, we detail how the dataset presented by  Delanoë et al. (2014)  was used to refine the microphysical model of the Varcloud algorithm.
The mass-diameter relationship Figure  4  shows the comparison between the measured IWC and the retrieved IWC, for different mass-diameter relationships: the RETRIEVED parameterization, for which a specific power law is selected for each campaign (panel a), the parameterization used in DARDAR-CLOUD V 2 , namely BFM, applied to all the campaigns (panel b), and finally the "Composite" parameterization, also applied to the entire dataset (panel c). The Composite was developed by Heymsfield et al. (  2010 ) using the measurements of all campaigns, combining different types of clouds and situations. As we want to keep a single M(D) in our algorithm, it is interesting to compare this more recent parameterization to BFM. It is clear that using dedicated parameterizations for specific atmospheric conditions and/or cloud types (that is, the RETRIEVED parameterization) gives better results when comparing the model to the measurements.
Impact of the new microphysical model The analysis of a more recent and larger in situ dataset including bulk IWC measurements allowed the microphysical model to be refined as explained in Sect. 3. 2. In this section, we show the consequences of this new parameterization in the IWC retrievals. To do so, the Varcloud algorithm was run using the V 2 and V 3 LUT and N 0 a priori value one after the other, both associated with the V 3 lidar ratio a priori. In the same way as for the study on the new lidar ratio a priori, we can look at the differences in the distribution of log 10 (IWC) (Fig. 9 ). The impact of the microphysical model is more com-  The first column shows the distributions for all retrieved pixels, the second column, for lidar-only pixels, the third column, for radar-only pixels, and the last column for lidar-radar pixels.
IWC retrievals As a summary of all these modifications in the retrieval code, Fig. 10  presents the difference between the new distribution (V 3 ) of retrieved log 10 (IWC) and the distribution of DARDAR-CLOUD V 2 (panels a to d) as well as the relative differences in IWC between the two versions (panels e to h). The new version includes all the updates presented above. Also taken into account is the update of CALIPSO Level 1 products (v4) consisting of the use of better ancillary datasets: a more accurate DEM (digital elevation model) and a new reanalysis product for the atmospheric variables (MERRA-2), which is shown to allow for more reliable CALIOP calibration coefficients. Information on this update can be found on the NASA website at the following address: https: //www-calipso. larc.
This information was obtained by running the algorithm several times with a different configuration. Each histogram is a comparison between two retrievals, processed with only one modification in the algorithm: changing the version of the CALIPSO Level 1 product (b), the DARDAR-MASK classification product (c), the parameterization of the lidar ratio a priori (d), or the microphysical model (e). When each contribution is taken separately, it can be seen that the highest percentage of occurrence is found for differences < 5 %. However, the combination of the new a priori value for the lidar ratio and the new microphysical model leads to an average reduction of -16 % from DARDAR-CLOUD V 2 to DARDAR-CLOUD V 3. As said previously in Sect. 4. 1, the 18 % of the data showing -100 % difference account for the evolution of the hydrometeor classification (Fig.
11a, c ). The new updates on CALIPSO product can also modify the classification and the retrieval, although to a lesser extent. Indeed, more than 80 % remain with differences < 5 % (Fig. 11b ). The largest differences are due to the impact of the new classification, which accounts for the broadening of the probability density observed in Fig. 10. This analysis shows that fewer than 10 % of the data remain with differences < 5 %.
More work could be done on the subject, adding radiometric instruments or looking at new instrumental platforms, such as the upcoming ESA/JAXA EarthCARE satellite, with a more sensitive radar, and High Spectral Resolution Lidar, which could help refine our analyses. This sensitivity study was done to help us identify improvements to be considered in the new version that will be made available at the AERIS/ICARE Data and Services Center. Our approach here is to use information and datasets validated by the literature to determine the microphysical assumptions and study the sensitivity of our algorithm to those assumptions. Further improvements are aimed at relying on more in situ and satellite observations to make parameterizations and combination of instruments more efficient, benefiting from CALIPSO-CloudSat extension and EarthCARE advent.
Acknowledgements. Quitterie Cazenave's research is funded by  CNES  and the DLR/VO-R young investigator group, and we thank the  AERIS/ICARE Data and Services Center  (http://www. icare. univ-lille1. fr/,last access: 9 May 2019) as well as the CloudSat and CALIPSO projects for providing access to the data used in this study. Review statement. This paper was edited by  Patrick Eriksson  and reviewed by two anonymous referees.
Data availability. DARDAR-CLOUD retrieval products v2 as well as the DARDAR-MASK v1. 1. 4 and v2. 11 products are publicly available on the AERIS/ICARE website (http://www. icare. univ-lille1. fr/, last access: 11 January 2017). Microphysical data from the ARM program can be found on the ARM archive website (https://www. arm. gov/data, last access: 20 January 2017).
Measurements were performed using ImageJ software (V1. 51k). 79 All quantitative variables were analyzed using R,  80  and we used SD to quantify the amount of variation within our sampling. Binary and multinomial logistic regression models were applied with the package "rms" on R to determine the best variable between age and SL explaining the qualitative criteria. 81 o define the developmental stages, individuals were categorized according to the chronological appearance of the different qualitative criteria.
ACKNOWLEDGMENTS We thank the aquariology service of the marine station of Banyuls-sur-Mer, who provided the larvae used in this study. We also thank the  Biopic platform of the marine station of Banyuls-sur-Mer  for its help on the morphological observations. We thank the technological center of the microstructures where the scanning electron microscopic observations were made. We thank  Yves Desdevises  and  Laurence Besseau  ( BIOM UMR 7232 ) for their help with the statistical analyses and the writing of the manuscript. We thank  Sarah Mc Menamin  for critical reading of the manuscript and many useful suggestions. Our rearing structure of clownfish has been supported by the  CNRS  (interdisciplinarity actions) and  EMBRC France.
Locomotion analysis. On the raw data (digitized markers), we first applied a fourth order low-pass Butterworth filter with a cut-off frequency of 60 Hz. This is well above the mean stride frequency in our study (mean frequency = 11. 74 Hz in A. boskianus and 11. 69 Hz in P. muralis). Second, a general filter using a piecewise cubic spline interpolation method was applied for missing data. For instance, the very fast movement of the limbs during the swing phase sometimes made few dots impossible to digitize correctly. In these occasional cases, we kept the running sequence with the few missing dots (usually 1-5 frames). If more than one third of the frames was missing, we removed the complete stride from the dataset. We then estimated the position of the body centre of mass (CoM) based on the dissections of three A.
Acknowledgements We are very grateful to  Gregory Desor  and  Jan Scholliers  for their valuable help for designing and building the complex terrain. We thank  Lise Eerdekens ,  Nick Van Hul  and  Ilse Goyens  for their valuable help in digitising a large number of running sequences. We thank the referees for their constructive and detailed comments on the first version of the manuscript. We are very grateful to  Josie Meaney-Ward  who revised and improved the English of the manuscript. This work was supported by  Fonds Wetenschappelijk Onderzoek  ( FWO  project  G0E02. 14N ). J. G. was funded by an  FWO   postdoctoral fellowship  ( 12R5118N ). M. V. -K. was funded by the  Department of Biology, University of Antwerp.
, 2017]. However, our current knowledge of the spatial structure and variability of the jets system is far from being comprehensive, and remains an intense subject of investigation. The very existence of these zonal jets has even been questioned by some studies, claiming that the sparse observations only captured aliased planetary waves signals [e. g. Jochum and Malanotte-Rizzoli , 2003 ]. More recent and comprehensive observational datasets however corroborated the existence of mean zonal currents, on which seasonal Rossby waves currents superimpose. They suggest that the near-equatorial zonal currents should not be considered as isolated pieces  [Cravatte et al. , 2012 [Cravatte et al. , , 2017]] , but rather appear as embedded into a broader meridional system of zonal jets extending to the tropics.
Acknowledgments We dedicate this review to the memory of  Bach Lien Hua , who contributed so much in the past years to the understanding of the DEC. We thank  Bertrand Delorme  and  Patrice Klein  for their help in improving the manuscript. This study has also been possible thanks to the amount of Argo data, collected and made freely available by the  International Argo Program  and the national programs that contribute to it (http ://www. argo. ucsd. edu; http ://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System (Argo 2000). The authors also thank  M. Ollitrault  and  J. -P. Ranou  for making the  ANDRO Atlas available  ( 10. 17882/47077 ). -24-Confidential manuscript submitted to Earth and Space Science.
Our results demonstrate that ISEC is the most accurate and robust method to simultaneously measure δ 34 S in sulfite, sulfate and thiosulfate. Thus, this approach was chosen and applied for sample analyses. Optimization of the Peak Zone for LRS Calculation. It has been demonstrated that better results are obtained when LRS is performed on the full peak zone (100%) ra-ther than smaller zones and that the standard deviation of the measured isotopic ratio depends on the regression coefficient (R²) of the linear function. 17 In this study, we evaluated the possibility of calculating the isotopic ratio with larger peak zones, up to 300% of the peak (Figure  4C ). Furthermore, we determined the peak zone between 100% and 300% of the peak providing the maximum R² of the linear function and the peak zone providing the mini-mum slope standard deviation by an iterative algorithm implemented as a Visual Basic code within Microsoft Excel (more details in SI).
At Harletz, the 100 lower record of MIS6 indicates 5 paleosols to incipient weathered horizons of various maturation (ISp). They are distributed along a decreasing trend in the clay content, in the color reflectance and the magnetic susceptibility, but also along an increasing trend in the coarse sand and in the grains size ratio (coarse + fine sand/clay) (Fig. 1 ). Just above the cryptotephra layer, there are two more thin incipient weathered horizons only observed through the analytical data (cryptopaleosols ISm). Moreover, ongoing magnetic studies (isothermal remanent magnetization and coercivity of 105 remanence) identified 3 more incipient weathered horizons on top of the loess unit at 6. 2m, 7. 4m and 8. 4m  (Lagroix et al. , 2016). Therefore, considering the Lake Ohrid recalculated dates of the tephra layer and the luminescence age available, there are 10 horizons including paleosols and incipient weathered horizons identified above the MIS7 Ogosta interglacial pedocomplex.
305 Reporting all the interstadials in table 1 supports the previous interpretation that these interstadials are hemispheric and can be interpreted as DO-like events, without any doubt at least for the early MIS6. However, the question remains why these marine records did not show all of the interstadials described in the other domains, contrary to what has been observed during the last climatic cycle. Is this related to the time resolution of the studied cores, which would not be high enough? This is an on-going problem that this paper cannot address, as it requires further investigations and additional data with 310 higher resolution to be obtained.
Acknowledgments The comments from  Peter Clark  on a preliminary version of this manuscript were very useful and welcomed. Thanks to  Kathy Roucoux  for providing the Ionnina I-284 data,  Steve Barker  for providing the Greenland GL t _syn and ODP 983 N pachyderma s data. This study benefited from research funds granted by the  ANR  to  DDR  ( ACTES  project  ANR-08-BLAN-.
Fire suppression agencies will be increasingly overwhelmed  (Bowman et al 2017 , Wotton et al 2017). Recently, an analysis of fire weather climatology from 1979 to 2015, which used the North American Regional Reanalysis dataset and the Canadian Fire Weather Index System, has suggested an ongoing significant, increasing trend in fire-season length in eastern Canada  (Jain et al 2017). To date, detection of temporal changes in the frequency (or in the return interval) of major wildfire activity remains a major challenge, owing to the short temporal coverage of circumboreal fire data (usually limited to post-1950), the large interannual and spatial variability in area that is burned, and evolving wildfire monitoring and reporting methods, which tend to mask long-term and subtle changes that can be a consequence of changing climate  (Krezek-Hanes et al 2011 , Waito et al 2015).
The need for a comprehensive assessment of historical wildfire, climate, and vegetation at all scales, including enhanced datasets from historical fire maps and synthesis of climate, vegetation, and fire interactions from past climates, including past warm periods, was identified as national priorities for building the capacity of wildland fire science in the 2020s  (Sankey 2018). Lake-sediment charcoal records are used to reconstruct long-term fire trends and analyze the sensitivity of fire activity to various ecosystem and climatic processes, and can ultimately improve our ability to project future fire impacts  (Heyerdahl et al 2008 , Girardin et al 2013b , Waito et al 2018). Charcoal particles that are produced during fire are transported by wind and runoff across landscapes and deposited in lake sediments.
e. S3 ). Charcoal particles were measured and the resulting data were converted into charcoal accumulation rates (CHAR raw ; mm 2 cm -2 yr -1 ) that were based on numerical age-depth models to reconstruct past fire frequencies for each lake (table  S1 ). The dates of local fire events were extracted from the CHAR raw series using CharAnalysis v1. 1 software  (Higuera et al 2010). CHAR raw series were interpolated (CHAR interpolated ) to equal time steps based on the median time-resolution computed from the sediments for each lake (table  S1 ). CHAR interpolated series were decomposed into a lowfrequency component (CHAR background ) and a highfrequency component (CHAR peak ). CHAR background results from long-distance burning or redeposition processes of charcoal particles that are unrelated to local fire occurrences.
Each CHAR peak that exceeded the threshold at the 99th percentile of the noise distribution was assumed to originate from a local fire event (Gavin et al 2006,  Higuera et al 2011). Given the short fire rotations in some parts of the study area (e. g. northwestern Quebec, figure  1 ), we recognize that some peaks may include more than one fire  (Higuera et al 2011). Forest fire data from the Canadian National Fire Database  (Stocks et al 2002)  were used for this study to described current fire activity. The database contains information on the location, date of detection, size (ha), and cause (lightning or human) of all fires that were recorded in Canada, for the period 1959-2016. Starting from the 0. 5°×0. 5°grid covering the study area (figure  1 ), we extracted all fires and computed the annual area burned for each cell and year.
Weibull smoothing To avoid overestimating the importance of single fire events, the FRI distributions were smoothed independently for each lake using the two-parameter Weibull probability density distribution  (Johnson and Gutsell 1994, Cyr et al 2009)  and a 5-observation moving window. The b and c parameters, which respectively corresponded to the shape and scale parameters of the Weibull distribution, were estimated using the fitdist function of the 'fitdistrplus' R package (Delignette-Muller and Dutang 2015). For each lake, we used a gamma (Γ) function  (Johnson and Gutsell 1994)  to calculate the mean of the Weibull distribution, where within each 5-observation window, the mean FRI was calculated as: b c Mean FRI 1 1. 1 = G + ⎜ ⎟ ⎛ ⎝ ⎞ ⎠ ( ) Bootstrap replications (1000 iterations) were used to construct equi-tailed (1-2σ) confidence intervals for the estimated mean FRI within each window.
Extreme biomass burning detection Extreme biomass burning was defined as periods during which CHAR krig exceeded a detection threshold that was computed from the long-term median (MED) and the median of absolute distances to the median (MAD): x z MED MAD , 2 i x x > + ( ) ( ) ( ) ( ) with i=1, K , n, for a grid-cell's CHAR krig record x of size n time points  (Hampel 1985)  (figures S5 and S6). The parameter z is a threshold that was selected. The number of extreme events under analysis was arbitrarily set to approximate the highest 15th percentile (a function of the distance to the median z, which varies by increments of 0. 5). For this study, we set z=3. We preferred to use this extreme event detection method on CHAR krig records rather than using local fire events that were detected using CharAnalysis software.
Binomial proportion confidence intervals (95% CIs) were computed using a Bayesian calculation with an uninformative prior distribution  (Brown et al 2001). This uncertainty estimate is important since n′ is unevenly distributed in time, and because small n′ or values of k that are close to 0 or 1 can lead to large errors in the estimation of p. The Bayesian approach that was used here (Jeffreys prior with Beta [1/2, 1/2]) has been shown to be particularly robust under small samples sizes, while also being well-suited for uncertainty estimation under large sample sizes. The extreme event detection method was repeated on modern area burned data that were extracted from the Canadian National Fire Database, and for each cell over the 1959-2016 period. A year in a given grid cell was deemed as being extreme when the sum of its area burned exceeded the detection threshold set by equation (1) for the particular annual area burned dataset for that particular cell.
In contrast, WTC examines the significance and magnitude of coherence (i. e. correlation) between two time series and, furthermore, reveals information about the phase relationship (delays) in a lag-period space (at a time and frequency localization). This analysis of phase relationships is relevant in a context in which the direct correlations between variables may be difficult to assess due to dating uncertainties and offset paleorecords. For each time-series, the required frequency spectrum was constructed using continuous wavelet transform analysis. We used the Paul wavelet (order 4) as it is not very localized in frequency space, while allowing signals that are relatively aperiodic to be detected by the analysis  (Moore et al 2007). The statistical significance level (90%) of the WTC against red noise backgrounds was estimated using Monte Carlo generated noise with 1000 surrogate data set pairs (having the same first-order autocorrelation coefficients as the input datasets; see  Grinsted et al 2004).