dataesr/scientific-paragraphs-categorization huggingface dataset:
Applying to this dataset a common algorithm procedure and the proposed cirrus Match approach, a test case of upper tropospheric thin cirrus observed by the two instruments during the night from 13 to 14 March 2008 has been identified and studied. To estimate the main uncertainties characterizing the adopted approach, assumptions on a quasi-isentropic air mass transport and cirrus "frozen conditions" during the advection between the two sites have been made. These hypotheses allowed for using the first and the last quasi-stationary periods, measured, respectively, above the OHP and the RTV, to estimate the crystal cloud mean sedimentation velocity (v s ) between the two sites. The range of retrieved values for v s (1. 4-1. 9 cm s -1 ) is consistent with crystals of simple shapes and small dimensions.
g. geometrical and optical thickness, back-scattering profile distribution) observed between two sites; -lidar data have to be integrated with ancillary measurements (in situ and space-borne) of cirrus crystal size distribution, water vapour and temperature to characterize the processes to which cirrus are subjected during the transport from one site to another; -the assumption of isentropic transport and "frozen" conditions inside the cirrus must be verified by using a microphysical cirrus model (WRF) to monitor the impact of mesoscale fluctuations and the evolution of microphysical cirrus processes between the two sites. The cirrus persistence could also be verified by the employment of CALIOP data to track cirrus advection. Dedicated campaigns, which will fulfil the requirements listed above, are planned to study, through this approach, the mean micro-physical changes of mid-latitude cirrus clouds during advections.
Acknowledgements. The authors thank  Gian Luigi Liberti  for the helpful discusssions and  Federico Angelini  for his support on backward trajectory statistical processing. The authors acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYPLIT transport and dispersion model. OHP lidar activities are supported both by  INSU  and  CNES. This work was also supported by the bilateral  French-Italian  project  STRACLIMA. The research leading to these results has received funding from the  European Union Seventh Framework Programme  (FP7/2007-2013) and  University of Versailles and Saint Quentin en Yvelines (UVSQ)  under grant agreement RBUCE-UP No. 246556. Edited by: R. Schofield The publication of this article is financed by  CNRS-INSU.
Data Lycaon skulls were accessed from the Natural History Museum Zimbabwe (103), Ditsong National Museum of Natural History  (25)  and Kruger National Park  (5). These sources held a total of 133 skulls collected between 1913 and 2001, originating from Zimbabwe  (38) , Botswana  (14) , Zambia  (53)  and South Africa  (28) , and representing a continuous region of south east Africa. Permission to conduct research in Zimbabwe was obtained from the Director General of the Parks and Wildlife Management Authority, with access to the collection of the Natural History Museum of Zimbabwe permitted by Albert Kumerai. Permission was also obtained from the Ditsong National Museum of Natural History, Pretoria and South African National Parks for access to their collections. All specimens were accessed temporarily for the purposes of this study with measurements and photographs taken on site.
1. Traits excluded from further analyses are listed in Tables  S1  and S2. In total, four traits were removed from the photogrammetric dataset, and one trait from the calliper dataset. Subsequent analyses were conducted on the absolute FA values, |FA|  [39]. We also calculated a composite index for each individual (CFA), as the mean |FA| across traits. Combining multiple traits in this way may provide a more accurate measure of FA  [39, 44] , assuming a consistent overall level of DS for a particular individual. A linear model was used to measure changes in FA over time. To reduce heterodescasity, the |FA| values were log-transformed, in common with many studies of evolutionary rate in morphological characteristics  [45] [46] [47]. Sex differences in FA were tested but found not to be significant and the sexes were therefore pooled.
Acknowledgments We are grateful to the  Director General of the Parks and Wildlife Management Authority  for permission to conduct research and logistical support in Zimbabwe. We would like to thank  Albert Kumerai ,  Woody Cotterill ,  Donald  and  Sheila Broadley  of the  Natural History Museum of Zimbabwe  for logistic help and access to the collection. Preliminary studies were conducted using material supplied by the  Oxford Museum of Natural History. Access  to this material, along with collections held by the  Ditsong National Museum of Natural History, Pretoria , and  South African National Parks  is gratefully acknowledged. We would also like to acknowledge the contributions of two anonymous reviewers, whose comments and suggestions for further analyses improved the manuscript.
Data are reported in standard notation in ‰ with respect to the V-PDB standard for carbon. The analytical error was lower than 0. 3 ‰ and three internal standards were measured during the isotopic analyses to continuously check the accuracy of the measurements. Acid soluble manganese oxides and iron oxides were determined on homogenised dried sediments using 1M HCl solution. About 250 mg of dry sample was leached with a 12 mL solution during 24 h while shaking continuously at room temperature. The centrifuged and filtered solution was then 10-fold diluted in 0. 2 M HCl and analysed with a flame atomic absorption spectrometer (5100PC Perkin-Elmer). The analytical precisions were better than ±3 % and ±7 % for manganese and iron, respectively. Acid soluble iron (Fe HCl ) represents amorphous iron oxides, ironS, some iron phyllosilicates and iron carbonates.
Specific tests on particulate manganese extraction with 1M HCl (Mn HCl ) have shown that Mn HCl represents the whole fraction of manganese oxides and manganese associated with carbonates  (Anschutz et al. , 2006). Sinking fluxes of particulate organic carbon (POC) reaching the benthic boundary layer (approximately 20 m above bottom) were derived from a particle size distribution dataset obtained with an Underwater Vision Profiler 5 (UVP5,  Picheral et al. , 2010)  deployed at every station (Table  1 ). Full methodology on the functioning of the UVP5  and on the estimation of sinking POC fluxes can be found in  Forest et al. (2013). Briefly, particles in the range 0. 08-4. 2 mm (in equivalent spherical diameter) recorded with the UVP5 were transformed into sinking fluxes by applying a regional empirical algorithm linking sediment trap fluxes and the UVP5 dataset.
Incubations were stopped when oxygen levels had decreased by more than 20 % to avoid hypoxic conditions and biogeochemical transformations. Hall et al. (1989)  demonstrated the importance of maintaining the O 2 supply to obtain consistent and relevant data during core incubations. To determine changes in nutrient concentration, samples of the overlying water phase were taken at three times during the incubation, including the onset and end. Water samples withdrawn for analysis were immediately replaced by an equivalent volume of bottom water of known nutrient composition. The total amount of water withdrawn and replaced during each sampling never exceeded 10 % of the total overlying water volume to prevent artefacts. Three additional incubation cores containing bottom water only acted as controls for assessing the oxygen consumption and nutrient changes due to processes within the water column or sample handling.
We tested for collinearity of variables retained in the bestsubset model using the variance inflation factor (VIF), with VIF > 10 indicating critical collinearity  (Quinn and Keough, 2002). This was not the case for any of the best-subset models. Principal component analysis (PCA) was used to determine the influence (eigenvector) of the six measured benthic boundary fluxes on the ordination of samples in a multidimensional space. Prior to PCA, homogeneity of each variable was assessed using a draftsman plot, and ammonium fluxes were transformed using natural logarithm. Moreover, flux data was standardized using the "normalise" routine in PRIMER-E  (Clarke and Gorley, 2006). No pair of fluxes was correlated with R > 0. 7, with the exception of nitrate and oxygen (R = 0. 82). A stepwise distance-based linear model permutation test  (DistLM, McArdle and Anderson, 2001)  was performed to identify which set of environmental variables predict the multivariate variation of benthic boundary fluxes, thus taking into account possible interactions between different fluxes (in contrast to the multiple regression approach described above).
The resemblance matrix quantifying the betweensample similarities in terms of all six standardized fluxes was calculated based on Euclidean distances  (Clarke and Gorley, 2006). Predicting environmental variables allowed to enter the model were content of Chl a, phaeopigment, manganese oxides and iron oxides in surface sediments, sediment surface porosity and δ 13 C org as well as bottom water oxygen concentration. To represent natural variation as much as possible in the analysis but to avoid arbitrary attribution, each environmental sample of a site was attributed to each replicate flux sample, thus triplicating the original dataset. Sinking POC rates were excluded from this analysis due to a lack of replicates and due to its correlation with sediment Chl a. While the influence of multicollinearity on the model results can be tested for multiple regression (VIF, see above), this is not the case for the DistLM.
After assessing normality and collinearity of the other predictor variables using a draftsman plot, sediment Chl a data was transformed using the natural logarithm to correct for skewness  (Anderson et al. , 2008). No pair of variables was correlated by R > 0. 85 and hence all variables were retained for possible inclusion in the model. The stepwise routine was run employing 9999 permutations and using the AIC c (Akaike's information criterion corrected) selection criterion. The AIC c was devised to handle situations where the number of samples (N) is small relative to the number (v) of predictor variables (N/v < 40)  (Anderson et al. , 2008). Results were visualized with a distance-based redundancy analysis (dbRDA)  (Anderson et al. , 2008). All multivariate statistical analyses were performed using the PRIMER 6 statistical package with the PERMANOVA+ add-on (PRIMER-E, Plymouth Marine Laboratory, UK).
Discussion In the literature, benthic activity is most often derived from sediment oxygen demand  (Graf, 1992; Link et al. , 2011; Wenzhöfer and Glud, 2002)  and assumed to decrease with increasing depth and distance from the continental source of particles and carbon. However, the heterogeneous pattern of oxygen and nutrient fluxes at the sediment-water interface in the Beaufort Sea emphasizes that benthic remineralisation function is more complex than oxygen fluxes. Here we present for the first time a multiple dataset of benthic boundary fluxes, i. e. for oxygen, silicic acid, phosphate, nitrate, nitrite and ammonium, and their relation with environmental variables in the Canadian Arctic. We discuss their spatial pattern and influences of the environment at different timescales with the aim to estimate dynamics of benthic ecosystem functioning related to variation in the quality and quantity of organic matter export.
Acknowledgements. We would like to thank the CCGS Amundsen officers and crew, Malina chief scientist  M. Babin , and all scientists and technicians for their support on board. Special thanks go to  J. Gagnon  and  J. -E. Tremblay  for nutrient analyses. We are grateful for the help of V. Galy in the laboratory. We also thank  Jens Holtvoeth  for his valuable comments on our manuscript. Financial support was received from the  National Science and Engineering Research Council (NSERC) Strategic Canadian Healthy Oceans Network (CHONe) and ArcticNet. Partial funding was provided by the Fonds québécois de la recherche sur la nature et les technologies (FQRNT) and  Québec-Océan  for  H. Link , and the  Canadian Research Chair and Institutional UQAR  funding for G. Chaillou. This study was conducted as part of the  Malina Scientific Program  funded by  ANR (Agence nationale de la recherche) ,  INSU-CNRS (Institut national des sciences de l'univers -Centre national de la recherche scientifique) ,  CNES  ( Centre national d'études spatiales)  and  ESA  ( European Space Agency).
In addition, simulations of future climate were calculated using emissions derived from Representative Concentration Pathway (RCP) scenarios  (van Vuuren et al. , 2011). This approach can help quantify the role of SLCF in past and future climate response across a range of models, which was difficult to do previously  (Shindell and Lamarque, 2009). Consequently, ACCMIP will be a valuable resource for the IPCC AR5. Furthermore, a constellation of satellites that can observe a number of trace gas constituents are now available with global coverage and data records of sufficient length to be useful for the evaluation of ACCMIP simulations. Expanding from the analysis of  Aghedo et al. (2011b) , we evaluate how ACCMIP tropospheric ozone bias effects OLR bias using global, simultaneous observations of tropospheric ozone and sensitivity of OLR to tropospheric ozone from the Tropospheric Emission Spectrometer, which was launched aboard the NASA Aura spacecraft in 2004  (Beer, 2006) , using data collected from 2005-2010.
(2010) , which showed that formal measurement and species interference errors were consistent with empirical calculations. We use TES V004 ozone profile product for this study, which has been updated relative to the V002 product. Ozonesonde profile comparisons with the Intercontinental Chemical Transport Experiment Ozonesonde Network Study (IONS), the World Ozone and Ultraviolet Data Center (WOUDC), the Global Monitoring Division of the Earth System Research Laboratory (GMD-ESRL), and the Southern Hemisphere Additional Ozonesonde archives (SHADOZ) have been performed. Approximately 5000 matches with one or more of these data sources are found using the coincidence criteria of ±3 h and a 300 km radius, spanning a latitude range and time-span from 72. 5 • S to 80. 3 • N from 2004 to 2008. Analysis of the comparisons are documented in the TES Validation Report V4 http://eosweb.
Application to TES data TES observations directly measure the 9. 6 micron ozone band radiances (the primary band where ozone absorbs thermal infrared radiation) and have the spectral resolution to disentangle the geophysical quantities, e. g. , temperature and clouds, driving ozone band radiative flux variability. These data have the potential to assess model results with respect to the longwave component of ozone radiative forcing. However, differences between satellite and climate model calculations need to be considered. TES only measures thermal radiance at the top-of-the-atmosphere (TOA). Consequently, these measurements do not provide information on the shortwave irradiance. Chemistry-climate models calculate the full global and diurnal cycle of ozone whereas TES has a global repeat cycle of 16 days and can only measure twice a day through its ascending and descending nodes.
, 2011). As described by  Lamarque et al. (2013)  the level of complexity in the chemistry schemes varied significantly between the models. The physical climate was based on prescribed SSTs for most models with the notable exception of the GISS model, which is integrated with a fully coupled ocean-atmosphere model  (Shindell et al. , 2013). Following  Young et al. (2013) , model simulations for the 2000 decade were averaged and then interpolated to the domain of the gridded TES product archived along with the ACCMIP simulations at the British Atmospheric Data Archive (BADC), which is composed of 64 pressure levels at 2 × 2. 5 • spatial resolution. These decadal mean model simulations are then compared against TES tropospheric ozone for 2005-2010.
, 2005 (Eyring et al. , , 2006;; Waugh and Eyring, 2008)  can help test radiatively important processes against observations in a manner that can reduce our uncertainty in ozone RF and increase the reliability of future projections. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. The TES monthly ozone and IRK dataset was developed using techniques from the obs4MIP activity (http://obs4mips. llnl. gov:8080/ wiki/) The National Center for Atmospheric Research (NCAR) is sponsored by the National Science Foundation. KB and HW acknowledges the support of the NASA Aura ROSES program. KB also acknowledges some useful codes from Adetutu Aghedo and Rachel Hodos in the initial processing of TES data to netcdf files. ACCMIP is organized under the auspices of Atmospheric Chemistry and Climate (AC&C), a project of International Global Atmospheric Chemistry (IGAC) and Stratospheric Processes And their Role in Climate (SPARC) under the International Geosphere-Biosphere Project (IGBP) and World Climate Research Program (WCRP).
The satellite derived soil moisture products are by essence areal averages; the spatial scale of some of these products is comparable to the resolution of land surface models  (Wagner et al. , 2007a). Furthermore, data are acquired at frequencies higher than most in-situ measurements. Among these datasets, the soil moisture products derived from the SCAT (onboard the ERS-1 & ERS-2 satellites) and the ASCAT (onboard MetOp satellite) instruments are particularly suited for comparison to model output at high latitudes due to their spatial resolution (25 to 50 km), global coverage and temporal sampling (80 % daily global coverage;  Naeimi et al. , 2009b). Several local to regional-scale analyses have shown the skill of these products to capture the observed soil moisture dynamics in the few areas of the world where a dense network of field measurements exists  (Wagner et al.
, 2007b; Pellarin et al. , 2006; R üdiger et al. , 2006). Most of them are located in temperate or tropical climate. The effective use of remotely-sensed surface soil moisture products in assimilation mode by hydrological or weather forecast models additionally revealed the potential of such datasets for hydrological modelling  (Scipal et al. , 2008; Yang et al. , 2007; Reichle and Koster, 2005; Walker and Houser, 2004). Up to now, very few studies have however focused on the large-scale remotelysensed and modelled surface soil moisture over high-latitude regions  (Bartsch et al. , Figures Back  Close Full 2011). Dirmeyer et al. (2004)  revealed the large divergence of several model and remotely-sensed soil moisture datasets over these regions; at this time, most of the model-derived datasets relied on very simple, bucket-like hydrological schemes.
Wagner et al. (2003)  compared the ERS-derived soil moisture product  (Scipal, 2003)  to global precipitation data and soil moisture modelled by the LPJ dynamic vegetation model: rather low to negative or insignificant correlations between these datasets were inferred over the circum-arctic regions, and the authors called for refinements of both the land-surface model and the soil moisture retrieval algorithm to address patent shortcomings for these regions. Similar low correlations were found over high-latitudes by  Naeimi et al. (2009b)  between the ERA-Interim soil moisture dataset and ERS and MetOp-derived surface soil moisture, despite the use of an improved retrieval algorithm. Meanwhile, the recognition of the potential climatic impact of circum-arctic regions (e. g. ACIA, 2005) and of the power of remote-sensing in this area of the globe have spurred both model developments designed to address high-latitude processes (e.
g. Gouttevin et al. , 2012a; Wang et al. , 2013)  and efforts to improve the availability and capabilities of remote-sensing products over these regions  (Bartsch et al. , 2008 (Bartsch et al. , , 2011;; H ögstrom et al. , 2013). In this context, it may be time to investigate closely the remaining discrepancies between modelled and remotely-sensed soil moisture products over the circum-arctic regions, and derive guidelines for future developments. Within the European Space Agency (ESA) DUE Permafrost project  (Bartsch et al. , 2012)  several permafrost-related earth observation products were investigated and integrated, including a combined surface soil moisture and surface freeze-thaw status dataset  (Paulik et al. , 2012). The evaluation of this dataset is a constitutive part of the DUE Permafrost project and also an ongoing task as we write this manuscript  (Heim et al.
Back Close Full performance over permafrost landscapes pending necessary adjustments  (Bartsch et al. , 2009 (Bartsch et al. , , 2011) )  which have now been integrated in the retrieval algorithm. This fuels our knowledge as to the accuracy and limitations of the ASCAT surface soil moisture dataset. The present study intends to compare the DUE-Permafrost surface soil moisture and freeze-thaw products to relevant variables as modelled by the land-surface model ORCHIDEE, which includes a physically-based hydrological scheme refined for high-latitudes processes. It is part of the blended evaluation framework of the DUE-Permafrost datasets, as the comparison of both products over a large continental domain may reveal previously unknown inconsistencies in the remote-sensing product, based on the known and different strengths of both products.
This strategy is fully in line with the one adopted by previous cross-comparison studies (e. g. Wagner et al. , 2003; Heim et al. , 2011). Section 2. 2 lists the plausible weaknesses of the surface soil moisture product that arose or were confirmed by our study. Here, these plausible inconsistencies of the remote-sensing dataset will be considered as such, and we will focus on the remaining model-to-data discrepancies, for which we derive likely modelling flaws. Parallel investigations are ongoing to determine to which extent these discrepancies could also or in part proceed from issues in the satellite product  (H ögstrom et al. , 2013) , but are beyond the scope of our study. The first section of this article details the characteristics of the compared datasets and clarifies the methodological framework of the comparison.
Remote-sensing data The ASCAT (Advanced SCATterometer) instrument onboard the Meteorological Operation (MetOp)-A satellite is an active microwave sensor operating in C-band (5. 3 GHz), a low frequency in the microwave domain suitable for soil moisture and surface status retrieval due to its ability to penetrate vegetation and to the sharp contrast of dielectric constant from dry or frozen to wet soil or melting snow at this frequency  (Ulaby et al. , 1982). This contrast is the theoretical basis for the remote sensing of soil moisture and surface status. Microwave frequencies are unimpeded by cloud cover, and active sensing allows acquisition during day and night. MetOp orbit and ASCAT instrument characteristics provide 80 % global daily coverage at 25 and 50 km resolutions. In this study, we make use of two complementary datasets derived from ASCAT time-series at 25 km resolution: the ASCAT Surface Soil Moisture (SSM) product and the ASCAT Freeze-Thaw (or Surface State Flag, hereafter SSF) product  (Paulik et al.