dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The source data underlying Figs. 1, 2, 5, and 7 and Supplementary Fig. 7 are provided as a Source Data file. Data associated with this paper, such as topography, bathymetry and seismological data are available. TOPOGRAPHY: NASA JPL (2013). NASA Shuttle Radar Topography Mission Global 1 arc second [Data set]. NASA EOSDIS Land Processes DAAC. https://doi. org/10. 5067/MEaSUREs/SRTM/SRTMGL1. 003. BATHYMETRY: EMODnet Bathymetry Consortium (2018): EMODnet Digital Bathymetry (DTM). https://doi. org/10. 12770/18ff0d48-b203-4a65-94a9-5fd8b0ec35f6. REGIONAL CENTROID MOMENT TENSORS: The repository of regional centroid moment tensors can be downloaded at this link: https://digital. csic. es/handle/10261/ 177887, https://doi. org/10. 20350/digitalCSIC/8623. DATA FROM IRIS DATA CENTRE: This work included data from the II, IU, GE, GT, and G seismic networks obtained from the IRIS data centre.
Data and methods Three independent datasets were used to provide evidence of temporal variability in the extension of CW into the GoM. We used ADT and surface velocity fields (geostrophy and Ekman) from the GEKCO (Geostrophic Ekman Current Observatory;  Sudre et al. , 2013)  product from 1993 to 2017 with a resolution of 0. 25 • × 0. 25 • , in conjunction with Chl a ocean color data derived from the reprocessing R2014. 0 product suite from Aqua MODIS (Moderate Reso- lution Imaging Spectroradiometer) and from SeaWIFS (Sea-Viewing Wide Field-of-View Sensor), using the OCx algorithm with a spatial resolution of 9×9 km (https://oceancolor. gsfc. nasa. gov/cgi/l3, last access: 19 November 2019). The 2003-2017 monthly Chl a ocean color product was derived from Aqua MODIS, and the 1998-2002 monthly Chl a ocean color product was derived from SeaWIFS.
Climatology was created from maps of ADT that result from the elevation of the sea surface height referenced to the geoid using the product from DUACS (Data Unification and Altimeter Combination System) available on the AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic data) website at https://www. aviso. altimetry. fr/en/data (last access: 19 November 2019). The ADT climatology was constructed using the 25 years of daily satellite maps from 1993 to 2017, averaging for each month in the different years. We considered LCEs in any stage of formation, detaching, and reattaching to the LC as evidence of the incursion of CW. After the ADT climatology was obtained, the predominant boundary contour of CW was extracted from each climatological month. It was observed that the 40±2.
2017. We used the 40 cm contour of each set of 3-year averages because this was the contour with the highest EKE observed in the 25-year dataset. To retrieve the CWF contours, we first determined the initial latitudinal position of the CWF to be at 80. 7 • W with the respective corresponding longitudinal positions between Cuba and Florida. The CWF contour lines that run from east to west and finish close to the tip of the Yucatán Peninsula were separated by 0. 2±0. 1 •. However, some ADT contour "islands" appeared next to the CWF with a typical distance of > 0. 3 • from the CWF contour. Additionally, a spectral analysis was done using a daily time series of 25 years of ADT data to build a spatially averaged region influenced by the LC between 91. 25 • W and 23. 125 • N and between 83. 5 • W and 28.
12 • N. When ADT island distances were > 0. 3 • from the front, we used a MATLAB code procedure to eliminate them from the CWF contours. Once the CWF's contours were retrieved, the next step was to visually corroborate the quality and coherence of each CWF contour over the monthly field maps of ADT, sea surface currents, and Chl a distribution. In this way, inconsistencies were detected and corrected. The MAT-LAB code procedure satisfactorily corrected 91. 3 % of the contours. The remaining sets were corrected by hand via visual analysis. The main mesoscale instabilities were obtained from calculations of the climatological monthly EKE maps of geostrophic and Ekman currents obtained from 25 years of daily satellite observations from GEKCO using the follow- ing equation: u = u + U ; u = u -U, (1) v = v + V ; v = v -V , (2) EKE = 1/2(u 2 + v 2 ), (3) where u and v represent the total current (u = u E + u g and v = v E + v g ), u E and v E represent the Ekman current, u g and v g represent the geostrophic current, U and V are the means of the oceanic currents, and u and v are the anomalies of the current.
To find the relationship between ADT and EKE patterns, the 40 cm ADT isoline was overlaid on the monthly EKE maps. This made the EKE means representative of the energy of the mesoscale eddy field  (Jouanno et al. , 2012). For consistency between the different satellite datasets, all monthly climatological spatial fields were standardized at 0. 25 • × 0. 25 • spatial resolution by bilinear interpolation. 3 Results and discussion.
West and northward Caribbean Water extension The monthly intrusions of the CWF were tracked by taking as a reference the northernmost latitudes and westernmost longitudes of the 40 cm ADT isoline representing 1993-2017 monthly average values of the ADT (not spatially averaged). The climatological position of the CWF for each month of the year is shown in Fig. 3. These results confirm the annual intrusion of CW as follows: (1) analysis of the maximum north and westward penetration of the front over 25 years shows that from January to February, it is retracted south- east to ∼ 26. 55 • N and ∼ 88. 32 • W (Fig. 3a  and b , respectively) and intrudes to 28 • N, 90. 45 • W in August; (2) an ADT spectral analysis derived from 25 years of daily data from the CWF region shows a strong annual signal that originates from the back and forth of the ADT signal (Fig.
(2015) , who did not indicate a time trend in Chl a concentration in the GoM. As the data were taken with different sensors and to eliminate the uncertainty that this difference is not caused by a systematic difference between the SeaW-IFS and MODIS datasets used in our analysis, we calculated least squares regressions to the SeaWIFS and MODIS time series at four stations corresponding to the northwest, northeast, southwest, and southeast regions of  Müller-Karger et al. (2015)  (Fig. 11 ). For each dataset, inner slopes and overall slopes were calculated. For all four stations, the SeaW-IFS  (1998) (1999) (2000) (2001) (2002)  and the  MODIS (2003 MODIS ( -2017) )  data series merged exactly and all stations show negative trends; equivalently, the combined time series (1998-2017) also show a negative tendency, supporting the conclusion that the Chl a concentration over the deep GoM has decreased over time.
Acknowledgements. We thanks the reviewers for their positive criticisms and comments that helped us to improve the paper. This study was carried out as part of the PhD thesis research conducted by the lead author at the  Faculty of Marine Science  and the  Oceanographic Research Institute (FCM-IIO/UABC) ,  Postgraduate Coastal Oceanography Program , and it was supported by the  Graduate Professional Development Mexican Program  grants (PRODEP:  DSA/103. 5/16/5801 ), the  National Institute of Technology of Mexico (TecNM) , and the  Mexican Energy Bureau and Hydrocarbons Mexican Trust , project  201441. This is a contribution of the  Gulf of Mexico Research Consortium (CIGoM). Financial support. This research has been supported by the  National Council of Science and Technology of Mexico -Secretariat of Energy -Hydrocarbons Trust  (grant no.
Data availability. Sea surface water and absolute dynamic topography were processed by SSALTO/DUACS and distributed by AVISO+ (https://www. aviso. altimetry. fr/en/data, last access: 19 November 2019) with support from CNES. The GEKCO (Geostrophic Ekman Current Observatory; Sudre et al. , 2013;  http: //www. legos. obs-mip. fr/members/sudre/gekco_formwithsupport, last access: 19 November 2019) product used in this study was developed by Joël Sudre at LEGOS, France. MODIS and SeaWIFS Chl a maps were derived from two NASA products: Aqua MODIS (Moderate Resolution Imaging Spectroradiometer; https://oceancolor. gsfc. nasa. gov/l3/, last access: 19 November 2019) and SeaWIFS (Sea-Viewing Wide Field of View Sensor) using the OCx algorithm with a spatial resolution of 9×9 (https://oceancolor. gsfc.
, 2010). Anthropogenic emissions are generated from EMEP inventory for 2007 (European Monitoring and Evaluation Programme; http://www. emep. int, last access: 19 April 2019). Biogenic emissions are estimated with the Model of Emissions of Gases and Aerosols from Nature (MEGAN-LHIV,  Guenther et al. , 2006). Sea-salt emissions are parameterized following  Monahan (1986). The soil and surface database of  Menut et al. (2013)  is used to calculate the dust emissions considering the spatial extension of potentially emitted area in Europe described in  Briant et al. (2017). The daily fire emissions are calculated using the APIFLAME fire emission model v1. 0  (Turquety et al. , 2014) , as described in  Majdi et al. (2019). Two domains are considered in this study (Fig. 5 ): one nesting domain covering Europe and North Africa and one nested over the Mediterranean.
The data suggest that this fluvial succession preserves a record of hyperthermal "U" event at ca. 50 Ma, adding important constraints to the age of this Formation. Second, we used the major and trace element composition of bulk floodplain material in order to explore the climatic impact of such minor hyperthermal, including empirical reconstructions of mean annual precipitation, allowing us to 85 discuss soil dynamics during global warming. This study identifies for the first time in a continental succession an event so far only recorded in marine sediments, thereby demonstrating the global breadth of these climatic events and the complementarity of oceanographic and terrestrial archives.
Correlation with target curves 175 The measured δ 13 C dataset was compared with a time-equivalent ODP 1263 global δ 13 C record reported by  Westerhold et al. (2017)  using the Analyseries software  (Paillard et al. , 1996). The δ 13 C record of site 1263 was favoured over those of ODP 1209 and 1258 covering the Castissent Fm time-period, because it is continuous and has a higher resolution. Correlations between the δ 13 C record of site 1263 and the δ 13 C record of the Chiriveta section were performed in order to optimize the Pearson correlation coefficient (r) and by minimizing abrupt 180 variations in sedimentation rates. Well-defined peaks in both δ 13 C records were used as tie-points for the correlation and the number of tie-points was kept minimum (<10) so as not to force the correlations.
1 and 0. 29 mm/yr. These values are within sedimentation rates of Eocene floodplain succession  (Kraus and Aslan, 1993). Using an average sedimentation rate of 0. 17 m/kyr, we obtained a mean Tc of 22,000 yrs. A hyperthermal event of 40 kyr, being approximately twice as long as the estimated Tc, should be recorded. 385 There are five Eocene hyperthermal events (PETM, ETM2/ELMO/H1, H2, I1, I2) identified worldwide (e. g. ;  Abels et al. , 2016; Schmitz and Pujalte, 2003). If we add the data from this study (U event), we can estimate a mean thickness for hyperthermal events of ca. 27 m for continental and 1. 3 m for deep-marine succession, respectively  (Abels et al. , 2016; Bowen et al. , 2001; Lauretano et al. , 2015; Lourens et al. , 2005; Nicolo et al. , 2007; Schmitz and Pujalte, 2003; Slotnick et al.
Acknowledgements. The authors would like to acknowledge the lifetime work of  Josep Serra Kiel , whose research and scientific contributions in the Pyrenees have been fundamental to this work and much beyond. This study has benefited from scientific discussion and field work with  M. Perret ,  A. Nowak ,  C. Läuchli ,  T. Hunger ,  J. Vernier  and  T. Maeder. Work  supported by an  Augustin Lombard grant  from the  SPHN Society of Geneva. 415 Field work was supported by an  Equinor  grant to SC. Isotopic, majors and trace data can be found in the supporting information (Table S2 in the Supplement) https://doi. org/10. 5194/cp-2019-88Preprint. Discussion started: 20 August 2019 c Author(s)  2019. CC BY 4. 0 License.
For this aim, we analyse how this period is simulated by the Weather Research and Forecasting (WRF) model and the HIRLAM ALADIN Research on Mesoscale Operational NWP In Euromed (HARMONIE) model set with the AROME configuration and with similar configuration than WRF. Hence, the second objective of this work is focused on detecting the strengths and weaknesses of these models under different fog-type specific conditions. These findings are expected to be valuable to improve the operational forecasting of fog, as well as the model development. This work is organised as follows: Section 2 presents the observational data used to characterize the events at the site, the configuration of the models and an introduction to the analysed period. Section 3 presents an exhaustive observational description and analysis of the model skill for each fog event.
Observational data The observational data analysed and used for the model evaluation in this study have been collected at CIBA site during the period comprising from 19 to 27 January 2016. This site (41 • 48. 92'N; 4 • 55. 92'W, 840 m above sea level) is located over Los Montes Torozos, an elevated and extended plateau of 800 km 2 situated over the large and wide Spanish Northern Plateau. A more complete description of this site is provided in  Cuxart et al. (2000) , while the fog climatology of the site was studied in  Román-Cascón et al. (2016a). This work showed how most of the radiation-fog events at CIBA are formed between November and February. The site is prone to the formation of radiation fog, in many cases alternated with low-stratus clouds and CBL fog, affecting considerably to the nearby airport of Valladolid  (Guijo-Rubio et al.
, 2018). Table  1  shows information about the instruments measuring the different variables. Note how in this study we analyse, among other variables, the observed fog thickness calculated using data from visibilimeters installed at 2, 30, 70 and 100 m. Hence, its uncertainty is determined by the distance between visibilimeters. For example, if the fog is observed at 30 m but not a 70 m, it indicates a fog thickness between 30 and 70 m. When the fog is observed at the four levels, we can only determine that the minimum fog thickness was 100 m.
A great part of the analyses shown hereinafter compares observed and simulated visibility. While direct visibility data are provided by the BIRAL SWS-100 visibilimeters, the visibility in the models have been calculated from liquid water content (LWC) output at the model levels. Since no information about the droplet spectra was available from the models, the LWC-visibility relationship given in  Kunkel (1984)  (equations 4 and 11) was used.
Acknowledgements This research has been funded by two  Spanish National Projects [Ref. CGL2012-37416-C04-02  ( MINECO/FEDER)  and  Ref. CGL2015-65627-C3-3-R (MINECO/FEDER )]. Jon A. Arrillaga  is supported by the  Predoctoral Training Program for No-Doctor Researchers of the Department of Education, Language Policy and Culture of the Basque Government  ( PRE 2017 2 0069 ,  MOD = B). Thanks to ECMWF for providing the data for the initialization of the models. We thank www. meteociel. com and NCEP for the reanalysis maps shown in the supplementary material.
These DEMs were used to assess from which resolution the model deteriorates too much with respect to observations. The simulations were done with SnowModel  (Liston & Elder, 2006a) , a distributed snow evolution model that was already tested in various regions, including semiarid mountains (e. g. ,  Gascoin et al. , 2013). Evaluation data include continuous snow depth measurements from an automatic weather station, but the model evaluation mainly relies on a time series of 8-m resolution SCA maps derived from Formosat-2 acquisitions during the 2008-2009 snow season. These Formosat-2 data can be seen as a surrogate of the Sentinel-2 data (the operational revisit frequency of 5 days was achieved in the Atlas during the 2017/2018 snow season). The accuracy of the simulations is balanced with the computation time.
Data 2. 2. 1. Meteorological Forcing We used hourly meteorological data from four Automatic Weather Stations (AWS). These stations provide standard meteorological variables (Table  1 ), and the snow depth was recorded at Oukaimeden AWS using an acoustic snow gauge  (Boudhar et al. , 2016). BABA ET AL. All these stations were installed during the early phase of the SUDMED project between 2002 and 2004  (Chehbouni et al. , 2008)  and were maintained as part of the Tensift observatory by the staff of the joint international laboratory TREMA  (Jarlan et al. , 2015). Only Oukaimeden station is equipped with a wind-sheltered Geonor T-200B precipitation gauge. The other stations (Aremd, Imskerbour, Tamatert, and Asni) have tipping bucket rain gauges. However, these stations rarely receive snow in contrast to Oukaimeden station.
The acquisition dates were constrained to summer to avoid the presence of snow. The images of 18 August 2015 are partly covered by clouds near their western edge. The base to height ratios (B/H) were respectively 0. 32, 0. 35, and 0. 3 for each date. B/H ratios lower than 0. 4 are recommended for mountainous terrain  (Hasegawa et al. , 2000). The operator (Airbus Space and Defense) provides a Rational Polynomial Coefficient (RPC) file with each image to allow the geolocalization of the images. These images and associated RPC files were used to generate a high-resolution DEM with the Ames Stereo Pipeline (ASP, version: 2. 5), an open source stereogrammetry software developed by NASA  (Shean et al. , 2016) , which supports Pléiades data and is efficient in mountainous regions  (Marti et al. , 2016).
We manually delineated these erroneous pixels in a GIS software and changed their value to No-data (They represent 2% of the data). These remaining gaps were isolated and covered areas generally smaller than the SRTM pixel size; hence, we filled them using the gdal_fillnodata. py utility with a radius of 11 pixels as a maximum search distance (44 m). The final DEM is shown in Figure  2. To evaluate the DEM accuracy, seven ground control points were collected with a SX Blue dGPS in the Rheraya catchment on 14 May 2015. The mean absolute error in elevation at the ground control points is equal to 4. 72 m, and the normalized median absolute deviation (indicator of the random error) is 4. 10 m (defined as NMAD = 1. 4826 median(|Δh m Δh  |) where Δh j refers to the individual errors and m Δh denotes the median of errors  Höhle & Höhle, 2009).
Images were geometrically corrected by cross correlation with a SPOT (Satellite Pour l'Observation de la Terre) reference image, which was previously georeferenced in the UTM-29N projection system  (Marchane et al. , 2015). Atmospheric correction was performed with the method of  Hagolle et al. (2008)  for the retrieval of aerosol optical thickness. Then, the snow-covered masks were computed from a supervised classification based on bands 2 (green), 3 (red), and 4 (near-infrared) using the parallelepiped approach as implemented in the ENVI software  (Lillesand, 1979). The training data were sampled in every image separately using three classes: snow, no snow, and shadowed snow (between 5 and 10 polygons of each class for each image). The two snow classes were then BABA ET AL. 2015 ) is that we refined the geolocalization of the Formosat-2 images stack using the Pléiades orthoimage (section 2.
Model Description and Setup We used SnowModel, a distributed snowpack evolution model  (Liston & Elder, 2006a). SnowModel is composed of four submodels: MicroMet, EnBal, SnowPack, and SnowTran-3D. MicroMet generates a distributed meteorological forcing over the model domain from AWS measurements  (Liston & Elder, 2006b). The Snow-Pack submodel uses the outputs from MicroMet and the energy balance submodel EnBal to simulate the evolution of the height and density of the snowpack  (Liston & Elder, 2006a). We used the one-layer option as recommended by the default settings. The SnowTran-3D submodel redistributes the snow depth over BABA ET AL. Note. The precipitation factors were first derived from automatic weather station data (AWS) and then optimized using the snow cover area data (Formosat-2).
Q li is estimated by assuming that the snow behaves like a gray body with an emissivity of 0. 98  (Liston & Hall, 1995). The turbulent exchange of sensible and latent heat are computed using the bulk equation with a stability correction  (Liston & Hall, 1995). The conduction of heat through the snowpack was neglected. We modified some aspects of MicroMet. Spatial interpolation in MicroMet is based on the Barnes objective analysis scheme, which uses a Gaussian distance-dependent weighting function to interpolate the station data to the regular model grid. Prior to this interpolation, elevation-dependent corrections are applied to remove the effect of elevation on the precipitation, air temperature, and humidity, that is, as if the domain was flat. Once the Barnes interpolation is performed, the data are lapsed back to the grid elevation as given by the DEM.
If a meteorological station is located on a grid cell with a high slope value, the simulated incoming shortwave at this station will be affected by the cos B factor. However, incoming shortwave radiation is typically measured in the horizontal plane by pyranometers. For this study it can be problematic since the terrain of the Rheraya catchment is very rugged. For example, Oukaimeden AWS is located near a ski hill. In addition, the local slope can vary significantly depending on the DEM resolution, which could introduce a spurious dependence of the radiation forcing to the input DEM resolution. Therefore, we modified the routine for radiation data assimilation as follows: first, we compute the incoming shortwave radiation for a flat domain. Then, we compute the differences between the simulated and measured shortwave radiation at the stations.
Model Calibration It is important to reduce as much as possible the bias in the model inputs to be able to pinpoint the effect of the model grid resolution. For instance,  Baldo and Margulis (2017)  used a meteorological data set that was optimized for snowpack modelling from Landsat observations. In our study area, the main source of bias presumably comes from the uncertainty in the distributed precipitation field. We used precipitation data from five stations for a catchment area of 220 km 2. It is by far the highest density of precipitation gauges in the Atlas Mountains. However, there remain large uncertainties on the high elevation precipitation since BABA ET AL. the elevation of the highest gauge is 3,230 m asl, while the maximum elevation in the catchment is 4,167 m asl (Table  1 ).
Conclusion The objective of this study was to evaluate the performance of a distributed snowpack energy balance model at different resolutions. We applied Snow-Model at different resolutions using AWS measurements as input data. We found that the model performed relatively well at resolutions from 8 to 250 m but the performance dropped significantly at 500 and 1,000 m. The same conclusion was drawn from both in situ observations (snow depth at Oukaimeden station) and remote sensing data (Formosat-2 snow maps). However, this conclusion was drawn using precipitation correction factors that were optimized at a resolution of 250 m. An additional experiment using precipitation correction factors optimized at 1,000 m suggests that the optimal resolution is probably between 250 and 90 m. At this level, the choice of the optimal resolution remains subjective since it has to be made based on a trade-off between the accuracy of the simulation and the computational cost of the simulation.