dataesr/scientific-paragraphs-categorization huggingface dataset:
Seismic data Seismic data for this study were obtained at 75 seismic stations in northeastern Brazil. These stations belong to a variety of seismic networks, both permanent and temporary. The Rede Sismográfica do Nordeste (RSISNE) consists of 19 broadband stations equipped with RefTek 151-120 sensors feeding RT-130 digitizers (24-bit) sampling at 100 Hz, with an interstation spacing of about 250 km and a network aperture of ∼ 800 km. The RSISNE network has been in operation since 2011 and was initially funded by the national oil company Petrobras. The Instituto Nacional de Ciência e Tecnologia em Estudos Tectônicos (INCT-ET) network, consists of 7 broadband stations and 22 short-period stations. The broadband stations were arranged along an approximately 1000 km long line with interstation spacing of about 100 km.
They were equipped with STS-2. 5 Streckeisen sensors and Q330 data loggers (24-bit) sampling at 100 Hz. The 22 short-period stations were equipped with L4A-3D Sercel sensors (2 Hz cutoff frequency) and 24-bit RT-130 digitizers sampling at 100 Hz. They were in operations between 2011 and 2012 and recorded continuously for between 6 months and 1. 5 years. The INCT-ET network was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Up to six broadband stations operated during the period from 2007 to 2009 under the "Institutos do Milênio" project. These stations were equipped with KS-2000 Geotech sensors and Geotech digitizers sampling continuously at 100 Hz. They recorded continuously for periods ranging from 6 months to 2 years, with two of them still currently in operation.
Receiver function processing and migration Receiver functions were computed for the 75 stations comprising the combined network for northeast Brazil. Most of the receiver function estimates were developed by  Luz et al. (2015a)  in order to investigate lateral variations in crustal thickness and the bulk Vp / Vs ratio across the Borborema Province. This dataset was later utilized by  Almeida et al. (2015)  to study the crustal architecture of the region from receiver function common conversion point (CCP) stacks. We developed 1400 new receiver function estimates at 11 temporary stations from the BODES network, following the same procedure as  Luz et al. (2015a). The receiver function approach aims at retrieving P-to-S converted phases within the coda of teleseismic P waves that result from the interaction of the teleseismic P-wave-front with crustal and upper mantle discontinuities under the recording station  (Langston, 1979 (Langston, , 1977) )  in order to produce estimates of the depth of the discontinuities.
7. For a better comparison we chose to represent the k = 2 harmonics at stations where SKS splitting was measured because: (i) we expect only horizontal (recorded on the k = 2 harmonics) or slightly dipping anisotropy in such geodynamical context; (ii) we observe in our data that k = 1 and k = 2 harmonics display energy within the same Table  1. Results of anisotropic symmetry directions for several depth ranges: 0-32, 32-100 and 0-100 km. One direction corresponds to either the trend of the dip in the case of dipping interface or to the trend of the fast axis in the case of plunging anisotropy. When two directions are indicated, they refer to the fast axis and to its perpendicular direction (horizontal anisotropy). Uncertainties were estimated from bootstrap quantification (resampling 200 times at every station).
Data availability. Raw data can be accessed via either the Rede Sismográfica Brasileira (RSBR) website at http://www. rsbr. gov. br (last access: 20 June 2019), or the Pool de Equipamentos Geofísicos do Brasil (PEGBR) website at http://www. pegbr. on. br (last access: 20 June 2019). The receiver function waveforms utilized in this study can be made available from Jordi Julià upon request (jordi@geofisica. ufrn. br). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-10-893-2019-supplement. Author contributions. GL processed the receiver functions and harmonic decomposition, carried out the tectonic interpretation of the results and prepared the paper. JJ provided the dataset utilized in this study, helped with the assessment and tectonic interpretation of the results, and actively contributed to the preparation of the paper.
Acknowledgements. We used the GMT v. 5. 4 open-source toolbox (Wessel et al. , 2013) to produce the figures. Thanks are due to  Nicola Piana Agostinetti  for constructive discussions regarding the harmonic decomposition method and to  Andrea Tommasi  for interesting debates on the tectonics of northeast Brazil. Jordi Julià  thanks the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  for his research fellowship ( CNPq , process no. 304421/2015-4 ). Financial support. Data used in this study were acquired due to funding from the  national oil company Petrobras  and the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Gaelle Lamarque  was supported by a 1-year scholarship from the  Programa Nacional de Pósdoutorado da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (PNPD/CAPES).
The measurement data A large part of this study is focused on model-model comparisons. However, in order to evaluate the realism of the reference simulation, we compare the modelled outputs to the AErosol RObotic NETwork (AERONET) and Met Office Integrated Data Archive System (MIDAS) measurements. The data of these stations were previously used in  Menut et al. (2018)  for the study of biomass burning plumes transport in the Gulf of Guinea, and it has been shown that they are well adapted for these kinds of studies with large modelled domains. Aerosol optical depth (AOD) level 2 measurements are used from the AERONET dataset  (Holben et al. , 2001). Comparisons between measurements and model outputs are performed using hourly time series of AOD at a wavelength of λ = 550 nm. The MIDAS meteorological surface station data (MetOffice, 2012) are used for precipitation rate, 2 m temperature and 10 m wind speed measurements.
Soundings Comparisons between observations and model output in the first 4 km of the troposphere are presented in Fig. 4. Observations are in the left panel and represent soundings made in several places: Abidjan, Accra, Cotonou, Savè, Lamto and Parakou. For each location, all soundings recorded between 1 and 31 July are averaged. The same is done for the model outputs, where hourly gridded data are interpolated to fit the time and location of the observations (central panel). This methodology was already used in  (Deroubaix et al. , 2019)  for similar vertical profiles. All information about the soundings and the experimental campaign are in  (Flamant et al. , 2018). Finally, the difference (mod-obs) is displayed in the right panel. For the wind speed, and at all sites, it is shown that a local maximum is present at 500 m above ground level (a.
Significance of differences To quantify the statistical significance of differences, the Mann-Whitney test is applied (also called Wilcoxon signed rank test)  (von Storch and Zwiers, 2001). This test is nonparametric: there are less restrictive assumptions, for example, the fact that the distribution does not have to be normal. This test examines the two sets of data (in our case, two different simulations) by combining all data and by sorting them in ascending order. The first and second simulations, denoted x 1,i and x 2,i , have the same dimension here (N ). We first calculate the difference between the two datasets: d i = x 2,i -x 1,i For i = 1,. , N. (7) A reduced dataset with dimension N r is built by removing data where d i = 0. The remaining data d i are sorted in ascending order and their rank (R i ) is stored.
Acknowledgements. The authors are thankful to the two anonymous referees whose comments helped improve the overall quality of the paper. Paolo Tuccella  was supported by an  AXA Research Fund  postdoctoral grant. Financial support. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502. The  Centre National d'Etudes Spatiales (CNES)  also provided financial support to the project. The  Service des Avions Francais Instrumentés pour la Recherche en Environnement  (SAFIRE, a joint entity of  CNRS ,  Météo-France  and  CNES , and operator of the  ATR 42 ) and the  Deutsches Zentrum für Luft-und Raumfahrt  (operator of the Falcon 20) are thanked for their support. The authors acknowledge their colleagues in charge of the radiosounding station network during DACCIWA.
Filtered D. magna and fresh BG11 media for the treatments and for the controls, respectively, were mixed with the one week old cyanobacterial pre-cultures in 4:1 ratio in 2 L Erlenmeyer flasks. Cyanobacterial density at the beginning of the experiment was 2 × 10 6 cells/mL for low density DM (1w and 2w) exposures and 5 × 10 5 cells/mL for high density DM (24 h) exposure. The initial nutrients (PO 4 and NO 3 ) and pH were measured for the low density DM (2w) exposure to ensure similar conditions. Dissolved nutrient concentrations were measured from filtered (GF/F 0. 7 µm) water using common colorimetric methods  [73]. Nitrate was measured after reduction to nitrite on a cadmium-copper column  [74]. Phosphate was measured following the method of  Murphy and Riley (1962)   [75]. Data presented in Table  S1  show no significant difference between control and treatment (p > 0.
Autosampler was maintained at 6 • C at all times. UV data was acquired from 200 to 400 nm. Mass data was acquired in positive ion electrospray scanning from m/z 50 to 2000 with a scan time of 2 s and inter-scan delay of 0. 1 s. Ion source parameters, i. e. , a capillary and sampling cone, were 2. 9 V and 25 V respectively; desolvation temperature, 300 • C; and source temperature, 80 • C. Cone gas and desolvation gas flows were 50 L h -1 and 400 L h -1 respectively. Sodium iodide (2 µg µL -1 211 in 50/50 Propan-2-ol/H2O) was used as the calibrant with leucine-enkephalin (0. 5 mg mL -1 212 in 50/50 methanol/Milli-Q) as the lock spray. Instrument control, data acquisition (centroid) and processing were achieved using MassLynx v4. 1 software (Waters, Milford, MA, USA). We monitored secondary metabolites produced by Microcystis that were observed to be changing due to abiotic or biotic factors  [36, 50, 78]  (Table  5 ).
Acknowledgments: We are grateful to  D. Azam  and  M. Coke  from the  PEARL INRA   1036 U3E  system (The  National Infrastructure in Biology and Health, France ) for providing the Daphnia magna strain (originating from l'INERIS) and  Bertrand Le Rouzic  ( University of Rennes 1 ) for providing us with the Scenedesmus communis culture (originating from the lake of Grand Lieu). The help during the experiments of master student Naoual Bendahhane is greatly acknowledged. We would like to thank  Len Montgomery  ( Robert Gordon University ) for for proofreading the manuscript.
We start by discussing observational needs for ocean data assimilation in support of ecosystem modeling, understanding and forecasting (see section Ocean Data Assimilation). We then review some statistical and dynamical approaches that have proven very useful for marine ecosystem modeling and forecasting in specific regions near the U. S. coasts (see section Modeling and Forecasting Marine Ecosystems and Their Physical Drivers). Finally, we review novel approaches to large-scale marine ecosystem monitoring that will not only allow early detection of distribution changes, but can also support modeling and forecasting efforts, in particular using acoustics or the approaches employed during the Tara Oceans expeditions (see section Novel Approaches to Large-Scale Ecosystem Monitoring in Support of Modeling Efforts).
OCEAN DATA ASSIMILATION Ocean data assimilation, which combines observations with dynamical ocean models to provide state estimates that are better than those offered by either on its own, has advanced significantly over the past 15 years  (Edwards et al. , 2015; Moore et al. , 2019). Ocean reanalyses, which use data assimilation to produce ocean state estimates for historical periods or in near-real-time, have a number of characteristics that make them attractive for marine ecosystem modeling and forecasting. In particular, they provide output for the three-dimensional structure of the ocean that is spatiotemporally resolved, gap-free, and continuous across periods of changing observational assets. Ocean data assimilation also considers the different uncertainties inherent in different measurements and sensors when adjusting the ocean state estimates, and enables observations of an observed variable (e.
g. , sea surface temperature, SST) to adjust, through the model dynamics, estimates of other less easily observed variables (e. g. , subsurface currents;  Moore et al. , 2017 ). An improved physical ocean state estimate can also translate into improved biogeochemical estimates in coupled physical-biogeochemical models, although assimilation of physical variables in the presence of a coupled biogeochemical component is a very challenging process due to the high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation  (Park et al. , 2018). The importance of data assimilation for ocean analyses and forecasting has motivated the establishment of the GODAE OceanView program 2 , whose main goal is the consolidation and improvement of global and regional reanalysis and forecasting activities using both physical and biogeochemical models.
While there have been technical advances to improve the quality of reanalysis products, both in data assimilation 2 https://www. godae-oceanview. org methods and computing technology, the accuracy of these products ultimately depends on the availability of qualitycontrolled observations. These products have benefited greatly from improvements to the ocean observing system guided in part by the OceanObs'99  (Smith and Koblinsky, 2001)  and OceanObs'09  (Hall et al. , 2010)  efforts. At present, the ocean variables most commonly assimilated are sea surface height (SSH), SST, and salinity, of which the first two have now been measured from satellites for several decades. Observation impact analyses indicate that satellite data have by far the largest impact in terms of correcting the modeled ocean state, owing to the sheer number of measurements they provide.
For example, regional ocean reanalyses in the California Current System have been used to improve understanding of bottomup drivers of primary production  (Jacox et al. , 2016)  and to model the response of marine fishes  (Brodie et al. , 2018)  and marine mammals  (Becker et al. , 2019)  to environmental change. Based on such relationships, near-real-time data assimilative models can be used to aid in management applications, for example of living marine resources (e. g. ,  Hazen et al. , 2018). Establishing robust links between marine species and their environment, either statistically or mechanistically, requires datasets that are self-consistent, or at least comparable, and maintained over long periods. Leveraging these relationships for management applications additionally requires that datasets be updated consistently and in a timely manner to enable forecasts and validation.
In operational forecast systems, observations are also used to perform bias-corrections of the forecast in the presence of systematic model errors. The physical models considered in this paper are primarily high-resolution regional ocean models, for which the lateral ocean boundary conditions as well as surface forcing need to be provided. Initial and boundary conditions and surface forcing are obtained from global models, which are themselves constrained by observations through initial conditions and data assimilation. Depending on the forecasting time scale τ, different observational inputs may be relatively more important. For forecasts up to several months, initial conditions are critical, but the memory of initial conditions is lost at longer lead times, so that boundary conditions and forcing become more important.
Equation (  3 ) is used for forecasting applications. The LIM approach has been extensively used to model and predict SST anomalies in the tropical Pacific  (Penland and Sardeshmukh, 1995; Newman and Sardeshmukh, 2017) , but it has also shown skill for the prediction of SST anomalies along the U. S. west coast  (Dias et al. , 2018, Figure 2). For the latter application, the Hadley Center Sea Ice and Sea Surface Temperature (HadISST) data were used over the period 1900-2015. While a LIM constructed using only SST information has proven very useful in many applications, the inclusion in the state vector of information about subsurface ocean dynamics, for example thermocline depth or SSH has improved both modeling and prediction of the tropical Pacific system  (Newman et al. , 2011; Capotondi and Sardeshmukh, 2015; Capotondi and Sardeshmukh, 2017; Newman and Sardeshmukh, 2017) , and may improve predictions in other regions.
SSH is in itself a very important quantity to predict, given its dynamical link to thermocline depth, as well as its relevance for coastal sea level. The extension of the state vector to other variables (e. g. , surface wind stress, ocean currents, sea surface salinity) may further improve the LIM system. An approach similar to the LIM has been successfully used to estimate the potential predictability of Net Primary Production (NPP) and SST over the global ocean, using satellite-based data of the two quantities over the period 1997-2017  (Taboada et al. , 2019) , although the short duration of the NPP record appeared to severely limit the skill of out-of-sample predictions of the last year of the data. Thus, future LIM applications can be expected to extend existing efforts to improve the modeling and forecasting of biological quantities.
The NMME provides a large multi-model ensemble dataset to evaluate and forecast climate variability on seasonal to interannual timescales. NMME model data is provided at a relatively coarse 1 • ×1 • resolution, but is useful for capturing large-scale oceanic variability and drivers of that variability that offer predictability. The NMME's ability to capture relevant modes of variability has been well-documented  (Becker et al. , 2014; L'Heureux et al. , 2017)  and the consistent set of hindcasts offers the opportunity to evaluate the applicability of the system for targeted predictions. In particular, the NMME model output can supply lateral boundary conditions and surface forcing, after proper downscaling, for high-resolution regional forecasting models like those described in Sections "JISAO's Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE)" and "Bering Sea Modeling and Forecasting.
Due to the large number of variables in the biogeochemical model relative to the physical model component the spin-up of the model biogeochemistry may require thousands of years  (Wunsch and Heimbach, 2008)  compared to weeks for the atmosphere and centuries for the ocean and ice components  (Stouffer et al. , 2004). Also, biogeochemical data are very sparse. Three-dimensional climatology datasets available for biogeochemical initialization exist only for few variables (macronutrients, oxygen, dissolved carbon and alkalinity) while data are extremely sparse for other variables, including dissolved organic carbon, dissolved iron, as well as the biomass of various plankton types. As a result, the latter fields are often initialized as constant fields, producing a mismatch with the physical ocean state, and leading to incomplete equilibration in coupled simulations  (Seferian et al.
This has allowed validating both the physical and biogeochemical model components, as well as the model forecasts. In particular, J-SCOPE model performance and predictability were examined for SST, bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, forecast (forcing without data assimilation), and re-forecast (forecasts performed on past years) comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales  (Kaplan et al. , 2016; Siedlecki et al. , 2016). Bottom ocean conditions (e. g. , bottom temperature and bottom oxygen), which are very important for benthic dwelling species, like Dungeness crab, were shown to have more predictability than SST in this region  (Siedlecki et al. , 2016). Observations are critical to the development and testing of systems like J-SCOPE.
Thus, this model has provided very important insights on climate change in the Northwest Atlantic  (Saba et al. , 2016). Temperature and salinity measurements in the Gulf of Maine Northeast channel, the major throughway for Slope Waters to enter the shelf at depth  (Mountain, 2012 ) have allowed the model validation at the fine scale of the Channel. This modeling activity has also largely benefited from relatively highresolution (0. 25 • ) NOAA Optimum Interpolation SST (OISST) data  (Reynolds et al. , 2007)  over a long enough period  to allow the establishment of a reliable SST climatology at the spatial scales relevant for this high-resolution model. Bottom temperature and salinity data from the NOAA Northeast Fisheries Science Center's fall and spring bottom trawl survey and ecosystem monitoring survey also provided critical in situ ocean data to estimate model biases.
, 2013 (Hermann et al. , , 2016)). The BESTNPZ-Bering 10k-ROMS is additionally coupled to a fish model, the Forage and Euphausiid Abundance in Space and Time (FEAST), which is a 2-dimensional, gridded, daily scaled multispecies length-based foraging, bioenergetics movement and recruitment model for post larval forage and predatory fish  (Ortiz et al. , 2016). Together, these models form the Bering10K Modeling Suite (hereafter Bering10K), and were originally designed under the supervision of an ecosystem modeling committee as part of an integrated ecosystem research program, "The Bering Sea Project 7  , " to synthesize up-to-date information about the eastern Bering Sea ecosystem and be used both for research and management purposes  (Punt et al. , 2016). An ocean acidification module has recently been added to the Bering10K modeling suite.
Skill and performance of the Bering10K modeling suite have been evaluated primarily over the eastern Bering Sea shelf using ocean in situ observations (Figure  8 ) and remotely sensed sea ice cover and ocean color data. Year-round vertical temperature and salinity profiles, as well as surface chlorophyll measurements, have been measured at four fixed moorings along the 70 m isobath on the Bering Sea shelf for the past 20+ years  (Stabeno et al. , 2012) , and summertime hydrographic surveys span much of the southeastern shelf  (Cokelet, 2016). These data have proven critical for model evaluation and improvement  (Hermann et al. , 2013 (Hermann et al. , , 2016;; Ortiz et al. , 2016). For instance, satellite and in situ observations pointed to warm biases in the modeled water column at an early stage of model development and led to improvements in the modeling of light attenuation (Kelly Kearney, personal communications); the mooring observations have allowed the detection of biases in the modeled vertical profiles of temperature and salinity, leading to the adjustment of the bulk flux algorithms used for surface heat and momentum fluxes.