dataesr/scientific-paragraphs-categorization huggingface dataset:
(1997). Scaled to give ∼7 Tg N yr -1 (PD: 7. 2) Offline stratospheric O 3 from CMIP5 dataset 1-D, two-stream model,  Hough (1988). Uses cli-matological O 3 above tropopause and modelled O 3 below. Prescribed globally uniform concentrations, different for each timeslice following CMIP5 dataset 2. 45 × 10 -12 e -1775/T UM-CAM  Zeng et al. (2008 Zeng et al. ( , 2010) )  RCP2. 6, RCP4. 5, RCP8. 5 Interactive, based on model's convection,  Price and Rind (1992, 1994).
Acknowledgements. ACCMIP is organised under the auspices of the  International Global Atmospheric Chemistry (IGAC)  and  Stratospheric Processes And their Role in Climate (SPARC)  projects under the  International Geosphere-Biosphere Project  ( IGBP ) and  World Climate Research Program (WCRP). The authors are grateful to the  British Atmospheric Data Centre (BADC) , which is part of the  NERC National Centre for Atmospheric Science (NCAS) , for collecting and archiving the ACCMIP data. For CESM-CAM-superfast, DB and PC were funded by the  U. S. Dept. of Energy (BER) , performed under the auspices of LLNL under Contract  DE-AC52-07NA27344 , and used the supercomputing resources of  NERSC  under contract No. DE11AC02-05CH11231. The CICERO-OsloCTM2 simulations were done within the projects  SLAC  ( Short Lived Atmospheric Components ) and EarthClim funded by the  Norwegian Research Council.
DP would like to thank the  Canadian Foundation for Climate and Atmospheric Sciences  for their long-running support of CMAM development. For EMAC, the work of VE and MR was funded by the  DLR Earth System Model Validation (ESMVal)  project and used the supercomputing resources of the  German Climate Computing Center (DKRZ)  and the  Leibniz Supercomputing Centre (LRZ) , and the work of IC was funded by the  ENEA National Integrated Model to support the international negotiation on atmospheric pollution (Minni)  project. The GEOSCCM work was supported by the  NASA   Modeling, Analysis and Prediction program , with computing resources provided by  NASA 's  High-End Computing Program  through the  NASA Advanced Supercomputing Division. VN and  LWH acknowledge efforts of GFDL's Global Atmospheric Model Development Team  in the development of the GFDL-AM3 and Modeling Services Group for assistance with data processing.
0) normalised so that the area-al. (1997) (PD: 6. 0) Shine (1995)) strat. O 3 and surf. albedo, al. (2010). Fixed present weighted mean matches the RCP6. 0, RCP8. 5 model's convection, Price et (climatology from Li and tion for modelled clouds, sions from Lamarque et CMIP5 value for the times-RCP2. 6, RCP4. 5, Interactive, based on Offline stratospheric O 3 Look-up table with correc-Anthropogenic emis- lice. and Rind (1993) (PD: 1. 3) for modelled fields different in each timeslice. RCP2. 6, RCP4. 5, Interactive, based on Full stratospheric chemistry Look-up table with correc-Prescribed surface concen-RCP8. 5 model's convection, Price from CMIP5 dataset Pyle (1993); no correction trations following CMIP5, RCP6. 0 RCP8. 5 model's convection, Price tion for modelled clouds, trations following CMIP5, RCP2.
10 -12 e -1690/T * Value in the last parenthesis represents global total present day emissions in Tg N yr -1. 5. 2) (PD: from CMIP5 dataset Offline stratospheric O 3 for modelled fields Pyle (1993); no correction Look-up table Law and different for each timeslice with no spatial variation; atmospheric concentration Prescribed globally uniform 1. 85 ×.
doi:10. 5047/eps. 2013. 07. 008 expansion degree. After ten years of the CHAMP mission, the most recent models describing the magnetic field of the lithosphere, such as the MF7 model (an upgrade of  Maus et al. , 2008;  http://geomag. org/models/MF7. html) expanded to SH degree 133, and the CHAOS-4 model (an upgrade of  Olsen et al. , 2010a)  expanded to SH degree 100, agree reasonably well up to degree 85, thus providing a robust bound of about 470 km on the maximum horizontal spatial resolution. The geometrical attenuation with altitude and the prominence of external fields at satellite level are the two main factors limiting our ability to reach higher SH degrees and thus to detect smaller scale structures. However, for equivalent altitude datasets, the differences between MF7 and CHAOS-4 mainly lie in the way the different models deal with source field separation and, more specifically, with the rapidly time-varying external fields.
This second potential is required to bypass a problem of incompleteness of the potential expressed in Eq. (1) when the geomagnetic field is to be represented (see  Thébault et al. , 2004). The solution for the magnetic field B is then obtained considering the gradient of the two potentials B = -(V SCHA + V SH ). (3) Empirically, we observe that a solution defined by the Neumann boundary conditions converges fast for the vector magnetic field components. This is a desired property when dealing with anomaly fields still possibly containing large spatial scales magnetic field contaminations, which is usually the case when processing real satellite measurements or realistic synthetic Swarm datasets built from SH models. The spherical and spherical cap harmonics both allow an exact representation of any poloidal magnetic field so that the two solutions converge in norm to each other.
The Dedicated Lithospheric Field Inversion (DLFI) Chain The full chain is represented in Fig. 1. The Swarm Level 1b data are first selected according to specific criteria and are corrected for various sources fields. As already discussed, this step is crucial for deriving the lithospheric model. It involves four processes (Process 1 to Process 4 in Fig. 2 ) that require auxiliary information and operator's decisions. The selected and corrected measurements are then iteratively processed with the R-SCHA modeling. The data are distributed into geographical bins (Process. 5. 1) corresponding to each spherical cap covering the Earth's surface and transformed into the cap's reference frame (Process 5. 2). The inverse problem is solved, and the regional parameters stored, after building the design matrix (Process 6.
Data selection For this simulation, we use only the vector Level 1b data simulated for the lowest pair of satellites Swarm A and Swarm B  (Olsen et al. , 2013)  since the third satellite, Swarm C, is expected to be too high to detect the fine lithospheric field structures. Nevertheless, its data could prove useful for the chain in case of a failure of Swarm A or B and all data selection and correction procedures described below were also made operational for processing Swarm C magnetic field measurements. Considering that vector magnetometers may suffer from malfunctions, the chain was also adapted to ingest scalar magnetic measurements if required. More than four years of simulated vector Swarm A and B data are selected between a simulated time period extend- ing from July 1998 to December 2002.
Among other selection criteria we included a processing block based on the measure of the Interplanetary Magnetic Field (IMF), whose value is related to the solar wind activity. We included also a selection block based on the ap index, which measures the general magnetic activity at the planetary scale, and a selection block based on quality flags such as the on/off status of the star camera. Other selection blocks are likely to be added as more and more experience is gained on the Swarm satellite data during the calibration/validation and operational phases.
The secular variation error of the main field is of secondary importance. In total, the correction errors accumulated using SCARF scientific products reach almost ±20 nT in two lobes located near the South geographic pole. We next identify the Swarm A and B data with the com- mon reading times and built up a set of so-called gradient data. The terminology of gradient is misleading here as it refers to a point to point difference between the measured data across the two orbits of the lowest pair of satellites (Swarm A and B). This difference is expressed in the same units as the vector measurements (in nT), which avoids the unnecessarily complication of solving the inverse problem with different data types (the true gradients are in nT/km or nT/degree). Considering the nature of the residuals, and their variations in space and in time, taking the difference between the data from the lower pair of satellites in the East-West direction, spaced about 1.
In particular, the cut-off frequency must be adjusted depending on how well the AUX LIT 2 model values temporarily removed from the data agree with the measurements in the filtered waveband. Finally, the redundant data that were kept to improve the continuity between the processing in mid and polar latitudes (see Subsection 3. 1) are removed from the dataset. Then, the standard deviation and the mean of the residuals are computed for each vector component. All values outside the 3σ range are considered as outliers and thus rejected and the auxiliary lithospheric field model values subtracted before the series of dedicated corrections are added back. Figure  4  shows the residual map for the vertical component after all corrections (where, again, the lithospheric field contributions have been subtracted for the purpose of illustration).
From a practical viewpoint, it can be seen in Fig. 5  that the corrections for the explicit main and external field models being imperfect, the gradient of the lithospheric field is also affected by correction errors (note the similarity between the structures seen in Figs. 3  and 5 ). These correction errors are different from those seen in the vector data (Fig. 4 ). It thus appears that both corrected datasets have different and maybe contradictory information contents. The issue, difficult to address, is to assess which of the contamination affecting the gradient data, and of the reduction errors affecting the vector data, is the largest. These formal and experimental differences will need to be considered carefully on a case by case basis when the Swarm mission will be in operation. For these reasons, the block processes 6.
1 and 6. 2 (Fig. 1 ) solve the least-squares inverse problem independently for each spherical cap G = A T C -1 d A + λR A T d ( 8 ) where G is the column matrix of the regional parameters to be recovered (Eqs. (  1 ) and (  2 )) A the matrix of basis functions for the vector and gradient data, C -1 d the covariance matrix of the vector and gradient data, d the vector of magnetic field measurements and their gradients, and R a regularization matrix multiplied by a scalar Lagrange parameter λ. Finding the best trade-off between mitigating the data noise, rejecting the correction errors, and exploiting the information content of each dataset is not trivial. We did not try to tackle this problem in the present simulation. We set the covariance matrix to identity and neglect the fact the gradient and the vector data errors could be correlated.
1 ) at 400 km altitude (this corresponds to the median altitude of the considered dataset). This grid satisfies the sampling theorem to SH degree 300. To ensure that the numerical solution is not affected too much by the lack of measurements in the polar gap, we rely on a final module. Indeed, large errors, or lack of data, near the geographic pole have a clear adverse effect on the recovery of some constituents of the lithospheric field. The zonal SH coefficients (of order m = 0) are mostly constrained by the magnetic field vector components (mostly the horizontal ones) at high latitudes. The Swarm orbital configuration tested in this study has a polar gap of about 5. 2 •. This represents a diameter of about 615 km at 400 km altitude. Discontinuities caused by the polar gaps introduce ringing when local functions are used.
Conclusions In this paper we presented the Swarm SCARF Dedicated Lithospheric Field Inversion chain, which will be used to process the magnetic data of the forthcoming Swarm satellite mission and to produce a model for the Earth's lithospheric field. This approach differs significantly from the comprehensive approach  (Sabaka et al. , 2013)  as it heavily relies on data selection, corrections, and visual inspections. It also uses a modeling scheme based on local functions that were designed to better detect the small scales of the lithospheric field. During the operational phase, the criteria discussed in this paper will be subject to changes and will therefore be described in a companion report of the Swarm SCARF dedicated lithospheric field model. With the current setting, the algorithm provides a model that meets the required performance at least up to SH degree 133 when considering and the synthetic data provided to us for the full mission duration.
This is a better spatial resolution than achieved so far by single satellite missions at comparable altitudes. This result confirms that we are able to develop strategies for recovering the lithospheric magnetic field to a high spatial resolution in a favorable situation when high quality vector magnetic field measurements are available for the two lower Swarm A and B satellites. This simulation was realistic in the sense that it was realized with a mathematical method different from the one used to synthesize the simulated data, with a separation into regions that inevitably introduces discontinuities, with imperfect main and external field models for correcting the synthetic measurements for non-lithospheric magnetic fields, and with real proxies for selecting the data for quiet magnetic days along the entire simulated mission duration.
An improvement from n = 85 to n = 120 means that twice as many SH Gauss coefficients would be recovered (the number of Gauss coefficients is n(n + 2)). This spatial resolution limit may not be a hard bound but it shows that the recipe we followed may need to be revised and fine-tuned when real data will become available. The Swarm calibration/validation phase will be a valuable learning experience to better control the intrinsically different information content and contamination errors of the vector and the gradient data. Some tools have been developed to account for the statistical differences between these two types of datasets. In this paper, we did not try to optimize the weights or to regularize the inverse problem but this possibility was left open on both the regional (Process 6. 2) and the global scales (Process 8).
One key advantage of the Dedicated Lithospheric Field Inversion chain is that it is based on a regional modeling approach. This allows the inverse problem to be solved independently for each region. Depending on the available computer resources, the process divided in as many independent jobs is intrinsically fast. For the present simulation, a few hours were necessary to select and process more than five million out of more than 250. 10 6 available vector synthetic measurements of the Swarm A and B satellites. A fast algorithm is advantageous considering the chosen philosophy. Isolating the lithospheric field relies on a careful selection of data and on compromises made in correcting the data more or less empirically. Therefore, fast processes open an avenue for computing parades of statistically equivalent models in order to estimate the variability of each SH degree part depending on the applied selections and corrections.
Acknowledgments. The authors gratefully acknowledge support from the  Centre National des Etudes Spatiales (CNES)  within the context of the " Travaux préparatoires et exploitation de la mission Swarm " project and from the  European Space Agency (ESA)  through the  ESTEC  contract No. 4000102140/10/NL/JA  " Development of the Swarm Level2 Algorithms and Associated Level2 Processing Facility ". We also acknowledge the comments of two anonymous referees that helped to improve the manuscript. This is IPGP contribution number  3410.
(2012). We used here LMDZ and INCA with a horizontal resolution of 96 × 95 grid points in longitude and latitude, and with a vertical discretisation of 19 layers. Finally, the ORCHIDEE land surface model serves as the land surface boundary condition for LMDZ and describes exchanges of energy and water between the atmosphere, the soil and the biosphere  (Krinner et al. , 2005) , including a dynamic snow module. The coupling between LMDz and ORCHIDEE is described by  Hourdin et al. (2006) , and those between LMDz and INCA is detailed by  Hauglustaine et al. (2004)  for chemistry and tracers and by  Balkanski et al. (2007 Balkanski et al. ( , 2010) )  and  Déandreis et al. (2012)  for the computation of the aerosols radiative forcings. For this work, we used the detailed representation of snowcover implemented in ORCHIDEE by  Krinner et al.
8 nm, characteristic of freshly emitted soot  (Dentener et al. , 2006; Jacobson et al. , 2004). In the real world, this diameter increases quickly, as BC undergoes ageing and coagulation and can be coated by other aerosols in the atmosphere. However, as we do not consider internal mixtures for BC in snow, we consider that BC aerosols regain their initial size when incorporated in the snowpack. We considered a BC density of 1 g cm -3 , and the refractive index for BC is taken to be m = 1. 75-0. 45i. Refractive indices for ice are taken from the GEISA database  (Jacquinet-Husson et al. , 1999). The corresponding mass absorption cross sec-tion (MAC) of BC resulting from these assumptions of size distribution, density, and refractive index reaches a value of 7. 6 m 2 g -1 at 545 nm (mid-visible, see the MAC definition of  Bond and Bergrstrom, 2006, and Boucher, 2011).
Acknowledgements. This work was supported by  Agence Nationale de la Recherche  under contract  ANR-09-CEP-005-02/PAPRIKA  and by funding from the  European Union   7th Framework Programme  under project  LIFE SNOWCARBO. We would like to thank the " Commissariat à l'Energie Atomique et aux Energies Alternatives " and  GENCI  for providing computer time for the simulations presented in this paper. The figures have been prepared with Ferret free software. We thank  J. J. Corbett  for providing ship traffic emissions scenarios in the Arctic, and the  National Snow and Ice Data Centre (NSIDC, Boulder, CO)  for providing IMS daily Northern Hemisphere snow and ice analysis. We thank  Marie Dumont  and  Ghislain Picard  for their useful comments on aerosol radiative properties computation. We thank the editor and the 4 anonymous reviewers for their relevant comments, which helped to refine our study and to improve the manuscript.
The merging of abyssal hill roughness with SRTM30_PLUS is described in  Timko et al. [2009]  and  Timko et al. (in preparation, 2013). A system of weights based on the SID data is used to prevent the addition of a synthetic roughness field to the SRTM30_PLUS product in locations where high resolution acoustic sounding data, which resolve abyssal hills, were used to derive the depth of the ocean in SRTM30_PLUS. Hereafter, the bathymetric product in which synthetic abyssal hill roughness is overlaid on SRTM30_PLUS is referred to as SSG. An illustration of the bathymetric products is given in Figure  1  for the two regions located over the Mid-Atlantic Ridge (MAR) and East Pacific Rise (EPR) studied in section 3. The synthetic abyssal hill roughness used in this study is shown in Figure  2. Abyssal hills are morphological indicators of mid-ocean ridge spreading rates and directions.
Energy Conversion [28] The impact of the addition of synthetic abyssal hill roughness on the energy flux into internal tides is shown in Figure  5  for MAR and EPR. Clearly, abyssal hill roughness strongly influences regional tidal energy conversion. Over the EPR region, the total energy flux into M 2 internal tides is 0. 78 GW for the SRTM30_PLUS bathymetry, and 0. 95 GW for SSG, which corresponds to an increase of 23% in the tidal energy conversion. As expected (section 3. 1), the impact of abyssal hills is much greater in the MAR region, where the total energy flux increases from 4. 5 GW with the SRTM30_PLUS bathymetry to 8. 5 GW in SSG (an increase of 89%). Note that these values may underestimate the true effect of abyssal hills because portions of the SRTM30_PLUS topography in these regions are derived from acoustic data and hence also resolve abyssal hills.
[29] The conversion of tidal energy over the MAR region with the standard bathymetric product (SRTM30_PLUS) is consistent with the previous estimate by  St. Laurent and Garrett [2002]  of 3. 8 6 1. 7 GW, made using the  Smith and Sandwell [1997]  bathymetry. It can also be noted that although the energy flux in the MAR with SRTM30_PLUS is much smoother than with SSG, small-scale energy flux patterns can be spotted even with SRTM30_PLUS in places where multibeam data are included in SRTM30_PLUS (Figure  1 , see around 26 S 14 W and 30-34 S 14 W in the MAR region). When abyssal hill roughness is added to Figure  2. Abyssal hill roughness. Root mean square differences between the SRTM30_PLUS and SSG bathymetric products (in meters) on a 1/4 grid (computed with an area weighted mean from the 1/120 grid).
The purple boxes locate the EPR and MAR regions where regional estimates of energy flux are calculated. Oceanic regions with no values (white regions) are regions where no abyssal hill statistical parameters were available. Areas with small abyssal hill roughness (compared to its surroundings) over midocean ridges are areas where abyssal hills are captured by acoustic data in SRTM30_PLUS. SRTM30_PLUS, much more small-scale features are evident in the energy flux map (Figure  5 ).
[48] Acknowledgments. The authors would like to thank three anonymous reviewers as well as  Sonya Legg  for helpful comments leading to improvements in the manuscript. They also acknowledge the use of the SOSIE software for interpolation of data. The work of AM and BKA on this paper is a component of the Internal-wave driven mixing Climate Process Team funded by  National Science Foundation  grants  OCE-0968721  and  OCE-096878  and the  National Oceanic and Atmospheric Administration, U. S. Department of Commerce , award  NA08OAR4320752. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the  National Oceanic and Atmospheric Administration , or the  U. S. Department of Commerce. BKA, PGT , and JAG also acknowledge support from a  University of Texas Jackson School of Geosciences   Development grant ,  Naval Research Laboratory  contract  N000173-06-2-C003 ,  Office of Naval Research  grants  N00014-07-1-0392 ,  N00014-09-1-1003 , and  N00014-11-1-0487 , and  National Science Foundation  grant  OCE-0924481.
(2009)  and  Tuccella et al. (2012). In addition, this work examines the sensitivity of predictions to horizontal grid resolution and biogenic emissions that were not addressed in previous WRF/Chem applications over Europe. The results from this study will be presented as a sequence of two parts. Part 1 describes the two modeling systems: WRF/Polyphemus and WRF/Chem-MADRID, their configurations and the simulation setup, evaluation protocols and observational databases used, the evaluation of meteorological predictions and sensitivity to horizontal grid resolutions using WRF, and the effect of aerosol/meteorology interactions on meteorological fields using WRF/Chem-MADRID. Part 2  (Zhang et al. , 2013)  describes the evaluation for chemical concentrations and intercomparisons between chemical predictions from the two models, sensitivity of the model predictions to horizontal grid resolutions, and the effect of interactions between meteorology and aerosols predicted with WRF/Chem-MADRID on air pollutant concentrations.