dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , potential low-consistency patterns between the satellite-derived and MARS SST fields, differences between the very superficial temperature conditions measured by the satellite and the temperature conditions in the upper sea layer (0-10m), diurnal sea surface temperature cycles,…. ). The calibration of these models could combine both prior calibration from in situ datasets (for instance, for possible temperature offsets between the surface and the subsurface) as well as model parameter inference from the processed DST data. Regarding the depth observation model, our approach disregards the uncertainty inherent in comparing the depth observation with the bathymetry. The depth observation from the tag has a minor uncertainty, but the main uncertainty comes from the potential variability of the bathymetry within a grid cell.
, 2009; Navarra et al. , 2010; Delworth et al. , 2012; Kinter et al. , 2013; Mizielinski et al. , 2014; Davini et al. , 2016). Due to the large computer resources needed for these simulations, synergy will be gained if they are carried out in a coordinated way, enabling the construction of a multi-model ensemble (since the ensemble size for each model will be limited) with common integration periods, forcing, and boundary conditions. The CMIP3 and CMIP5 databases provide outstanding examples of the success of this approach. The multi-model mean has often proven to be superior to individual models in seasonal  (Hagedorn et al. , 2005)  and decadal forecasting  (Bellucci et al. , 2015)  as well as in climate projections  (Tebaldi and Knutti, 2007)  in response to radiative forcing. Moreover, significant scientific understanding has been gained from analysing the inter-model spread and attempting to attribute this spread to model formulation  (Sanderson et al.
Initialization and spin-up of the atmosphere-land system As discussed in  Eyring et al. (2016) , the initialization of land surface and atmosphere requires several years of spin-up to reach quasi-equilibrium before the simulation proper can begin. We recommend this is done using the first few years of the forcing datasets before restarting in 1950. We further recommend that the initial condition for the atmosphere and land for 1950 (for the highresSST-present and the highres-1950 experiment) come from the ERA-20C reanalysis from January 1950. If this is not possible, then the exact procedure used should be fully documented by each group. 3 Detailed description of tiers 3. 1 Tier 1 simulations: ForcedAtmos runs 1950-2014 -highresSST-present The target for high resolution is 25-50 km, which is significantly higher than the typical CMIP5 resolution of 150 km.
SST and sea-ice forcing Although there is a significant forcing of the ocean by the atmosphere, in particular at the mid-latitudes  (Wu and Kirtman, 2007) , many recent studies have shown that gradients in SST associated with fronts and ocean eddies can have a significant influence on the atmosphere via changes in airsea fluxes  (Minobe et al. , 2008; Parfitt et al. , 2016; Ma et al. , 2015; O'Reilly et al. , 2015). Similarly, there is evidence that daily variability rather than monthly smoothed forcing can influence model simulations  (de Boisséson et al. , 2012; Woollings et al. , 2010). Since the high-resolution simulations will approach 25 km, this means there is a requirement for a daily, 1 4 • dataset for a period longer than satellite-based datasets (such as  Reynolds et al. , 2002)  are able to provide.
Hence, we will use a new dataset based on HadISST2  (Rayner et al. , 2016; Kennedy et al. , 2016)  which has these properties for both SST and sea-ice concentration for the period 1950-2014 -in addition, it provides an ensemble of historic realizations which can potentially be used to produce multiple ensemble members. It should be noted that the use of a daily, 1 4 • dataset will also have adverse effects. This is an inevitable consequence of AMIP runs. In these runs the ocean has an infinite heat capacity, with a deteriorative impact on the phase relationships between SSTs, the overlying atmosphere, and surface fluxes  (Barsugli and Battisti, 1998; Sutton and Mathieu, 2002). Although beneficial for the processes discussed above, the daily, 1 4 • data will be for instance less optimal for the simulation of extremes over land  (Cassou, 2015)  and MJOs  (DeMott et al.
Controlcontrol-1950 These coupled runs will be the HighResMIP equivalent of the pre-industrial control, here being a 1950s control using fixed 1950s forcing. The forcing consists of GHG gases, including O 3 and aerosol loading for a 1950s (∼ 10-year mean) climatology. This will allow an evaluation of the model drift. The initial ocean conditions are taken from version 4 of the Met Office Hadley Centre "EN" series of datasets of global quality controlled ocean temperature and salinity profiles and monthly objective analyses (EN4,  Good et al. , 2013)  over an average period of 1950-1954. As described below, a short spin-up with these forcings is required (∼ 50 years) to produce initial conditions for both the 100-year simulation within this control as well as for the Historic simulation described in Sect.
On the other hand, there is strong evidence of the importance of coupled ocean-atmosphere interactions for the summer monsoon dynamics  (Robertson and Mechoso, 2000; Robertson et al. , 2003; Wang et al. , 2005; Nobre et al. , 2012). Consideration was given to starting the HighResMIP from 1870 to better compare with GMMIP, but it would not be affordable for many groups. In addition, the quality of observational and reanalysis datasets during the earlier period, to assess the modelled variability and processes, is questionable.
CORDEX CORDEX regional downscaling experiments provide focused downscaling over particular regions. Comparison between these and global HighResMIP simulations can give insight into the relative importance of global-scale teleconnections and interactions, against enhanced local resolution and local processes. HighResMIP can (potentially) provide boundary conditions for downscaling and provide a stimulus to cloud resolving simulations, but data volumes are likely to be prohibitive, so this will be left to individual groups to coordinate.
Data storage and sharing The storage and distribution of high-resolution model data are challenging issues. Since the resolution of HighResMIP approaches the scales necessary for realistic simulation of synoptic weather phenomena, daily and sub-daily data will be stored to allow the investigation of weather phenomena such as those related to mid-latitude storms, blocking, hurricanes, and monsoon systems. However, high-frequency output of all three-dimensional fields will not be affordable to store. Careful considerations are needed to limit the highfrequency output. The considerations should take into account that the information relevant for the end users is concentrated at or near the land surface where people live, so that it is desirable to store surface and near-surface variables at high temporal and spatial resolutions.
Furthermore, in order to evaluate the HighResMIP ensemble, the high-frequency output should contain variables for which high-frequency observations are available as well. HighResMIP output data will conform to all the CMIP requirements for standardization. The CMIP6 data and diagnostic plan  (Juckes et al. , 2016)  describes the diagnostic request for all the CMIP6 MIPs. This data request, including that of HighResMIP, is available from the CMIP6 website. The data and diagnostic plan will be finalized during the boreal summer of 2016. An estimate of the amount of data that need to be stored is given at http://clipc-services. ceda. ac. uk/ dreq/tab01_3_3. html. The data storage is divided into three priorities. This is based on a balance between the HighResMIP data request (http://clipc-services.
ceda. ac. uk/dreq/u/HighResMIP. html) to answer scientific questions and the large data volumes involved. Priority 1 should be possible for all centres. Priorities 2 and 3 involve large data volumes and more specific questions. HighResMIP groups commit to archiving at least the priority 1 data request diagnostics on an Earth System Grid Federation (ESGF) node. The very large data vol-umes mean that it may be difficult to transfer all of the priority 2 and 3 data, and hence a different methodology is needed to cope with this. Discussions with other international data centres are planned to further enable collaborative analysis. In European Horizon 2020 project PRIMAVERA, the JASMIN platform (STFC/CEDA, UK) will be used for data exchange and as a common analysis platform. In future, it would be a more efficient management of global resources to move analysis tools to data storage centres.
The European Copernicus Climate Data Store may also provide useful future avenues for data storage and sharing, which will be explored. Further, the project will explore a close collaboration with the European EUDAT initiative (http://www. eudat. eu), which is developing data storage, preservation, staging, and sharing services suitable for extremely large datasets. One useful approach may be to provide spatially and/or temporally coarsened model output on the ESGF, which would enable initial analysis compared to DECK simulations, and indicate which avenues of analysis may require full model resolution output, with manageable remaining volumes. It would then also be available for any automated assessment tools on the ESGF.
Process studies Process-level assessment of the simulated climate will give us some insights to improve the physics scheme in the climate models at a range of resolutions. Satellite simulators will be applied to the HighResMIP model output to evaluate cloud and precipitation processes in detail (e. g. Hashino et al. , 2013). After the launch of the EarthCare satellite (planned in 2018;  Illingworth et al. , 2015) , a new dataset including vertical distribution of cloud, precipitation, and vertical velocity is expected to be available. The fact that the horizontal resolution of the climate model is approaching that of the satellite observations also motivates us to accelerate synergetic studies between models and observations. Process studies will aim to pin down the reasons for potentially better capturing small-scale and consequently largescale phenomena with increasing resolution.
Changes in water availability HighResMIP is very relevant to this grand challenge. Resolution affects the hydrological cycle by modifying the landsea partitioning of precipitation. Increasing resolution in general increases the moisture convergence over land  (Demory et al. , 2014) , although regionally this can be reversed, such as for instance in Europe during the winter due to changes in the position of the storm track  (Van Haren et al. , 2014). In addition, simulations of extreme precipitation events are highly sensitive to increasing resolution. How robust are these results across the multi-model ensemble? Can higher-resolution models help to give insight into inconsistencies between global precipitation and energy balance datasets? How surface water availability (P minus E) changes with warming is of significant societal relevance.
Along with the data themselves, the provenance of the data will be recorded, and DOIs will be assigned to collections of output so that they can be appropriately cited. This information will be made readily available so that published research results can be verified and credit can be given to the modelling groups providing the data. The WIP is coordinating and encouraging the development of the infrastructure needed to archive and deliver this information. In order to run the experiments, datasets for natural and anthropogenic forcings are required. These forcing datasets are described in separate invited contributions to this Special Issue. The forcing datasets will be made available through the ESGF with version control and DOIs assigned. 2013;  Mao et al. , 2013). For the targeted experiments we will provide a 1 4 • mean LAI3g dataset.
The other boundary conditions (e. g. greenhouse gases and aerosols, SST, and seaice conditions) will be identical to those in Tier 1. The new targeted simulations will be directly compared to the Tier 1 results, for which each modelling centre has used their preferred LAI. If significant positive impacts are found, then the next CMIP might consider applying LAI3g as a new common high-resolution LAI dataset. C2 Impact of SST variability on large-scale atmospheric circulation -highresSST-smoothed The impact of mesoscale air-sea coupling on the large-scale circulation (in atmosphere and ocean) is a growing area of research interest. Ma et al. (2015)  have shown that mesoscale SST variability in the Kuroshio region can exert an influence on rainfall variability along the US North Pacific coast.
Acknowledgements. PRIMAVERA  project members ( Malcolm J. Roberts ,  Reindert J. Haarsma ,  Pier Luigi Vidale ,  Torben Koenigk ,  Virginie Guemas ,  Susanna Corti ,  Jost von Hardenberg ,  Jin-Song von Storch ,  Wilco Hazeleger ,  Catherine A. Senior ,  Matthew S. Mizielinsky ,  Tido Semmler ,  Alessio Bellucci ,  Enrico Scoccimarro ,  Neven S. Fučkar ) acknowledge funding received from the  European Commission  under grant agreement  641727  of the  Horizon 2020 research programme. Chihiro Kodama  acknowledges  Y. Yamada ,  M. Nakano ,  T. Nasuno ,  T. Miyakawa , and  H. Miura  for analysis ideas. Neven S. Fučkar  acknowledges support of the  Juan de la Ciervaincorporación postdoctoral fellowship  from the  Ministry of Economy and Competitiveness of Spain. L. Ruby Leung  and  Jian Lu  acknowledge support from the  U.
LAI to evolve repeat (monthly or otherwise) orography, land surface consistent with land use cycle types, soil properties, leaf change. area index/canopy height, river paths Initial atmosphere, ocean, Continuation from Historic Continuation from Tier 1 Continuation from Tier 2 sea-ice state simulation simulation historic simulation Ensemble number Typically ≥ 3 ≥ 1 1 a. Leaf area index (LAI) experiment -highresSST-LAI: e. Tiers 2 and 3 using RCP8. 5 instead of SSPx -highres- impact of using a common LAI dataset in models, link- RCP85: for centres that need to start their simulations ing with LS3MIP before the availability of SSPx b. Impact of SST variability on large-scale atmospheric circulation -highresSST-smoothed: impact of using a 4 Connection with DECK and CMIP6 endorsed MIPs smoothed SST and sea-ice forcing dataset, linking with 4.
Fluidized bed packings This experimental dataset addresses the question of mechanical stability by preparing loose packings of glass spheres which are (a) completely at rest and (b) stable under a finite pressure, i. e. their own weight. The sample consists of 14 000 monodisperse quartz glass spheres with a diameter of 351 AE 0. 5 mm from Sandoz Fils S. A. Individual packings are created by fluidizing the particles with flow pulses of water in a fluidized bed. 10  Then X-ray tomographies of the samples were obtained at the European synchrotron radiation facility (ESRF) in Grenoble at beamline ID15A. The high X-ray flux allowed us to take a full tomogram with up to 1500 projections in less than 20 seconds in the monochromatic X-ray beam. This enabled us to gather several hundred tomograms (cf.
Acknowledgements We thank  Sibylle Na  ¨gle for creating Fig. 2 and  Udo Krafft  and  Wolf Keiderling  for building the fluidized bed setup. X-ray tomographies were performed on the ID15A beamline at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. Computational resources were provided by  FZJ (Forschungszentrum Ju ¨lich, Germany ). The authors would like to acknowledge the funding of the  Deutsche Forschungsgemeinschaft (DFG)  through the  Cluster of Excellence Engineering of Advanced Materials. The  French National Research Agency (ANR)  is acknowledged for support via EQUIPEX grant  ANR-11-EQPX-0031  (project  NanoimagesX ). We are grateful to the  John von Neumann Institute for Computing (NIC)  and the  Ju ¨lich Supercomputing Center (JSC)  for the allocation of a special CPU-time grant ( NIC  project number:  8214 ,  JSC  project  ID :  HMR10 ).
, 2015). The genomes of 13 different B. cinerea isolates have been re-sequenced to measure their genetic diversities, which pointed out the fact of the broad host range of the species, and their potential ability to adapt to new hosts. Arabidopsis is a small flowering plant that offers important advantages for basic research in genetics and molecular biology. Arabidopsis genome sequence was first completed in 2000, with a genome size of ∼135 Mbp (Arabidopsis Genome  Initiative, 2000). A full public database of the Arabidopsis complete genome, genome maps, genetic and physical markers, gene structure and gene expression, DNA and seed stocks, can be accessed via The Arabidopsis Information Resource (TAIR 1 ) database. The "flexibility" of the Arabidopsis genome allows this plant to adapt to various environmental conditions.
Black carbon (BC) emitted from fires in northern Eurasia is transported and deposited on ice and snow in the Arctic and can accelerate its melting during certain times of the year. Thus, we developed a high spatial resolution (500 m × 500 m) dataset to examine daily BC emissions from fires in this region for 2002-2015. Black carbon emissions were estimated based on MODIS (Moderate Resolution Imaging Spectroradiometer) land cover maps and detected burned areas, the Forest Inventory Survey of the Russian Federation, the International Panel on Climate Change (IPCC) Tier-1 Global Biomass Carbon Map for the year 2000, and vegetation specific BC emission factors. Annual BC emissions from northern Eurasian fires varied greatly, ranging from 0. 39 Tg in 2010 to 1. 82 Tg in 2015, with an average of 0.
Such information is critical for assessing the impacts of BC on accelerated melting of Arctic ice and snow and on solar radiation in the atmosphere. In addition, BC deposition on the Arctic is further complicated by the dome effects of atmospheric circulation that limits the transport of air masses from lower latitudes into the Arctic  (Stohl, 2006). Only certain weather patterns allow for the transport of pollutants to the Arctic. It is therefore necessary to develop daily emission sources for the assessment of the transport and deposition of BC on Arctic ice and snow. The Global Fire Emissions Database (GFED4; http:// www. globalfiredata. org; Giglio et al. , 2013)  provided the most detailed fire emission inventory daily or monthly at a spatial resolution of 0. 25 • × 0. 25 • globally for 1997-2015.
This dataset has been widely used to study the effects of fires on atmospheric chemistry, air quality, and climate. However, it underestimated the seasonality of atmospheric aerosols in the Arctic in comparison to ground-based and satellite observations (e. g. ,  Stohl et al. , 2015). In this study, we developed the Fire Emission Inventorynorthern Eurasia (FEI-NE), a dataset of daily BC emissions from forest, grassland, shrubland, and savanna fires over northern Eurasia at a 500 m × 500 m resolution for 2002-2015. We examined the spatial and temporal variability of BC emissions from fires in different ecosystems in the geopolitical regions of Russia, eastern Asia, central and western Asia, and Europe. The estimates of BC emissions in different regions will assist policy makers in developing effective mitigation policies for reducing BC emissions from fires and reducing the BC impacts on accelerated ice and snow melting in the Arctic.
For mapping burned areas in northern Eurasia, the original burn scar algorithm was unchanged; however, the contextual filter was modified. In this study, potential burn scars not within 5 km and 10 days of active fire detection were classified as false detections and were eliminated. For the western United States, the thresholds of the contextual filter were 3 km and 5 days. Land cover classification of burned areas was based on the MODIS land cover/land cover change product (MOD12) at a 500 m resolution  (Friedl et al. , 2010). The date of a burned pixel in FEI-NE was taken as the first date the pixel satisfied the contextual filter. There is no comprehensive geospatial dataset of large fires (> 4 km 2 ) in northern Eurasia, as in the United States, to compare with our MODIS-derived burned areas.
Fuel loading Since limited information was available on the fuel loading for different land cover types over northern Eurasia, we developed a fuel-loading dataset for forested and nonforested areas over northern Eurasia at a 500 m × 500 m resolution circa 2010. The data sources were (1) the MODIS land cover map (MOD12, v5), (  2  The fuel loading of non-forested areas at a 1 km × 1 km resolution was derived from the IPCC Tier-1 Global Biomass Carbon Map for the year 2000  (Ruesch and Gibbs, 2008). The data product was based on biomass carbon stored in aboveground living vegetation created using the IPCC Good Practice Guidance for reporting national greenhouse gas inventories  (Penman et al. , 2003).
As a check on the assumed fuel moisture used in our consumption calculations, the WFDEI meteorological forcing dataset  (Weedon et al. , 2014)  was used to estimate FMFWD and FMCWD using the National Fire Danger Rating System basic equations  (Cohen and Deeming, 1985). We found the areas affected by fire in Russia had average values of FMFWD = 6 % and FMCWD = 12 %. To gauge the sensitivity of the fuel consumption estimates to the fuel moisture content, we conducted a set of simulations with fuel moisture set at twice our best estimate values. The effect reduced combustion completeness to 56 % for dead woody debris and 50 % for litter and duff. The amount of the fuel burned, or fuel consumption, was estimated as the product of fuel-loading and combustion completeness.
Discussion We present the daily spatial and temporal distribution of BC emissions from biomass burning over northern Eurasia at a 500 m × 500 m resolution from 2002 to 2015 in Fig. 5. This BC emission inventory is essential for modeling air quality in high latitudes and ice and snow melting in the Arctic. The dataset has been used for studying the transport and deposition of BC on the Arctic from 2002 to 2013  (Evangeliou et al. , 2016). The study found that approximately 8. 2 ± 2. 7 % of the BC emitted by northern Eurasian fires was deposited on Arctic ice during the period of 2002-2013, accounting for 45-78 % of the BC deposition from all the sources  (Evangeliou et al. , 2016). About 42 % of the BC emitted during spring and summer was deposited on Arctic ice, which is the most effective period for acceleration of ice and snow melting.
Acknowledgements. We thank  Vlady Alexeyev  at the  Sukachev Institut of Forest and Sergey Bartalev at the Space Research Institute of the Russian Academy of Sciences  for providing the essential datasets for mapping the fuel loading. This project was supported by the  US Department of State, US Forest Service Research and Development , and  NASA   Terrestrial Ecology Program. Edited by: J. Williams Reviewed by: two anonymous referees.
ACKNOWLEDGMENTS. I. V. P. thanks  Maria Grazia Giuffreda  [ Swiss National Supercomputer Center (CSCS)]  for her support. This work is supported by  National Institutes of Health (NIH)  Grants  U01HL114476  and  R01HL121386 , a  Swiss Platform for Advanced Scientific Computing grant ,  CSCS  Grant  s583 ,  National Science Foundation   XSEDE  Project  TG-MCB130124 , and the new  DOE Collaboratory on Mathematics for Mesoscopic Modeling of Materials  ( CM4 ). I. V. P, Z. P. , M. D. , and S. S. acknowledge partial support from the  Singapore-MIT Alliance for Research and Technology (SMART) Center. Simulations were carried out at  CSCS  under Projects s583 and u4, Kraken and Darter at the  National Institute for Computational Sciences (NICS)  under Project  TG-MCB130124 , and also at the  Argonne Leadership Computing Facility (ALCF)  through the  Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program  at  Argonne National Laboratory (ANL).