dataesr/scientific-paragraphs-categorization huggingface dataset:
The first objective of ESM-SnowMIP is to assess the current state of the art of snow models on spatial scales rang-ing from the site scale to global scales. On the site scale, the inclusion of newly developed multi-physics ensemble models  (Essery, 2015; Lafaysse et al. , 2017)  will allow highly controlled experiments to determine the influence of model structure on model performance in snow simulations. The availability of longer-term high-quality observations at a larger range of sites than in previous intercomparison exercises provides the opportunity for a more comprehensive assessment of the current modelling capacity in different climate settings (see  Sect. 3. 1). Similarly, on the global scale, a wealth of new Northern Hemisphere datasets based on advanced remote-sensing techniques and land surface models driven by reanalysis allows for more meaningful evaluations than has been possible in the past (see  Sect.
3. 2. 1). In this respect, one particular motivation of ESM-SnowMIP is to take advantage of the multi-model CMIP6 setting and at the same time to make particular snow modelling and observational expertise available to climate modelling groups that in the past have not focused their attention on the representation of snow in their coupled models. CMIP6 provides the opportunity to evaluate the representation of the historical evolution of seasonal snow in global simulations with varying degrees of freedom, ranging, for a given model, from global coupled ocean-atmosphere simulations to atmosphere-only (AMIP) climate simulations with prescribed oceanic boundary conditions  (Gates, 1992) , to land-surface-only simulations (LMIP) forced by observationally based meteorological data  (van den Hurk et al.
Experimental design and analysis strategy As in CMIP6, experiments in ESM-SnowMIP are tiered. Tier 1 simulations are mandatory for all participating groups, provided their model structure is adapted to the specific experiment; exceptions are snow models that are not part of an ESM and which therefore do not participate in the coupled experiments, even those that are labelled as Tier 1. The number of groups participating in Tier 2 simulations will necessarily be lower than or equal to those participating in Tier 1 experiments; we anticipate that not all proposed Tier 2 experiments will necessarily attract a sufficient number of participating groups for a meaningful multi-model analysis to be possible. Global snow simulations are subject to uncertainty in the meteorological data used to drive models (whether provided by bias-corrected reanalyses as in LS3MIP offline land model experiments or by coupling with atmospheric models as in CMIP6); global products providing vegetation and soil characteristics for model parameters are often contradictory, and global observations of snow properties for evaluation of models (e.
g. for snow density and thermal conductivity) are limited. To gain more insight into the behaviour of models in coupled and uncoupled global snow simulations, ESM-SnowMIP includes experiments using high-quality driving and evaluation data from well-instrumented reference sites. Only climate models will be able to perform the global coupled simulations required for CMIP6, and their land surface models will carry out global uncoupled simulations driven with a prescribed meteorological forcing. However, all models -both those that are coupled to an atmospheric model and those that are not (i. e. stand-alone snow models) -can perform the local uncoupled reference site simulations for ESM-SnowMIP at much lower computational expense. Models that have already completed the first round of reference site simulations, listed in Table  1 , include land surface schemes (LSS) of CMIP6 models, stand-alone snow physics models, hydrological models, and multi-physics ensemble models.
Downscaled large-scale forcing (LSF-downscaled-Site). Most of the mid-latitude ESM-SnowMIP reference sites were established for snow research in mountainous regions and are at higher elevations than much of the surrounding terrain. Meteorological variables in large-scale forcing datasets, such as the GSWP3 (Global Soil Water Project Phase 3) meteorological forcing data provided at 0. 5 • spatial resolution for LS3MIP  (Kim et al. , 2017) , would therefore be expected to be biased relative to in situ measurements at the sites even if they were perfect on the grid scale. Col de Porte, for example, is located at an elevation of 1325 m in the French Chartreuse Mountains but lies within a 0. 5 • grid cell with an average elevation of 870 m. Figure  8a  shows that an FSM simulation for winter 2009-2010 at Col de Porte with GSWP3 driving data gives almost no snow accumulation; this is because temperature on the grid scale, because of the lower mean altitude, is higher than at Col de Porte, while total precipitation is lower and snowfall is much lower.
Site and grid elevations for Sodankylä, in contrast, only differ by 40 m because this site is situated in a flat area. The large-scale simulation shown in Fig. 8b  is not so strongly influenced by driving data biases. Downscaling is commonly required when using regional climate predictions in hydrological impact studies. An ESM-SnowMIP experiment with altitude-adjusted large-scale driving data will be helpful in using reference site observations and simulations to evaluate the performance of models in large-scale simulations. Simply removing average altituderelated errors in the GSWP3 driving data, with no attempt to adjust variations on interannual and shorter timescales, greatly improves the SWE simulation for Col de Porte, re-placing a massive underestimate with a slight overestimate (Fig.
Large-scale observational data Observation-based estimates of SWE and snow cover fraction (SCF) are required for the evaluation of historical mean and model spread from ESM-, AMIP-, and LMIP-type simulations, as well as prescribed historical SWE and SCF as required for specific experiments. For this purpose, we will employ a blended dataset of snow products from ERA-Interim/Land  (Balsamo et al. , 2015) , MERRA  (Rienecker et al. , 2011) , ERA-Crocus  (Brun et al. , 2013) , ESA-GlobSnow  (Takala et al. , 2011) , and GLDAS-2  (Rodell et al. , 2004). These SWE datasets were assessed previously for temporal and spatial consistency by  Mudryk et al. (2015) , who proposed a climatology derived from a combination of these five datasets. The rationale for using a blended suite of snow products is threefold.
First, it provides a measure of historical observational uncertainty given by the range of estimates from indi-  vidual analyses. This is demonstrated in Fig. 9 , which shows the daily median and spread (5th-95th percentile) among the five snow products listed above for the 1981-2010 seasonal SWE climatology. For a given day, statistics are calculated from the pooled distribution of data across the 30-year period and across all five datasets. As analysed in  Mudryk et al. (2015) , the range across the snow products likely results from differences in the snow schemes within the land surface models, differences in precipitation and temperature in the forcing meteorology (from various reanalyses), and the impact of satellite and weather station measurements (used in GlobSnow and ERA-Brown). Still, the illustrated spread is a useful proxy for observation-based uncertainty (which cannot be determined when a single product is applied for evaluation) and may be used to evaluate the corresponding level of agreement from LS3MIP and ESM-SnowMIP simulations over a similar historical period.
A second reason to use a suite of analyses for MIP evaluation is that the bias and error of individual datasets vary with geographical location and datasets that perform well in some regions may perform more poorly in others. The lack of a clear "best" dataset provides minimal reason to favour one analysis over another. In fact, it has been explicitly demonstrated that combinations of products have both lower bias and RMSE than individual products when evaluated over domains with in situ data  (Schwaizer et al. , 2016). For this reason, a blended combination of snow products will be used for time-varying prescribed SWE simulations (see Sect. 3. 2. 2) and could also be used for evaluation of SWE output from simulations which are observationally constrained by nonsnow-related variables. Finally, the use of SWE analyses also allows for the definition of a mutually consistent set of snow cover extent (SCE) data by choosing a seasonal and climatological SWE threshold above which a grid cell is considered snow-covered.
Note that these considerations also apply for the land-only simulations of LS3MIP in which soil wetness and SWE are to be pre-scribed. In cases of snow modules for which an unequivocal relationship ties surface albedo to SWE, it might be sufficient to run only the albedo scheme with prescribed SWE as input. While a number of snow products are available to serve as prescribed SWE, we recommend the  Mudryk et al. (2015)  combined climatology (see Sect. 3. 2. 1) for ESM-SnowMIP simulations. Because biases in individual products are compensated for through averaging, this dataset represents an improved reference for model evaluation compared to any single-component dataset  (Sospedra-Alfonso et al. , 2016) , just like a climate model ensemble mean is often preferred over a single member. The simulated surface albedo will be compared to surface albedo as derived from satellite observations (MODIS,  Schaaf et al.
Again, in the multi-model context, we expect a relationship between the sensitivity of the simulated potential permafrost extent to thermal insulation by soil and diagnosed errors of the simulated near-surface permafrost extent, which we hope will be useful to identify ways for model improvement. Tier 2: fixed land cover (FLC-LSM). Previous studies show that inaccurate representation of vegetation distribution and parameters in LSMs may result in large biases in simulated surface albedo for snow-covered forests  (Essery, 2013; Wang et al. , 2016). Most current LSMs represent vegetation from a set of plant functional types (PFTs), which are usually de-rived from global land cover datasets  (Bonan et al. , 2002; Poulter et al. , 2015). There are large differences among PFTs used in LSMs, which may result from the differences in the land cover datasets, the cross-walking tables used to map land cover datasets into PFTs represented in LSMs, or uncertainties in dynamic PFT simulations  (Hartley et al.
, 2017; Poulter et al. , 2011). In order to separate biases due to differences in vegetation distribution from those due to physical processes in LSMs, we propose an experiment in which models derive their PFTs from the same land cover dataset and using the same cross-walking table. Several global land cover datasets are available with spatial resolutions ranging from 300 m to 1 km  (Bontemps et al. , 2012). The newly released European Space Agency (ESA) Climate Change Initiative (CCI) land cover datasets are developed specifically to address the needs of the climate modelling community  (Poulter et al. , 2015). The CCI maps include 22 level-1 classes and 15 level-2 subclasses based on the United Nations Land Cover Classification System, which was identified as a suitable thematic legend and compatible with the PFT concept of most LSMs  (Bontemps et al.
, 2012). While most previous land cover datasets are for a single year, the CCI datasets are available from 1992-2015 at 300 m resolution  (ESA, 2017). The finer spatial resolution of 300 m (versus 1 km) makes it inherently superior for land cover mapping in heterogeneous landscapes for which different datasets tend to disagree  (Fritz et al. , 2011; Herold et al. , 2008). In addition, a cross-walking table to convert the categorical land cover classes to the fractional area of PFTs was provided with the CCI datasets  (Poulter et al. , 2015). We thus suggest the use of the CCI land cover dataset for the year 2000 as the common land cover dataset from which to derive PFTs. Different models usually have their own unique set of PFTs. For cases in which both the phenology type and the associated climate zone are considered, the Köppen-Geiger climate classification can be used as in  Poulter et al.
(2015). The simulation period of 1980-2014 matches the LS3MIP Land-Hist offline reference simulations. A comparison of the surface albedo with the reference simulation will isolate the impact of PFT distribution on surface albedo and associated feedbacks in snow-covered areas. Since the PFTs are from the same land cover data in the participating models, the differences in surface albedo among the models will reveal differences in snow-vegetation interactions and other vegetation-related parameterizations (e. g. leaf area index) used in the models.
Timeline, data request, and data availability The first set of reference site simulations (Ref-Site) have already been carried out and are currently being analysed. The additional site simulations will be carried out in the near future. In 2018 and 2019, the global climate modelling community will be heavily involved in CMIP6. To decrease peak workload on the modelling groups while at the same time optimizing synergies with the CMIP6 activities and in particular with LS3MIP, it was decided to launch the ESM-SnowMIP simulations after the main CMIP6 activities. This, however, has the disadvantage that ESM-SnowMIP global simulation results will not be available for analysis in time for the sixth IPCC assessment report. For these global land-only and coupled simulations, the request for output variables is identical to the LS3MIP data request (https://www.
earthsystemcog. org/projects/wip/ CMIP6DataRequest, last access: 3 December 2018) for the respective reference simulations indicated in Table  2 , and the model set-up will be very similar, as described in the preceding sections of this paper. This further keeps the additional workload for the ESM-SnowMIP coupled simulations to a minimum. The ESM-SnowMIP site simulation output is sufficiently small to be easily handled via an ftp server at one of the participating institutes (see the dedicated website at https: //www. geos. ed. ac. uk/~ressery/ESM-SnowMIP. html, last access: 3 December 2018). Gridded Northern Hemisphere SWE data are freely available from the National Snow and Ice Data Center (http://nsidc. org/data/NSIDC-0668, last access: 3 December 2018). Large-scale meteorological forcing (GSWP3) for the distributed simulations, also used in LS3MIP, will be made available via the ESGF at https: //esgf-node.
Acknowledgements. This project was supported by the  World Climate Research Programme's Climate and Cryosphere (CliC)  core project. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Analysis of ESM-SnowMIP reference site simulations is supported by  NERC  grant  NE/P011926/1. Gerhard Krinner ,  Chris Derksen , and  Richard Essery  acknowledge support by the  ESA  in the framework of the  ESA CCI  snow project. Simulations by the SWAP model were supported by the  Russian Science Foundation  (grant  16-17-10039 ;  Yeugeniy M.
Gusev  is the recipient). Simulations by the SPONSOR model were supported by the  Russian Foundation for Basic Research  (grant  18-05-60216 ;  Vladimir A. Semenov  is the recipient). The work of  Gabriele Arduini  leading to this paper was funded through the  APPLICATE  project. APPLICATE has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  727862. Nander Wever  was supported by the  Swiss National Science Foundation  (project  200021E-160667 ). IGE and CNRM/CEN are part of  LabEX OSUG@2020  ( ANR10 LABX56 ). Stefan Hagemann  and  Tobias Stacke  were partially supported by funding from the  European Union  within the  Horizon 2020 project  CRESCENDO (grant no. 641816 ). Hyungjun Kim  acknowledges support by a  Grant-in-Aid for Specially Promoted Research   16H06291  from  JSPS.
Data availability. Site forcing data are available through https: //www. geos. ed. ac. uk/~ressery/ESM-SnowMIP/netcdf. zip (last access: 7 December 2018). The Weissfluhjoch data are accessible via https://doi. org/10. 16904/16 (WSL, 2017). A paper describing the site-scale input data is currently in preparation.
ACKNOWLEDGEMENTS The authors are thankful to commentaries from  Dr. Camille Risi. EF received support from the  Marie Skodowska-Curie  actions (grant  658997 ) in the framework of the project ExMeCy. This work was also supported by computational time granted from the  Greek Research and Technology Network (GRNET)  in the National HPC facility ARIS under project pr003009. Finally, we would like to thank the two anonymous reviewers for their valuable comments which improved this manuscript.
The hydrographic data The GEOVIDE cruise (GEOTRACES-GA01 section) was conducted in June-July 2014 and consisted of 78 stations along the eastern SPNA and the Labrador Sea (Fig. 1 ). During the first cast in each station, a classical rosette equipped with 22 Niskin bottles and CTD SBE-911 equipped with a dissolved oxygen sensor SBE-43 was deployed. This first station cast was used as a reference for the physical and chemical characterization of water masses. Discrete sampling for oxygen, nutrients and salinity was performed at all 78 stations. Nutrient concentrations (nitrate and silicic acid) were measured by the continuous flow analyser, giving the concentration values in µM (µmol L -1 ). We transformed measured nutrient concentrations in µM to µmol kg -1 by dividing by (where σ 1 is potential density referenced to 1000 dbar), which marks the limit between the upper and lower limbs of the Atlantic Meridional Overturning Circulation (AMOC) at the GEOTRACES-GA01 section  (Zunino et al.
Acknowledgements. We are grateful to the captains, staff and researchers who contributed to the acquisition and processing of hydrographic data. We are especially grateful to  Morgane Gallinari ,  Emilie Grosstefan  and  Manon Le Goff , who contributed to the analysis of nutrients, and to the technical team:  Pierre Branellec ,  Floriane Desprez de Gésincourt ,  Michel Hamon ,  Catherine Kermabon ,  Philippe Le Bot ,  Stéphane Leizour ,  Olivier Ménage ,  Fabien Pérault  and  Emmanuel de Saint-Léger. For this work  Maribel I. García-Ibáñez  and  Fiz F. Pérez  were supported by the  Spanish Ministry of Economy and Competitiveness  through the  BOCATS  ( CTM2013-41048-P ) project, co-funded by the  Fondo Europeo de Desarrollo Regional   2014-2020  (FEDER). Maribel I. García-Ibáñez  was also supported by the  Centre for Climate Dynamics at the Bjerknes Centre.
Pascale Lherminier  was supported by  IFREMER. Patricia Zunino  was supported by  CNRS  and  IFREMER , within the framework of the projects At-lantOS ( H2020-633211 ) and  GEOVIDE  ( ANR-13-BS06-0014-02 ). Herlé Mercier  was supported by  CNRS  and the  AtlantOS H2020  project under grant number  633211. Paul Tréguer  was supported by the  LABEX-Mer  ( French Government "Investissement d'Avenir" programme ,  ANR-10-LABX-19-01 ). Edited by:  Gilles Reverdin Reviewed  by: three anonymous referees.
Data availability. The 2014 GEOVIDE (GEOTRACES-GA01 and GO-SHIP A25) data from the classical rosette is available at SEANOE: http://doi. org/10. 17882/54653 (Perez et al. , 2018). The Greenland-Portugal ADCP data are available at SEANOE: http: //doi. org/10. 17882/52153 (Lhermier and Sharthou, 2017). The Supplement related to this article is available online at https://doi. org/10. 5194/bg-15-2075-2018-supplement. Author contributions. All authors contributed extensively to the work presented in this paper. MIGI, FFP and PL designed the research. MIGI, FFP, PL, PZ, HM and PT analysed the physical and chemical data. MIGI and FFP developed the code for processing the data. MIGI wrote the paper and prepared all figures, with contributions from all co-authors.
Habitat The specimens studied in the present work were all collected in natural pools in an Amazonian savannah in the Serra dos Carajás (Canga deposits), see Fig. 7B. In the same study period, approximately 30 phytoplankton species, 30 zooplankton species and 15 aquatic macrophyte species were recorded in the pool, but no fish were observed. Allotype BRAZIL: mature ♀, total length 12. 8 mm, brood pouch 2. 9 mm reaching the extremity of the 6 th abdominal segment (including genital segments), cercopods 2. 7 mm, same collecting data as for holotype (MNRJ 2974).
Acknowledgements Special thanks to  Túlio Paiva Chaves ,  Leandro Braga Godinho  and  Didier Pillet  for providing photos and collecting specimens of Dendrocephalus in the field and making this work possible. We also acknowledge the staff of the  Laboratório de Biossistemática Animal (UESB, Brazil ) for help and photographs, and particularly  Muriel Lima de Oliveira ,  Natália de Souza Silva ,  Herick Gonçalves ,  José Guilherme de Almeida Neto. We also thank  David Montero  for his help at the scanning electronic microscope, the  Company VALE  for financial support during the sampling in Serra dos Carajás and the  Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio)  for providing the license for sampling (SISBIO  33647-2 ) and especially the staff of the  Floresta Nacional de Carajás.
The fluxes of water, energy, and matter within and between hydrological compartments occur across a variety of spatial and temporal scales as a consequence of the complexity and heterogeneity of catchment characteristics. In recent years, new developments in ground-based (in situ), airborne, and satellite-borne instrumentation have made it possible to measure states and fluxes at unprecedented spatial and temporal resolutions. This has enabled the creation of databases of long-term time series of states and fluxes, providing a basis for improving our understanding of integrated hydrological behavior and for testing of crucial scientific questions. With this special section, we hope to give a representative overview of existing hydrological observatories and to increase the awareness and knowledge of these infrastructures and their associated databases.
, 2017; Stisen et al. , 2018). The selection of catchments within existing networks often accounts for the existence of already well-monitored systems having long-term databases on both environmental and socioeconomic information, e. g. , population and agricultural and industrial activities affecting water resources  (Mollenhauer et al. , 2018). An important aspect of hydrological observatories is the accessibility of data for the research community. Often the observatory data are disseminated through common information and exchange platforms, e. g. , the TEODOOR portal of the TERENO network (www. tereno. net) or the ENOHA data portal (www. enoha. eu). In addition, some hydrological observatories have implemented individual solutions for publishing their measurement data. The data management and data dissemination solutions of each hydrological observatory are described in detail in this special section.