dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , only urban vs. only rural) using the standard approach for comparing regression coefficients  (Zar, 1999 ; see Dingemanse, Dochtermann, & Wright, 2010 for a similar approach). We compared path coefficients that had a p <. 05 in either urban or rural models. For acquiring goodness-of-fit indices of the path models, we applied Shipley's d-sep test  (Shipley, 2002)  which tests for any unrealized paths in the model, and provides Fisher's C statistics where the significance of unrealized paths is combined (see  Shipley, 2009  for the calculations). By comparing the Fisher's C statistics to a χ 2 distribution, we rejected or accepted each of the three models (i. e. , models with p >. 05 were accepted). All analyses were performed using R version 3. 2. 3 (R Core Team 2013). We used the "lme4" package  (Bates, Maechler, Bolker, & Walker, 2015)  for mixed-effect models, and the "piecewiseSEM" package  (Lefcheck, 2016)  for conducting path analyses.
Clear sky irradiance model The clear sky irradiance model is used to predict both DNI and GHI values as if there were no clouds present. It is derived using the Linke turbidity calculated from ground measurements of the DNI. Firstly, the Linke turbidity is calculated for all DNI measurements in the last 24 h using the Linke turbidity model from  Ineichen and Perez (2002). Then cloudy data points are sorted out using the temporal variation of the Linke turbidity as explained in Hanrieder et al. (  2016 ) and  Wilbert et al. (2016a). Afterwards, the current Linke turbidity is determined using a time-weighted average of the N most recent Linke turbidities for cloud-free DNI measurements. Together with the airmass of future timestamps this Linke turbidity is used to derive the clear sky irradiances of GHI and DNI based on  Ineichen and Perez (2002).
const mn is the same for every orthoimage since it is determined by camera properties, which are set to be fixed. For this assumption, the constants are allowed to be different between every pixel of each of the six shadow cameras utilized. The DNI in Eq. (  15 S 0 mn;j are the linearized images described in Section 3. 4. To calculate spatially resolved DNI maps for all pixels mn, the three orthoimages S 0 mn;j , clear sky DNI data for the time of the sunny reference orthoimage and three DHI maps are needed. For the sunny reference, the ground measurements DNI mn;sunny and DHI mn;sunny can safely assumed to be constant over the whole imaged area. For the shaded reference, we also assumed DNI mn;shaded to be zero for all pixels (Eq. (  8 )). In general, DHI mn;shaded and DHI mn;current are not homogeneous over the whole imaged area, especially for scattered clouds during the timestamp under evaluation.
The clear sky irradiance model (see Section 3. 3) is based on station 1 but the corresponding irradiance maps' pixel is not specially treated with e. g. normalizations. Therefore, station 1 is included in the validation. Mean DNI is the daily mean DNI as Fig. 8. Comparison of DNI values measured by station 1 (southernmost in Fig. 3 ) on 2015-09-18 to the corresponding pixel in the DNI maps derived from the shadow camera system. On this day, very few clouds were present. measured by the ground stations for the validation timestamps T d. The same dataset in one-minute resolution as for the visual validation in Section 4. 1 is taken. The spatial average (Spatial avg) depicts the deviation of the ground measurements to the corresponding pixels of the shadow camera system averaged for every considered minute among the three stations and pixels.
Quantitative validation of GHI maps The validation of GHI maps derived from the shadow camera system is conducted over the same dataset as the validation of the DNI maps in the previous section. For the validation of GHI maps, 23 radiometers (see Fig. 3 ) are used. Station avg is the average of the daily deviations for each station. The mean GHI averaged for all stations and timestamps is 714. 8 W/m 2 for 2015-09-18 and 599. 2 W/m 2 for 2015-09-19. The deviations are similar amongst the stations with a standard deviation of the absolute RMSE between the stations of 6. 0 W/m 2 for 2015-09-18 and 7. 5 W/m 2 for 2015-09-19. The spatial average (Spatial avg) depicts the deviation of the ground measurements to the corresponding pixels of GHI maps calculated by the shadow camera system averaged for every considered minute among the 23 stations and pixels, taking spatial aggregation effects into account.
Acknowledgements The research presented in this publication has received funding from the  European Union 's  Horizon 2020 programme  for the initial development of the shadow camera system ( PreFlexMS, Grant  Agreement No. 654984 ). With founding from the  German Federal Ministry for Economic Affairs and Energy  within the  WobaS  project, the generation of irradiance maps was implemented. The European Union's  FP7 programme  under Grant Agreement No. 608623  ( DNICast  project) financed operations of all-sky imagers and other ground measurements.
Snow cover variability has significant effects on local and global climate evolution. By changing surface energy fluxes and hydrological conditions, changes in snow cover can alter atmospheric circulation and lead to remote climate effects. To document such multi-scale climate effects, atmospheric reanalysis and derived products offer the opportunity to analyze snow variability in great detail far back to the early 20th century. So far only little is know about their quality. Comparing snow depth in four long-term reanalysis datasets with Russian in situ snow depth data, we find a moderately high daily correlation (around 0. 6-0. 7), which is comparable to correlations for the recent era , and a good representation of sub-decadal variability. However, the representation of pre-1950 inter-decadal snow variability is questionable, since reanalysis products divert towards different base states.
, 2015,). Therefore, large-scale monitoring and quantifying of snow cover is crucial for assessing climate change and its representation in climate models (e. g. ,  Frei and Gong, 2005; Brown and Mote, 2009; Brown and Robinson, 2011; Liston and Hiemstra, 2011; Ghatak et al. , 2012; Zuo et al. , 2015)  and for analyzing cryosphere-climate feedbacks (e. g. ,  Flanner et al. , 2011; Orsolini and Kvamstø, 2009; Zhang et al. , 2013). Here we analyze snow depths in climate reanalyses in comparison to in situ data, with the aim to better assess cryosphere-atmosphere coupling processes in the context of the 20th century climate evolution. To this end, reanalysis products provide a compromise between the high temporal resolution and length of in situ observational datasets (e. g. ,  Bulygina et al.
, 2010)  and the large spatial, but relatively short-term, coverage of satellite products  (Siljamo and Hyvärinen, 2011; Frei et al. , 2012; Hüsler et al. , 2014). Comprehensive reanalyses datasets are well suited to investigate processes and mechanisms, and a variety of reanalyses are now routinely produced by meteorological prediction centers such as (but not limited to) NCEP-DOE (National Centers for Environmental Prediction-Department of Energy), ERA-40 (ECMWF Re-Analysis-40) and ERA-Interim, and JRA-25 (Japanese reanalysis-25) and JRA-55 (e. g. ,  Uppala et al. , 2005; Onogi et al. , 2007; Compo et al. , 2011; Dee et al. , 2011; Rienecker et al. , 2011; Poli et al. , 2013). However, so far only a few studies analyzed snow representation in reanalysis products. Khan et al. (2008)  compared measured snow data with snow water equivalents and snow depth in the NCEP-DOE  (Kanamitsu et al.
, 2002) , ERA-40  (Uppala et al. , 2005)  and JRA-25  (Onogi et al. , 2007)  reanalysis products over Russian river basins. They found that the ERA-40 outperformed the NCEP-DOE and JRA25 in terms of correlations and mean values. Despite reproducing well the seasonal variability, all reanalysis products struggled with snowmelt season values. Brown et al. (2010)  compared ERA-40 and NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) snow cover extent to satellite and in situ datasets. They found that for the period 1982-2002, ERA-40 shows higher correlations and smaller root mean square errors (RMSEs) than the NCEP reanalysis, and that May values were considerably better approximated than June values. Brun et al. (2013)  forced the CROCUS snow model with atmospheric conditions from  ERA-Interim (1970 -1993)  and found very high agreements with Eurasian in situ snow measurements.
However, the authors only investigated the snow dataset in a binary fashion (snow/no snow). Given the lack of inter-comparison studies of snow depth between reanalyses products, we evaluate snow depth in four centennial state-of-the-art reanalyses. The goal of this study is to assess the consistency between in situ observations and reanalyses estimation of snow depths. To assess this performance, we focus on early snowfall season (October and November) and early snowmelt season (April). This assessment also includes specialized reanalyses for land surface processes, driven by input from the atmosphere. This article is structured as follows. Section 2 gives an overview of the various datasets analyzed, whereas Sect. 3 defines the methods used in the comparison. Section 4 presents the results for the evaluation.
Reanalysis Datasets The Twentieth Century Reanalysis version 2 dataset allows retrospective 4-dimensional analysis of climate and weather between 1871 and 2012  (Compo et al. , 2011). It was achieved by assimilating synoptic observations of surface pressure into the NCEP GFS (Global Forecast System) model using an Ensemble Kalman Filter variant. Prescribed boundary conditions are HadISST1. 1  (Rayner et al. , 2003)  monthly sea surface temperature (SST) and sea ice cover data as well as forcing of CO 2 , volcanic aerosols and solar radiation. The Twentieth Century Reanalysis version 2c (20CRv2c) uses the same model as version 2 with new sea ice boundary conditions from the COBE-SST2 (Centennial Observation-Based Estimates of SST version 2;  Hirahara et al. , 2014) , new pentad Simple Ocean Data Assimilation with sparse input version 2 (SODAsi.
2; Giese et al. , 2016)  sea surface temperature fields, and additional observations from ISPD (International Surface Pressure Databank) version 3. 2. 9 (Cram et al. , 2015). SODAsi. 2c is generated by tapering SODAsi. 2 at 60 • N and S to COBE-SST2 SSTs, which makes the Arc- tic sea ice and SSTs consistent. For both products, we use the mean of the 56-member ensemble, at a 6-hourly temporal resolution. The spatial resolution corresponds to a Gaussian T62 grid. The ERA-20C reanalysis  (Poli et al. , 2016)  uses the Integrated Forecast System (IFS) as a framework to assimilate observations of surface pressure and marine surface winds. It is a global atmospheric reanalysis for the period 1900-2010 with a 3-hourly temporal resolution and a horizontal resolution of T159 with 91 vertical levels, reaching from the surface up to 1 Pa.
Snow depth observations This study uses time series of daily snow depths for 820 Russian meteorological stations (distributed as shown in the Supplement Fig. S1 ). The time series are prepared by RIHMI-WDC (All-Russian Research Institute of Hydrometeorological Information -World Data Centre). Meteorological datasets are automatically checked for quality control. Since the procedure of snow observations changed in the past, particular attention was given to the removal of all possible sources of inhomogeneity in the data. However, there have been no changes in the observation procedures since 1965. Daily observations are measured on three stakes at the weather station, where the average of all three is registered in the time series. When using monthly data, we use the maximum snow depth during that month instead of the mean value, because it reflects the process of snow accumulation (snow depth is a cumulative and highly inertial characteristic of the climate system).
Choice of long-term daily snow observations Out of the over 800 stations, 15 stations were selected with a record extending back to the beginning of the 20th century on a daily basis. Stations with records extending into the 19th century were shortened to start from 1901. All time series end in 2011. Stations with different starting years are indicated in Table  2. Furthermore, Table  2  displays the location of the 15 stations, including the elevation above sea level. To correlate daily measurements with daily reanalysis values, values from the closest grid cell to the station location were chosen. The results therefore include uncertainties concerning the surrounding topography of the stations. Moreover, the relative amount of missing data is shown for the average of all 3 months. As can be seen, data availability differs considerably between months and stations.
Calculation of extreme event detection To evaluate the detection rate of extreme daily snow depth events, we calculate the 98th percentile values in all reanalysis products in two different ways. Extreme events were calculated for both absolute daily snow depth and accumulated daily snow depth, with the latter being the snow depth difference between 2 consecutive days. The selected dates in the reanalyses are then compared to the station dates. Based on the number of dates selected using station data, a percentage hit rate is calculated, namely the amount of extreme events in station data divided by the amount of correctly selected dates in reanalyses. Snow observations were performed at 08:00 local time, which is different than any of the available reanalysis output. To allow some margin of error, we also perform this hit-rate analysis for a ±1 day shift.
Sub-decadal and daily performance Moving away from decadal tendencies, we now evaluate the daily and the inter-annual snow variability over the 13 selected stations with records extending back to the early days of the 20th century. Figure  5  presents the daily performance between station data and the reanalyses over the recent period. The melting season (April) generally exhibits the weakest correlation between grid and station, with slightly better values for October and highest values for November. However, this ranking can differ for individual station locations. For the period 1981-2010, the ERA-20C reanalysis achieves better results than the 20CR reanalyses, especially so in April, indicating that melting and temperature evolution is somewhat more accurate in the ECMWF reanalyses. November and, even more so, October correlations are very similar in all four long-term reanalysis products.
Moreover,  Khan et al. (2008)  state that all evaluated reanalysis snow products showed the worst matching in April. The same result was found in our analysis, where April values showed the smallest correlation and highest absolute error (RMSE). Therefore, it can be assumed that models used for creating the reanalysis datasets still struggle with properly representing melting season  (Slater et al. , 2001). Looking at the RMSE, it could be shown that the 20CRv2 and 20CRv2c generally overestimate snow depth, and that ERA-20C and ERA-20C land are closer to the station data. This is true for the recent past as for the centennial analysis. The same applies to the variability comparison. Interestingly, the snow depth RMSE in October is smaller than in the other months, but day-to-day variability (correlation) appears to be better in November.
Among the explanations for this underwhelming performance are (a) the assimilation of only surface data in the reanalyses (which challenges the computation of the complex conditions for extreme snowfall), (b) the long time frame in which assimilated data quantity is decreasing back in time and (c) the spatial resolution of the reanalyses which can not resolve features like small-scale uplift or orographic precipitation or, at an even smaller scale, snowdrift. With these deficiencies in mind, the achieved correlation coefficients for the centennial time series are even more remarkable. However, analysis of inter-decadal tendencies of snow depth revealed a peculiar evolution, even though snow schemes and assimilated data are comparable. Generally, the ECMWF datasets compute a stronger snow depth decrease before the 1940s than the 20CR products for the main Russian Arctic snow field.
Moreover, stable atmospheric conditions prevent vertical motion and therefore condensation. Knudsen et al. (2015)  showed that, in the recent era, Arctic anticyclonic circulation patterns also promote low snowfall in summer over the Russian sector of the Arctic, and a similar association with (too) high pressure could be at play in ERA-20C in the pre-1950s. On the other hand, if compared to station data, the ERA-20C snow depths show a good agreement for anomalies early in the 20th century. Furthermore, near-surface temperatures influence snow depth evolution. The new 20CRv2c dataset uses alternative sea ice and SST representations as boundary conditions, which improves the 2 m temperature performance over the Arctic compared to 20CRv2. Nevertheless, it is generally still colder than  ERA-20C or CRUTEM4.
4 (Climatic Research Unit Temperature version 4. 4; Jones et al. , 2014). However, ERA-20C is most probably much too warm during April, whereas the 20CR reanalyses seem to be too cold during November and December, thus they might be overestimating snow depths (see Supplement Figs. S8 and S9). Ultimately, there is no clear and simple answer to this issue and our anal-ysis can only provide an initial assessment of the discrepancy between the two families of reanalyses. The results of the snow climatologies hint towards heterogeneous dataset issues. Decadal tendencies in the second half of the 20th century are better represented by the 20CR datasets (relative to their baseline), whereas tendencies for the first half of the century are better represented in ERA-20C. Unfortunately, only 13 stations could be used to verify long-term evolution in snow depth.
Conclusions Snow depth and its evolution from a variety of centennial reanalyses has been tested against in situ observations over the Russian territory. Long-term reanalyses are able to reproduce daily and sub-decadal snow depth variability very well; however, they generally overestimate snow depths. Moreover, computing the exact day of extreme snow accumulation is still a difficult task for these datasets. Spatially, the region of high and low snow and the snow cover boundaries are well represented. However, inter-decadal comparison of snow depth revealed some issues with pre-1950s snow climates over northern Russia. The ECMWF and NOAA reanalyses show diverging snow states (low or high, respectively), most likely a consequence of assimilation schemes or model biases rather than input data.
found best case basin-wide correlations of around 0. 65 in ERA-40 and JRA-25, with much worse correlations for the NCEP-DOE reanalysis. All these reanalyses assimilated a variety of input data, not only surface data as is the case with the centennial reanalyses ex-Taylor diagrams showing the median of the 13 station locations using daily data for the longest period available (see Table1). The x axis and y axis indicate the standard deviation, the radians indicate correlation values and the green circles indicate centered RMSE. The green dot shows the observed variability. For more details concerning the datasets statistics, see Supplement Figs. S3-S6. ERA-20C is labeled as ERA20C, and ERA-20C land is labeled as ERA20CL.
Acknowledgements. Y. Orsolini was supported by the  Norwegian Research Council  (project  SNOWGLACE  # 244166  and EPOCASA # 229774/E10 ). A. Sterin  and  S. Brönnimann  were supported by the  EU-FP7  project  ERA-CLIM2 (607029 ). M. Wegmann,  Y. Orsolini ,  S. Brönnimann ,  A. Sterin  and  O. Bulygina  acknowledge funding by the  European ERAnet. RUS programme, especially within the project ACPCA. M. Wegmann also benefitted from the  ARCTIC-ERA  project funded by the  Agence Nationale de la Recherche (ANR)  through the  Belmont Fund initiative. Edited by: M. Schneebeli Reviewed by:  T. Vihma  and one anonymous referee.
Data availability. The NOAA-CIRES Twentieth Century Reanalysis version 2 can be downloaded at https://www. esrl. noaa. gov/ psd/data/gridded/data. 20thC_ReanV2. html, and version 2c can be downloaded at https://www. esrl. noaa. gov/psd/data/gridded/data. 20thC_ReanV2c. html. The ECMWF reanalysis datasets can be accessed via http://apps. ecmwf. int/datasets/. The Russian in situ snow depth observations can be found at the data website of the All-Russian Research Institute of Hydrometeorological Information -World Data Centre http://meteo. ru/english/data. Concerning ERA-.
found best case basin-wide correlations of around 0. 65 in ERA-40 and JRA-25, with much worse correlations for the NCEP-DOE reanalysis. All these reanalyses assimilated a variety of input data, not only surface data as is the case with the centennial reanalyses ex-Taylor diagrams showing the median of the 13 station locations using daily data for the longest period available (see Table1). The x axis and y axis indicate the standard deviation, the radians indicate correlation values and the green circles indicate centered RMSE. The green dot shows the observed variability. For more details concerning the datasets statistics, see Supplement Figs. S3-S6. ERA-20C is labeled as ERA20C, and ERA-20C land is labeled as ERA20CL. Apr Oct Nov Correlation Correlation Correlation 0 0. 1 0. 2 0. 3 0. 4 0.
MINERAL RESOURCE TOXICITY TO INDIVIDUAL ORGANISMS CANNOT BE RELIABLY PREDICTED For many commonly-studied metals, existing acute toxicological data [lethal concentrations (LC 50 ) and effective concentrations (EC 50 )] are available, but only for shallowwater biological species (e. g. ,  Crompton, 1997). These data identify concentrations of metals which are either lethal, or "effective, " for 50% of the exposed population over a designated period, conventionally 72 or 96 h. Alternatively, more recent toxicological studies have adopted a variable exposure duration that matches a desired sub-lethal endpoint  (Simpson et al. , 2017). The US EPA ECOTOXicology Database (ECOTOX; https:// cfpub. epa. gov/ecotox/) is an online resource that summarizes all available metadata included within each ecotoxicology publication, and this database is updated quarterly.
However, interrogation of this database at the end of 2013, at the start of the MIDAS project, identified that no data were available for any deep-sea taxa. Through DSM, the toxic effects of metals will act potentially at in situ deep-sea temperatures and pressures (high pressureup to 60 MPa, low temperature-down to 2 • C), which are very different from those of laboratory exposures reported in the ECOTOX database (conventionally set at standard conditions of a temperature of 20 • C and a pressure of 0. 1 MPa)  (Mestre et al. , 2014). Brown et al. (2017a)  have contrasted the toxic limits of metals in solution at low temperature (10 • C) and high hydrostatic pressure (10 MPa) with those recorded under standard conditions of temperature and pressure (20 • C and 0. 1 MPa). These initial experiments made use of the shallow-water shrimp Palaemon varians as an experimental model; a species that has a close phylogenetic relationship to deep-water hydrothermal-vent shrimp species  (Tokuda et al.
THE PHYSICAL STATE OF THE METAL TOXICANT IS IMPORTANT Any metals released during the mining cycle will occur in different physical states  (Simpson and Spadaro, 2016). Metals may enter solution/aqueous phase and be taken up across the gills, body wall and digestive tracts of exposed animals. Alternatively, crushed mineral particles, and dissolved metals that may adsorb onto sediment particles or flocculates may be ingested; this may particularly be the case for metals released during dewatering of ore slurry (for example  Campana et al. , 2012 Campana et al. , , 2013;; Camusso et al. , 2012). These various exposure routes have implications to predictions of toxicity as well, not least because the vast majority of data listed within databases such as the US EPA ECOTOX database are based on organisms exposed to metals in solution.