dataesr/scientific-paragraphs-categorization huggingface dataset:
Retrieving threshold of wind erosion The monthly climatological threshold of wind erosion is retrieved by matching the frequency distribution of the MODIS DOD at certain level with the frequency Step1: Since dust is emitted from the dry and sparsely-vegetated surface, the daily DOD data is first masked out to remove the influences of non-erodible factors and unfavorable environmental conditions that are known to prevent dust emission using criteria as follows: daily VSM less than 0. 1 cm 3 cm -3 ; monthly LAI less than 0. 3; monthly snow cover less than 0. 2%; monthly top-layer soil temperature higher than 273. 15 K, i. e. , over unfrozen surface; and soil depth thicker than 15 cm. Similar criteria have been used in previous studies to detect or confine dust source regions. For instance,  Kim et al.
(2014) found that the annual threshold of wind erosion at the 25% level, i. e. , when surface condition is favorable for dust emission, can be lower than 6 m s -1 at some sites in the Sahel (their Fig. 5 ). Several factors may contribute to the discrepancies. First, studies suggest that reanalysis datasets may underestimate surface wind speed in spring and for monsoon days in Africa (e. g. ,  Largeron et al. , 2015) , and therefore could lead to a lower value of V threshold than that derived from station observations. In fact, Bergametti et al. (2017) found even 3-hourly wind speed record at stations may miss short events with high weed speed. As mentioned earlier, using DOD frequency to approximate dust emission may lead to an overestimation of dust emission over regions such as the southern Sahel where transported dust is a large component and consequently an underestimation of V threshold.
Climatology of surface dust concentration While DOD is a key parameter associated with the climate impact of dust, surface dust concentration is an important factor affecting local air quality. Here we compare the modeled surface dust concentration with RSMAS station observations. Model output is Great Plains and Midwest (e. g. ,  Pu and Ginoux, 2018a)  may also add uncertainties to the data itself. V thresh 12mn and V thresh Ann simulations (Fig. 10d ), while the JJA peak over India is largely overestimated (Fig. 10e ). Over the U. S. dusty region, the seasonal cycle in the V thresh 12mn simulation is slightly underestimated compared to MODIS DOD but overestimated from May to August in the V thresh Ann simulation (Fig. 10f ). DOD is underestimated in South Africa in all three simulations (Fig.
Acknowledgements. This research is supported by  NOAA  and  Princeton University's Cooperative Institute for Climate Science  and  NASA  under grant  NNH14ZDA001N-ACMAP  and  NNH16ZDA001N-MAP. The authors thank  Drs. Veronica Chan  and  Hyeyum Shin  for their helpful comments on the early version of this paper and  Dr. Sophie Vandenbussche  for her valuable suggestions. We also thank the  AERONET program  for establishing and maintaining the sunphotometer sites used in this study and the  IMPROVE network  for the data. IMPROVE is a collaborative association of state, tribal, and federal agencies and international partners. The  US Environmental Protection Agency  is the primary funding source, with contracting and research support from the  National Park Service. The Air Quality Group at the  University of California , Davis is the central analytical laboratory, with ion analysis provided by  Research Triangle Institute , and carbon analysis provided by  Desert Research Institute.
E. in a South Pole ice core 7. The Greenland ice sheet acidity record also shows a modest increase at 1450 C. E. 8 , which was not thought to be a tropical volcanic event at the time. Later, Pang 9 tied the southern hemisphere ice core anomaly to a large volcanic event from Kuwae volcano in Vanuatu primarily utilizing historical reports and tree ring data. The Kuwae caldera collapse was first suggested to have occurred in 1425 C. E. , based on historical reconstructions and radiocarbon dating 10. The timing was shortly revised by Robin et al. 11  to 1452-1453 C. E. by combining 14 C and ice core chronologies. Published tree ring frost damage 12 and density data 13 were interpreted as being the result of a strong cooling in the northern hemisphere in 1453 C. E. , seemingly confirming 1452 C.
In addition, the microprobe results show generally higher Na 2 O wt% compared to the SEM-EDS measurements. This is expected because microprobes are configured to measure Na 2 O in the first counting period during EPMA tephra analyses, whereas all elements are measured simultaneously during SEM-EDS analyses (longer counting time could result in Na loss on the SEM/ EDS), and the SEM-EDS measurements are on unpolished particles, which increases variability of the results. Figure 2. Geochemical composition of individual glass shards extracted from the SPICEcore compared with possible volcanic sources using Harker variation diagrams. Open red triangles represent ice core volcanic glass particle data analyzed using SEM-EDS. The same volcanic glass particles analyzed using EPMA-WDS method are shown with filled red triangles.
Acknowledgements This work is directly supported by the  National Science Foundation  grants  PLR1543454 ,  1142069  and  1542778. We would like to thank  Mark Twickler ,  Joseph Souney  and the  SPICEcore Science Coordination Office ; the  U. S. Ice Drill Program Office for SPICEcore  recovery; the  109 th New York Air National Guard  for transport in Antarctica; the members of  South Pole station  for field operations support. The  U. S. NSF Ice Core Facility  support for allocation and storage of SPICEcore samples and the many student researchers assisting on core packaging and processing are acknowledged.
Study design and population SAFEHEART is a real-world clinical practice multicentre, nationwide, long-term prospective cohort study in Spain, which includes molecularly defined heterozygous FH patients treated as per local guidelines with LLT, with or without ASCVD at baseline  [16]. Data analysed for this work were obtained between January 2004 and October 2015 and only those ≥18 years old subjects with molecularly determined FH were included. This study was approved by the ethics committees and all subjects gave written informed consent. Based on data from this study, the SAFEHEART-RE can be used to estimate the risk of cardiovascular events at 5 and 10 years using 8 simple variables  [13]. The SAFEHEART registry was able to provide information to develop a risk prediction equation, the SAFEHEART-RE that can estimate the risk of incident ASCVD events in FH patients using the following variables: Age, gender, history of atherosclerotic cardiovascular disease, presence of hypertension, body mass index (BMI), smoking, and plasma LDL-C and Lp(a) levels.
This relative risk reduction was applied to the baseline risk to calculate the predicted event rate of therapy. In this case, the endpoint was the occurrence of major vascular events, defined as the combined outcome of major coronary event, non-fatal or fatal stroke or coronary revascularisation  [15]. Variables were analysed for normality using the Kolmogorov-Smirnov test. Qualitative data were expressed as absolute number (percentage). Quantitative data were expressed as mean and standard deviation (SD) or median and IQR if they were not normally distributed Average absolute risk was estimated using the SAFEHEART-RE in every individual using the on-treatment LDL-C after LLT and after the simulated use of PCSK9 mAb. Absolute risk reduction (ARR) was obtained as the difference between absolute risk before and after the simulated use of PCSK9 mAb.
This is reflected by the number of candidates receiving treatment with PCSK9 mAb that varies according to different guidelines  [10, 11]. An accurate predictive tool would provide risk assessment more precisely on a case by case basis for physicians, payers, and patients. In this study, our findings suggest that SAFEHEART-RE could be a useful tool to select those subjects for potential therapy with PCSK9 mAb based on real-life data. The strengths and limitations of our study merit careful consideration. The SAFEHEART registry is a nationwide, long-term prospective contemporary cohort of a molecularly-defined FH population that allow the development of a robust FH specific risk prediction equation (SAFEHEART-RE). Given that FH patients have a greater cumulative exposure to LDL-C, the beta coefficient for LDL-C would incorporate this and be more reliable than data derived from non-FH populations  [3].
This opens the possibility of creating unique time series of elevation changes of the Icelandic glaciers, thereby expanding knowledge of the last century of glacier variations and allowing further studies of glacier response to climate forcing. The processing of optical stereo imagery has improved during recent years due to advances in computer vision and image processing. New tools and algorithms are available to solve the image orientation, such as structure from motion (SfM, e. g. Pierrot  Deseilligny and Clery, 2011). Image correlation can be performed with high precision and detail using semi-global matching  (Hirschmuller, 2008). These tools are accessible to the community with open-source software such as MicMac (IGN, France; Pierrot  Deseilligny and Clery, 2011; Rupnik and others, 2017)  and the NASA Ames Stereo Pipeline (ASP)  (Shean and others, 2016).
Moreover, publicly accessible archives of high-resolution DEMs with sub-meter uncertainties have become available in recent years. The main glacierized regions in Iceland were surveyed with airborne lidar between 2008 and 2012, an initiative during the 2008 International Polar Year (IPY)  (Jóhannesson and others, 2013). In addition, the current state of the glaciers and ice caps is being monitored by satellite sub-meter stereo imagery, such as Pléiades and WorldView  (Berthier and others, 2014; Noh and Howat, 2015; Willis and others, 2015; Shean and others, 2016; Belart and others, 2017). The high-resolution DEMs are not only useful for updating the glacier's topography, but also provide valuable data to generate improved DEMs from the archives of stereo imagery. This is achieved by using co-registration techniques in overlapping off-glacier areas between the historical datasets and the modern DEMs  (Barrand and others, 2009; Papasodoro and others, 2015; Fieber and others, 2018).
Frame stereo imagery The frame imagery was subdivided into four groups: (   Bindschadler and Vornberger, 1998; Surazakov and Aizen, 2010). In this study, we use six images from the Hexagon KH-9 mission # 1216, acquired in 1980, crossing Iceland from north to south, including, among other glacierized areas, a complete coverage of Eyjafjallajökull, Mýrdalsjökull, Höfsjökull as well as numerous smaller (size <50 km 2 ) glaciers and ice caps. The images have a format of 23 × 46 cm and a focal length of 305 mm  (Surazakov and Aizen, 2010). The AMS, DMA and LMÍ series were obtained from the National Land Survey of Iceland (http://www. lmi. is), which stores negatives and prints of the aerial surveys carried out from the 1940s to the 1990s. All the data are publicly available upon request, and scanning of the negative films was carried out with a photogrammetric scanner (further details in online Supplementary material S2).
Climate data The gridded daily temperature of Iceland, produced by  Crochet and Jóhannesson (2011) , was used in this study. This dataset consists of 1 × 1 km gridded daily air temperature at 2 m above ground, spanning 1949-2016, deduced by interpolation of weather station observations reduced to sea level with a constant vertical temperature lapse rate of 6. 5°C km -1 , and adjusted back to the topography with a 1 × 1 km DEM and the same constant vertical lapse rate. Gridded daily precipitation of Iceland was obtained from two sources: (1) the linear theory model of orographic precipitation (LT-Model,  Crochet and others, 2007) , available for the period 1958-2007 in 1 × 1 km resolution, and the (2) HARMONIE numerical model (HM-Model,  Bengtsson and others, 2017; Nawri and others, 2017) , spanning 1980-2016 with 2.
Pushbroom stereo imagery (ASP) ASP is a robust, open-source and automated photogrammetric package commonly used for processing pushbroom satellite stereo images (NASA;  Shean and others, 2016). The pipeline (Fig. 2 ) used the orbital information from the imagery, generally presented as Rational Polynomial Coefficients (RPCs), and no GCPs were used. The processing of ASTER and SPOT5 DEMs was performed with an ASP setup slightly modified (Supplement S3) from that of  Brun and others (2017) , and inspired by  Lacroix (2016). Pléiades imagery was processed as described in  Belart and others (2017). SPOT5, ASTER and Pléiades DEMs were co-registered to the lidar using the Iterative Closest Point (ICP) algorithm in ASP  (Shean and others, 2016; Belart and others, 2017). The 3D transformation served to refine the orbital information and produce a co-registered orthoimage.
Correction and uncertainty assessment of the obtained mean elevation change The difference of DEMs (dDEM) can be affected by spatiallyvariable errors due to residual errors in the sensor orientation (interior and exterior). This effect can be particularly enhanced in the oldest datasets, e. g. 1945 and 1960, where the camera geometry is not fully constrained (e. g. Magnússon and others, 2016). To correct the spatially-variable errors in the dDEM, we used a modified version of the Sequential Gaussian Simulation (SGSim) described by  Magnússon and others (2016). In this study, it is included in an automated pipeline using the command-line interface for the open-source software GSLib  (Deutsch, 1998). The SGSim calculates 1000 realizations of simulated maps (2D grids) of spatially-variable errors for each dDEM within the ice cap, using as input the off-glacier areas of the dDEM and a modeled semivariogram, also constrained by the off-glacier areas of the dDEM.
Details of the SGSim methodology are described in online Supplementary material S4. This method takes into account the spatial autocorrelation of the dDEMs, producing a spatially-variable error correction. This results in significantly lower uncertainties in glacier-wide mean elevation change and volume change than proxies based on descriptive statistics  (Rolstad and others, 2009; Magnússon and others, 2016) , such as standard deviation or Normalized Median Absolute Deviation (NMAD,  Höhle  and Höhle, 2009) (Table 2 ). To verify the robustness of the SGSim, we used the two independent datasets at similar dates in  1960 and 1980 , respectively (1960 DMA, 1960 LMÍ, 1980 LMÍ and 1980 KH-9 ; Table  1 ), for which we calculated the glacier-wide mean elevation changes compared with the 2010 lidar DEM (considered as a ground truth).
Calculation of seasonally corrected mass balance The volume changes dV t2 t1 for the selected time intervals were calculated from SGSim-corrected maps of elevation change. Since they contained some data gaps (up to 15% of the ice cap area, in the worst case of the 1989 series), a gap interpolation and outlier filtering were also performed, as described in online Supplementary material S5. The glacier-wide geodetic mass balance _ B was calculated with the following equation  (Fischer and others, 2015; Magnússon and others, 2016) : _ B t2 t1 ¼ dV t2 t1 c A t2 t1 dt ; (1) where A t2 t1 is the average area for the two dates, dt is the time difference, in absolute years, and c is the conversion factor of volume to water equivalent, here chosen as 0. 85 ± 0. 06  (Huss, 2013). This value is recommended over time periods longer than 5 years  (Huss, 2013) , hence we assume twice as large uncertainty ( ± 0.
12) for the conversion factor in time periods of 4 and 5 years  (1980-1984; 1984-1989; 1989-1994; 1994-1998; 2004-2009; 2010-2014). For the period 2009-2010, a conversion factor of 0. 90 ± 0. 10 is chosen, assuming the elevation change is mostly due to ice melted by the April 2010 eruption. The glacier-wide geodetic mass balance was then temporally adjusted to the end of the summer, _ B EOS , to facilitate comparison between the different time periods, avoiding the effect of seasonal signals that are particularly strong in a relative sense for short time periods. A volume correction was calculated between the date of each DEM dataset (t1 and t2), and 1 October of the same year  (Magnússon and others, 2016). For simplicity, the  DEMs of 1945 DEMs of , 2004 DEMs of and 2009  were not shifted seasonally as they were acquired at most 1 week apart from 1 October (Table  1 ).
75 ± 0. 1 and c s is the conversion factor of snow to water of 0. 5 ± 0. 1. α and β are binary switches; α = 1 and β = 0, meaning that the glacier surface at the location analyzed contains firn or ice, until P ðT<1 ○ CÞ < 0, when they switch to α = 0 and β = 1, meaning that new snow is present at the location, changing the ddf and conversion factor. If the new snow is completely melted, the switches turn back to α = 0 and β = 1 as firn and ice reappear on the glacier surface (Fig. 3 ). The gridded temperature was used to calculate the positive degree-days, T + , and to set the 1°C threshold between rain and snow in precipitation (e. g. Jóhannesson and others, 1995). The HM-Model was utilized to infer P for the period 1980-2014 and the seasonal corrections involving the 1960 datasets were computed with the LT-Model after scaling it toward the HM-Model by linearly fitting summer precipitation in the overlapping time period.
The glacierized areas were always digitized by the same operator and using the same criteria of definition of the ice cap boundaries. Uncertainties in the area were neglected; given the size of Eyjafjallajökull, the maximum area uncertainty was assumed of 5%, as digitized from the coarsest resolution (ASTER) dataset  (Raup and others, 2014). This would only lead to <0. 01 m w. e. a -1 increase in uncertainty of the geodetic mass balance. Thus, δ _ B t2 t1 and δ _ B t2 EOS t1 were calculated as δ _ B t2 t1 ¼ 1 A t2 t1 dt ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (cδV t2 t1 ) 2 þ (dV t2 t1 δc) 2 q (4) δ _ B t2 EOS t1 ¼ 1 A t2 t1 dt ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi cδV t2 t1 À Á 2 þ cδV 1 Oct t1 À Á 2 þ cδV 1 Oct t2 À Á 2 þ dV t2 t1 À dV 1 Oct t1 þ dV 1 Oct t2 À Á δc À Á 2 v u u t ; (5) where δV t2 t1 δV 1 Oct.
97, N = 7), Eqn (6) can be simplified to _ B EOS ¼ φTs þ ωPw þ γΔA þ k; (7) where γ includes both terms b g and b a and allows solving the least-square fit with four unknowns (φ, ω, γ, k). Both scenarios yielded highly similar results. We did not attempt to solve the terms b g and b a , as additional unknowns in the least-square fit since the system may become unstable, due to limited amount of observations and high correlation between terms. Finally, using Eqn (7) with the solved parameters, we computed the annual mass balance from 1958 to 2014 using the annual winter precipitation, summer temperature and glacier area, the latter assumed to change at the same annual rate between each two datasets. Alternative equations to estimate mass balance from climatic variables were tested but did not yield a better fit than using Eqn (7).
Elevation changes and their uncertainties The SGSim-correction applied to the dDEMs yielded uncertainties of sub-meter to a meter (Table  2 ) in the glacierwide elevation difference. Other proxies to assess bias and uncertainties of the dDEM that were based on descriptive statistics, such as the median and NMAD off-glacier, led to substantially different biases and larger uncertainties (Table  2  and Table  S1 ), as already observed by  Magnússon and others (2016)  and  Rolstad and others (2009). For the years 1960 and 1980, two independent surveys were carried out on Eyjafjallajökull. This provides a unique opportunity to compare the results of the descriptive statistics and SGSim. When using the SGSim-correction for two datasets with close dates of survey, 1960 DMA and 1960 LMÍ series (Table  1 ), the glacier-wide mean elevation change during 1960-2010 is -13.
83 ± 0. 31 and -13. 38 ± 0. 41 m, respectively (Table  1 ). The remaining difference can be attributed to -0. 36 ± 0. 14 m of melting between datasets, calculated from the seasonal correction (Section 4. 2). Similarly, the glacier-wide mean elevation change during 1980-2010 is -20. 13 ± 0. 41 m (LMÍ dataset) and -19. 35 ± 0. 99 m (KH-9 dataset). In this case, the seasonal effect is larger, -1. 43 ± 0. 44 m, also likely explaining most of the observed difference. Much larger mismatches of glacierwide mean elevation difference were found when applying the median correction based on the dDEM's off-glacier areas, up to 3. 4 m difference (both series of 1960-2010) and up to 3. 5 m difference (both series 1980-2010), which further diverge when applying seasonal correction. The time series of elevation changes reveal strong fluctuations in the elevation of the ice cap through recent decades, showing overall thinning in  1945 -1960 and 1994 -2014 , and thickening in 1960 -1994 (Figs 4 and 5) (Figs 4 and 5).
g. Aðalgeirsdóttir and others, 2000) , leading to a maximum thickening of 100 m in 2010-2014 (Fig. 4 ). The DEM and orthoimage created from the declassified KH-9 imagery cover approximately one-third of Iceland and about one-third of the glacierized areas (∼3400 km 2 ) of the country (Fig. 6 ). From the comparison with the 2010 lidar, the SGSim-corrected glacier-wide elevation change has an uncertainty of 1 m at the 95% confidence level (online supplementary Table  S1 ). The KH-9 DEM and EMISAR DEMs have a large overlap, allowing the comparison between the datasets and analysis of large-scale elevation changes, not only for Eyjafjallajökull. Results show a good fit between the KH-9 DEM and EMISAR, with a median elevation difference of 2. 08 m and NMAD of 5. 73 m in all unchanged areas, including areas far away (>50 km) from the GCPs (Fig.
Geodetic mass balance The geodetic records yield a negative mass balance of _ B   3. There are differences up to 0. 4 m w. e. a -1 between uncorrected and seasonally-corrected geodetic mass balances for periods as short as 4-6 years, but the difference becomes negligible for long periods (⩾20 years, Table  3 ). The seasonal correction is particularly high when using the LMÍ datasets from the 1980s due to seasonal variations in acquisition dates (Table  1 ). The largest seasonal correction was -1. 98 ± 0. 69 m (glacier-wide lowering) for the LMÍ DEM of 28 July 1980, since it was acquired 2 months before the end of the summer, whereas -0. 56 ± 0. 40 m was estimated between 22 August 1980 (KH-9) and 30 September 1980. The seasonal corrections for each date of survey are listed in online Supplementary material S6 and Table  S2.
Remote-sensing and open-source alternatives: MicMac, ASP and GSLib The pipeline of processing stereoscopic data (aerial, spaceborne, frame and pushbroom) and assessing uncertainties was carried out in a semi-automated workflow. Manual steps in the workflow are: (1) measurement of fiducial marks in the frame imagery, (2) measurements of GCPs, (3) digitization of glacier outlines, (4) manual mask of unfiltered artifacts and (5) fitting of a semivariogram model. Many studies have used photogrammetry and SfM for DEM generation for glaciers with historical aerial photographs generally using commercial software (e. g. Barrand and others, 2009; Mölg and Bolch, 2017; Fieber and others, 2018). Here the entire processing was carried out using only open-source software (MicMac). The obtained uncertainties of elevation changes are similar to previous studies using commercial software, such as ERDAS Imagine for frame stereo imagery processing (e.