dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology The focus of this work is on retrieving new information from mass spectra by applying new analytical approaches. Therefore, we chose a dataset that has been presented earlier, though without PMF analysis, by  Zha et al. (2018)  and was also used in the first study describing the binPMF method  (Zhang et al. , 2019). The measurements are described in more details below in Sect. 2. 1, while the data analysis techniques used in this work are presented in Sect. 2. 2.
Positive matrix factorization (PMF) After the model of PMF was developed  (Paatero and Tapper, 1994) , numerous applications have been conducted with different types of environmental data  (Song et al. , 2007; Ulbrich et al. , 2009; Yan et al. , 2016; Zhang et al. , 2017). By reducing the dimensionality of the measured dataset, the PMF model greatly simplifies the data analysis process with no requirement for prior knowledge of sources or pathways as essential input. The main factors can be further interpreted with their unique or dominant markers (elements or masses). The basic assumption for PMF modeling is mass balance, which assumes that ambient concentration of a chemical component is the sum of contributions from several sources or processes, as shown in Eq. (  1 ). X = TS × MS + R (1) In Eq.
001 Th. Then the interpolated data were averaged into bins of 0. 02 Th width. We selected three ranges for further analysis based on earlier studies  (Ehn et al. , 2014; Yan et al. , 2016; Bianchi et al. , 2019; Peräkylä et al. , 2020). -Range 1, m/z 250-300 Th, 51 unit masses ×25 bins per unit mass = 1275 bins/variables, consisting mainly of molecules with five to nine carbon atoms and four to nine oxygen atoms in our dataset. -Range 2, m/z 300-350 Th, 51×25 = 1275 bins, mainly corresponding to HOM monomer products, with 9 to 10 C atoms and 7 to 10 O atoms. -Range 3, m/z 510-560 Th, 51×30 = 1530 bins, mainly corresponding to HOM dimer products, with carbon numbers of 16 to 20 and 11 to 15 O atoms. To avoid unnecessary computation, only signal regions with meaningful signals in the mass spectra were binned  (Zhang et al.
General overview of the dataset and spectrum During the campaign, in autumn 2016, the weather was overall sunny and humid with average temperature of 10. 8 • C and relative humidity (RH) of 87 %  (Zha et al. , 2019). The average concentrations of NO x and O 3 were 0. 4 and 21 ppbv, respectively. The average total HOM concentration was ∼ 10 8 molecules cm -3. Figure  1  shows the 1 h averaged mass spectrum taken at 18:00 LT (all times in this paper are in Finnish local time (UTC+2) unless stated otherwise) on 12 September, as an example of the analyzed dataset. In addition to exploring the benefits of this type of subrange analysis in relation to different formation or loss pathways, separating into subranges may also aid factor identification for low-signal regions. As shown in Fig. 1 , there is a difference of 1-2 orders of magnitude in the signal intensity between Range 3 and Range 1-Range 2.
2  shows the result of Range 1, with spectral profile, time series, diurnal cycle, and averaged factor contribution during the campaign. As shown in Fig. 2d , factors 1-3 are all daytime factors, while factor 4 has no clear diurnal cycle but a distinct sawtooth shape. Factor 4 comes from a contamination of perfluorinated acids from the inlet's automated zeroing every 3 h during the measurements  (Zhang et al. , 2019). The zeroing periods have been removed from the dataset before binPMF analysis, but the contamination factor was still resolved. This factor is discussed in more detail in Sect. 4. 1 and 4. 4.
(2019) , where the zeroing periods were not removed, causing larger signals for the contaminants. In our dataset, where the zeroing periods were removed, no sawtooth pattern was discernible in the diurnal trend, yet it could still be separated even though it only contributed 3 % to Range 2. More about the contamination factors from different ranges will be discussed in Sect. 4. 4. We chose to show the four-factor result below, to simplify the later discussion and comparison. Figure  3  shows four-factor result of Range 2, with spectral profile, time series, diurnal cycle, and averaged factor contribution during the campaign.
In the results by  Yan et al. (2016)  the combined UMR-PMF of monomers and dimers did yield a considerable amount of compounds in the monomer range also for the NO 3 radical chemistry factor. There may be several reasons for this discrepancy. One major cause for differences be-tween the spring dataset by  Yan et al. (2016)  and the autumn dataset presented here is that nighttime concentrations of HOMs were greatly reduced during our autumn campaign. The cause may have been fairly frequent fog formation during nights and also the fact that the concentration of ozone decreased nearly to zero during several nights  (Zha et al. , 2018). It is also possible that the NO 3 radical-related factor by  Yan et al. (2016)  is probably a mixture of NO 3 and O 3 radical chemistry, while the monomer may thus be attributed to the O 3 part.
9721 Th), and (CF 2 ) 6 CO 2 HF • NO - 3 (425. 9653 Th), were examined in fine time resolution, i. e. , 1 min. The time series and 3 h cycle of the three fluorinated compounds were shown in Figs. S3  and S4. The correlation coefficients dropped greatly before and after the zero period was removed: from 0. 9 to 0. 3 for R 2 between m/z 276 and 339 Th and 0. 8 to 0. 1 between m/z 276 and 426 Th (Fig. S5a, b ). A similar effect is also found with the 1 h averaged data (Fig. S5c, d ). It is evident that the three fluorinated compounds were from the same source (zeroing process), but due to their different volatilities, they were lost at different rates. This, in turn, means that the spectral signature of this source will change as a function of time, at odds with one of the basic assumptions of PMF.
Conclusions The recent developments in the field of mass spectrometry, combined with factor analysis techniques such as PMF, have greatly improved our understanding of complicated atmospheric processes and sources. In this study, we applied the new binPMF approach  (Zhang et al. , 2019)  to separate subranges of mass spectra measured using a chemical ionization mass spectrometer in the Finnish boreal forest. By using this method, we were able to identify a daytime dimer factor, presumably initiated by OH/O 3 oxidation of monoterpenes, forming from RO 2 + RO 2 reactions despite competition from daytime NO. This compound group, showing a diurnal peak around noon, may contribute to new particle formation at the site. In addition, we successfully separated NO 3 -related dimers which would not have been identified from this dataset without utilizing the different subranges.
In our dataset, this method was crucial for identifying different types of dimers and dimer formation pathways, which are of great importance for the formation of both new particles and SOA. Data availability. The data used in this study are available from the first author upon request: please contact Yanjun Zhang (yanjun. zhang@helsinki. fi). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-5945-2020-supplement. Author contributions. ME and YZ designed the study. QZ and MR collected the data; data analysis and article writing were done by YZ. All coauthors discussed the results and commented on the article. Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. We thank the tofTools team for providing tools for mass spectrometry data analysis. The personnel of the Hyytiälä forestry field station are acknowledged for help during field measurements. Financial support. This research has been supported by the  European Research Council  (grant no. 638703-COALA ), the  Academy of Finland  (grant nos. 317380  and  320094 ), and the  Swiss National Science postdoc mobility grant  (grant no. P2EZP2_181599 ).
In this study, we propose a method to retrieve the monthly global threshold of wind erosion (hereafter, V threshold ) for dry and sparsely vegetated surface (i. e. , under favorable conditions for dust emission) using high-resolution satellite products and reanalysis datasets. This two-dimensional threshold of surface 10 m winds is then implemented into the Geophysical Fluid Dynamics Laboratory (GFDL) coupled landatmosphere model, AM4. 0/LM4. 0 (Zhao et al. , 2018a, b). The benefits of using this spatially and temporally varying threshold in simulating present-day climatology and seasonal cycles of dust are analyzed by comparing the model results with observations. The data and method used to retrieve the threshold of wind erosion are detailed in Sect. 2. The distribution of the derived V threshold and its implication in the climate model is presented in Sect.
Data and methodology In this section we first introduce the satellite products, observational data, and reanalyses used to retrieve the threshold of wind erosion and validate model output (Sect. 2. 1). The processes to retrieve the threshold of wind erosion are detailed in Sect. 2. 2. The uncertainties of V threshold associated with the retrieval criteria and selection of surface wind datasets are discussed in Sect. 2. 3. Section 2. 4 introduces the GFDL AM4. 0/LM4. 0 model, its dust emission scheme, and simulation designs.
, 2013)  than with a high-quality flag (i. e. , QA = 3); as retrieved aerosol products were poorly flagged over dust source regions, we also use DOD with the flag of QA = 1. Both daily DOD retrieved from Aqua and Terra platforms are used by averaging the two when both products are available or using either one when only one product is available. Since Terra passes the Equator from north to south around 10:30 local time (LT) and Aqua passes the Equator from south to north around 13:30 LT, an average of the two combines the information from both morning and afternoon hours. This process also largely reduces missing data  (Pu and Ginoux, 2018b). This combined daily DOD, hereafter MODIS DOD, is available from January 2003 to December 2015 at a resolution of 0. 1 × 0. 1 • grid. Note that due to the temporal coverage of MODIS products, the diurnal variations in dust (e.
Station data Multiple ground-based datasets are used to validate AM4. 0/LM4. 0 simulated aerosol and dust optical depth and surface dust concentrations. and Ångström exponent between wavelengths λ A and λ B (α) are used to derive AOD 550 nm using the following equations: τ 550 = τ A 550 λ A -α if τ A i s available, (2) τ 550 = τ B 550 λ B -α if τ B is available. ( 3 ) In a manner similar to the process of screening SDA COD data, monthly AOD 550 nm data with less than 3 d of records in a given month are removed. When calculating the annual means we excluded years having less than 5 months of records. Finally, to calculate the climatology of 2003-2015, only stations with at least 3 years of records during this period are used, totaling 351. We also developed a method to derive DOD at 550 nm from AOD at 550 nm based on the relationship between the Ångström exponent and fine-mode AOD established by  Anderson et al.
(2005; their Eq. 5). This adds a few more sites over the Sahel than the SDA COD stations. DOD is calculated by subtracting fine-mode AOD from total AOD. Due to the large uncertainties of singlescattering albedo in AERONET records over regions where AOD is lower than 0. 4 (e. g. ,  Dubovik and King, 2000; Holben et al. , 2006; Andrews et al. , 2017) , we did not use single-scattering albedo to screen AOD to further separate dust from scattering aerosols. Therefore, the derived AERONET DOD over coastal stations may be contaminated by sea salt. 2. RSMAS surface dust concentration. The Rosenstiel School of Marine and Atmospheric Science (hereafter the RSMAS dataset) at the University of Miami collected mass concentrations of dust, sea salt, and sulfate over stations globally, with most of the stations on islands  (Savoie and Prospero, 1989).
The dataset has been widely used for model evaluation (e. g. ,  Ginoux et al. , 2001; Huneeus et al. , 2011). Only stations with records longer than 4 years were used, and of those stations only those years with at least 8 months of data are used for calculating climatological annual means. So, in total 16 stations are used. Station names, locations, and record lengths are listed in Table  S1  of the Supplement. We compare the climatology of annual mean surface dust concentration with model output during 2000-2015. Note that since most station records end earlier than 1998, the dataset largely represents the climatology during the 1980s and 1990s. Thus the discrepancies between model output and the RSMAS data include both model biases and the difference in surface dust concentration from the 1980s to the 2000s.
3. IMPROVE surface fine dust concentration. The Interagency Monitoring of Protected Visual Environments (IMPROVE) network has collected near-surface particulate matter 2. 5 (PM 2. 5 ) samples in the US since 1988  (Malm et al. , 1994; Hand et al. , 2011). IMPROVE stations are located in national parks and wilderness areas, and PM 2. 5 sampling is performed twice weekly (Wednesday and Saturday;  Malm et al. , 1994)  prior to 2000 and every third day afterwards. Fine dust (with an aerodynamic diameter less than 2. 5 µm) concentration is calculated using the concentrations of aluminum (Al), silicon (Si), calcium (Ca), iron (Fe), and titanium (Ti) by assuming oxide norms associated with predominant soil species  (Malm et al. , 1994; their Eq. 5). This dataset has been widely used to study variations in surface fine dust in the US (e.
Retrieving the threshold of wind erosion The monthly climatological threshold of wind erosion is retrieved by matching the frequency distribution of the MODIS DOD at a certain level, namely, DOD thresh , with the frequency distribution of surface 10 m winds from the NCEP1 reanalysis over the period from 2003 to 2015. The process can be summarized by the following steps: -Step 1. Since dust is emitted from the dry and sparsely vegetated surface, the daily DOD data are 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 % (since the snow cover percentage is rounded up to an integer in the MODIS product, this criterion actually requires no snow cover); monthly top-layer soil temperature higher than 273.
On the other hand,  Cowie 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. Firstly, 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 they 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 records at stations may miss short events with a high wind speed. As shown in Table  3 , among the reanalysis wind products tested here, NCEP1 actually produced a lower V threshold in northern Africa than the other two reanalyses.
g. ,  Kurosaki and Mikami, 2007; Ginoux and Deroubaix, 2017). In the following section, we will exam if the spatially and temporally varying V threshold would improve model simulation of the DOD spatial pattern, seasonal variations, frequency distribution, and surface dust concentrations in the GFDL AM4. 0/LM4. 0. Results using V threshold with DOD thresh = 0. 2 (or 0. 02) are shown in Sect. 3. 2-3. 3, and results using V threshold with DOD thresh = 0. 5 (or 0. 05) are briefly discussed in Sect. 4. 3. 2 V threshold in the GFDL AM4. 0/LM4. 0 model In this section we analyze the model output using the default setting (Control; Table  4 ), 12-month (V thresh 12 mn), and annual mean V threshold (V thresh Ann) by comparing model results with multiple observational datasets and MODIS DOD.
The results indicate the importance of including the seasonal cycle of V threshold in the model. Using time-varying 2-D V threshold , the model was also able to capture a strong dust storm in the US Great Plains in October 2012, which created deadly accidents, while some dust forecasting models failed to reproduce it. Finally, this method to retrieve the global threshold of wind erosion can be conducted under different resolutions or surface wind reanalyses or applied to surface friction velocity datasets to match the resolution/scheme of dust models and may help improve their simulations and forecasting of dust distribution. As discussed in Sect. 4, there are uncertainties associated with this method, and future studies to better quantify the influence of transported dust to the overall DOD frequency distribution and incorporate station-based surface wind records into the retrieval process will further improve the dataset.
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 the Research Triangle Institute and carbon analysis provided by the Desert Research Institute. Financial support. This research has been supported by NASA (grants no. NNH14ZDA001N-ACMAP and NNH16ZDA001NMAP). Review statement. This paper was edited by Yves Balkanski and reviewed by two anonymous referees.
Figure11. Seasonal cycle of DOD from MODIS (black), the Control (grey), V thresh 12 mn (orange), and V thresh Ann (yellow) runs, with gridded AERONET SDA COD (blue) averaged over nine regions. The annual mean of each dataset in each region is listed on the top of the plot. Note that in (i) DOD from the V thresh 12 mn and V thresh Ann simulations is multiplied by 0. 1 for clarity.
the oceanic uptake of atmospheric carbon dioxide (co 2 ) emitted by human activities alters the seawater carbonate system. Here, the chemical status of the Northeast Atlantic is examined by means of a high-quality database of carbon variables based on the GO-SHIP A25 section (1997-2018). The increase of atmospheric co 2 leads to an increase in ocean anthropogenic carbon (c ant ) and a decrease in carbonate that is unequivocal in the upper and mid-layers (0-2,500 m depth). In the mid-layer, the carbonate content in the northeast Atlantic is maintained by the interplay between the northward spreading of recently conveyed Mediterranean Water with excess of carbonate and the arrival of subpolar-origin waters close to carbonate undersaturation. In this study we show a progression to undersaturation with respect to aragonite that could compromise the conservation of the habitats and ecosystem services developed by benthic marine calcifiers inhabiting that depth-range, such as the cold-water corals (CWC) communities.
Materials and methods Two-decades of ocean acidification trends across the whole water column of the Northeast Atlantic are evaluated by means of high-quality CO 2 measurements. The observations came from ten hydrographic cruises across the Iberian Abyssal Plain between 1997 and 2018 (Fig. 2 ): the 1997 FOUREX cruise (CLIVAR Carbon Hydrographic Data Office site, https ://cchdo. ucsd. edu/cruis e/74DI2 30_1) and 9 repeats of the OVIDE section (OVIDE group, 2020). They are almost evenly spaced in time since all cruises except the first one belong to the OVIDE sampling program, a high-resolution hydrographic survey that has been carried out every other year during spring-summer since 2002 (https ://www. umr-lops. fr/en/Proje ts/Proje ts-actif s/OVIDE ). The assembled high-quality CO 2 system database between the Azores Biscay Ridge (45° N, 18° W) and the Iberian Peninsula therefore spans 21 years (1997-2018; Fig.
0014 pH units  9, 27. The large amount of measurements from the deeper waters sampled at the Iberian Abyssal Plain (n = 1,633) show a very low standard deviation (7. 9146 ± 0. 0006 pH at in situ conditions of temperature and pressure in the total scale), which is a useful indicator of consistency and reproducibility since these old waters are expected to be in near steady state  9. Total alkalinity (TA) was analyzed by single point titration  52  and calibrated with CRM with a measurement precision of ± 2 μmol kg -1. Over 3,200 samples of TA were analyzed for this study, with around three hundred per cruise for the stations shown in Fig. 2. The amount of total alkalinity measurements with WOCE flags values different from "Acceptable" is less than 1%. The datasets were subject to primary and secondary quality control procedures  53  consistent with the GLODAP data products  54, 55  and neither carbon related variables (pH and total alkalinity) nor oxygen have been modified at the secondary quality control procedure  55.
3, 4, 5). Atmospheric co 2 : past concentrations and shared socioeconomic pathways projections. In this study we contrasted the anthropogenic changes in the ocean carbon cycle with respect to its main driver, the atmospheric CO 2 concentration, in contrast to previous studies in the region using time as dependent variable  8, 9. Note that this methodology develops rates with respect to atmospheric CO 2 rather than time, so the units of the rates are μmol kg -1 ppm -1. Atmospheric CO 2 concentration is a more meaningful variable than time, since it allows to make projections directly based on greenhouse gas concentrations resulting from different policy decisions and socioeconomic pathways. Past atmospheric CO 2 concentrations were taken from the Mauna Loa database (https ://www. esrl. noaa.
Acknowledgements For this work  M. Fontela  was funded by the  Spanish Ministry of Economy and Competitiveness  ( BES-2014-070449 ) supported by the  Spanish Government  and co-funded by the  Fondo Europeo de Desarrollo Regional   2007-2012  (FEDER) and by Portuguese national funds from  FCT-Foundation for Science and Technology  through project  UID/Multi/04326/2019  and  CEECINST/00114/2018. F. F. Pérez was supported by the  BOCATS  Project ( CTM2013-41048-P ) and  ARIOS  project ( CTM2016-76146-C3-1-R ) both co-funded by the  Spanish Government  and the  Fondo Europeo de Desarrollo Regional (FEDER). This project has received funding from the  European Union 's  Horizon research and innovation programme  under grant agreement No  820989  (project  COMFORT , Our common future ocean in the Earth system-quantifying coupled cycles of carbon, oxygen, and nutrients for determining and achieving safe operating spaces with respect to tipping points).
Data availability Data were collected and made publicly available by the International Global Ship-based Hydrographic Investigations Program (GO-SHIP; https ://www. go-ship. org/) and the national programs that contribute to it. Global Distribution of Cold-water Corals version 5. 0 (June 2018) is distributed under UNEP-WCMC's General Data License (excluding WDPA). URL: https ://data. unep-wcmc. org/datas ets/3.
Gleckler et al. (2008)  proposed a multidimensional metric based on the comparison of the spatio-temporal variability of many climatic fields with respect to reference datasets, and found that creating a scalar comprehensive metric is nontrivial. Eyring, Righi, et al. (2016)  and  Eyring et al. (2020)  have combined metrics and diagnostic tools designed to assess specific features of the climate system, whilst  Lembo et al. (2019)  have provided a tool to test the models' skill in representing the thermodynamics of the climate system. Hence, it seems highly desirable to have a scalar metric that summarizes the information associated with model performance and that satisfies the mathematical axioms associated with the concept, as for the usual Euclidean distance. These axioms are listed in Text S1 of the Supplementary Information (SI) and they are satisfied by the rootmean-square distance, known as an L 2 metric in mathematics.