dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016; Cools and De Vos, 2020) , altitude, and geographic coordinates. At the tree level, metadata consist of tree species, tree number, and diameter at breast height  (Dobbertin and Neumann, 2016). Meteorological in situ measurements of hourly temperature and relative humidity  (Raspe et al. , 2013)  were available for 82 forest Level II plots for both 2015 and 2017. Furthermore, we amended the dataset with satellite-based values of transpiration from the Global Land Evaporation Amsterdam Model (GLEAM)  (Miralles et al. , 2011; Martens et al. , 2017)  and of hourly soil water (layer 1, 0-7 cm) and surface air temperature (2 m height) from  ERA5-Land (Muñoz Sabater, 2019)  for the respective regions of every forest plot. GLEAM (v. 3. 3a)  data were available at daily Figure  1. Overview of forest plots at which Hg foliage samples were harvested from different tree species groups during the sampling year 2017.
We detected outliers of time-normalized foliar Hg concentrations (see Sect. 2. 3) within each tree species and needle age class by applying the modified Z score method according to  Iglewicz and Hoaglin (1993)  using an absolute threshold value of 3. 5, above which a modified Z score value was considered an outlier. As a result, 3. 2 % of values within the dataset were removed as outliers.
Correction of foliar Hg concentrations for drying temperature Drying and grinding of foliage samples were carried out by ICP Forests laboratories and BFW. All foliar concentration values (Hg, N and C org ) within the dataset are normalized to dry weight for a sample drying temperature of 105 • C in or-der to make values internally consistent. The actual drying temperature differed between foliage samples (40-80 • C). In order to adjust for actual drying temperature, the laboratories determined the drying factor to correct for water content of each sample by drying an aliquot of foliage sample at actual drying temperature and subsequently at 105 • C. The drying factor was available for 62 % of samples within the dataset. For the rest of the samples an average drying factor per tree species and needle age class was applied for drying temperature correction.
Determination of the beginning of the growing season for calculating daily foliage Hg uptake rates Mercury concentrations in leaves and needles have been demonstrated to increase linearly over the course of the growing season  (Rea et al. , 2002; Laacouri et al. , 2013; Blackwell et al. , 2014; Wohlgemuth et al. , 2020). In this study, foliage samples within the dataset were harvested at various points in time, making a direct comparison of measured Hg concentrations unfeasible. We therefore calculated foliar Hg uptake rates (in ng Hg g -1 d. w. d -1 ) of current-season samples by normalizing foliar Hg concentrations to their respective life period in days from the beginning of the growing season (emergence of new foliage) to date of harvest. These resulting foliar Hg uptake rates are net Hg accumulation rates per gram dry weight on a leaf basis and should not be confused with foliar Hg fluxes on a whole-tree basis.
These data sources comprise in situ phenological observations, which were available for 15 % of samples, and observations of the emergence of current-season needles of coniferous tree species from the Pan European Phenological database PEP725  (Templ et al. , 2018). We assigned observations from PEP725 to the corresponding closest forest plot of the respective sampling year (2015 or 2017) by using the nearest neighbor function matchpt from the Biobase package in R  (Huber et al. , 2015)  such that differences between PEP725 observation and forest plots did not exceed 3 • in latitude or 30 m in altitude but matched longitude as closely as possible. For details on the matching procedure and results see Sect. S3. 1 in the Supplement. To model the beginning of the growing season for deciduous trees, we utilized the leaf area index (LAI) product of Copernicus Global Land Service based on PROBA-V satellite imagery at a resolution of 300 m and 10 d  (Dierckx et al.
Thus, the difference of average daily Hg uptake between winter and growing season spruce needle samples indicates a decrease in Hg accumulation in spruce needles during winter. However, the potential of needle Hg uptake in winter needles requires further investigation, for example, by performing a full winter sampling at multiple forest plots. For this study, we shortened the calculated life period of spruce needles from winter sampling plots to 15 November  (Rötzer and Chmielewski, 2001)  to improve comparability of spruce needle Hg uptake rates within the dataset.
There are distinct differences in median daily Hg uptake rates between current-season foliage of tree species groups (Fig. 3 ). The median daily foliar Hg uptake rate of deciduous leaf samples is 0. 23 ng Hg g -1 d. w. d -1 , a factor of 3. 2 larger than the median daily foliar Hg uptake rate of current-season conifer needle values (0. 07 ng Hg g -1 d. w. d -1 ). The difference between deciduous and coniferous leaves in the European dataset is smaller than a previous observation from a mixed forest site in Switzerland in 2018, where Hg uptake rates of coniferous species were reported to be 5 times lower than those of deciduous trees  (Wohlgemuth et al. , 2020). Similarly,  Navrátil et al. (2016)  reported higher foliar Hg concentrations in beech leaves (36. 3 ng Hg g -1 ) than in current-season spruce needles (14.
This observation supports the notion that the physiological activity of trees controls foliar Hg(0) uptake, thereby explaining the large variation among tree species groups  (Wohlgemuth et al. , 2020). We compared foliar Hg uptake rates and leaf N concentrations with values of median stomatal conductance for beech, oak, pine, and spruce included in a global leaf-level gas exchange database compiled by  Lin et al. (2015)  (see description of database calculation in Sect. S7 in the Supplement). Albeit stomatal conductance measurements for tree species of interest within the database  (Lin et al. , 2015)  originated from one or only a few sites (n = 1-5; Table  1 ), beech and oak exhibited higher median stomatal conductance values than spruce and pine, corresponding to higher daily Hg uptake rates and foliar N concentrations in beech and oak compared to spruce and pine.
Thus, we observed a strong control of plant functional traits on foliar Hg(0) uptake with tree species of high photosynthetic activity (high N concentration) and stomatal conductance exhibiting the highest foliar Hg(0) uptake rates. Within tree species groups, linear regression coefficients of daily Hg uptake and foliar N concentration were significant (p < 0. 001) for beech (R 2 = 0. 15; n = 312) and fir (R 2 = 0. 27; n = 66). Corresponding statistical significance for hornbeam, oak, pine, and spruce could not be evaluated since the respective data used for the linear regression was heteroscedastic. Blackwell and Driscoll (2015)  found a significant relationship between foliar Hg concentration and foliar N percentage for yellow birch, sugar maple, and American beech but not for pine (red pine and white pine), red spruce, or balsam fir.
Foliar Hg uptake and leaf mass per area Within the whole dataset, leaf mass per area (LMA; g d. w. m -2 leaf ) data were available in a subset of 349 foliage samples from 48 sites (from both 2015 and 2017). LMA is an important parameter in plant ecophysiology because carbon gains of plants via photosynthetic activity and gas diffusion are optimized per unit of leaf area as plants adapt their LMA, i. e. , their foliage thickness and/or tissue density, to the availability of sunlight during growth  (Ellsworth and Reich, 1993; Niinemets and Tenhunen, 1997; Rosati et al. , 1999). This LMA adaptation of foliage to sunlight had been suggested to be more effective for optimizing photosynthetic capacity than within-leaf N partitioning of photosynthesizing biomass  (Evans and Poorter, 2001). Therefore, we analyzed the connection of foliar Hg uptake to LMA across tree species.
Conclusions We created a large European forest dataset for investigating the control of tree physiology and climatic conditions on foliar stomatal Hg(0) uptake. We observed that foliar Hg concentrations were highly correlated with foliage sampling date (Fig. 2 ), confirming the notion that foliage takes up Hg(0) over the entire growing season and over multiple growing seasons in the case of coniferous needles (Fig. 7 ). Consequently, it is necessary to calculate foliar Hg uptake rates by normalizing foliar Hg concentrations by the time period of Hg(0) accumulation to make foliar Hg values from different sites comparable. For reasons of comparability, foliar Hg uptake rates should ideally be normalized to ambient air Hg(0) concentrations when large variation in atmospheric Hg(0) is expected (e.
6 ). We therefore suggest that foliar Hg measurements bear the potential to serve as a proxy for stomatal conductance, providing a time-integrated measure for stomatal aperture when taking into account the spatial and temporal variation in atmospheric Hg(0). We call for the implementation of a stomatal Hg(0) deposition model that takes tree physiology and environmental conditions like VPD or soil water content into account in order to make projections about this important Hg deposition flux under climate change. The diminution of the vegetation mercury pump in response to drought stress as a result of climate change could result in elevated Hg concentrations in the ocean and potentially in marine fish in future, a potential risk which warrants further quantitative studies. Data availability.
Foliar Hg concentrations, foliar Hg uptake rates, and Hg-related metadata are available for download at https://doi. org/10. 5281/zenodo. 5495179. Please note that coordinates (latitude, longitude) were rounded to minutes. R scripts for data analysis and plots of this paper can be found at https://github. com/wohle/Hg_Forests  (Wohlgemuth, 2022). ICP Forests proprietary data (N concentrations and forest plot attributes) fall under the publication policy of ICP Forests (Annex II of  Seidling et al. , 2017)  and can be accessed from the ICP Forests database (http://icp-forests. net/page/data-requests, ICP Forests, 2022) upon request from the Programme Co-ordinating Center (PCC) in Eberswalde, Germany. Foliar Hg concentration values from the Austrian Bio-Indicator Grid can be obtained from BFW upon request (https://bfw.
ac. at/rz/bfwcms. web?dok=3687, BFW, 2022). ERA5-Land data (Muñoz Sabater, 2019) were downloaded from the Copernicus Climate Change Service (C3S) Climate Data Store (https://cds. climate. copernicus. eu/#!/home, Copernicus, 2022a). Data for the beginning of the growing season of coniferous trees in 2015 and 2017 were provided by members of the PEP725 project (http://www. pep725. eu/, PEP725, 2022). PROBA-V leaf area index values of 300 m resolution and GLEAM transpiration values from 2015 and 2017 were obtained from the VITO Product Distribution Portal (https://land. copernicus. eu/global/products/lai, Copernicus, 2022b) and the GLEAM server (https://www. gleam. eu/, GLEAM, 2022), respectively. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-1335-2022-supplement.
Author contributions. LW managed the project, coordinated foliar Hg measurements, assembled the Hg dataset, performed the data analysis, and wrote the manuscript. PR, BA, AR, LV, PW, VT, NE, MG, PR, AT, MN, AK, MI, PM, SB, DZ, and CI supplied foliage samples and metadata and gave scientific input to the manuscript. AF contributed foliar Hg concentrations from the Austrian Bio-Indicator Grid and gave scientific input to the manuscript. GH and CA provided valuable scientific support. MJ designed and set up the SNSF project (174101), provided valuable scientific support, and contributed to manuscript writing. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. We thank  Fabienne Bracher  and  Judith Kobler Waldis  for assistance in foliage sample analysis. The evaluation was based on data that were collected by partners of the official  UNECE ICP Forests network  (http://icp-forests. net/contributors, last access:  16 February 2022 ). We are grateful to all ICP Forests participants who supported the project through foliage sampling, nutrient analysis, and cooperation in the logistics of this project. In this context, we particularly thank  Martin Maier ,  Andrea Hölscher , and their team from the  Department of Soil and Environment at FVA Baden-Württemberg ;  Daniel Žlindra  from the  Slovenian Forestry Institute ;  Nils König  from  Northwest German Forest Research Institute (NW-FVA );  Hans-Peter Dietrich  and  Stephan Raspe  from the  Bavarian State Institute of Forestry (LWF Bayern) ;  Michael Tatzber  from the  Austrian Research Centre for Forests (BFW) ;  Arne Verstraeten  and  Luc De Geest  from the  Belgian Research Institute for Nature and Forest (INBO) ;  Sébastien Macé  from the  French National Forest Office (ONF) , and  Panagiotis Michopoulos  from the  Forest Research Institute of Athens (FRIA).
We are grateful to  Samantha Wittke  and  Christian Körner  for their helpful advice and support on leaf area indices and plant phenology. Special thanks go to  Till Kirchner  and  Anne-Katrin Prescher  from  Thünen Institute  for their assistance in accessing the ICP Forests Database. Financial support. This research was funded by the  Swiss National Science Foundation (SNSF  (grant no. 174101 )). The participating countries from the  UNECE ICP Forests Network  funded the sampling using national funding; among funding agencies are the  Natural Resources Institute Finland (Luke) ,  Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) ,  Norwegian Institute of Bioeconomy Research (NIBIO) ,  Norwegian Ministry of Agriculture and Food (LMD) ,  Polish Forest Research Institute  (IBL), Pol-.
Acknowledgements The authors wish to thank  Cecilia Bértola ,  Federico De León ,  Marcelo Taibo , and  Ian Tayler  for their help in designing and assessing the experimental items,  Cecilia Bértola  and  Federico de León  for recruiting the participants and administering the experiment, and  Céline Pozniak  for advice on the statistical analysis. Our sincere thanks also to the instructors of the courses in which the participants were recruited. The authors are deeply indebted to three anonymous Glossa reviewers. Their suggestions contributed invaluably to improve our analysis, its results, and their presentation.
Observational evidence of the dipolar dust response Changes in dust transport and emission will shape changes in human environment. Of particular importance will be those changes in air quality impacting the local population of the affected regions. Here we use a range of observational datasets to quantify the impact of SSWs on the air quality of countries in Southern Europe and West Africa. First, we use the daily-mean PM 10 concentrations collected at background stations in Greece and Senegal (see Methods). Overall, the regressed SLP anomaly onto the PM 10 time-series at the two stations suggests that, higher-than-normal PM 10 concentrations in Greece tend to occur under the influence of a cyclonic SLP anomaly (Fig. 4a ), while higher-than-normal PM 10 concentrations in Senegal tend to occur in the presence of an anticyclonic SLP anomaly (Fig.
4g ). We note nonetheless that changes in the expected number of poor air quality days during SSW episodes are robust across a wide range of thresholds (Supplementary Fig. 6 ), and that exposure to air pollutants at any level has observable health impacts  [48] [49] [50]. To sum up, SSWs lead to a significant increase in the level of PM 10 concentrations in Greece and a significant decrease in the level of PM 10 concentrations in Senegal. The oppositely signed air quality changes in response to SSWs (worsened in Greece and improved in Senegal) detected in station observations of PM 10 concentrations show good agreement with the dipolar dust response derived from the modeled datasets (Fig. 1c ; Supplementary Fig. 2a ). Using the ground-based Aerosol Robotic Network (AERONET) direct measurements of aerosol optical depth (AOD) yields responses similar to the above results, which consist of above-normal AOD (increased by 10%) in Greece and below-normal AOD (reduced by 11%) in Senegal (Supplementary Table  2 ).
Discussion Saharan dust intrusions are a recurrent problem threatening public health and economic well-being of a large population in Southern Europe and West Africa. Forecasting such high impact but short duration events poses profound scientific challenges  51. Major progress in the basic understanding and predictive capability can take decades  17. Today, many developing countries across Africa do not have the computing power to forecast air quality  17. In many parts of Europe, most forecasts of air quality only cover 2-5 days. Extending this to longer term forecasts would give communities more time to prepare for risks associated with air pollution. Using a wide range of datasets, our results demonstrate the existence of large-scale stratospheric impacts on North African dust emission and transport on S2S timescales with the consequent impact on air quality, implying the potential for long-term dust and air pollution predictions.
The stratospheric impacts on dust transport and air pollution at the surface are driven by the role of SSWs in establishing a negative NAO-like signal at the surface. This NAO phase is typically associated with a weakened subtropical ridge, which, through increasing southwesterly winds in the Eastern Mediterranean and weakening northeasterly trade winds in West Africa, creates large-scale meteorological conditions favorable for the dipolar dust response. The agreement between models and the consistency with station observations and the meteorological causes lend strong support to the stratospheric impacts on air pollution at the surface. Overall, the changes in air pollution in response to SSWs range between 10 and 30% (with a couple of outliers reaching 60%) compared to the non-SSW mean, depending on the region of concern and on the dataset.
Datasets The meteorological fields examined include daily-mean sea level pressure (SLP), 10 m zonal wind (U10 m), and 10 m meridional wind (V10 m) from MERRA2 (the Modern-Era Retrospective Analysis for Research and Applications, version 2)  55. These meteorological fields have a 1. 25°× 1. 25°latitude-longitude resolution. For air quality related fields, we use daily data from a wide range of sources, including model outputs, station observations, and aerosol reanalysis. Firstly, we examine simulated daily-mean surface dust concentrations from CESM2 (Community Earth System Model, version 2)  56  and MERRA2 (The MERRA2 Aerosol Reanalysis)  57. Note that the dust field from MERRA2 is not directly constrained by the aerosol assimilation included in MERRA2 although the overall optical depth is constrained  57.
7  for the station location and Supplementary Table  3  for the temporal coverage). The PM 10 concentrations are measured at four stations, including three sites in West Africa (M'Bour, Senegal; Cinzana, Mali; Banizoumbou, Niger) from INDAAF (International Network to study Deposition and Atmospheric composition in Africa) and one site at Finokalia (southern Greece) from Finokalia Atmospheric Observatory. The station AOD at the aforementioned four stations comes from AERONET (Aerosol Robotic Network)  60. Unlike the model outputs, the observational data are not recorded everyday due to network and/or power failures, instrument maintenance, etc. In this study, the days with missing values are discarded. This is unlikely to affect the representativity of the measurements given their high recovery rate  47.
Note that the potential impacts of sampling error from short observational records and limited background stations may represent a potential source of uncertainty in observed air quality responses (Supplementary Note 1). Thirdly, the gridded AOD from the MERRA2 aerosol reanalysis 57 is analyzed. Unlike the MERRA2 dust mentioned above, the MERRA2 AOD is directly constrained by the aerosol assimilation and therefore is a blend of observations with model hindcasts  57. To enable evaluation against the station AOD from AERONET, the MERRA2 AOD time-series at the grid point closest to each AERONET station is evaluated. For the gridded datasets, daily anomalies at each grid point are obtained by subtracting the seasonal cycle on that calendar day. The seasonal cycle is defined as the mean and first three Fourier harmonics of the daily climatology to remove the high-frequency sampling variability, following previous studies  [61] [62] [63].
Sudden stratospheric warmings Sudden stratospheric warming events (SSWs) are defined by the reversal of the zonal-mean westerlies at 60°N and 10 hPa  30. The first day on which the daily-mean zonal-mean zonal wind at 60°N and 10 hPa is easterly, is defined as the central date of the warming. Using the dailymean zonal-mean zonal wind at 60°N and 10 hPa from the MERRA2 reanalysis, 24 SSW events are identified in the 40 extended winters (November-March, NDJFM) during 1980/81-2019/20 (Supplementary Table  4 ) where the MERRA2 dust is available. Of these, 22 SSWs occur during the 1980/81-2013/14 extended winters (NDJFM) when the CESM2 dust is available. The definition of SSWs is insensitive to the reanalysis dataset because the central dates for SSWs in each reanalysis are in good agreement for the analyzed time period  64.
The level of PM 10 concentrations during each set of non-SSW episodes is computed in the same way. To obtain the second air quality indicator, we first count the number of poor air quality days during SSW episodes and during each set of non-SSW episodes. However, a direct comparison of the number of poor air quality days between SSW and non-SSW episodes would not be fair if the number of days with available data differs between SSW and non-SSW episodes, given the fact that the observational data are not recorded everyday due to network and/or power failures, instruments maintenance, etc. To account for this uncertainty, the number of poor air quality days is divided by the number of available data days to obtain the rate of poor air quality days. To facilitate understanding, the rate of poor air quality days is multiplied by 30 to obtain the expected number of poor air quality days per 30-day period.
Acknowledgements Y. D. acknowledges support from  Cornell University. Support from the  Swiss National Science Foundation  through projects  PP00P2_170523  and  PP00P2_198896  to  D. I. V. D. is gratefully acknowledged. M. K. and N. M. acknowledge support through the project  PANACEA  ( MIS 5021516 ) funded by the  Operational Programme "Competitiveness, Entrepreneurship and Innovation"  ( NSRF 2014-2020 ) and co-financed by Greece and the  European Union Regional Development Fund. The  French National Observatory Service INDAAF  is supported by the  INSU/CNRS , the  IRD (Institut de Recherche pour le Développement) , and the  Observatoires des Sciences de l'Univers EFLUVE and Observatoire Midi-Pyrénées. We would like to thank the PIs and operators from  France ,  Niger ,  Mali , and Senegal for maintaining the INDAAF stations (https:// indaaf.