dataesr/scientific-paragraphs-categorization huggingface dataset:
This analysis focuses on ozone observations from 27 remotes sites, selected from over 9000 ozone time series in the TOAR Surface Ozone Database  [Schultz et al. , 2017]. Although few in number, these are the best available sites for understanding long-term ozone trends at remote locations worldwide, and they are well suited for evaluating the ozone trends calculated by the global chemistry-climate models that simulate the evolution of tropospheric ozone on multi-decadal time scales  [Collins et al. , 2017; Myhre et al. , 2017; Young et al. , 2018]. Our analysis goes beyond the scope of TOAR, which only addressed five of the 27 sites selected for this paper  [Gaudel et al. , 2018] , by using the most recently available data, and by using a more refined trend analysis method that is based on monthly anomalies.
Method All ozone observations reported in this paper were made using the standard modern method, based on ozone's ultraviolet (UV) absorption cross-section  [Galbally et al. , 2013] , with the exception of some early observations made in the 1950s-1970s, as described below. A history of the UV-absorption method and its comparison to a wide range of other ozone measurement techniques is provided by the Tropospheric Ozone Assessment Report (TOAR)  [Tarasick and Galbally et al. , 2019]. We report ozone observations from 27 remote surface sites, listed in Table  1 , along with their locations and the original data sources. Many of these sites are part of the World Meteorological Organization's (WMO) Global Atmosphere Table  1 : Ozone monitoring sites used in this analysis, with information on the primary data source prior to processing by the TOAR Database 1.
Site name lat. long. alt. (m a. s. l. ) Years with data Monitoring institution or network 2   Primary data source Minamitorishima, N. Pacific 24. 3° N, 154° E, 8 m (M) 1994-2018 GAW/Japan Meteorological Agency Watch (GAW) program and therefore follow strict data quality control guidelines  [Galbally et al. , 2013; Schultz et al. , 2015] , with station audits and calibration information available from the World Calibration Centre for Surface Ozone, Carbon Monoxide, Methane and Carbon Dioxide  [Klausen et al. , 2003; Buchmann et al. , 2009; WCC-Empa, 2019]. All surface ozone data were uploaded to the TOAR Surface Ozone Database  [Schultz et al. , 2017] , processed by the TOAR Database team and then retrieved from the TOAR Surface Ozone Database via the JOIN web interface: https://join. fz-juelich.
Therefore, we report trends based on monthly mean ozone anomalies, which also reveal the influence of unforced climate variability on interannual ozone fluctuations  [Logan et al. , 2012; Oltmans et al. , 2013; Tarasick et al. , 2016; Lin et al. , 2014; 2015; 2017]. The observed monthly means are calculated by the TOAR Surface Ozone Database from hourly observations (for either daytime or nighttime conditions, as described below). Acceptance of a monthly mean depends on the hourly data availability being greater than 75%; the data availability requirement was relaxed to 50% for Kislovodsk due to intermittent data sampling in the early part of the record. To calculate a monthly anomaly we first calculated monthly mean ozone at a particular site for the entire time series, and then calculated the 16-year mean for all 12 months of the year over the base period 1995-2010.
Data selection The focus of this study is to quantify long-term ozone trends worldwide, at as many remote monitoring sites as possible. We require each site to have at least 20 years of data, beginning no later than 1995 and extending to the present  (2013) (2014) (2015) (2016) (2017) (2018). Sites must be in remote locations far from fresh anthropogenic emissions, which could lead to localized production or destruction of ozone  [Escudero et al. , 2014; Simon et al. , 2015; Strode et al. , 2018]. The TOAR Surface Ozone Database, which holds ozone metrics from more than 9,000 sites worldwide  [Schultz et al. , 2017] , was scanned for sites that meet these criteria, yielding the 27 remote surface sites reported in Table  1. Twenty sites are in the Northern Hemisphere and seven are in the Southern Hemisphere; six sites are in the polar regions and two are in the tropics; thirteen sites are high elevation (>1500 m), of which eight are mountaintop sites; six sites are influenced by the marine boundary layer due to their island or coastal locations (see Table  1  for a listing of the sites in each of these environments).
The diurnal ozone cycle at each site was examined to select the time of day when atmospheric conditions are well-mixed. This criterion typically resulted in daytime (08:00 to 19:59 solar time) data being selected for low elevation sites, to avoid the nighttime hours when ozone is likely to be depleted under the nighttime temperature inversion. Conversely, nighttime (20:00 to 07:59 solar time) data were selected for mountaintop sites to focus on regionally representative ozone, and to avoid local air masses with depleted ozone that are transported from the valleys to the mountaintops under daytime upslope wind conditions  [Price and Pales, 1963; Weiss-Penzias et al. , 2006; Gheusi et al. , 2011; Gallagher et al. , 2012; Cristofanelli et al. , 2013]. The mountaintop sites in this analysis are Mauna Loa, Hawaii  [Oltmans et al.
Remote sites in the Southern Hemisphere American Samoa (24) 0. 2 ± 0. 2 p = 0. 05 1. 5 ± 0. 7 p = 0. 00 2. 2 ± 0. 9 p = 0. 00 El Tololo (N) 0. 2 ± 0. 6 p = 0. 52 0. 2 ± 0. 9 p = 0. 63 Cape Point (  24 ) 1. 6 ± 0. 3 p = 0. 00 1. 2 ± 0. 6 p = 0. 00 0. 9 ± 0. 9 p = 0. 05 Cape Grim (D) 0. 4 ± 0. 2 p = 0. 00 0. 3 ± 0. 3 p = 0. 08 -0. 2 ± 0. 4 p = 0. 27 Ushuaia (D) -0. 9 ± 0. 5 p = 0. 00 -0. 9 ± 0. 5 p = 0. 00 -0. 8 ± 0. 6 p = 0. 00 Table  3 : As in Table  2  but the change in ozone is reported as the total increase or decrease of ozone from the beginning of the period to the end of the period, in units of ppbv or percent, based on the linear regression values in Every surface site selected for this analysis was screened to ensure its remote location, using information provided by the TOAR Surface Ozone Database.
The first step was to identify all rural sites with data spanning 1995-2015, using an objective algorithm developed by TOAR  [Schultz et al. , 2017]. This algorithm takes advantage of several global gridded geo-data sets (human population density, satellite-detected tropospheric column NO 2 , a bottom-up NO x emission inventory, satellite-detected nighttime lights of the world, and satellite-detected land cover), which are cross-referenced with the locations of each site in the TOAR database. Using thresholds of human population, satellite-detected tropospheric column NO 2 , and satellitedetected nighttime lights of the world, the algorithm identified 400 rural sites in the Northern Hemisphere and just 8 in the Southern Hemisphere. Next, the JOIN web interface (https://join. fz-juelich. de) was used to visually inspect the surroundings of each rural site, as revealed by current satellite imagery (provided by Google maps; see  Schultz et al.
Finally, Section 2. 3. 8 described the IAGOS commercial aircraft observations above Western Europe. Focus is placed on the observations at 650 hPa to demonstrate that the IAGOS data reveal the ozone trend in the lower free troposphere above Europe, while ozone observations at the same altitude in the Alps are impacted by the European boundary layer. The longest continuous time series of surface ozone observations in remote locations are from the four baseline observatories maintained by the NOAA Global Monitoring Laboratory (GML)  [Oltmans et al. , 1986]  ). The technique is reliable and compares very well to the UV-absorption method  [Tarasick and Galbally et al. , 2019]. A Regener Automatic instrument also measured ozone at SPO from February 1961 to July 1963  [Oltmans and Komhyr, 1976a, b].
Zugspitze merged ozone records Ozone was measured at the summit of Zugspitze (2960 m) in the Alps of southern Germany from 1978 to 2010 by the late H. -E. Scheel of the Fraunhofer Institute (IFU) and the Karlsruher Institut für Technologie, Garmisch-Partenkirchen, Germany  [Scheel et al. , 1997]. These data have been widely reported in the literature  [Oltmans et al. , 2006; Gilge et al. , 2010; Logan et al. , 2012; Gaudel et al. , 2018]  and are available from the TOAR Surface Ozone Database. A chemiluminescence instrument was used from 1978 to 1996, when a UV-absorption instrument was installed  [Logan et al. , 2012]. The two instruments overlapped for three years and the data from the two methods were found to have similar accuracy, according to WMO/GAW audits  [Herzog et al. , 1996]. In particular, the long-term stability of the sensitivity of the chemiluminescence instrument (Bendix 8002) has been comparable or even better than the stability of the different UV instruments used at the site.
In 2000 the German Federal Environment Agency (UBA) commenced ozone measurements from the Schneefernerhaus station (2656 m), 300 m below the Zugspitze summit  [Gilge et al. , 2010] , however the record in the TOAR Surface Ozone Database begins in 2002. Comparison of the overlapping time period shows that the summit and Schneefernerhaus ozone records are very similar in terms of diurnal, seasonal and interannual variations, with the main difference being that the Schneefernerhaus ozone values are slightly lower  [Zellweger et al. , 2011]. To estimate the ozone trend at Zugspitze from 1978 to the present, the summit and Schneefernerhaus ozone anomaly records must be merged. A complication arises because reference ozone values spanning the 1995-2010 base period can't be calculated for the Schneefernerhaus record, which only begins in 2002.
Therefore, the following procedure was employed. First, monthly mean nighttime ozone at Schneefernerhaus was compared to the corresponding summit values for the period 2002-2010. Due to its lower elevation, ozone at Schneefernerhaus is 1. 3 ppbv less than the summit values, on average. Monthly ozone anomalies were calculated at the summit site using the summit's 1995-2010 base period. The summit ozone base period was then reduced by 1. 3 ppbv (making the two overlapping times series virtually indistinguishable from each other) and used to calculate monthly ozone anomalies at Schneefernerhaus. The two monthly ozone anomaly records could then be merged, using 1978-2001 data from the summit, and 2002-2016 data from Schneefernerhaus.
Cape Point, South Africa Another coastal monitoring site used in this analysis is Cape Point, South Africa. This site is not remote due to its proximity to Cape Town Metropolitan Municipality, 25 km to the north (population 4. 0 million). However, the station's principal investigator uses observed wind direction to filter the data set and omit ozone observations influenced by recent emissions from the nearby urban area, equal to 22% of the total data set  [Brunke et al. , 2004]. The subset of data filtered by wind direction is used for this analysis; no other remote ozone time series in the TOAR database was archived with a full time series and a subset filtered by wind direction. Due to the filtering of this time series by wind direction these ozone observations are representative of the air masses originating to the west of Cape Point.
Cape Grim, Tasmania Australia's Cape Grim Baseline Air Pollution Station is located on a rural coastal bluff in northwestern Tasmania and was established in 1976  [Derek et al. , 2016]. The ozone monitoring program commenced in 1976 and surface ozone data with quality assured measurements have been obtained since 1982  [Galbally et al. , 2000]. The Cape Grim ozone record is the longest mid-latitude record available from the Southern Hemisphere and it is an important data set for monitoring ozone changes in this part of the world. While the air sampled at Cape Grim generally approaches from the Southern Ocean to the west, the observed ozone can often be modified due to influence from the boundary layer of Australia or Tasmania. Molloy and Galbally [2014]  used co-located observations of radon, wind speed, wind direction and condensation nuclei to filter 10 years  (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001)  of Cape Grim ozone data to identify baseline transport conditions from the Southern Ocean (wind and radon data are available from the TOAR database).
They found that approximately 40% of the data set was representative of baseline conditions and that the positive ozone trend was approximately 8% greater in baseline air compared to the full data set for those years. The data set used here is the recently produced 1982-2017 Cape Grim surface ozone on the WMO GAW/BiPM standard scale, available at the World Data Centre for Reactive Gases (https://www. gaw-wdcrg. org/). This record has not yet been selected for baseline conditions.
, 2012] , which demonstrates that the high sampling frequency by IAGOS aircraft above Western Europe can easily produce accurate monthly mean ozone profiles. IAGOS aircraft can take-off and land at any time of day and all data are used in this analysis. No diurnal ozone cycle occurs in the free troposphere above Europe (above the 750 hPa level), although a clear ozone cycle occurs in the boundary layer, and is strongest below 950 hPa  [Petetin et al. , 2016]. For this analysis ozone trends are estimated for several pressure levels from 950 to 250 hPa, which avoids the lowest layers with very strong diurnal cycles. Any data points in the stratosphere were removed from the analysis, as determined from the potential vorticity values associated with each ozone profile, and available from the IAGOS data portal (https://doi.
org/10. 25326/20). Particular attention is given to the ozone trend at 650 hPa because this pressure surface resides in the lower free troposphere, and because it is close to the average pressure of the high elevation ozone monitor at the summit of Jungfraujoch (located at 3580 m above sea level with an annual average pressure of 655 hPa, based on observations by MeteoSwiss, as reported in the TOAR Surface Ozone Database). Following the methods of  Schultz [1995]  and Weiss-Penzias et al. [2006]  this analysis also takes advantage of IAGOS observations of relative humidity, temperature and pressure to calculate vertical profiles of specific humidity. Relative humidity was measured by the MOZAIC Capacitive Hygrometer  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011)   [Helten et al.
[2014]. Comparison of European boundary layer specific humidity values to the values at 650 hPa and at Jungfraujoch (see Section 2. 3. 8) indicates that approximately 18% of the air at Jungfraujoch is from the boundary layer during the daytime or nighttime hours of winter (December-February), increasing to 33% during the night and 45% during the day in the summer months (June-August). This measurement-based analysis demonstrates that the Alpine observations are not fully representative of the lower free-tropospheric air masses observed by the IAGOS aircraft, especially in summer. Figure  7  shows ozone trends above Western Europe  as measured by IAGOS aircraft on 10 pressure levels from the lower troposphere to the upper troposphere. The monthly means of ambient ozone are shown to illustrate the pronounced seasonal cycle of ozone that occurs above Western Europe, with a strong peak in summer at all levels (the data were filtered according to potential vorticity to remove stratospheric observations from the highest levels).
IAGOS is the only time series with a clear positive trend, whereas ozone at Jungfraujoch is relatively unchanged since 1995, and ozone has decreased at Sonnblick, Zugspitze and the three low-elevation sites. These trends are based on data from all months of the year, and as shown next, the difference between IAGOS and the Alpine sites is driven by the summer months. Figure  9  shows the ozone anomaly trends at the same sites as in Figure  8 , but for the warm months of May-August when the European boundary layer is deep, and for the cold months of November-February when the boundary layer is shallow. During the cold months differences among the sites are small. IAGOS and Jungfraujoch show small positive trends, while ozone is flat at Zugspitze and Sonnblick, similar to the low-elevation sites.
However, there is a greater difference between IAGOS and the other    2003 [Tressol et al. , 2008]] ). Further analysis is provided in Supplemental Figure S-6, to determine if the trends of the high and low ozone extremes (5 th and 95 th percentiles) are similar between IAGOS and Jungfraujoch. During the warm months there are no clear similarities between the two sites, but during the cold months positive trends are strongest for the 5 th percentiles (1. 0 -1. 9 ppbv decade -1 ), at both sites. In conclusion, the negative trends at Sonnblick and Zugspitze, based on data from all months (Figure  8 ), are driven by the summertime decreases (Figure  9 ), which are also seen at the low elevation sites in southern Germany. The relatively flat trend at the higher elevation site of Jungfraujoch is in between the positive IAGOS trend and the negative trends at Sonnblick and Zugspitze (Figure  8 ).
Using a global atmospheric chemistry model they determined that these sites are representative of 25% of the Earth's surface area (33% in the Northern Hemisphere and 18% in the Southern Hemisphere). The coverage was limited despite the large number of sites because most sites were clustered in North America, Europe and Japan. Therefore, we conclude that the trends reported by the present analysis are representative of far less than 25% of the Earth's surface. To place the 27 remote sites in the context of the global ozone monitoring network, Figure  10  shows the global distribution of observed surface ozone using all available monitoring sites (approximately 5000) from the TOAR Surface Ozone Database, below 2000 m elevation, and averaged onto a 2° × 2° grid for the period 2005-2014  [Schultz et al.
, 2017]. These data were retrieved from the TOAR archive (https://doi. pangaea. de/10. 1594/ PANGAEA. 876108), which contains many observationbased data products (data through 2014) to facilitate research on regional and global ozone distributions. The ozone metric shown in Figure  10  is the monthly mean of the maximum daily 8-hour average, selected because the majority of these sites (urban, suburban and rural) experience diurnal cycles  [Strode et al. , 2018] , and this metric avoids the nighttime and early morning hours when surface ozone is at a minimum due to surface deposition under stable conditions. The value shown for each grid cell is the annual maximum of the 12 monthly values to reveal the time of year and the ozone values associated with peak photochemical ozone production, which is strongest in low and mid-latitudes (plots of standard seasonal ozone values from the TOAR database are provided by  Schultz et al.
, 2016; Ziemke et al. , 2019]. Focusing on the variable ozone trends at northern mid-latitudes, one might conclude that overall, ozone decreases are more common than increases since 1995 (Figure  5 ). But as discussed above, the number of available sites is too small to construct an area-weighted trend that is representative of the entire latitude band. An obvious shortcoming of this analysis is the lack of long-term rural monitoring sites across South and East Asia where ozone increases are expected due to increasing ozone precursor emissions  [Zhang et al. , 2016; Lin et al. , 2017; Ziemke et al. , 2019]. Relying on the peer-reviewed literature and the TOAR surface ozone database (with data through 2014),  Gaudel et al. [2018]  concluded that surface and lower tropospheric ozone has generally increased across East Asia over the past two decades.
Clearly, the highly variable positive and negative trends at the surface, as shown in Figure  5 , are not indicative of ozone trends in the free troposphere, which are overwhelmingly positive. This comparison is a further demonstration that scattered surface ozone trends cannot be assumed to be representative of ozone trends in other regions and levels of the troposphere. While the TOAR database reveals the observation-based surface ozone distribution from all available monitoring sites (see Figure  10 ,  and Gaudel et al. [2018] ), very large data gaps exist and models must be used for a complete global picture. The surface ozone distribution from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) multi-model ensemble generally captures the same ozone peaks as the TOAR observations but with a high bias  [Young et al.
Data accessibility statement The surface ozone observations reported in this paper, from 1970 to the present, can be downloaded from the TOAR Surface Ozone Database via the JOIN web interface: https://join. fz-juelich. de. The historical ozone observations from MLO (1957MLO ( -1959) )  and SPO (1961SPO ( -1963) )  are available from NOAA's Global Monitoring Laboratory: ftp://aftp. cmdl. noaa. gov/data/ozwv/SurfaceOzone/Historical/. IAGOS aircraft observations are available from: https://doi. org/10. 25326/20. In addition, the monthly surface ozone values upon which the trends were calculated can be easily downloaded as ASCII text files from the following URL: http://doi. org/10. 34730/e792cad833174ebcafd9f052711e5660.
By leveraging Tara Oceans data from all major oceanic provinces, including the Arctic Ocean, we inferred a comprehensive global-ocean cross-domain plankton cooccurrence network from sequencing data. We built statistical niche models to predict realized niche widths of planktonic taxa, across kingdoms, and from pole-to-pole. These were then mapped onto the network and used to evaluate both local and global robustness of plankton community structures to simulated environmental changes. Noticeable efforts have used the ecological niche concept to identify open ocean physical conditions governing phytoplankton biogeography  (34) , and also to better formalize central biogeochemical processes through the definition of key plankton functional types  (35). The niche representation of planktonic diversity affords a more effective integration of abiotic and biotic constraints to better predict perturbations of primary productivity under climate change scenarios  (26, 36).
Today, highthroughput co-culture experiments using microfluidics  (79)  and fabricated synthetic microbial ecosystems may help fill this gap  (80). Our resource constitutes useful guidance for co-culture experiments, as well as a database for hypothesis testing. Climate scenarios predict global changes in temperature, pH, and nutrient concentrations, which all greatly influence plankton physiology. Temperature can directly impact bacterial growth  (81) , grazing rates  (82) , and phytoplankton metabolism  (83). Nitrogen availability is a primary limiting factor for marine phytoplankton  (84). Ocean acidification caused by rising atmospheric CO2 can impact phytoplankton growth rates, and is predicted to have a greater impact than warming or reduced nutrient supply on plankton ecological functions  (85).
Data description From 2009 to 2013, the Tara Oceans expedition collected samples at more than 200 stations across all significant oceanic provinces from oligotrophic to polar regions. Sampling stations were selected to represent distinct marine ecosystems at global scale  (90)  describes the sampling strategy and the methodology applied, and sample provenance is described in Table  S1. Environmental data measured or inferred at the depth of sampling are available in Table  S2  and published at PANGAEA, Data Publisher for Earth and Environmental Science (http://www. pangaea. de). In this study, we limited our analyses to the euphotic zone, including only the samples from surface (SRF) and the Deep Chlorophyll Maximum (DCM). Two prokaryotesenriched size fractions (0. 2-1. 6 µm and 0. 2-3 µm) were available and included in the analyses.