dataesr/scientific-paragraphs-categorization huggingface dataset:
It can dissociate to release NO2 allowing transport of reactive NOx away from the source regions  (Fischer et al. , 2014). The length of the NOx 115 cycle depends on both the chemical mechanism and the rate parameters employed, determining the regional O3 production efficiency. Therefore, to fully understand differences in O3 production efficiency between the various chemical schemes requires analysis of the major NOy components. In this study we present an evaluation of key trace gas products (tropospheric O3, NO2, CO) simulated by the chemistry modules implemented in the CAMS system for the contiguous United States. This evaluation is 120 performed for the years 2014-2015, spanning an entire summer and winter period. We also include the corresponding products from the CAMS Reanalysis dataset  (Inness et al.
Model Description 130 In this section we provide a brief description of the various configurations of the CAMS system for global atmospheric chemistry modelling. Here we focus on the upgrades which have been made to the three chemistry modules (CB05BASCOE, MOCAGE, MOZART) available in the IFS as compared to the extensive description of each of the modules as provided in  Huijnen et al. (2019). Hereafter we refer to these model configurations as 135 IFS(CBA), IFS(MOC) and IFS(MOZ), respectively. A brief overview of the contents and parameterized differences for each of the various chemical modules is provided in Table  1 , below. For details pertaining to the CAMSRA reanalysis dataset the reader is referred to Innes et al. (2019). There is significant variability in the number of thermal (photolytic) reactions across schemes, with IFS(MOC) and IFS(MOZ) being the most explicit (condensed).
The reaction rates have been updated to follow the recommendations from IUPAC (http://iupac. pole-ether. fr; last access: 21 Sept 2021) or  JPL-2015 (Burkholder et al. , 2015). Updates have also been introduced to the photolysis 275 look-up table, which includes explicit calculations of photolysis frequencies for most of the IFS(MOZART) species. The heterogeneous chemistry in the troposphere is implemented according to the corresponding module from IFS(CBA) to account for heterogeneous uptake of N2O5, HO2 and NO3 on aerosols as described in the previous section. However, the heterogeneous uptake on ice and cloud droplets is currently not included in IFS(MOZ).
The experiments are archived with experiment identifiers b28w for IFS(CBA), b0ov for IFS(MOC) and b0yw for IFS(MOZ). For comparison, the CAMS Reanalysis (CAMSRA), which includes data assimilation of O3, CO, NO2 and Aerosol Optical Depth (AOD), uses IFS cycle 42r1. CAMSRA was run at the same horizontal resolution (TL255).
295 as the experiments presented here, but with only 60 model levels, a model top of 0. 1hPa  (Inness et al. , 2019). Different emission estimates were also employed for the CAMSRA dataset meaning that interpretation of differences need to be done with care. The emissions adopted in this configuration are taken from CAMS_GLOB_ANT v4. 2-R1. 1  (Granier et al, 2019 ; https://eccad. aeris-data. fr/), which is a modification of the v4. 2 global anthropogenic emission dataset used 300 operationally in CAMS, with reduced emission strength over China. Biogenic emissions are taken from the CAMS_GLOB_BIO v1. 1 dataset  (Doubalova et al. , 2018 ; http://eccad. aeris-data. fr/), and biomass burning emissions are taken from GFAS v1. 2  (Kaiser et al. , 2012)  which are applied using the methodology as described in  Ye et al.
Additionally, a filtering procedure was applied to the data before computing the daily mean values to remove stations where the time series displayed more than 50% of identical values, denoting a failure in the measurement sensor. Moreover, for CO the observational values < 50 μg m -3 and >1200 μg m -3 were filtered out at the rural 400 stations to avoid spurious instrumental effects. Model 3-hourly outputs were spatially interpolated from the model grid to the stations network. Time-series of daily mean composites were then obtained by computing the daily mean values across stations with identical station classification for each domain, using both the observed and simulated data during 2014, providing a spatial mean of the concentrations. Details on the accuracy of the instrumentation used across the network can be found in  Williams et al.
405 Table  9 : Details of the various regions defined for assessing simulated surface concentrations for O3, CO and NO2 using data from the AirNow measurement network. The total number of individual stations for each respective species is given in the right most column. locations away from strong NOx emission sources. This dataset is therefore a measure of the chemical processing which occurs in transported air-masses at regional scale. The inferred mixing ratios result from the application of the Multilayer Model  (Finkelstein et al. , 2000)  to these samples, which uses site specific meteorological parameters to determine deposition values. We only use the near-surface mixing ratios here, as assessing the wet deposition component of the chemical modules is beyond the scope of this manuscript and more associated with 420 the aerosol modules applied in IFS.
565 Organic Compounds (VOC), combined with differences in the chemical lifetimes as a result of OH variability  (Huijnen et al. , 2019). For IFS(MOZ) we diagnose a comparatively low tropospheric CO burden associated with a fast oxidation rate due to higher mixing ratios of OH in the LT of between 20-50% (c. f. Fig  S1 ). This is directly associated with the higher O3 (c. f. 595 More extended regional composites for surface CO have been assembled using data available from the AirNow measurement network, with three regional comparisons of the daily variability in surface CO at rural locations being shown in Figure  9. The number of stations used for the comparison is lower than that used for the other species as limited by data availability (c. f. Table  10)   S2 ). Comparing IFS(CBA) against CAMSRA shows there is only a modest difference in CO, with slight increases in the associated negative biases.
Biases presented in Table  S2  shows that, assuming comparable data quality, the influence of local effects related to positioning and selection of stations can result in more extreme biases. For the aircraft composites surface effects are not so important where mixing in the boundary layer provides a more homogenized value. For wintertime and 625 springtime the higher observed mean values of 130-175 ppb are not captured by the mini-ensemble with underestimates of 10-40 ppb depending on the month and region suggesting too low primary emissions over a wide area, with CAMSRA exhibiting the lowest biases. One dominant precursor for the chemical production of CO is the oxidation of formaldehyde (CH2O) e. g. Zeng et al. (2015). The corresponding comparisons of CH2O for all chemical modules are shown in the Supplementary.
630 Material in Figure  S4 , with associated biases in Table  S3. In the LT during boreal summertime conditions, C5H8 acts as a dominant source of CH2O resulting in high mixing ratios over Colorado of 1. 7-2. 5 ppb. Generally, all chemical modules exhibit negative biases, except for the CAMSRA dataset, associated with the higher C5H8 emissions which are applied (MEGAN-MACC;  (Sindelarova et al. , 2014) ). There is a higher variability associated with the simulations than that observed showing the sensitivity towards both photolysis and dissolution into cloud 635 droplets, which introduces complications in short-term (daily) modelling of CH2O. Note that the higher CH2O in CAMSRA is not directly correlated with higher CO, due to effective CO data assimilation being applied. During wintertime biogenic fluxes are low due to the seasonality in biogenic activity, thus CO comparisons shown for February and March can be considered to be representative of the background supplemented with regional anthropogenic emission sources, considering the tropospheric lifetime of 1-2 months (e.
Conclusions In this study we have presented a detailed description of the recent updates which have been made to the chemistry modules that are integrated in ECMWFs IFS global model for the purpose of performing global Air Quality 890 forecasts. We have evaluated a set of three simulations covering the years 2014/2015, using the latest model configuration as developed in an experimental version of the ECMWF IFS cycle 47R1. This provides insight in the performance of the modeling of trace gases (here excluding data assimilation) in the CAMS global system. This study has focused on lower tropospheric composition for the contiguous United States with an emphasis on tropospheric O3, NO2 and CO. We also included comparisons against the most recent reanalysis dataset that is based on a previous CAMS configuration (CAMSRA).
This allows assessment of model changes compared to this established dataset. By comparing seasonal means in the lower troposphere between the various chemistry modules we have shown a strong seasonality in the regional inter-model differences for O3, CO and NO2 in the US. For O3 these differences are limited to ±5% during boreal summertime, during which higher mixing ratios occur. The ability to capture the 900 regional seasonality in surface concentrations for the background is somewhat region dependent, with relatively good agreement for the West Coast and an overestimation towards the East Coast. Comparing seasonal composites against ozonesondes shows that there is generally good agreement in more remote locations and high positive biases of 10-30 ppb for more polluted regions, especially at the surface near the US East Coast.
Comparisons for more southerly regions show lower mean daily biases in Texas and California-Nevada with limited correlation in 905 the daily variability. For the Colorado region, there are biases of ±6 ppb across chemistry modules (±10-15%). At the surface there are small negative biases of around 5 μgm -3 for IFS(CB05BASCOE) and 15 μgm -3 for CAMSRA. For boreal wintertime a significant variability in the O3 production efficiency occurs across chemistry modules resulting in IFS(MOCAGE) and IFS(MOZART) exhibiting increases in mixing ratios of +6-15% and +20-25% across a wide region as compared to IFS(CB05BASCOE), especially in the Northern US. A significant positive 910 bias in surface concentrations occurs for 2014 in the Northern US indicating too efficient O3 production, whereas CAMSRA exhibits a significant negative bias.
Other regions show less difference across the simulations. Associated differences occur for the OH radical for both seasons, which leads to significant differences in the tropospheric distribution of CO of between 8-20%, especially during wintertime. In general, the seasonal cycle at the surface is captured well when compared to both ESRL background observations and surface AirNow, with between 5-40% depending on the region, which contributes to the negative CO biases. Biases of CO for all chemical modules are typically larger than the CAMSRA dataset which is strongly constrained by assimilation of CO observations from satellite retrievals. As was the case for O3, also NOx shows a seasonal variation in the simulated inter-model differences within the 925 order of 5-10% for NO2 and up to 50% for NO.
Comparing profiles for both trace gases against aircraft measurements shows significant negative biases exist for both NO and NO2 for the NOx-rich environment of Colorado across all chemical modules indicating regional emissions which are too low. The performance of the three chemistry versions is overall better than for CAMSRA, which can be understood by the fact of the limited impact of NO2 data assimilation in the CAMS reanalysis. Comparisons against AirNow surface observations 930 shows that the regional annual cycles are captured well across the simulations with negative biases and showing only a weakly correlated daily variability. Examining NO/NO2 ratios shows that the equilibrium between NO and NO2 is mostly captured well by IFS(CB05BASCOE) in the boundary layer, with the other chemical modules overestimating the fraction of NO (albeit with lower NOx mixing ratios).
940 There is generally an overestimation in HNO3, both at surface and in the free troposphere, which may be due to too efficient N2O5 hydrolysis on wet surfaces under some conditions. Model analysis suggests that this conversion on cloud surfaces is not a dominating term with respect to associated N2O5 comparisons for the East Coast during the wintertime period. One dominating factor on the seasonal distributions of NO2 is the fraction stored as PAN and transported out the 945 source regions. For boreal summertime, IFS(MOZART) simulates 20-50% less resident PAN than IFS(CB05BASCOE), which contributes to the more efficient O3 formation in IFS(MOZART). When comparing against aircraft profiles around Colorado for July and August, there is generally an underestimation in resident PAN of 40-60% across chemical modules, suggesting a lack of Volatile Organic Compounds precursors and subsequent acetyl-peroxy radicals, in line with previous studies  (Huijnen et al.
, 2019). For boreal wintertime, when 950 there is an extended tropospheric lifetime under cold temperatures, significant positive biases in regional PAN were diagnosed as compared against aircraft profiles for IFS(MOCAGE), pointing at differences in model assumptions regarding the stability of PAN, as determined by the rate data employed. This is also reflected by the PAN/NOx ratios which show a strong overestimate in IFS(MOCAGE) and requires future developments. As presented in this manuscript, a significant divergence of key air quality products simulated by each of the 955 chemistry modules exists, depending on seasonal and regional conditions. These are due to fundamental differences associated with the oxidative capacity and the regional efficiency for the production of tropospheric O3, which are in turn determined by the chemical mechanism, the parametrizations adopted and the rate data used.
of the chemical loss of NOx(Jaegle et al. , 2018), with reaction of NO2 with OH accounting for another third (thus the two most dominant chemical processes). In-situ observations of N2O5 are rare, where daytime mixing ratios are typically of the order of tens of ppt due to the efficient loss by photolysis, and, therefore, subject to high measurement uncertainty. During nighttime, surface observations range from 50-3000 ppt, with high daily variability (e. g. Wood et al. , 2005; Brown et al. , 2009). The WINTER campaign includes observations taken during 760 night, where accumulations of N2O5 occur, allowing model evaluations to be made. Evaluations against the nighttime N2O5 measurements from the WINTER campaign are shown in Figure15, along with the corresponding profiles of HNO3. Unfortunately, no model data was available for N2O5 in the CAMSRA dataset for comparison.
Acknowledgements We acknowledge funding from the  Copernicus Atmosphere Monitoring Service (CAMS)  under contract  CAMS_42_II , which is funded by the  European Union 's  Copernicus Programme. We acknowledge the  World Ozone and Ultraviolet Radiation Data Centre (WOUDC)  for providing ozone-sonde observations and the Earth 990  System Research Laboratories network  for providing the surface measurements of carbon monoxide and ozone. We acknowledge the  AirNow monitoring network  for allowing access to surface observational data for carbon monoxide, nitrogen dioxide and ozone. Finally, we acknowledge NOAA for access to the multiple aircraft campaigns used in this manuscript. https://doi. org/10. 5194/gmd-2021-318Preprint. Discussion started: 1 October 2021 c Author(s)  2021. CC BY 4. 0 License.
755 of the chemical loss of NOx(Jaegle et al. , 2018), with reaction of NO2 with OH accounting for another third (thus the two most dominant chemical processes). In-situ observations of N2O5 are rare, where daytime mixing ratios are typically of the order of tens of ppt due to the efficient loss by photolysis, and, therefore, subject to high measurement uncertainty. During nighttime, surface observations range from 50-3000 ppt, with high daily variability (e. g. Wood et al. , 2005; Brown et al. , 2009). The WINTER campaign includes observations taken during 760 night, where accumulations of N2O5 occur, allowing model evaluations to be made. Evaluations against the nighttime N2O5 measurements from the WINTER campaign are shown in Figure15, along with the corresponding profiles of HNO3. Unfortunately, no model data was available for N2O5 in the CAMSRA dataset for comparison.
Parasitological data Prevalence estimates presented here focus on data from cattle (live Bos taurus, Bos indicus and cross breeds) coprological surveys conducted in 2017 in the Senegal River Basin area of northern Senegal, West Africa, as part of the Zoonoses and Emerging Livestock System (ZELS) program  (16, 25). In this human schistosomiasis endemic area, Richard Toll and Lac de Guiers (estimated urogenital schistosomiasis prevalence in children and adults was 87% and 79% in 2016, and 88% and 41% in 2017-18, respectively (  16 )), henceforth referred to as RT, schistosome transmission is perennial due to the year-round persistence of freshwater habitats for intermediate snail hosts (i. e. , the Senegal River and the Lac de Guiers lake)  (16). Faecal samples from 200 sympatric live-sampled bovines in eight RT site locations (Didjiery; Mbane; Medina Cheikhou; Ndombo; Sanente; Temeye; Thiago and Yetti Yone) were collected following randomized protocols and processed using two schistosomiasis tests: Kato-Katz (KK) for presence of Schistosoma spp.
Acknowledgments We are thankful to the Senegalese communities, local facilitators and field teams involved in the  ZELS  project, particularly the late  Cheikh Thiam , his family, and  Alassane Ndiaye. Without their assistance in the collection and analysis of the samples, the data on which this work is based upon, would not have been created. We also thank  Dr. Benjamin Collyer  for discussions on modelling the overdispersion dynamics.
Box and Jenkins methodology Time series data is a collection of measurements recorded sequentially and equally spaced across a discrete time interval. The fundamental assumption in time series modeling is that past characteristics will continue to occur in the future  (Raman et al. 2018). The autoregressive integrated moving average (ARIMA) model developed by  Box and Jenkins (1976)  is the most widely used approach for time series analysis. The box and Jenkins approach rely on a mathematical model developed to characterize the time series data based on autocorrelation analysis  (Chen et al. 2015). Following the creation of the model, it is utilized to anticipate future values using historical and current time series data  (Chen et al. 2015). A major characteristic of time series models is the assumption of some form of statistical equilibrium  (Hyndman and Athanasopoulos 2013).
To do so, the ACF and quantile-to-quantile (Q-Q) figures of residuals are plotted. In addition, the forecast accuracy of the model is evaluated by splitting the data into two sets. The last observations in our data set are used to compare between simulated values and actual ones. Finally, the chosen SARIMA model is applied to predict the monthly Chl-a future values. The long time series, which covers approximately three decades of monthly Chl-a historical data, enabled us to perform multiple time steps ahead  (Wei 1990)  from May 2018 to December 2025. The modeling of the SARIMA model was performed using Econometrics toolbox in the MATLAB software MATLAB® software  (version 7,860,349 (R2020b) , The Mathworks, MA, USA).
, 2007)  and resampled to half-hourly time series. Based on all-sided needle area measurements the specific leaf area was 0. 010 m 2 g -1. Resulting data coverage for fluxes of CO 2 and water were 98% and 63%, respectively, for mid-April to mid-October in 2018-2019. Sampling and isotopic analysis of water Samples of 1-yr-old needle (1N) water, twig water, soil water and atmospheric water vapor were collected every 3-5 wk during May to October in 2018 and 2019, except for 1N water sampled more frequently in 2019 (14 times + diurnal course on 23 May). Furthermore, 1N and twig samples were collected from five mature Scots pine trees between midday and 15:00 h (UTC + 2) from a sun-exposed position in canopy top. For each tree, 1N samples and the linked twigs with barks peeled off were collected into separate 12 ml exetainer vials (Labco, Lampeter, UK).
Based on CSIA, needle sugars comprised mainly of sucrose, while the total contribution of glucose and fructose was on average 25%. The concentration-weighted average δ 13 C of the sugars was highly correlated with δ 13 C of sucrose (r > 0. 95; Fig. S1 ), and hence δ 13 C of sucrose is used to indicate the δ 13 C of the total needle sugars hereafter. The δ 18 O and δ 13 C of bulk WSC, and δ 13 C of sucrose and pinitol were each combined over the two needle generations (0N and 1N) to form time series covering the whole growing season (Fig. S2 ). The δ 18 O of bulk WSC and δ 13 C of sucrose were similar for 0N and 1N and the combined dataset was obtained as their average (Fig. S2a, c ). Bulk WSC in 2018 and pinitol showed a difference in level of δ 13 C between 0N and 1N, which was first corrected for before averaging over the two data series (Fig.
Acknowledgements The authors thank  Juho Aalto ,  Bartosz Adamczyk ,  Teuvo Hietajärvi ,  Esko Karvinen ,  Petri Kilpeläinen ,  Ari Kinnunen ,  Salla Kuittinen ,  Tarmo Kylli ,  Jukka Kärki ,  Janne Levula ,  Haoran Li ,  Marine Manche ,  Aino Ovaska ,  Aino Seppänen  and  Fana Teferra  for help in fieldwork and sample preparation;  Manuela Oettli  for HPLC-IRMS δ 13 C analysis;  Kei Yoshimura  for sharing the IsoGSM data; Sylvia Englund Michel for the data of δ 13 C of atmospheric CO 2. The authors are grateful to  Nerea Ubierna  and three anonymous reviewers for constructive comments. The work was supported by the  Academy of Finland  (nos. 332141 ,  295319 ) and the  European Research Council  (no. 755865 ). PS-A acknowledges funding from the  Knut and Alice Wallenberg Foundation  (no. 2015.
, 2009; Suryan and Fischer, 2010)  as well as the west coast of Canada  (Žydelis et al. , 2011) , whereas post-breeding Laysan albatross are distributed in pelagic regions of the central and western Pacific  (Gutowsky et al. , 2014a; Henry et al. , 2021). Analyses of environmental characteristics during the post-breeding period have been limited to a few years of observations, often from a single population (but see  Henry et al. , 2021) , and it is unclear the extent to which previous observations can be generalized. The degree to which post-breeding habitat is shared or segregated between species and between colonies of the same species is similarly unclear. Here, we assess whether resource partitioning between Laysan and black-footed albatrosses persists during postbreeding. We use data from global location sensing (GLS) loggers deployed on albatross at two breeding colonies across 5 consecutive years to address the following questions: First, do known differences in the spatial distributions of Laysan and black-footed albatrosses persist into and throughout the postbreeding period? Second, do post-breeding albatrosses of the same species but from different colonies share similar at-sea distributions? Third, if post-breeding segregation occurs, what environmental variables characterize each species' habitats? Lastly, do Laysan and black-footed albatrosses show shifts in spatial distribution and changes in the environmental characteristics of their habitats over the post-breeding period? Addressing these questions will improve our understanding of habitat use and resource partitioning in North Pacific albatross, and may identify potential competitive interactions between species and colonies outside of breeding.