dataesr/scientific-paragraphs-categorization huggingface dataset:
MOCAGE model MOCAGE is an off-line global three-dimensional chemistry transport model  (Josse et al. , 2004; Guth et al. , 2016). The operational version of MOCAGE simulates the atmospheric chemistry without data assimilation. It provides the time evolution of the chemical air composition from the surface to the stratosphere (top at 5 hPa). This model is used for operational daily forecasts (e. g. ,  Marecal et al. , 2015)  and also for research studies (e. g. ,  Bousserez et al. , 2007; Anderson et al. , 2017; Morgenstern et al. , 2017; Guth et al. , 2018). The meteorological fields (temperature, wind, specific humidity, pressure, cloud and precipitation) used in this study for MOCAGE come from outputs of ARPEGE. The advection of chemical species in MOCAGE follows the semi-Lagrangian approach of  Williamson and Rasch (1989).
, 2015). Photolysis rates are calculated in MOCAGE via a look-up table computed off-line. This table was updated using TUV (The Tropospheric Ultraviolet and Visible) software version 4. 6  (Madronich, 1987). Also the photolysis of PAN (Peroxyacetic Nitric Anhydride) was added. All these changes provide improvements to ozone in MOCAGE, particularly in the Upper Troposphere and Lower Stratosphere (UTLS) where ozone plays an important role on the radiation. For this study, we carried out a simulation from the MOCAGE model on 60 hybrid vertical levels, from the ground up to 0. 1 hPa and a global domain with a horizontal resolution of 0. 1 • (11 km). Indeed, as mentioned above, the ARPEGE model has its top at 0. 1 hPa while that of the operational MOCAGE model is at 5 hPa. For the purpose of consistency, we used a research version of the MOCAGE model with a top at 0.
1 hPa. The research version of MOCAGE was run from September 2015 to April 2017, with the first 6 months being considered as spin-up. Figure  1  illustrates the differences between the ozone profile provided by RTTOV (red line) and the actual ozone variability from MOCAGE, for two days in summer and winter. The ozone profile of RTTOV is obtained as the average of typical and extreme ozone profiles from the  Chevallier et al. (2006)  database, used to compute RTTOV coefficients. The profiles described in  Chevallier et al. (2006)  have a variability which is consistent with statistic profiles computed with MOCAGE ozone fields. Even though the mean ozone profiles are similar, there are still significant differences. Indeed, the ozone profile from RTTOV appears to overestimate the concentration (by up to 0.
CONCLUSIONS AND PERSPECTIVES The radiative transfer model for the simulation of satellite radiances is one of the major ingredients of current data assimilation systems. At Météo-France, ozone is not Ozone can have a significant impact both on simulations of radiative transfer, particularly in the infrared, and on the modelling of atmospheric dynamics due to its radiative contribution. Thus, the objective of this study was to evaluate the impact of using a realistic O 3 in the observation operator RTTOV instead of the single O 3 profile. As ozone is not analysed or forecasted by ARPEGE, we used realistic ozone fields from the MOCAGE CTM. This work is a first step towards integrating ozone into the 4D-Var data assimilation system for the ARPEGE model. First, the impact of a realistic O 3 in RTTOV was evaluated in the observation operator for the simulation of the four main infrared instruments used for ARPEGE.
Finally, we evaluated our experiments in terms of forecast scores. The results achieved with the O3EXP experiment are surprising since the slight reduction of the bias obtained at the beginning of the data assimilation process translates into significant improvements in terms of standard deviation in forecasts over a large part of the atmosphere and over forecast ranges. The reduction in forecast errors for temperature can reach 2. 5%, for humidity 4. 0% and for wind 3. 5%. We suggest that the use of more realistic ozone in RTTOV allows more accurate simulation, especially of CO 2 -sensitive channels, some of which are also sensitive to ozone. Reducing the bias in the observations provides better quality control which seems to be beneficial in terms of the temperature information brought to the analysis.
Export-based residence times for prior studies were calculated where some combination of particulate and dissolved Fe inventories was available at a given location close to the sampling period of the export measurement (bolded studies, Table  1 ). If total Fe inventories were calculated from individual, published particulate and dissolved inventory datasets, the studies were cited in Table  1. Where sediment traps were used, the Fe inventories have been calculated from surface to the trap depth and the residence times determined accordingly. At a couple of locations (e. g. ,  Bowie et al. , 2015; Lemaitre et al. , 2016) , the traps were located below the inventory measurements (e. g. , mixed layer inventory measured to 123 m and sediment trap deployed at 200 m), and the measured inventories were extrapolated to cover unquantified inventory gap.
Ancillary Data Net primary production (NPP) is from Carbon-based Production Model-2 outputs  (Westberry et al. , 2008)  derived from SeaWiFS (studies 1997 to mid-2002) or MODIS (studies after mid-2002) products. NPP was determined for the 16 days prior to sampling using a weighted average of 8-day NPP products. For trap studies where average Fe fluxes were reported for a multi-day event, the average NPP used was from 16 days prior to sampling to the last sampling day. No satellite products were available for comparison with  Smith et al. (2014)  and the decadal SeaWiFS average for the 16 days prior to the sampling event was used instead  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007). Annual chlorophyll-a concentrations were determined with MODIS-Aqua products (0. 083°r esolution, 2006 to 2016).
The Residence Time of Total Iron in the Upper Ocean Is Sub-Annual We begin with an assessment of the residence time of total iron in the upper ocean. The full range in τ tot from this study is 1 day to 2. 2 years (Figure  2 ) and the few previous estimates of τ tot (n ¼ 3) fall within this range (Figure  3 ;  Buck et al. , 2010; Jickells, 1999). The median is 33 days, and the mean is 86 days. Sixty-seven percent of τ tot from this study fall in a narrow, 10-to 100-day range (bolded lines, Figure  2 ), shorter than most of the prior estimates of τ, which were almost all τ diss (Figure  3 ). The small range of variability in τ tot is surprising considering the diversity of methods, export depths, and locations represented in this dataset (Table  1 ). By consolidating these estimates, we provide ocean models with a range in rate constants for Fe removal (λ tot ), which is defined as 1/τ tot.
This study quadruples the existing dataset, adding new export-based τ tot (stars) and τ diss (right panel). These new τ are from diverse regions and are shorter, on average, than prior upper ocean estimates (center panel). Minimum τ diss and maximum τ diss are determined with respect to removal by biological and abiotic processes (F bio+auth , Equation  5 ) and biological processes alone (F bio , Equation  4 ). Reference numbers are indicated along the upper axis (1-  Bergquist & Boyle, 2006; 2-Boyle et al. , 2005; 3-Bruland et al. , 1994; 4-Buck et al. , 2010; 5-Croot et al. , 2004; 6-Hayes et al. , 2015; 7-Hayes, Anderson, et al. , 2018; 8-Jickells, 1999; 9-Kadko et al. , 2019; 10-Moore et al. , 2004; 11-Sarthou et al. , 2003; 12-Tagliabue et al. , 2016; 13-Ussher et al. , 2013). 10. 1029/2020GB006592.
Global Biogeochemical Cycles BLACK ET AL. Note. Residence times were determined at each individual location using semi-simultaneously measured Fe inventories and export fluxes at depths ranging from 40 to 225 m. Because the zonal residence times are averages of residence times for individual locations, the mean total inventory divided by the mean F tot will not equal the mean residence time. Means are reported ± 1 standard deviation. F POC is the export flux of particulate organic carbon. See Figure  S1  for a summary schematic combining data from Figure  4  and Table  2. Note that τ diss,bio for the sub-Antarctic is likely an overestimate because seven of the 11 F bio were determined using a weak acid leach noted to underestimate F bio  (Planquette et al. , 2011). A generally longer residence time for the (potentially) more reactive fraction of the Fe pool (i.
g. , proteomics) that indicate the response of organisms to their chemical environment, will help define the conditions under which marine ecosystems expend the additional energy needed to retain and recycle Fe  (Rafter et al. , 2017). These conditions are currently poorly constrained in global biogeochemical models. Lastly, we note that the uncertainties in τ diss can be reduced through additional investigation of two assumptions inherent in its derivation. First, the biogenic Fe content of particles used in our calculations is based on measured cell quotas of Fe and P for a relatively small number of samples. Expanding the database of these quotas to better establish their natural range and patterns of variability, and matching estimates of particulate biogenic Fe concentration to cell quotas determined concurrently with the biogenic Fe estimates, will reduce the uncertainty in the derived biogenic Fe fluxes.
The following were used to determine chlorophyll-a, net primary production, and ocean current speeds: Earth Space Research, ESR (2009), OSCAR third degree resolution ocean surface currents. Ver. 1. PO. DAAC, CA,  USA. https://doi. org/10. 5067/OSCAR-03D01. [last access:  2018-06-19. NASA Goddard Space Flight Center, Ocean Biology Processing Group  ( 2018 ), Moderate-resolution imaging spectroradiometer (MODIS) Ocean Color Data,  NASA OB. DAAC, Greenbelt, MD, USA. https://oceandata. sci. gsfc. nasa. gov/MODIS-Aqua/Mapped/Annual/[last access:  2018-06-22]. NOAA (2013 ),  World Ocean Atlas  statistical mean of percent oxygen saturation on 1°grid for all decades. Dataset version WOA2013. https://www. nodc. noaa. gov/cgi-bin/OC5/woa13/woa13oxnu. pl?parameter¼O. [last access:  2018-06-19 ]. Ocean Productivity,  Oregon State University , (2018), Custom products: updated Cup, http://www.
Data Availability Statement All original U. S. GEOTRACES data are available through the Biological and Chemical Oceanography Data Management Office (BCO-DMO) and derived data compilations of new and prior datasets have been made available in the supporting information. The BCO-DMO websites listed below include all the metadata and relevant links. To reach the data page for each, either scroll down to the "Data URL" link on the main page or simply add "/data" to the BCO-DMO http listed below (e. g. , http://lod. bco-dmo. org/id/dataset/3662/ data). The following were used to determine new iron export and residence times for TN303, KN199-04, and KN204-01:.
The calculated changes in gyre boundary position (ΔLat) are given in Table  S2 ; we calculate the uncertainty in ΔLat via bootstrapping  (Efron, 1979) , with 10,000 realizations of the data accounting for the uncertainties in the age (±500 yr, 1σ) and δ 18 O calcite (±0. 04‰, 1σ) of the records, as well as the whole ocean change in δ 18 O water (±0. 1‰, 1σ) and the global mean change in SST (±0. 6 °C, 1σ) with Monte Carlo simulation. The R code to perform the analysis (https://github. com/willyrgray/npac_gyres) and the compiled planktic foraminiferal δ18O data (https://doi. pangaea. de/10. 1594/PANGAEA. 912229) are availabe online.
Acknowledgments We are grateful to  Lloyd Keigwin  for imparting the wisdom "If the signal is big enough, you'll see it in the 18-O of calcite. "We thank  Gavin Simpson  for discussions regarding Generalized Additive Models. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling for the coordination of CMIP and thank the climate modeling groups for producing and making available their model output (https://esgf-node. llnl. gov/search/cmip5/). Insightful and constructive comments from four anonymous reviewers very much improved a previous version of this manuscript. W. R. G, J. W. B. R. , and A. B. were funded by the  Natural Environment Research Council (NERC)  Grant  NE/N011716/1  awarded to J. W. B. R. and A. B. R. C. J. W. was funded by the  Tamaki Foundation ,  NASA  (Grant  NNX17AH56G ) and  NSF  (Grant  AGS-1929775 ).
R. F. I. was funded by a  NERC   Independent Research Fellowship   NE/K008536/1. The planktic foraminiferal δ 18 O compilation used in this study is available on Pangea(https://doi. pangaea. de/10. 1594/PANGAEA. 912229 ), and the R code to perform the analysis will be available online (https://github. com/willyrgray/npac_gyres).
ACKNOWLEDGEMENTS We thank the three anonymous reviewers for their comments and suggestions. We acknowledge  François Catzeflis  ( Institut des Sciences de l'Evolution de Montpellier)  for providing access to the specimens used in this study. We would also like to thank  Anthony Herrel  and  Kévin Le Verger  for facilitating access to the specimens ( Muséum National d'Histoire Naturelle de Paris). We thank  Renaud Lebrun  for his assistance with the µ-CT scanner, as well as the MRI platform. We would additionally like to thank  Mélanie Debiais-Thibaud  ( Institut des Sciences de l'Evolution de Montpellier ) and  Samuel Ginot  ( Institut de Génomique Fonctionnelle de Lyon ) for discussions about staining protocols, and  Marie-Ka Tilak  ( Institut des Sciences de l'Evolution de Montpellier ) for her help in the molecular biology laboratory.
Funding Sérgio Ferreira-Cardoso ,  Lionel Hautier  and  Frédéric Delsuc  were supported by a  European Research Council (ERC)  consolidator grant ( ConvergeAnt  project # 683257 ). Lionel Hautier  and  Frédéric Delsuc  were supported by  Centre National de la Recherche Scientifique (CNRS). This work was supported by '' Investissements d'Avenir' ' grants managed by  Agence Nationale de la Recherche Labex CEBA  ( ANR-10-LABX-25-01 ),  Labex  CEMEB.
WaSP Data Processing The first step in calculating WSE for WaSP surveys using Trimble or Septentrio receivers is to convert native GNSS data to the Receiver Independent Exchange Format (RINEX) file type, a commonly used ASCII file format that can be opened with most text readers. GNSS surveys recorded by Trimble 5700/R7 receivers are stored as. tXX Trimble data files, whereas surveys recorded by Septentrio receivers are stored as. SBF Septentrio Binary Format data files. For Trimble files, we use Trimble's Convert To RINEX-TBC utility version 3. 0. 5. 0, which is freely available via the Trimble website. For Septentrio receivers, we use SBF Converter, which is part of the Septentrio RxTools software package, which is freely available via the septentrio website. When a GNSS survey is recorded as multiple files, we use TEQC software, freely available from UNAVCO, to splice files together (see Supplementary Text 1.
1. 3 for pseudo TEQC syntax). Next, the spliced RINEX file is submitted for postprocessing using PPP software. For the WaSP demonstration in the North Saskatchewan river and three prairie pothole ponds presented here (see section "WaSP and Near-Shore Static Surveys in Prairie Pothole Ponds, Saskatchewan, and North Saskatchewan River"), the Canadian Spatial Reference System-PPP web application is used, which is freely available via the Natural Resources Canada website. 3 Specifically, the ellipsoidal height data contained within the. pos output file, relative to the International Terrestrial Reference Frames (ITRF, as defined by the International GNSS Service at the epoch for the specific orbit ephemerides), are used. For the 63 lake surveys spanning ∼17 • latitude and ∼476 m vertical elevation collected during 2017 NASA ABoVE 3 https://webapp.
1. 4 3 for pseudo syntax) to a period in which WaSP is in the water and relatively stable (orange circles, Figures  4A, B ). After time windowing, prairie pothole pond survey times ranged from 1 to 4 h (see section "WaSP and Near-Shore Static Surveys in Prairie Pothole Ponds, Saskatchewan"), whereas NASA ABoVE survey times varied from 0. 9 to 10. 4 h (see section "Future WaSP Improvements"). Next, we use a Hampel filter with a 600-point window (10 min with 1 Hz logging interval) and 1. 5 sigma threshold to remove outliers from the data series. A Hampel filter defines the median of a moving window and replaces outliers with the median value. Such filtering can be achieved using proprietary software such as MATLAB, or open-source software such as Python. The mean latitude, longitude (Figure  4C ), and height (Figure  4D ) of the windowed and filtered GNSS time series data are calculated as the final vertical and horizontal coordinates.
Measurement uncertainty for a lake/pond (ε l ) survey is then calculated as ε l = σ h + σ t + 0. 01m (2) where σ h is the standard deviation of 1 Hz heights from WaSP PPP solutions, σ t is the standard deviation of antenna offset measurements, and the additional 0. 01 m is intended to account for antenna offset measurement error. Note that the mean horizontal coordinates are representative, not an exact survey location because the WaSP systems move even during anchored lake/pond surveys. For rivers, initial PPP results are converted to a. shp file and overlaid on nearly coincident satellite or airborne imagery in GIS software. The. shp file is visually inspected and locations in the data series when WaSP is not in transect are manually removed (red circles, Figure  5 ). This can be achieved using proprietary software packages such as ESRI's ArcMap, or open-source software such as QGIS.
After filtering, the North Saskatchewan River WaSP surveys presented here ranged from 9. 29 to 9. 40 h in duration, resulting in an average travel speed of 4. 80 km/h. Uncertainty in river WSE measurements (ε r ) is then calculated as follows: ε r = (σ h + r) 2 (3) where σ h is the standard deviation of the estimated vertical positions from the NRCAN PPP output, and r is range in antenna offset measurements. Next, a centerline for the study reach is defined. For North Saskatchewan River results presented here, we use the Global River Widths from Landsat (GRWL) database  (Allen and Pavelsky, 2018)  and remove right angles from the centerline  (Pitcher et al. , 2019 ; Figure  5 ). Channel orthogonals are then established at right angles to the centerline at 25 cm downstream intervals  (Ferreira, 2014 ; Figure  5 ).
AirSWOT Radar Processing AirSWOT is an airborne ka-band radar that produces swathbased maps of WSE  (Altenau et al. , 2017 (Altenau et al. , , 2019;; Denbina et al. , 2019; Pitcher et al. , 2019; Tuozzolo et al. , 2019). To map WSE, AirSWOT uses along-track interferometry, where two SAR images are taken along the flight track to create an interferogram. Using traditional InSAR processing techniques, phase differences shown in the interferogram indicate differences in elevation relative to the wavelength; such phase differences are used to produce an elevation model (e. g. ,  Wu et al. , 2011; Long and Ulaby, 2014)   (Fayne et al. , 2019). Each swath of AirSWOT radar data is delivered as a six-band geotiff with 3. 6 × 3. 6 m pixel size. Band 1 contains interferometrically derived heights, in meters, relative to the WGS84 ellipsoid.
Bands 2 through 6 are the radar incidence angle (radians), magnitude [which can be converted to dB using 20 * log10(mag)], correlation (dimensionless), height sensitivity (meter/radian), and height uncertainty (as "error" in meters). See  Fayne et al. (2020)  for discussion of specific limitations of these data. To aid interpretation of radar data, AirSWOT also integrates a color-infrared (CIR) digital camera. The images collected by the CIR system were processed into 1 m resolution orthomosaics FIGURE  5  | Water Surface Profiler data processing for river surveys requires manually inspecting the data series and removing points when the system is not in the water or in transect (red circles). Next, latitude, longitude, and height positions are assigned a downstream distance by snapping to their nearest-neighbor channel orthogonal (blue line).
The base map is a 1 m resolution digital orthomosaic collected by AirSWOT on August 17, 2017, as part of the NASA Arctic Boreal Vulnerability Experiment. (Kyzivat et al. , 2018) , which were then classified into binary openwater masks  (Kyzivat et al. , 2019b). The open-water classification uses on an object-based unsupervised classification routine, with manual quality control. A threshold was applied to a normalized difference water index  (Mcfeeters, 1996)  in order to identify open-water seed regions, and a custom region-growing procedure was developed to determine a final open-water boundary  (Kyzivat et al. , 2019a ; Figure  5 ). Here, we use the open-water mask created from August 17, 2017, CIR data over the North Saskatchewan River to systematically remove nonopen-water pixels from both July and August radar data.
North Saskatchewan River in situ Water Level Sensor Arrays In addition to the WaSP and motorboat surveys, the Water Survey of Canada, a branch of Environment and Climate Change Canada (ECCC), installed and surveyed in an array of nine temporary pressure transducers (PTs) in the North Saskatchewan River study reach (Figure  7  and Supplementary Table  1 ). These PTs were programmed to measure WSE at 5 minute intervals, and all nine PTs were operational during July 2017 WaSP, motorboat, and AirSWOT surveys. This array is used as an independent validation of WaSP WSE (Figure  10A ) and WSS (Figure  11A ). Unfortunately, data from only one of the nine PTs were recovered for the August 2017 surveys. The PT recovered was located 0. 37 km from the start of the study reach, near Petrofka Bridge (Figure  7 ).
The raw pressure data series from this PT was manually converted to water depth using a barometric pressure logger located near the end of the study reach (close to Wingard Ferry). Notable jumps in the data series suggest that the PT location shifted between installation and recovery. To rectify this, jumps in the data series were manually identified, and a constant offset calculated as the difference between neighboring highquality measurements was applied to subsequent data readings. After manual adjustment, outlier readings from the 5 min data series were removed using a Hampel filter with a 288-point (or 1 day given a 5 min logging interval) window. Finally, the daily average was computed (Supplementary Figure  1  and Supplementary Table  2 ) and is used here to determine the total water level drawdown between July and August 2017 (Figure  10B ).
First, given that the motorboat made several transects orthogonal to flow direction for hydrographic depth and velocity surveys, the WSE data had to be carefully inspected and manually filtered, resulting in data gaps (e. g. , ∼21. 7 km downstream; Figure  8A ). Second, the angle of the antenna on the motorboat would tilt when weight on the boat was redistributed due to people or equipment moving. Third, the motorboat was unable to survey shallow reaches of the river. This was particularly problematic for the August 16, 2017, motorboat survey when the antenna height changed three times to accommodate shallow water conditions. This resulted in three unique vertical offset corrections that were applied to the final data series. Shallow water is not a problem for WaSP because the platform has low draft.