dataesr/scientific-paragraphs-categorization huggingface dataset:
Dissolved organic phosphorus (DOP) concentration distributions in the global surface ocean inform our understanding of marine biogeochemical processes such as nitrogen fixation and primary production. The spatial distribution of DOP concentrations in the surface ocean reflect production by primary producers and consumption as an organic nutrient by phytoplankton including diazotrophs and other microbes, as well as other loss processes such as photolysis. Compared to dissolved organic carbon and nitrogen, however, relatively few marine DOP concentration measurements have been made, largely due to the lack of automated analysis techniques. Here we present a database of marine DOP concentration measurements (DOPv2021) that includes new (n = 730) and previously published (n = 3140) observations made over the last ~30 years (1990-2021), including 1751 observations in the upper 50 m.
2 µM vs. ~0. 06 µM)  6, 9, 21, 22. Despite the importance of DOP for fueling primary productivity and N 2 fixation, the spatial distribution of [DOP] and its variability in the ocean is still poorly resolved due to sparse measurements largely focused in the North Atlantic (Fig. 1 ). In particular, there are few published [DOP] measurements from the South Pacific, Indian Ocean and Southern Ocean. Prior efforts to compile marine [DOP] measurements, specifically, the Global Open Ocean DOP (GOOD) database  8  , were based on measurements made between 1932 and 1994, with 33% of the observations made prior to 1965 before the wet oxidation method (using potassium persulfate)  23  and UV oxidation method  24  were published, and 67% of the observations were made prior to 1980, before the publication of the ash/hydrolysis method  25  , which has been found to recover a higher fraction of some model DOP compounds than wet oxidation  26.
However, the [DOP] measurements in the GOOD database do not specify the method employed to measure [DOP], and many [DOP] measurements (2352 observations) reported in the GOOD database exceed 2 µM in open ocean surface waters, which is inconsistent with our current (i. e. , < 30 years old) understanding of marine DOP (Fig. 2 ). Although the GOOD database has advanced our understanding of marine DOP 20 , the shortcomings described above limit its utility. As DOP observations have accumulated over the last 30 years, an updated, open access marine [DOP] database of analyses made using modern measurement techniques  [23] [24] [25] [26] [27]  with corresponding metadata is required to facilitate marine biogeochemical research. Here, we present a new DOP database (DOPv2021) with [DOP] measurements from more than 42 cruises undertaken over the past 30 years (1990-2021) (Supplementary Table  1 ), which are distinct from the data reported in the GOOD database, where corresponding methodological details are not reported.
Methods The DOPv2021 database includes previously reported [DOP] measurements as well as new measurements from the WebbPacific2007, CoFeMUG (KN192-05; GEOTRACES GAc01), ETSP2010, ETSP2011, Gulf of Alaska 2013 (GOA2013), GO-SHIP P18-2016, GO-SHIP P06-2017, Gulf of Mexico 2019 (GOM2019), SCALE 2019, and GEOTRACES-SWINGS (MD229; GEOTRACES GS02) cruises. Previously reported [DOP] measurements published between 1990 and 2021 are also included in the DOPv2021 database. A criterion for including previously published [DOP] data was analysis by one of the three modern methods (wet oxidation  23, 27  , ash/hydrolysis  25, 26  , and UV oxidation  24  ). The database includes 730 new observations and 3140 observations from published literature. These new and previously reported [DOP] data were merged into one data file in the comma-separated format (i.
e. , a. csv file). Other non-proprietary community-established data formats include netcdf that can be downloaded directly from the BCO-DMO website (https://www. bco-dmo. org/dataset/855139/data). Figure  1  shows where [DOP] samples included in the DOPv2021 database were collected. Supplementary Table  1  summarizes the number of [DOP] observations from each cruise, the cruise year, the analytical method employed to measure [DOP], and the data sources. Any negative [DOP] values are reported as "BDL" for "below detection limit" in the data file. These "BDL" [DOP] values are found in samples from the Southern Ocean portion of the GO-SHIP P18-2016 line (n = 35) and we do not include these "BDL" values in our data analysis. Although the focus of the database is [DOP] measurements, we also gathered salinity, temperature, nitrate + nitrite concentrations, and [SRP] along with the [DOP] measurements when these data were available.
Data Records The DOPv2021 database and associated validation data are archived in the United States National Science Foundation-funded Biological and Chemical Oceanography Data Management Office (BCO-DMO) and can be accessed at both the BCO-DMO website (https://www. bco-dmo. org/dataset/855139) and the Woods Hole Open Access Server (https://doi. org/10. 26008/1912/bco-dmo. 855139. 3)  30. Data associated with the quality of TDP measurements can be accessed under the supplemental file tab on the BCO-DMO web page. The DOPv2021 database includes the following information for each record: The three modern methods employed to measure [DOP] differ mainly in the oxidation approach (Step 2). There are three oxidation approaches used for the samples reported in the DOPv2021 database: wet oxidation, UV oxidation, and ash/hydrolysis.
While we do not know whether the previously reported [DOP] measurements in the DOPv2021 dataset included the analysis of model compounds, prior work has shown that the wet oxidation, ash/hydrolysis, and UV oxidation [TDP] methods recover roughly >90% of model DOP compounds  26, 30  , implying similar levels of DOP recovery in the DOPv2021 dataset, regardless of analytical method employed. In order to quantitatively compare the contribution of DOP contamination from the methodological procedures relative to the DOP in the sample, we prepared ultra-pure ("Milli-Q") water in the same way as the samples and evaluated potential DOP contributions from reagents and sample handing during the sample analysis. The mean (±1 S. D. ) [DOP] of the Milli-Q water blank was 0. 00 ± 0. 03 µM (n = 34), indicating no significant contribution of DOP from reagents and sample analysis and handling, and that the [DOP] reported should only reflect the DOP within a seawater sample, and not from any analytical contamination.
Usage Notes Summary of [DOP] observations. The [DOP] distribution in the DOPv2021 database follows a log-normal distribution (Fig. 4a ), with most observations falling between 0. 10 and 0. 20 µM (n = 1726) and 179 observations with [DOP] >0. 30 µM (maximum = 0. 61 µM) (Fig. 4a ). There are 1746 [DOP] observations in the DOPv2021 database made using the UV oxidation method, 1321 [DOP] observations made using the wet oxidation method, and 768 [DOP] observations made using the ash/hydrolysis method (Fig. 4b ). We note that samples measured using the UV oxidation method are primarily from the Atlantic Ocean, with wet oxidation or ash/ hydrolysis methods mainly used in Pacific and Indian Ocean samples (Fig. 2 ) (Supplementary Table  1 ), potentially adding analytical bias to the global [DOP] distributions.
The DOPv2021 database includes measurements from the surface to 6000 m (Fig. 2 ). Of these, 1751 observations are from the upper 50 m, 3234 observations are from the upper 200 m, and 132 observations are from >1000 m (Fig. 2 ), reflecting interest in DOP's role in upper ocean biogeochemistry. The mean [DOP] in the upper 50 m of the global ocean was 0. 17 ± 0. 08 µM. Upper ocean [DOP] varies more between ocean basins than deep ocean [DOP] (Fig. 2 ). The deep ocean (>1000 m) [DOP] reflects the refractory portion of the DOP pool, with a mean deep ocean [DOP] in the DOPv2021 database of 0. 05 ± 0. 05 µM (n = 132), consistent with the deep ocean [DOP] reported at Station ALOHA  22  and in Letscher & Moore, 2015 4 (Table  2 ). In contrast, the mean deep ocean [DOP] and associated standard deviation in the GOOD database is much higher, 0.
12 ± 0. 2 µM (Table  2 ). As has been discussed previously  22  , [SRP] >2 µM limits the precision of [DOP] estimates in the deep ocean, especially when the [SRP] approaches the [TDP] and the difference between [SRP] and [TDP] is small. We find three outliers among deep ocean [DOP] observations based on Chauvenet's criterion, which is applied to normally distributed datasets and rejects data whose probability of deviation from the mean is <1/(2n) (where "n" is sample size)  31. The mean deep ocean [DOP] does not change if these three outliers are excluded, but the standard deviation without these three outliers decreases to 0. 03 µM. Additional [DOP] measurements from the deep ocean will better constrain the rates and locations of heterotrophic and abiotic DOP sink processes. The DOPv2021 database includes [DOP] measurements from all major ocean basins (Supplementary Table  1 ) (Fig.
1 ). However, these observations are not evenly distributed (Figs. 1,5 and 6). In particular, the Indian and Southern Oceans remain under-sampled (Figs. 5, 6 ). Sparse [DOP] observations from the Southern Ocean hinder our understanding of the spatial and temporal variability of [DOP] in the global surface ocean, as well as of the fertility of the adjacent low-latitude Southern hemisphere gyres. For example, we might expect low [DOP] in recently upwelled deep waters, with higher [DOP] resulting from high rates of new production in the Southern Ocean spring and summer, as has been observed for DOC and DON in regions impacted by upwelling  [32] [33] [34]. Since [DOP] observations in the DOPv2021 database are concentrated in the mid-latitudes (Fig. 6 ), where oligotrophic gyres with low [SRP] result in DOP being a significant assimilative P source 2,3,13 , we cannot evaluate the role of productivity in the Southern Ocean as a source of DOP to low-latitude gyres, although we expect it to be significant  35.
Temporal and spatial distribution of [DOP] observations. [DOP] measurements in the DOPv2021 database include observations from every month, although observations are concentrated in the spring and fall (Fig. 6a ). The small but detectable seasonal changes in [DOP] observed at the BATS site  6  raise the possibility of seasonal bias in the DOPv2021 database, and underscore the need for additional [DOP] measurements from samples collected during the summer and winter. There are several general features of the global ocean [DOP] distributions. First, [DOP] decreases with increasing depth (Figs. 2, 5 ). However, [DOP] in the upper 200 m varies significantly among ocean basins (Figs. 2, 5 ). Additionally, [DOP] gradients occur in the upper 50 m across ocean basins (Fig. 5 ). For example, in the Pacific Ocean, upper 50 m [DOP] is higher on the eastern side of the basin (mean ± 1 S.
D. of 0. 24 ± 0. 08 µM for observations east of 160°W, n = 319), and lower in the west (mean ± 1 S. D. of 0. 17 ± 0. 06 µM for observations west of 160°W, n = 418) (Fig. 5 ). Additionally, upper 50 m [DOP] is relatively high between 20°S and 20°N (mean ± 1 S. D. of 0. 20 ± 0. 08 µM, n = 391) and lower in the center of the oligotrophic gyres between 20° and 40° north or south (mean ± 1 S. D. of 0. 16 ± 0. 08 µM, n = 1097) (Fig. 5 ), reflecting regions of net production and consumption, respectively  16. Database summary. This DOPv2021 database reports global [DOP] observations made using modern methods and includes corresponding metadata such as data sources and methods. This dataset can be used to explore [DOP] distributions at basin and global scales, as well as the relationship between [DOP] and other parameters.
This dataset also functions as a [DOP] field that can be assimilated into ocean biogeochemical models. Researchers should, however, be aware of the potential biases inherent to the dataset, including: 1) seasonal bias with higher coverage in spring and fall; 2) poor coverage of the Southern and Indian Oceans; 3) methodological bias, with samples preferentially analyzed using the UV oxidation method in the Atlantic Ocean; and, 4) limited deep ocean [DOP] data. Finally, new [DOP] measurements can be added to the database by contacting the corresponding author. Updates to the database are reflected in updated version numbers of the database as well as in corresponding metadata recorded at the BCO-DMO site.
EXPOCODE: the code defines a standard nomenclature for cruise labels of a research vessel Cruise: cruise name LATITUDE (°N): latitude N (-90 to 90) LONGITUDE (°E):longitude E (-180 to 180) depth (m): sampling depth date (yyyymmdd):sampling date station ID: station number bottle ID: Niskin bottle identifier or underway sample identifier Temperature (°C): sample temperature Salinity: sample salinity NO3 + NO2 (µM): nitrate + nitrite concentration SRP (µM): soluble reactive phosphorus concentration DOP (µM): dissolved organic phosphorus concentration DOP_flag:quality flag for DOP using WOCE bottle code region: ocean basin sampled method: analytical method employed to determine DOP concentration reference: data source reference technical Validation DoP concentration measurements. Although details vary, the protocol to measure the [DOP] of a sample included in this database can be simplified to four steps: 1.
acknowledgements This research was funded by  NSF -OCE-1829797 (ANK) ,  NSF-OCE-1829916  (RTL),  NSF-OCE-1736652  ( DM via Daniel Sigman ),  NSF-OCE-1851420  ( DM via Daniel Sigman ),  NSF-OCE-0452883  (MAS), the  South African Departments of Science and Innovation and Forestry, Fisheries  and Environment, as well as the  National Research Foundation  through  SANAP-129232  (SEF). We are grateful to  Dennis Hansell  who was the  Chief Scientist of the GoA cruise ,  Eric Webb ,  Chief Scientist  of the WebbPacific2007 cruise,  Kevin Speer  and  Sabine Mecking  who were the Chief Scientists of the GO-SHIP 2017 P06 cruise, as well as  Rolf Sonnerup  and  Brendan Carter  who were the Chief Scientists of the GO-SHIP 2016 P18 cruise, and  Thomas Ryan-Keogh  who was the Chief Scientist of the SCALE 2019 cruise.
We also gratefully acknowledge the GEOTRACES-SWINGS cruise scientists and crew (https://doi. org/10. 17600/18001925 )and SCALE cruise participants, particularly  Raquel Flynn. Additionally , the OUTPACE dataset is managed by  CNRS ( 1)-INSU (2 ). Finally, we gratefully acknowledge  Mary Curci ,  Samantha Howe ,  Dylan Opitz , and  Roberto Gamez , who helped measure DOP concentrations in this study.
We recommend that you refer to the project website: http://forge. ipsl. jussieu. fr/igcmg_doc/wiki/Doc/Config/IPSLCM5A2 for a proper installation and compilation of the environment. In addition, source code of the version used for this paper is publicly available at https://doi. org/10. 5281/zenodo. 6772699(Pillot,  2022 ). The authors acknowledge the use of Generic Mapping Tool (generic-mapping-tools. org, Wessel et al. (2013)), pyferret (ferret. pmel. noaa. gov/Ferret/)and  NCL  (https://www. ncl. ucar. edu)for analysis and graphics. Colored figures in this paper were made with perceptually uniform, color-vision-deficiency-friendly scientific color maps (Crameri et al. , 2020), developed and distributed by  Fabio Crameri  (https://www. fabiocrameri. ch/colourmaps/). All model outputs used in this study are available as a NetCDF files on a Zenodo repository and accessible at https://doi.
org/10. 5281/zenodo. 6641306( Sarr & Laugié ,  2022 ). The repository also contains paleogeography grids used for the simulations. Acknowledgments The authors acknowledge  Olivier Aumont ,  Christian Ethé  and  Laurent Bopp  for their contribution to the development of the adapted version of the PISCES code for deep-time simulations. The authors thank the  CEA/ CCRT  for providing access to the HPC resources of TGCC under the allocation  2019-A0050102212 ,  2020-A0090102212  and  2021-A0110102212  made by  GENCI. The authors thank  TotalEnergies  for funding the project and granting permission to publish. The authors are also grateful to  David de Vleeschouwer  and  Michel Crucifix  for their comments and reviews.
Data Availability Statement Code availability LMDZ, XIOS, NEMO, and ORCHIDEE are released under the terms of the CeCILL license. OASIS-MCT is released under the terms of the Lesser GNU General Public License (LGPL). Up to date IPSL-CM5A2 source code is publicly available through svn, with the following commands line: svn co http:// forge. ipsl. jussieu. fr/igcmg/svn/modipsl/branches/publications/IPSLCM5A2. 1_11192019%20modipsl;%20 cd%20modipsl/util;. /model%20IPSLCM5A2. 1. The mod. def file provides information regarding the different revisions used, namely: • NEMOGCM branch nemo_v3_6_STABLE revision 6665 • XIOS2 branchs/xios-2. 5 revision 1763 • IOIPSL/src svn tags/v2_2_2 • LMDZ5 branches/IPSLCM5A2. 1 rev 3591 • branches/publications/ORCHIDEE_IPSLCM5A2. 1. r5307 rev 6336 • OASIS3-MCT 2. 0_branch (rev 4775 IPSL server).
For simplicity, assuming that samples of the input and output are taken at evenly spaced points in time separated by ∆t, Equation (  2 ) can be expressed in terms of V out at time k, V in at time k and dV out of V out at time k and k-1, respectively: V in (k) -V out (k) = RC• V out (k) -V out (k -1) ∆t (3) Rearranging terms in Equation (  3 ) gives a recurrence relation: V out (k) = V in (k)• ∆t RC+∆t + V out (k -1)• RC RC + ∆t (4) The first term on the right side of Equation (  4 ) is the contribution from the input, and the second term is the inertia from the previous output. This differential model of the LP filter described in Equation (  4 ) provides a way to determine the output data based on the input and the previous output values. Based on Equation (  4 ), the effect of the LP filter for frequency scan in continuous tuning mode is simulated by using a pseudocode algorithm with the help of Matlab.
Funding: This research was funded by the  Talent fund of Hefei University  under Grant, grant number  22040421002 ,  University Natural Science Research Program of Anhui Province , grant number  KJ2021A0979 ,  Key disciplines of Hefei University: Information and Communication Engineering , grant number  2018xk03 ,  French national research agency (ANR)  under the CaPPA, grant number  ANR-10-LABX-005  and the CPER ECRIN contracts.
The three infrared atmospheric sounding interferometers (IASIs), launched in 2006(IASIs), launched in  , 2012(IASIs), launched in  , and 2018, are key instruments to weather forecasting, and most meteorological centres assimilate IASI nadir radiance data into atmospheric models to feed their forecasts. The European Organisation for the Exploitation of Meteorological Satellites (EU-METSAT) recently released a reprocessed homogeneous radiance record for the whole IASI observation period, from which 13 years (2008-2020) of temperature profiles can be obtained. In this work, atmospheric temperatures at different altitudes are retrieved from IASI radiances measured in the carbon dioxide absorption bands (654-800 and 2250-2400 cm -1 ) by selecting the channels that are the most sensitive to the temperature at different altitudes.
We rely on an artificial neural network (ANN) to retrieve atmospheric temperatures from a selected set of IASI radiances. We trained the ANN with IASI radiances as input and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) as output. The retrieved temperatures were validated with ERA5, with in situ radiosonde temperatures from the Analysed RadioSoundings Archive (ARSA) network and with EUMETSAT temperatures retrieved from IASI radiances using a different method. Between 750 and 7 hPa, where IASI is most sensitive to temperature, a good agreement is observed between the three datasets: the dif-ferences between IASI on one hand and ERA5, ARSA, or EUMETSAT on the other hand are usually less than 0. 5 K at these altitudes. At 2 hPa, as the IASI sensitivity decreases, we found differences up to 2 K between IASI and the three validation datasets.
Long-term atmospheric temperature records can be obtained from in situ measurements (lidars and radio soundings). These observations are generally of excellent quality; however, they are sparse and unevenly distributed around the globe. More recently, satellite-derived temperatures have become a key component for climate change monitoring  (Li et al. , 2011; Yang et al. , 2013). Satellite observations have a better spatial coverage, but the construction of a long temperature record from these observations usually requires merging several different instruments, and corrections and adjustments between the observations are needed to obtain a homogeneous dataset  (Zou et al. , 2014; Seidel et al. , 2009). In 2006, the first infrared atmospheric sounding interferometer (IASI) was launched on the Metop satellite.
IASI measures radiance spectra from which surface and atmospheric temperatures  (Hilton et al. , 2012; Safieddine et al. , 2020a)  and trace gas concentrations can be retrieved  (Clerbaux et al. , 2009; Clarisse et al. , 2011). A second instrument and then a third were launched in 2012 and 2018, and the comparison between the three instruments has shown excellent agreement  (Boynard et al. , 2018; EUMETSAT, 2013a). Since IASI is planned to fly for at least 18 years, with the three instruments built at the same time and flying in constellation, continuity and stability are ensured, and the construction of a long-term climate data record at a range of altitudes is becoming possible. IASI radiance spectra and derived atmospheric temperature profiles are routinely processed by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).
Over the past 13 years, EUMET-SAT has performed several updates on the real-time processing of both radiances and temperatures, making the time series non-homogeneous. The impacts of these updates have been evidenced in several studies  (George et al. , 2015; Van Damme et al. , 2017; Parracho et al. , 2021)  and quantified in  Bouillon et al. (2020). For temperatures, the "jumps" in the time series due to these updates make them unfit for the computation of trends. In 2018, EUMETSAT reprocessed the Metop-A radiance dataset (https://doi. org/10. 15770/EUM_SEC_CLM_0014; EUMET-SAT, 2018), providing a new radiance dataset over time. After 2018, the radiances are stable and consistent with those reprocessed  (Bouillon et al. , 2020). In this work, we present a new atmospheric temperature product derived from the homogeneous IASI radiance dataset, using an artificial neural network (ANN) technique, to derive a homogeneous temperature data record.
In Sect. 2, we present the data used in this study, and in Sect. 3, we explain the method used to compute the temperatures, which consists of selecting the appropriate IASI channels and training the ANN. In Sect. 4, we compare the outputs of the neural network with both the latest ECMWF reanalysis (ERA5) and Analysed RadioSoundings Archive (ARSA) radiosonde temperatures to validate the new data. In Sect. 5 we compute atmospheric temperature trends for the past 13 years. Conclusions are listed in Sect. 6.
ERA5 reanalysis The European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5;  Hersbach et al. , 2018; Copernicus Climate Change Services, 2019)  is a 4D-Var data assimilation product. It is part of the Integrated Forecast System (IFS) that provides variables relevant to the atmosphere, land, and ocean  (ECMWF, 2016). The ERA5 atmospheric temperature product used in this work is hourly and is available on 37 pressure levels (from the surface up to 0. 01 hPa). ERA5 actually assimilates IASI radiances from Metop-A and Metop-B as well as high-spectral-resolution radiances from other instruments such as the Atmospheric InfraRed Sounder (AIRS) on Aqua and the cross-track infrared sensor from the Suomi National Polar-orbiting Partnership and the National Oceanic and Atmospheric Administration.