dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012; Yeager et al. , 2012; Carrassi et al. , 2016). The value and technical feasibility of a Deep Argo Program were recognized at OceanObs'09  (Roemmich et al. , 2010). Successful deployments of prototype deep floats and CTDs in 2012-2014 led to a Deep Argo Implementation Workshop in May 2015  (Zilberman and Maze, 2015) , where a plan for Deep Argo's progression to a global 5 • × 5 • array was endorsed. Regional pilot arrays have been established in the South Pacific, South Indian, and North Atlantic oceans, further demonstrating the feasibility of a global Deep Argo array.
Argo Data Management The Argo data system was designed in 2001 at the 1 st Argo Data Management meeting in Brest, France and its main components remain in place and function well (Figure  2 ). The national Data Assembly Centres (DACs) receive data via satellite transmission, decode it, and apply quality control according to a set of agreed NRT tests. Erroneous data are corrected if possible, flagged accordingly and then sent to two Global Data Assembly Centres (GDACs) and the GTS. The GDACs collect the data from the 11 DACs, synchronize their databases daily and serve the data on FTP sites. The AIC monitors the status of the Argo Program, including data distribution, and meta data that incorporate float location, model, transmission system, owner, etc. In addition, the AIC gathers feedback on data quality from users and relays it to float owners and DACs.
Argo leadership has a strong corporate memory, which ensures the stability of the program. Argo regularly audits the performance of the array so that systematic problems can be identified before they seriously degrade the data. Argo data acquisition is distributed across 25 national programs (Figure  5 ). The procedures for delivery of data in NRT and DM (see section "Argo Data Management" under the section "The Present Status, Problems, and Strengths") are comprehensively described in public documents. This ensures a global dataset of consistent quality and uniform format ready for public use. Reliability of data does not depend on the originating national program or investigator. Since 2013, Argo has undertaken a major revision of its data format in order to incorporate data from biogeochemical sensors and other auxiliary measurements.
This issue highlights the need to exercise care in the use of NRT data, and the requirements for reference data from GO-SHIP and other programs, to ensure the long-term consistency of the Argo dataset. While there have been many different designs of the hull and buoyancy engines on which sensors are deployed, Core Argo has depended almost exclusively on the SBE41 and SBE41CP CTDs by SeaBird Electronics (SBE). This has led to good consistency of data, and implementation of uniform procedures for data handling across the national programs, but exposes the program to the risk of single points of failure. Twice during Argo there has been a major problem with the supply of pressure sensors to SBE for use in float CTDs, resulting in lost deployment opportunities and impacting Argo coverage. To reduce this risk, the Steering Team welcomes initiatives to establish alternative sensor payloads.
BGC-Argo is the first program with the capability to monitor biogeochemical processes globally, under seasonal seaice, throughout the year, and across all weather conditions. This allows BGC-Argo to access processes and timescales that cannot be observed from oceanographic cruises. It will revolutionize understanding of ocean biogeochemistry and marine resources, including fisheries, providing the foundation for informed ocean management. This strength results in particular from the costeffectiveness of such robotic measurements, which lower the cost of a vertical profile by more than an order of magnitude compared to classical techniques of data acquisition. The consistency of the data collected and processed in this manner enable global assessments based on the entire data set. In some cases, analyses of these consistent datasets  (Poteau et al.
Finally, to accommodate users whose research requires all parameters in one file, M-files are generated by the GDACs and contain both Core Argo and BGC profiles. The transition into version 3 formats also allowed the development of GDAC File Checkers which check not only the format, but also the content of some variables. This has greatly increased the consistency and usability of Argo data. With all these changes, the GDACs today host over 2 million files comprising over 245 GB. Argo DMQC of Core Argo variables occurs about 12 months after observation. The  Owens and Wong (2009)  method is the Argo standard, with local modifications depending on profile location. An ongoing challenge is ensuring that Argo DMQC is performed consistently across the program, with the best possible quality reference database for comparison.
To this end, Argo is beginning to study the Argo DM dataset in a more robust manner. The North Atlantic ARC has analyzed a variety of floats from different DMQC operators in the North Atlantic to see if DMQC software is being applied properly and consistently  (Cabanes et al. , 2016). DMQC workshops continue to be held as needed to study the current status of DMQC data and to train new DMQC operators. To help with this, Argo is beginning to share source code more formally through Github, and with a Matlab version of the OW method that is publically available via https://github. com/ArgoDMQC/matlabow/. The AIC has upgraded to an interactive dashboard interface that allows users to perform searches on different float samples, make plots, and display statistics of the float samples. This helps Argo PIs, users, and the public track Argo floats and monitor the health of the array on a real time basis.
Ensuring a timely and citable Argo dataset is a high priority. Over the past several years, the Argo GDACs have served the Argo data in a variety of ways. To facilitate research reproducibility, monthly snapshots of the GDACs are captured and are assigned digital object identifiers (DOIs) 7. Through collaboration with the Research Data Alliance, Argo has moved ahead with issuing a DOI for its dataset with a monthly tag 8  that can be added to the end of the DOI string to indicate from which month Argo data was used in scientific papers. As work in this field progresses, if DOIs are further adapted to data sets that change over time, Argo will follow the recommended changes to adhere to the current DOI framework. The Argo Data Management System has been acknowledged as a model for other observing systems and widely copied.
Nevertheless, there are substantial challenges for sustaining this success. With additional Argo sensors comes increasing complexity, and the extra burden on human resources in the data management system is considerable to maintain consistent meta and technical data for all sensors and float types. Even the P/T/S elements of the Argo dataset increase in complexity as multiple vertical axes are accommodated, and new requirements for meta and technical data are implemented. Another strain on the human resources comes from producing high quality, consistent DM files in a timely manner for increasingly complex profile and trajectory files. Finally, integration of Argo and other elements of GOOS generates a need for improvement of interoperability across the GOOS data systems.
The choices of globally-uniform or basin-specific parking depth, and of preferred cycle time are still under discussion. As occurred with Core Argo, the global implementation of an evenly distributed Deep Argo float array will follow from the expansion of the regional pilots. Deep Argo's scientific objectives are to close the Earth's budgets of heat and freshwater, to assess the steric contribution to sea level rise, to improve our understanding of full-depth ocean circulation and water-mass transformation in the ocean interior, and to reduce uncertainties in coupled atmosphereocean models, reanalyses, and predictions. The deployment of new sensors for BGC or mixing parameters could enhance the value of Deep Argo, expand the scope of the Deep Argo dataset, and promote the integration of Deep Argo with other observing system elements.
This applies to Core Argo 2000 dbar CTDs, Deep Argo CTDs, and BGC-Argo sensors, and other prioritized parameters, and will involve coordination of the AST, ADMT, Deep Argo, BGC-Argo, and sensor manufacturers. The framework for incorporation into the Argo database explains what is required of float providers and articulates guidelines for the collection and distribution of all data collected by Argo floats. In summary, float providers must follow Argo governance rules for mandatory pre-deployment notification through the AIC and agree to timely data delivery of both NRT and DM data. In addition, float providers must have a plan for long term data ownership with a national Argo DAC. Floats that are to become part of the Core Argo Program should target the Core Argo profiling depth of 2000 dbar and the cycle time of 10 days.
Argo will also continue to develop more sophisticated code sharing among Argo DACs, PIs, and manufacturers to reduce the burden on DACs and PIs each time modifications are made to floats or sensors and to improve consistency across the Argo program. Code sharing may also help with turnover as scientists and DMQC operators within Argo retire and take some of their knowledge with them. Continuing to find interested and talented Argo data team members and train them appropriately in a timely manner will remain a challenge for the ADMT. Quality control of Argo data involves a complex sequence of both automatic and manual tests to produce data of high scientific quality. With 400 profiles arriving daily, the burden on human resources dedicated to QC is large and Argo is considering whether machine learning can help ease the burden and improve the QC process.
Core Argo The Argo Program began with a strong "user pull" that informed decisions relating to the original Argo design  (Argo Steering Team, 1998). Key beneficiaries of the Argo Program, along with the scientific community, include climate sensitive industries (e. g. , agriculture, aquaculture, tourism, and fisheries), and industries with a significant maritime presence (e. g. , oil and gas, shipping, and emergency response). In most cases, the Argo Community's end user engagement is maintained indirectlyeither through the forecasting community, or through the scientific community. The forecasting community (e. g. , CLIVAR GSOP, GODAE OceanView) has impact through the provision of forecasts that are underpinned by GOOS -primarily through Argo and satellite observations. Such forecasts depend on the routine delivery of NRT Argo data.
, 2019) , and the complementary nature of information available from Argo observations and satellite observations  (Ponte et al. , 2019; Vinogradova et al. , 2019) , has been demonstrated many times (e. g. ,  Oke and Schiller, 2007; Lea et al. , 2014). An example of the impact of Argo data on FOAM -the 1/4 • -resolution operational ocean forecast system run by the UKMet Office -is presented in Figure  11 , showing the change of temperature at 110 m depth in the FOAM system after Argo data are withheld for just 1 month  (Lea et al. , 2014). The delayed-mode Argo dataset underpins many efforts in global ocean reanalysis on climate-scales (e. g. ,  de Boisséson et al. , 2018)  and eddy-scales (e. g. ,  Oke et al. , 2013) , providing products that are the foundation for much scientific research.
g. ,  Wong et al. , 2003; Kobayashi and Minato, 2005; Owens and Wong, 2009). The Argo dataset facilitates assessment of the variability and trends in ocean heat content (Figure  12 , also see  Wijffels et al. , 2016) , freshwater distribution, steric sea level rise, and intermediate depth ocean circulation. This fundamental monitoring program for the world climate facilitates understanding of climate variability and change with increased certainty and more precise attribution (e. g. ,  Chen et al. , 2017). Core Argo and BGC Argo also underpin the assessments of ecosystem health  (Canonico et al. , 2019)  increasingly delivering metrics relevant to marine biodiversity and ocean acidification (e. g. ,  Bender et al. , 2016; Watanabe and Kawamiya, 2017; Cross et al. , 2019). A key Argo user community is Education, including K-12, undergraduate, and graduate levels.
Gridded versions of the Argo dataset have been developed, as well as data viewers, to serve the needs of education, research, and government agency users having widely varying backgrounds and levels of technical expertise. Education applications of Argo have included teaching units tailored to curricula as well as "after school enrichment" web-based activities  9. At the graduate level, about 300 Ph. D. theses have used Argo data. Despite substantial achievements in Education, there remains more to be done. All modern students should be aware of the Argo Program and able to explore the global ocean from their desktop. It's important to the Argo Community that the Program remains relevant to the wider community and continues to deliver societal benefits. As new opportunities and vulnerabilities arise, and as requirements for ocean observing are better understood, the Argo design will continue to be refined to better meet ongoing societal needs (e.
Argo The interaction with the Core Argo Program and its extensions (deep, marginal seas, polar seas) is a natural interaction. BGC-Argo floats also provide Core Argo measurements that are seamlessly integrated into the Argo data system. This interaction allows a more cost-effective and efficient usage of a multidisciplinary array. Additional synergies related to science have already been described (e. g. , physical -biogeochemical coupling, radiometry, and heating rate of the upper ocean).
Ocean Color Remote Sensing Chla and particle backscatter are quantities that are inferred from remote sensing of ocean color. BGC-Argo floats provide the vertical dimension to the global and synoptic measurements of the satellites, with a high degree of consistency  (Haëntjens et al. , 2017). The integration of both observation systems has to target the development of merged 3D/4D products (already prototyped, e. g. ,  Sauzède et al. , 2016). Additionally building on float arrays dedicated to satellite calibration  (IOCCG, 2011; Gerbi et al. , 2016; Leymarie et al. , 2018) , the progressive densification of an in situ database, with the addition of radiometry, is a way to validate remote sensing products and eventually to refine algorithm retrieval, including at regional scales. BGC-Argo can be used to validate space-based products (e.
SOCONET and SOCAT Floats with carbon system parameters, such as pH, would extend the surface pCO 2 observations collected by the SOCONET ship of opportunity and mooring network and served from the SOCAT database. SOCAT  (Bakker et al. , 2016)  produces a collated dataset with annual updates of in situ surface pCO 2 measurements. However, the observations are infrequent, at high latitude, and biased to summer months. Merged products of ship, mooring-based and float-based estimates have the potential to improve our understanding of seasonal and interannual variability of ocean acidification and air-sea CO 2 fluxes.
Finally, full-depth monitoring of ocean heat content will allow for synthesis with satellite observations for better closure of the global sea level rise and energy budgets. The Argo2020 design (Figure  10  and Table  1 ) describes the next-generation Argo Program that includes enhancements to Core Argo as well as BGC-Argo and Deep Argo elements, all fully integrated in their spatial distributions and through the comprehensive Argo Data Management System. In the face of increasing anthropogenic forcing and accelerating global and regional trends in sea level, heat content, ocean acidification and deoxygenation, there is urgent need to continuously monitor the physical and biogeochemical variability of the global ocean with sufficient spatial and temporal resolution. The Argo in situ profiling array is a crucial piece of GOOS, and essential for meeting this challenge.
Here, we have outlined a practical and scientifically compelling plan to expand Argo into a global full-depth physical and biogeochemical observational array. Introduction -WBO and DR; Core Argo subsections -DR, BK, SWi, PO, and TS; BGC Argo subsections -KJ, HC, and EB; Deep Argo subsections -NZ; Argo Data Management subsections -MS and SPo; ArgoMix subsections -MA and JM; Conclusion -SPu and DR; DR compiled the manuscript and edited it for consistency. Co-authors (DB, MBa, MBe, HB, EB, PC, FCa, TC, FCh, DC, FD, GD, DD, KF, IF, RF, GF, HF, TF, MG, BG, RH, TH, SH, SJ, MJ, GCJ, KK, NK, AK, TM, TN, JN, AG, AO, RP, SPr, SR, CSchmechtig, CSchmid, ES, AS, PS, LT, TTa, VT, ST, JTo, AT, TTr, JTu, PV, WW, HW, RW, AW, SWa, AWa, CW, AWo, JX, P-YT, Y-DL, GM, KM, and IY) wrote the passages, as coordinated and assembled by the lead authors, and edited the manuscript for completeness, clarity, and accuracy.
ACKNOWLEDGMENTS Argo data were collected and made freely available by the  International Argo Project  and the national programs that contribute to it (http://www. argo. ucsd. edu,http://argo. jcommops. org,http://doi. org/10. 17882/42182 ). The authors gratefully acknowledge support from their respective Argo National Programs and/or national agencies. This is the PMEL Contribution Number  4863. The  Surface Ocean CO2 Atlas (SOCAT)  is an international effort by numerous researchers and funding agencies to deliver a uniformly quality-controlled surface ocean CO2 database. BGC Argo data were collected and made freely available by the  Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM)  Project funded by the  NationalScience Foundation, Division of Polar Programs  ( NSF PLR -1425989 ), supplemented by both the NOAA and  NASA.
FUNDING DR, MS, and NZ were supported by the  US Argo Program  through the  NOAA Grant   NA15OAR4320071  (CIMEC). WO, SJ, and SWi were supported by the  US Argo Program  through the  NOAA Grant   NA14OAR4320158  (CINAR). EuroArgo scientists were supported by the two grants: (1) AtlantOS funding by the  European Union 's  Horizon 2020 Research and Innovation Programme  under the Grant Agreement No. 633211  and (2) Monitoring the  Oceans and Climate Change with Argo (MOCCA) Co- funded by the  European Maritime and Fisheries Fund (EMFF)  Project No. SI2. 709624. This manuscript represents a contribution to the following research projects for HC, CaS, and FD: remOcean (funded by the  European Research Council , grant  246777 ), NAOS (funded by the  Agence Nationale de la Recherche  in the frame of the  French "Equipement d'avenir" program , grant  ANR J11R107-F ),  AtlantOS  (funded by the  European Union 's  Horizon 2020 Research and Innovation Programme , grant  2014-633211 ), and the  BGC-Argo  project funded by the  CNES.
DB was funded by the  EU   RINGO  project ( 730944 H2020-INFRADEV-2016-1 ). RF was supported by the  AGS-1835576. GCJ was supported by the  Global Ocean Monitoring and Observing Program ,  National Oceanic and Atmospheric Administration (NOAA) ,  U. S. , and the  Department of Commerce and NOAA Research. LT was funded under the SOCCOM Grant No. NSF   PLR-1425989. VT's contribution was supported by the  French National Research Agency (ANR)  through the  EQUIPEX NAOS (Novel Argo Observing System)  under the reference  ANR-10-EQPX-40  and by the  European H2020 Research and Innovation Programme  through the  AtlantOS  project under the reference 633211. WW was supported by the  Argo Poland program  through the  Ministry of Sciences and Higher Education  Grant No. DIR/WK/2016/12. AmW was funded by the  NSF -OCE1434722.
(2010)  and  Schneider et al. (2013)  agree well overall. The  Schneider et al. (2013)  dataset depicts a long-term CO 2 increase starting at ∼137. 8 ka and ending at ∼128. 5 ka with a centennial-scale CO 2 rise above the subsequent LIG CO 2 values, also referred to as an "overshoot". The CO 2 overshoot is smaller in the  Schneider et al. (2013)  dataset compared to a similar feature measured in  Lourantou et al. (2010) : while the former displays a relatively constant CO 2 concentration of ∼275 ppm between 128 and 126 ka, the latter shows a CO 2 decrease from 280 to 265 ppm between 128 and 126 ka. The offsets between CO 2 records from the same EDC core are likely related to the different air extraction techniques used in the two studies  (Schneider et al. , 2013). The smoothed spline CO 2 curve we recommend using as forcing for the PDG is based on those two EDC dataset and the calculation method accounts for such potential difference in local maxima (details provided in  Köhler et al.
(2012) , an overlap is observed with the coral data over the MWP-2A interval, within the stated uncertainties. Still, both coral dataset suggest that MWP-2A occurs several millennia later (i. e. ∼135-134 ka) than in the Red Sea RSL reconstruction. This mismatch is likely to be related to the difficulty to precisely anchor the dating of the current Red Sea RSL age scale over this interval (as also discussed in the supplementary information of  Grant et al. (2012) ). Hence, we propose a revised chronology for the Red Sea RSL record in order to provide a better agreement with the absolutely-dated corals. Given the potential ambiguities of the tie point defined in  Grant et al. (2012)  to stretch the depth scale across this interval, we find it reasonable to adjust it such that the timing of MWP-2A is more consistent with the absolute ages provided by the Tahiti and Huon Peninsula coral data.
We note that reassigning the tie points across this interval (Table  S5 ), where tie points are placed at the beginning and end of MWP-2A (as defined by the coral data), results in a sea-level reconstruction that more closely approximates a linear agedepth model (Fig. 7a ). This revised age model for the Red Sea RSL is adopted as our preferred reconstruction for sea-level change during the PDG. This reconstruction also compresses the total duration of the sea-level rise during the entirety of the PDG transition, which has implications for the freshwater forcing in the NH and for making analogies between the last and penultimate deglaciations. This revised chronology is still attached to large uncertainties given the limits of the datasets. Also, considerable uncertainties remain with the magnitude of the sea-level pulse during MWP-2A because some of the corals cover a wide range of paleowater depth (0 to 6 m for the pre-MWP-2A Tahiti corals, ≥20 meters for the Tahiti corals during MWP-2A, 0-25 m for the post-MWP-2A Tahiti corals and 0 to 20 m for the Aladdin's Cave corals).
Equilibrium spin-up at 140 ka (PDGv1-PGMspin) If a LGM run is already available, then it is suggested to initialise the 140 ka spin-up from the climate fields and ocean state produced by the LGM equilibrium run. Starting from a LGM state may minimize the duration of the spin-up, as it should shorten the time to reach equilibrium in the deep ocean  (Zhang et al. , 2013). If starting from a pre-industrial set up, it is suggested to follow the PMIP4 LGM protocol  (Kageyama et al. , 2017)  to set up the 140 ka state, but using the 140 ka boundary conditions described below instead of the respective 21 ka boundary conditions. The model should be forced with 140 ka background conditions (Table  1)   2017 ) provides guidelines for computing land-ice fraction and orography from the ice-sheet reconstruction datasets.