Cited by 69Oceanographic and Atmospheric ProcessesMeteorological Phenomena and SimulationsUnderwater Vehicles and Communication Systems

Requirements for an integrated in situ atlantic ocean observing system from coordinated observing system simulation experiments.

Open access

Abstract

A coordinated effort based on Observing System Simulation Experiments (OSSEs) has been carried out for the first time by four European ocean forecasting centers in order to provide insights on the present and future design of the in situ Atlantic Ocean observing system from a monitoring and forecasting perspective. This multi-system approach is based on assimilating synthetic data sets, obtained by sub-sampling in space and time an eddy-resolving unconstrained simulation, named the Nature Run. To assess the ability of a given Atlantic Ocean observing system to constrain the ocean model state, a set of assimilating experiments were performed using four global eddy-permitting systems. For each set of experiments, different designs of the in situ observing system have been assimilated, such as implementing a global drifter array equipped with a thermistor chain down to 150 m depth or extending a part of the global Argo array in the deep ocean. While results from the four systems show similarities and differences, the comparison of the experiments with the Nature Run generally demonstrates a positive impact of the different extra observation networks on the temperature and salinity fields. The spread of the multi-system simulations, combined with the sensitivity of each system to the evaluated observing networks, allowed us to discuss the robustness of the results and their dependence on the specific analysis system. By helping define and test future observing systems from an integrated observing system view, the present work is an initial step toward better-coordinated initiatives for supporting the evolution of the ocean observing system and its integration within ocean monitoring and forecasting systems.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fmars.2019.00083

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Temperature and salinity errors were reduced by 5-10% in the upper 2000 m with an enhancement of Argo sampling in equatorial regions and by around 20-30% in the deep ocean due to the implementation of deep Argo.

    “Temperature and salinity errors were reduced by 5-10% in the upper 2000 m with an enhancement of Argo sampling in equatorial regions and by around 20-30% in the deep ocean due to the implementation of deep Argo (Gasparin et al., 2019).”

    From The tropical atlantic observing system (2019) · cites this paper as Gasparin et al., 2019

    The 'Doubling Argo in the WBC and Along the Equator' section reports a 5–10% reduction in temperature and salinity error across the Atlantic in the upper 2,000 m. 'Implementing a Deep Argo Array' reports an average improvement of 20% below 2,000 m, not a 20–30% reduction; its 20–30% figure is the standard deviation across systems. Page numbers are unavailable in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 52

  1. Atlas, R. (1997). Atmospheric observations and experiments to assess their usefulness in data assimilation. J. Meteorol. Soc. Jpn. 75, 111-130. doi: 10.2151/ jmsj1965. · DOI
  2. 1B_111 Atlas, R., Kalnay, E., Baker, W. E., Susskind, J., Reuter, D. C., and Halem, M. (1985). "Simulation studies of the impact of future observing systems on weather prediction, " in Proceedings of the 7th Conference on Numerical Weather Prediction, Montreal, QC, 145-151. · DOI
  3. Ballabrera-Poy, J., Hackert, E., Murtugudde, R., and Busalacchi, A. J. (2007). An observing system simulation experiment for an optimal moored instrument array in the tropical Indian Ocean. J. Clim. 20, 3249-3268. doi: 10.1175/ JCLI4149. · DOI
  4. Balmaseda, M. A., Hernandez, F., Storto, A., Palmer, M. D., Alves, O., Shi, L., et al. (2015). The ocean reanalyses intercomparison project (ORA-IP). J. Operat. Oceanogr. 8(Suppl. 1), s80-s97. doi: 10.1080/1755876X.2015.1022329 · DOI
  5. Bell, M. J., Schiller, A., Le Traon, P. Y., Smith, N. R., Dombrowsky, E., and Wilmer- Becker, K. (2015). An Introduction to GODAE OceanView. Abingdon: Taylor & Francis.
  6. Bonaduce, A., Benkiran, M., Remy, E., Le Traon, P. Y., and Garric, G. (2018). Contribution of future wide swath altimetry missions to ocean analysis and forecasting. Ocean Sci. 14, 1405-1421. doi: 10.5194/os-2018-58 · DOI
  7. Brankart, J. M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P. A., et al. (2015). A generic approach to explicit simulation of uncertainty in the NEMO ocean model. Geosci. Model Dev. 8, 1285-1297. doi: 10.5194/gmd-8-1285-2015 · DOI
  8. Brasseur, P., and Verron, J. (2006). The SEEK filter method for data assimilation in oceanography: a synthesis. Ocean Dyn. 56, 650-661. doi: 10.1007/s10236-006- 0080-3 · DOI
  9. Cabanes, C., Grouazel, A., von Schuckmann, K., Hamon, M., Turpin, V., Coatanoan, C., et al. (2013). The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements. Ocean Sci. 9, 1-18. doi: 10.5194/os-9-1-2013 · DOI
  10. Cronin, M. F., Nicholas, B., Talley, L. D., Booth, J., Joyce, T. M., Ichikawa, H., et al. (2012). "Monitoring ocean-atmosphere interactions in western boundary current extensions, " in Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society, Venice.
  11. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERAInterim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553-597. doi: 10.1002/ qj.828 · DOI · Paper page
  12. Ebita, A., Kobayashi, S., Ota, Y., Moriya, M., Kumabe, R., Onogi, K., et al. (2011). The Japanese 55-year Reanalysis "JRA-55": an interim report. SOLA 7, 149-152. doi: 10.1016/j.scitotenv.2018.10.126 · DOI
  13. Emery, W. J., Baldwin, D. J., Schlussel, P., and Reynolds, R. W. (2001). Accuracy of in situ sea surface temperatures used to calibrate infrared satellite measurements. J. Geophys. Res. 106, 2387-2405. doi: 10.1029/2000JC000246 · DOI
  14. Fujii, Y., Cummings, J., Xue, Y., Schiller, A., Lee, T., Balmaseda, M. A., et al. (2015). Evaluation of the tropical Pacific observing system from the ocean data assimilation perspective. Q. J. R. Meteorol. Soc. 141, 2481-2496. doi: 10.1002/qj. 2579 · DOI
  15. Gasparin, F., Garric, G., Remy, E., Drillet, Y., Drevillon, M., Greiner, E., et al. (2018). A large-scale view of oceanic variability from 2007 to 2015 in the global high-resolution monitoring and forecasting system at Océan. J. Mar. Syst. 187, 260-276. doi: 10.1016/j.jmarsys.2018.06.015 · DOI · Paper page
  16. Good, S. A., Martin, M. J., and Rayner, N. A. (2013). EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncer- tainty estimates. J. Geophys. Res. 118, 6704-6716. doi: 10.1002/2013JC009067 · DOI
  17. Guinehut, S., Dhomps, A., Larnicol, G., and Le Traon, P.-Y. (2012). High resolution 3-D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci. 68, 845-857. doi: 10.5194/os-8-845-2012 · DOI
  18. Halliwell, G. R., Srinivasan, A., Kourafalou, V., Yang, H., Willey, D., Le Hénaff, M., et al. (2014). Rigorous evaluation of a fraternal twin ocean OSSE system for the open Gulf of Mexico. J. Atmos. Ocean. Technol. 31, 105-130. doi: 10.1175/ JTECH-D-13-00011. · DOI
  19. Huang, B., and Wang, X. (2018). On the use of cost-effective valid-time-shifting (VTS) method to increase ensemble size in the GFS Hybrid 4DEnVar system. Mon. Weather Rev. 146, 2973-2998. doi: 10.1175/MWR-D-18-0009. · DOI
  20. Johnson, G. C., Lyman, J. M., and Purkey, S. C. (2015). Informing deep Argo array design using Argo and full-depth hydro-graphic section data. J. Atmos. Ocean. Technol. 32, 2187-2198. doi: 10.1175/JTECH-D-15-0139. · DOI
  21. Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., et al. (2015). The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn. 93, 5-48. doi: 10.2151/jmsj.2015-001 · DOI
  22. Kuo, T. H., Zou, X., and Huang, W. (1998). The impact of global positioning system data on the prediction of an extratropical cyclone: an observing system simulation experiment. Dyn. Atmos. Oceans 27, 439-470. doi: 10.1016/S0377- 0265(97)00023-7 · DOI
  23. Lea, D. J., Martin, M. J., and Oke, P. R. (2014). Demonstrating the complementarity of observations in an operational ocean forecasting system. Q. J. R. Meteorol. Soc. 140, 2037-2049. doi: 10.1002/qj.2281 · DOI
  24. Lellouche, J. M., Greiner, E., Le Galloudec, O., Garric, G., and Regnier, C. (2018). Re-cent updates on the Copernicus marine service global ocean monitoring and forecasting real-time 1/12 high-resolution system. Ocean Sci. Discuss. 2018, 1-70. doi: 10.5194/os-2018-15 · DOI
  25. Lellouche, J. M., Le Galloudec, O., Drevillon, M., Régnier, C., Greiner, E., Garric, G., et al. (2013). Evaluation of global monitoring and forecasting systems at Mercator Océan. Ocean Sci. 9:57. doi: 10.5194/os-9-57-2013 · DOI
  26. Li, Z., Chao, Y., McWilliams, J. C., and Ide, K. (2008). A three-dimensional variational data assimilation scheme for the regional ocean modeling system: implementation and basic experiments. J. Geophys. Res 113:C0500. doi: 10. 1029/2008JC004928 · DOI
  27. Lumpkin, R., Centurioni, L., and Perez, R. (2016). Fulfilling observing system implementation requirements with the global drifter array. J. Atmos. Ocean. Technol. 33, 685-695. doi: 10.1175/JTECH-D-15-0255. · DOI
  28. Lumpkin, R., Özgökmen, T., and Centurioni, L. (2017). Advances in the applications of surface drifters. Ann. Rev. Mar. Sci. 9, 59-81. doi: 10.1146/ annurev-marine-010816-060641 · DOI
  29. Luo, D., and Cha, J. (2012). The North Atlantic Oscillation and North Atlantic jet variability: precursors to NAO regimes and transitions. J. Atmos. Sci. 69, 3763-3787. doi: 10.1175/JAS-D-12-098. · DOI
  30. Maloney, E. D., and Chelton, D. B. (2006). An assessment of the sea surface temperature influence on surface wind stress in numerical weather prediction and climate models. J. Clim. 19, 2743-2762. doi: 10.1175/JCLI 3728. · DOI
  31. Oke, P. R., Larnicol, G., Fujii, Y., Smith, G. C., Lea, D. J., Guinehut, S., et al. (2015). Assessing the impact of observations on ocean forecasts and re-analyses: part 1, Global studies. J. Operat. Oceanogr. 8, s49-s62. doi: 10.1080/1755876X.2015. 1022067 · DOI · Paper page
  32. Purkey, S. G., and Johnson, G. C. (2010). Warming of global abyssal and deep southern ocean waters between the 1990s and 2000s: contributions to global heat and sea level rise budgets. J. Clim. 23, 6336-6351. doi: 10.1175/ 2010JCLI3682. · DOI
  33. Ratnam, J., Behera, S., Masumoto, Y., Takahashi, K., and Yamagata, T. (2011). Anomalous climatic conditions associated with the El Niño Modoki during boreal winter of 2009. Clim. Dyn. 39, 227-238. doi: 10.1007/s00382-011- 1108-z · DOI
  34. Ridley, J. K., Blockley, E. W., Keen, A. B., Rae, J. G. L., West, A. E., and Schroeder, D. (2018). The sea ice model component of HadGEM3-GC3.1. Geosci. Model Dev. 11, 713-723. doi: 10.5194/gmd-11-713-2018 · DOI
  35. Riser, S. C., Freeland, H. J., Roemmich, D., Wijffels, S., Troisi, A., Belboech, M. et al. (2016). Fifteen years of ocean observations with the global Argo array. Nat. Climate Change 6, 145-153. doi: 10.1038/nclimate2872 · DOI
  36. Roemmich, D., and Gilson, J. (2009). The 2004-2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Prog. Oceanogr. 82, 81-100. doi: 10.1016/j.pocean.2009.03.004 · DOI
  37. Roemmich, D., and Gilson, J. (2011). The global ocean imprint of ENSO. Geophys. Res. Lett. 38. doi: 10.1029/2011GL047992 · DOI
  38. Roemmich, D., Johnson, G. C., Riser, S., Davis, R., Gilson, J., Owens, W. B., et al. (2009). The Argo program observing the global ocean with profiling floats. Oceanography 22, 34-43. doi: 10.5670/oceanog.2009. · DOI
  39. Ryan, A., Regnier, C., Divakaran, P., Spindler, T., Mehra, A., Smith, G., et al. (2015). GODAE OceanView 26, Class 4 forecast verification framework: global ocean inter-comparison. J. Operat. Oceanogr. 8, s98-s111. doi: 10.1080/1755876X. 2015 · DOI
  40. Schiller, A., Wijffels, S. E., and Meyers, G. A. (2004). Design requirements for an Argo float array in the Indian ocean inferred from observing system simulation experiments. J. Atmos. Ocean. Technol. 21, 1598-1620. doi: 10.1175/1520- 0426(2004)021<1598:DRFAAF>2.0.CO;2 · DOI
  41. Storkey, D., Blaker, A. T., Mathiot, P., Megann, A., Aksenov, Y., Blockley, E. W., et al. (2018). UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions. Geosci. Model Dev. 11, 3187-3213. doi: 10.5194/gmd- 2017-263 · DOI
  42. Storto, A., Dobricic, S., Masina, S., and Di Pietro, P. (2011). Assimilating along- track altimetric observations through local hydrostatic adjustments in a global ocean reanalysis system. Mon. Weather Rev. 139, 738-754. doi: 10.1175/ 2010MWR3350. · DOI
  43. Storto, A., Masina, S., and Dobricic, S. (2013). Ensemble spread-based assessment of observation impact: application to a global ocean analysis system. Q. J. R. Meteorol. Soc. 139, 1842-1862. doi: 10.1002/qj.2071 · DOI
  44. Storto, A., Masina, S., and Navarra, A. (2015). Evaluation of the CMCC eddy- permitting global ocean physical reanalysis system (C-GLORS, 1982-2012) and its assimilation components. Q. J. R. Meteorol. Soc. 142, 738-758. doi: 10.1002/ qj.2673 · DOI
  45. Storto, A., Masina, S., Simoncelli, S., Iovino, D., Cipollone, A., Drevillon, M., et al. (2018). The added value of the multi-system spread information for ocean heat content and steric sea level investigations in the CMEMS GREP ensemble reanalysis product. Clim. Dyn. 1-26. doi: 10.1007/s00382-018-4585-5 · DOI
  46. Szekely, T., Gourrion, J., Pouliquen, S., and Reverdin, G. (2016). CORA, Coriolis Ocean Dataset for Reanalysis. SEANOE, doi: 10.17882/46219 · DOI
  47. Turpin, V., Remy, E., and Le Traon, P. Y. (2015). How essential are Argo observations to constrain global ocean data assimilation system. Ocean Sci. 12, 257-274. doi: 10.5194/os-12-257-2016 · DOI · Paper page
  48. Usui, N., Fujii, Y., Sakamoto, K., and Kamachi, M. (2015). Development of a four- dimensional variational assimilation system toward coastal data assimilation around Japan. Mon. Weather Rev. 143, 3874-3892. doi: 10.1175/MWR-D-14- 00326. · DOI
  49. Verrier, S., Le Traon, P.-Y., and Remy, E. (2017). Assessing the impact of multiple altimeter missions and Argo in a global eddy-permitting data assimilation system. Ocean Sci. 13, 1077-1092. doi: 10.5194/os-13-1077-2017 · DOI
  50. Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While, J. (2015). Implementing a variational data assimilation system in an operational 1/4 degree global ocean model. Q. J. R. Meteorol. Soc. 687, 333-349. doi: 10.1002/qj. 2388 · DOI
  51. Xue, Y., Wen, C., Kumar, A., Balmaseda, M., Fujii, Y., Alves, O., et al. (2017). A real-time ocean reanalysis intercomparison project in the context of tropical pacific observing system and ENSO monitoring. Clim. Dyn. 49, 3647-3672. doi: 10.1007/s00382-017-3535-y · DOI
  52. Zweng, M. M., Reagan, J. R., Antonov, J. I., Locarnini, R. A., Mishonov, A. V., Boyer, T. P., et al. (2013). NOAA Atlas NESDIS 74 World Ocean Atlas 2013, Volume 2: Salinity, ed. S. Levitus (Silver Spring, MD: NOAA Printing Officce), 39. doi: 10.1073/pnas.1303904110 · DOI · Paper page

Cited by

Sorted by citations

Authors
Gasparin, F., Guinehut, S., Mao, C., Mirouze, I., Rémy, E., King, R. R., Hamon, M., Reid, R., Storto, A., Le Traon, P.-Y., Martin, M. J., & Masina, S.
Affiliations
  • Mercator Ocean (France) (FR)
  • Collecte Localisation Satellites (France) (FR)
  • Met Office (GB)
  • Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (FR)
  • NATO Centre for Maritime Research and Experimentation (IT)
  • CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT)
Corresponding author
Gasparin, Florent
Venue
Frontiers in Marine Science
Published
2019-03-14
Volume
6
Issue
83
Pages
14p.
ISSN
2296-7745 (ISSN-L)
OpenAlex
W2911650121
Cited by
69
References
0
Citations by year
2026: 1 · 2025: 10 · 2024: 7 · 2023: 12 · 2022: 17 · 2021: 9 · 2020: 5 · 2019: 8
FWCI
5.39
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Marine Science (publishedVersion, cc-by)
  • HAL (submittedVersion)
  • Institutional Archive of Ifremer (French Research Institute for Exploitation of the Sea) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • IOC of UNESCO (Intergovernmental Oceanographic Commission) (cc-by)
Funders
Horizon 2020 Framework Programme
Topics
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
Keywords
observing system simulation experiments, ocean data assimilation, Argo floats, oceanographic monitoring, ocean forecasting, eddy-resolving ocean model, nature run, temperature and salinity fields, observing system design

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review