Articles | Volume 19, issue 3
https://doi.org/10.5194/os-19-887-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-887-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improving the thermocline calculation over the global ocean
Emmanuel Romero
Departamento de Oceanología, Instituto Politécnico Nacional–Centro Interdisciplinario de Ciencias Marinas (IPN–CICIMAR), Av. IPN s/n, La Paz, B.C.S., 23096, Mexico
Leonardo Tenorio-Fernandez
CORRESPONDING AUTHOR
Departamento de Oceanología, Instituto Politécnico Nacional–Centro Interdisciplinario de Ciencias Marinas (IPN–CICIMAR), Av. IPN s/n, La Paz, B.C.S., 23096, Mexico
CONAHCyT, Consejo Nacional de Humanidades Ciencia y Tecnología, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Alcaldía Benito Juárez, C.P. 03940, Mexico City, Mexico
Esther Portela
School of Environmental Sciences, University of East Anglia, Norwich, UK
Univ. Brest, Laboratoire d'Océanographie Physique et Spatiale, CNRS, IRD, Ifremer, Plouzané, France
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart 7001, Australia
Jorge Montes-Aréchiga
Departamento de Física, Universidad de Guadalajara, Gral. Marcelino García Barragán 1421, Olímpica, 44430 Guadalajara, Jal, Mexico
Laura Sánchez-Velasco
Departamento de Oceanología, Instituto Politécnico Nacional–Centro Interdisciplinario de Ciencias Marinas (IPN–CICIMAR), Av. IPN s/n, La Paz, B.C.S., 23096, Mexico
Related authors
Emmanuel Romero, Leonardo Tenorio-Fernandez, Iliana Castro, and Marco Castro
Ocean Sci., 17, 1273–1284, https://doi.org/10.5194/os-17-1273-2021, https://doi.org/10.5194/os-17-1273-2021, 2021
Short summary
Short summary
Due to the need to obtain a greater amount of data in less time in areas with little in situ hydrographic data, an algorithm based on cluster analysis is proposed. This algorithm allows real-time quality control of Argo data which has patterns similar to data in delayed mode. To test this, a study area of high scientific interest but with little concentration of in situ data was chosen. In this area 80 % of the data normally discarded because of salinity drifts was recovered.
Nicolas Kolodziejczyk, Esther Portela, Virginie Thierry, and Annaig Prigent
Earth Syst. Sci. Data, 16, 5191–5206, https://doi.org/10.5194/essd-16-5191-2024, https://doi.org/10.5194/essd-16-5191-2024, 2024
Short summary
Short summary
Oceanic dissolved oxygen (DO) is fundamental for ocean biogeochemical cycles and marine life. To ease the computation of the ocean oxygen budget from in situ DO data, mapping of data on a regular 3D grid is useful. Here, we present a new DO gridded product from the Argo database. We compare it with existing DO mapping from a historical dataset. We suggest that the ocean has generally been losing oxygen since the 1980s, but large interannual and regional variabilities should be considered.
Emmanuel Romero, Leonardo Tenorio-Fernandez, Iliana Castro, and Marco Castro
Ocean Sci., 17, 1273–1284, https://doi.org/10.5194/os-17-1273-2021, https://doi.org/10.5194/os-17-1273-2021, 2021
Short summary
Short summary
Due to the need to obtain a greater amount of data in less time in areas with little in situ hydrographic data, an algorithm based on cluster analysis is proposed. This algorithm allows real-time quality control of Argo data which has patterns similar to data in delayed mode. To test this, a study area of high scientific interest but with little concentration of in situ data was chosen. In this area 80 % of the data normally discarded because of salinity drifts was recovered.
Cited articles
Akpinar, A., Fach, B. A., and Oguz, T.: Observing the subsurface thermal
signature of the Black Sea cold intermediate layer with Argo profiling
floats, Deep Sea Res. Pt. I, 124,
140–152, https://doi.org/10.1016/j.dsr.2017.04.002, 2017. a
Argo: Argo, https://argo.ucsd.edu/ (last access: January 2022), 2022a. a
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo
GDAC) [data set], https://doi.org/10.17882/42182, 2022b. a, b, c
Argo Data Management Team: Argo user’s manual, https://doi.org/10.13155/29825, 2022. a
Bhogal, A. A., Siero, J. C., Fisher, J. A., Froeling, M., Luijten, P.,
Philippens, M., and Hoogduin, H.: Investigating the non-linearity of the BOLD
cerebrovascular reactivity response to targeted hypo/hypercapnia at 7T,
NeuroImage, 98, 296–305,
https://doi.org/10.1016/j.neuroimage.2014.05.006, 2014. a
Cao, L., Shi, P.-J., Li, L., and Chen, G.: A New Flexible Sigmoidal Growth
Model, Symmetry, 11, 204, https://doi.org/10.3390/sym11020204, 2019. a
Chu, P. C. and Fan, C.: Exponential leap-forward gradient scheme for
determining the isothermal layer depth from profile data, J. Oceanogr., 73, 503–526, https://doi.org/10.1007/s10872-017-0418-0, 2017. a
Chu, P. C. and Fan, C.: Global ocean synoptic thermocline gradient,
isothermal-layer depth, and other upper ocean parameters, Sci. Data, 6,
119, https://doi.org/10.1038/s41597-019-0125-3, 2019. a
de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A., and Iudicone, D.:
Mixed layer depth over the global ocean: An examination of profile data and a
profile-based climatology, J. Geophys. Res.-Oceans, 109, C12003,
https://doi.org/10.1029/2004JC002378, 2004. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Dong, S., Sprintall, J., Gille, S. T., and Talley, L.: Southern Ocean
mixed-layer depth from Argo float profiles, J. Geophys. Res.-Oceans, 113, C06013, https://doi.org/10.1029/2006JC004051, 2008. a, b, c
Duka, M. A., Shintani, T., and Yokoyama, K.: Thermal stratification responses
of a monomictic reservoir under different seasons and operation schemes,
Sci. Total Environ., 767, 144423,
https://doi.org/10.1016/j.scitotenv.2020.144423, 2021. a
Flexas, M. M., Thompson, A. F., Torres, H. S., Klein, P., Farrar, J. T., Zhang,
H., and Menemenlis, D.: Global Estimates of the Energy Transfer From the Wind
to the Ocean, With Emphasis on Near-Inertial Oscillations, J. Geophys. Res.-Oceans, 124, 5723–5746, https://doi.org/10.1029/2018JC014453,
2019. a
Harper, S.: Thermocline ventilation and pathways of tropical–subtropical
water mass exchange, Tellus A, 52,
330–345, https://doi.org/10.3402/tellusa.v52i3.12269, 2000.
a
Helber, R. W., Kara, A. B., Richman, J. G., Carnes, M. R., Barron, C. N.,
Hurlburt, H. E., and Boyer, T.: Temperature versus salinity gradients below
the ocean mixed layer, J. Geophys. Res.-Oceans, 117, C05006,
https://doi.org/10.1029/2011JC007382, 2012. a
Holte, J. and Talley, L.: A New Algorithm for Finding Mixed Layer Depths with
Applications to Argo Data and Subantarctic Mode Water Formation, J. Atmos. Ocean. Technol., 26, 1920–1939,
https://doi.org/10.1175/2009JTECHO543.1, 2009. a, b, c
Holte, J., Talley, L. D., Gilson, J., and Roemmich, D.: An Argo mixed layer
climatology and database, Geophys. Res. Lett., 44, 5618–5626,
https://doi.org/10.1002/2017GL073426, 2017. a, b, c, d
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020. a
Jiang, B., Wu, X., and Ding, J.: Comparison of the calculation methods of the
thermocline depth of the South China Sea, Mar. Sci. Bull, 35, 64–73, 2016. a
Jiang, Y., Gou, Y., Zhang, T., Wang, K., and Hu, C.: A Machine Learning
Approach to Argo Data Analysis in a Thermocline, Sensors, 17, 2225,
https://doi.org/10.3390/s17102225, 2017. a, b, c
Landerer, F. W., Jungclaus, J. H., and Marotzke, J.: Regional Dynamic and
Steric Sea Level Change in Response to the IPCC-A1B Scenario, J.
Phys. Oceanogr., 37, 296–312, https://doi.org/10.1175/JPO3013.1, 2007. a
Li, G., Cheng, L., Zhu, J., Trenberth, K. E., Mann, M. E., and Abraham, J. P.:
Increasing ocean stratification over the past half-century, Nat. Clim.
Change, 10, 1116–1123, https://doi.org/10.1038/s41558-020-00918-2, 2020. a, b
Liu, W. and Saint, D. A.: Validation of a quantitative method for real time PCR
kinetics, Biochem. Biophys. Res. Commun., 294, 347–353,
https://doi.org/10.1016/S0006-291X(02)00478-3, 2002. a
Lorbacher, K., Dommenget, D., Niiler, P. P., and Köhl, A.: Ocean mixed layer
depth: A subsurface proxy of ocean-atmosphere variability, J. Geophys. Res.-Oceans, 111, C07010,
https://doi.org/10.1029/2003JC002157, 2006. a, b
Luo, Y., Rothstein, L. M., and Zhang, R.-H.: Response of Pacific
subtropical-tropical thermocline water pathways and transports to global
warming, Geophys. Res. Lett., 36, C00A02, https://doi.org/10.1029/2008GL036705, 2009. a
Overland, J. E. and Wang, M.: Future climate of the north Pacific Ocean, EOS T. Am. Geophys. Un., 88, 178–182,
https://doi.org/10.1029/2007EO160003, 2007. a
Pellichero, V., Sallée, J.-B., Schmidtko, S., Roquet, F., and Charrassin,
J.-B.: The ocean mixed layer under Southern Ocean sea-ice: Seasonal cycle and
forcing, J. Geophys. Res.-Oceans, 122, 1608–1633,
https://doi.org/10.1002/2016JC011970, 2017. a
Peralta-Ferriz, C. and Woodgate, R. A.: Seasonal and interannual variability of
pan-Arctic surface mixed layer properties from 1979 to 2012 from hydrographic
data, and the dominance of stratification for multiyear mixed layer depth
shoaling, Prog. Oceanogr., 134, 19–53,
https://doi.org/10.1016/j.pocean.2014.12.005, 2015. a, b
Portela, E., Kolodziejczyk, N., Vic, C., and Thierry, V.: Physical Mechanisms
Driving Oxygen Subduction in the Global Ocean, Geophys. Res. Lett.,
47, e2020GL089040, https://doi.org/10.1029/2020GL089040, 2020. a
Ritz, C. and Spiess, A.-N.: qpcR: an R package for sigmoidal model selection
in quantitative real-time polymerase chain reaction analysis,
Bioinformatics, 24, 1549–1551, https://doi.org/10.1093/bioinformatics/btn227, 2008. a
Romero, E., Tenorio-Fernandez, L., Castro, I., and Castro, M.: Filtering method based on cluster analysis to avoid salinity drifts and recover Argo data in less time, Ocean Sci., 17, 1273–1284, https://doi.org/10.5194/os-17-1273-2021, 2021. a
Romero, E., Tenorio-Fernandez, L., Portela, E., Montes-Aréchiga, J., and
Sánchez-Velasco, L.: romeroqe/mld-mtd: MLD & MTD, [code],
https://doi.org/10.5281/zenodo.6985561, 2022. a, b
Ruvalcaba-Aroche, E. D., Sánchez-Velasco, L., Beier, E., Barton, E. D.,
Godínez, V. M., Gómez-Gutiérrez, J., and Martínez-Rincón, R. O.:
Ommastrephid squid paralarvae potential nursery habitat in the
tropical-subtropical convergence off Mexico, Prog. Oceanogr., 202,
102762, https://doi.org/10.1016/j.pocean.2022.102762, 2022. a
Sallée, J.-B., Matear, R. J., Rintoul, S. R., and Lenton, A.: Localized
subduction of anthropogenic carbon dioxide in the Southern Hemisphere oceans,
Nat. Geosci., 5, 579–584, https://doi.org/10.1038/ngeo1523, 2012. a
Sallée, J.-B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L.,
Schmidtko, S., Garabato, A. N., Sutherland, P., and Kuusela, M.: Summertime
increases in upper-ocean stratification and mixed-layer depth, Nature, 591,
592–598, https://doi.org/10.1038/s41586-021-03303-x, 2021. a, b, c, d
Southward, A. J. and Barrett, R. L.: Observations on the vertical distribution
of zooplankton, including post-larval teleosts, off Plymouth in the presence
of a thermocline and a chlorophyll-dense layer, J. Plankton Res.,
5, 599–618, https://doi.org/10.1093/plankt/5.4.599, 1983. a
Sprintall, J. and Cronin, M. F.: Upper Ocean Vertical Structure,
https://doi.org/10.1006/rwos.2001.0149, 2001. a, b
Sprintall, J. and Tomczak, M.: Evidence of the barrier layer in the surface
layer of the tropics, J. Geophys. Res.-Oceans, 97,
7305–7316, https://doi.org/10.1029/92JC00407, 1992. a
Timmermans, M.-L., Toole, J., Krishfield, R., and Winsor, P.: Ice-Tethered
Profiler observations of the double-diffusive staircase in the Canada Basin
thermocline, J. Geophys. Res.-Oceans, 113, L04601,
https://doi.org/10.1029/2008JC004829, 2008. a, b, c
Van der Graaf, P. and Schoemaker, R.: Analysis of asymmetry of agonist
concentration–effect curves, Journal of Pharmacological and Toxicological
Methods, 41, 107–115, https://doi.org/10.1016/S1056-8719(99)00026-X,
1999. a
Vecchi, G. and Soden, B.: Global Warming and the Weakening of the Tropical
Circulation, J. Climate, 20, 4316–4340, https://doi.org/10.1175/JCLI4258.1,
2007. a
Yamaguchi, R. and Suga, T.: Trend and Variability in Global Upper-Ocean
Stratification Since the 1960s, J. Geophys. Res.-Oceans, 124,
8933–8948, https://doi.org/10.1029/2019JC015439, 2019. a, b
Yang, H. and Wang, F.: Revisiting the Thermocline Depth in the Equatorial
Pacific, J. Climate, 22, 3856–3863, https://doi.org/10.1175/2009JCLI2836.1,
2009.
a
Yu, H., Tsuno, H., Hidaka, T., and Jiao, C.: Chemical and thermal
stratification in lakes, Limnology, 11, 251–257,
https://doi.org/10.1007/s10201-010-0310-8, 2010. a
Zelle, H., Appeldoorn, G., Burgers, G., and van Oldenborgh, G. J.: The
Relationship between Sea Surface Temperature and Thermocline Depth in the
Eastern Equatorial Pacific, J. Phys. Ocean., 34, 643–655,
https://doi.org/10.1175/2523.1, 2004. a
Short summary
In this study, we present a methodology to locate the minimum and maximum depths of the strongest thermocline, its thickness, and its strength by adjusting the sigmoid function to the temperature profiles in the global ocean. The results of the methodology are compared with the results of other methods found in the literature, and an assessment of the ocean regions where the adjustment is valid is provided. The method proposed here has shown to be robust and easy to apply.
In this study, we present a methodology to locate the minimum and maximum depths of the...