Articles | Volume 32, issue 1
https://doi.org/10.5194/npg-32-35-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-32-35-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jan 2025
Research article |
| 22 Jan 2025
| 22 Jan 2025
Scaling and intermittent properties of oceanic and atmospheric pCO2 time series and their difference in a turbulence framework
Kévin Robache
CORRESPONDING AUTHOR
Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, CNRS, IRD, UMR LOG 8187, 62930 Wimereux, France
François G. Schmitt
CORRESPONDING AUTHOR
Laboratoire d'Océanologie et Géosciences, Université du Littoral Côte d'Opale, Université de Lille, CNRS, IRD, UMR LOG 8187, 62930 Wimereux, France
Yongxiang Huang
State Key Laboratory of Marine Environmental Science, Center for Marine Meteorology and Climate Change, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China
Fujian Engineering Research Center for Ocean Remote Sensing Big Data, Xiamen University, Xiamen, China
Related authors
Zéline Hubert, Arnaud Louchart, Kévin Robache, Alexandre Epinoux, Clémentine Gallot, Vincent Cornille, Muriel Crouvoisier, Sébastien Monchy, and Luis Felipe Artigas
EGUsphere, https://doi.org/10.5194/egusphere-2024-1933, https://doi.org/10.5194/egusphere-2024-1933, 2024
Short summary
Short summary
This study provides the first assessment of decadal changes of the whole phytoplankton community addressed by flow cytometry in the highly productive waters of the Strait of Dover. A significant increase of 1 °C in surface seawater temperature, associated to an important change in nutrient concentration and balance, have triggered a change in phytoplankton communities characterized by higher total abundance and an increasing proportion of the smallest cells (picroeukaryotes, picocyanobacteria).
Zéline Hubert, Arnaud Louchart, Kévin Robache, Alexandre Epinoux, Clémentine Gallot, Vincent Cornille, Muriel Crouvoisier, Sébastien Monchy, and Luis Felipe Artigas
EGUsphere, https://doi.org/10.5194/egusphere-2024-1933, https://doi.org/10.5194/egusphere-2024-1933, 2024
Short summary
Short summary
This study provides the first assessment of decadal changes of the whole phytoplankton community addressed by flow cytometry in the highly productive waters of the Strait of Dover. A significant increase of 1 °C in surface seawater temperature, associated to an important change in nutrient concentration and balance, have triggered a change in phytoplankton communities characterized by higher total abundance and an increasing proportion of the smallest cells (picroeukaryotes, picocyanobacteria).
Xuebo Li, Yongxiang Huang, Guohua Wang, and Xiaojing Zheng
Earth Syst. Sci. Data, 13, 5819–5830, https://doi.org/10.5194/essd-13-5819-2021, https://doi.org/10.5194/essd-13-5819-2021, 2021
Short summary
Short summary
High-frequency observatory data (50 Hz 3D wind velocity, 50 Hz temperature and 1 Hz PM10) for studying the features of the fluid and dust field during sand and dust storms were presented. It is anticipated that data collected in this work will be of utility not only specifically for the boundary layer community in building a model for sand and dust storms but also broadly for communities studying the exchange of the dust and fluid field and energy transfer for the particle-laden two-phase flow.
Wei Wei, Hongsheng Zhang, Bingui Wu, Yongxiang Huang, Xuhui Cai, Yu Song, and Jianduo Li
Atmos. Chem. Phys., 18, 12953–12967, https://doi.org/10.5194/acp-18-12953-2018, https://doi.org/10.5194/acp-18-12953-2018, 2018
Short summary
Short summary
Heavy particulate pollution events have frequently occurred in the North China Plain. Using the intermittency factor, we found that the turbulence during the transport stage is intermittent and not locally generated. Turbulence results from the wind shear of low-level jets and then transports downward, causing intermittent turbulence at lower levels. The intermittent turbulence contributes positively to the vertical dispersion of particulate matter and improves the air quality near the surface.
P. R. Renosh, F. G. Schmitt, and H. Loisel
Nonlin. Processes Geophys., 22, 633–643, https://doi.org/10.5194/npg-22-633-2015, https://doi.org/10.5194/npg-22-633-2015, 2015
Short summary
Short summary
Intermittent dynamics of particle size distribution in coastal waters is studied. Particle sizes are separated into four size classes: silt, fine, coarse and macro particles. The time series of each size class is derived, and their multiscaling properties studied. Similar analysis has been done for suspended particulate matter and total volume concentration. All quantities display a nonlinear moment function and a negative Hurst scaling exponent.
Q. Xing, M. Gao, X. Gao, L. Tosi, F. G. Schmitt, Y. Zhang, P. Shi, J. Wei, and Y. Luo
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-7029-2014, https://doi.org/10.5194/bgd-11-7029-2014, 2014
Revised manuscript not accepted
Related subject area
Subject: Scaling, multifractals, turbulence, complex systems, self-organized criticality | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean
David Wichmann, Christian Kehl, Henk A. Dijkstra, and Erik van Sebille
Nonlin. Processes Geophys., 28, 43–59, https://doi.org/10.5194/npg-28-43-2021, https://doi.org/10.5194/npg-28-43-2021, 2021
Short summary
Short summary
Fluid parcels transported in complicated flows often contain subsets of particles that stay close over finite time intervals. We propose a new method for detecting finite-time coherent sets based on the density-based clustering technique of ordering points to identify the clustering structure (OPTICS). Unlike previous methods, our method has an intrinsic notion of coherent sets at different spatial scales. OPTICS is readily implemented in the SciPy sklearn package, making it easy to use.
Cited articles
Alola, A. A. and Kirikkaleli, D.: Global Evidence of Time-Frequency Dependency of Temperature and Environmental Quality from a Wavelet Coherence Approach, Air Quality, Atmos. Health, 14, 581–589, https://doi.org/10.1007/s11869-020-00962-z, 2021. a
Anderson, D. E. and Verma, S. B.: Turbulence Spectra of CO2, Water Vapor, Temperature and Wind Velocity Fluctuations over a Crop Surface, Bound.-Lay. Meteorol., 33, 1–14, https://doi.org/10.1007/BF00137033, 1985. a
Anderson, D. E., Verma, S. B., Clement, R. J., Baldocchi, D. D., and Matt, D. R.: Turbulence Spectra of CO2, Water Vapor, Temperature and Velocity over a Deciduous Forest, Agr. Forest Meteorol., 38, 81–99, https://doi.org/10.1016/0168-1923(86)90051-1, 1986. a
Anderson, T. R., Hawkins, E., and Jones, P. D.: CO2, the Greenhouse Effect and Global Warming: From the Pioneering Work of Arrhenius and Callendar to Today's Earth System Models, Endeavour, 40, 178–187, https://doi.org/10.1016/j.endeavour.2016.07.002, 2016. a
Calif, R. and Schmitt, F. G.: Modeling of Atmospheric Wind Speed Sequence Using a Lognormal Continuous Stochastic Equation, J. Wind Eng. Ind. Aerod., 109, 1–8, https://doi.org/10.1016/j.jweia.2012.06.002, 2012. a
Calif, R. and Schmitt, F. G.: Multiscaling and joint multiscaling description of the atmospheric wind speed and the aggregate power output from a wind farm, Nonlin. Processes Geophys., 21, 379–392, https://doi.org/10.5194/npg-21-379-2014, 2014. a
Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air–sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087–1109, https://doi.org/10.5194/bg-19-1087-2022, 2022. a
Corrsin, S.: On the Spectrum of Isotropic Temperature Fluctuations in an Isotropic Turbulence, J. Appl. Phys., 22, 469–473, https://doi.org/10.1063/1.1699986, 1951. a
Crisp, D., Dolman, H., Tanhua, T., McKinley, G. A., Hauck, J., Bastos, A., Sitch, S., Eggleston, S., and Aich, V.: How Well Do We Understand the Land-Ocean-Atmosphere Carbon Cycle?, Rev. Geophys., 60, e2021RG000736, https://doi.org/10.1029/2021RG000736, 2022. a
Crossland, C. J., Baird, D., Ducrotoy, J.-P., Lindeboom, H., Buddemeier, R. W., Dennison, W. C., Maxwell, B. A., Smith, S. V., and Swaney, D. P.: The Coastal Zone – a Domain of Global Interactions, in: Coastal Fluxes in the Anthropocene: The Land-Ocean Interactions in the Coastal Zone Project of the International Geosphere-Biosphere Programme, edited by: Crossland, C. J., Kremer, H. H., Lindeboom, H. J., Marshall Crossland, J. I., and Le Tissier, M. D. A., Global Change – The IGBP Series, Springer, Berlin, Heidelberg, 1–37, ISBN 978-3-540-27851-1, 2005. a
Derot, J., Schmitt, F. G., Gentilhomme, V., and Morin, P.: Correlation between Long-Term Marine Temperature Time Series from the Eastern and Western English Channel: Scaling Analysis Using Empirical Mode Decomposition, C. R. Geosci., 348, 343–349, https://doi.org/10.1016/j.crte.2015.12.001, 2016. a
Emerson, S. R. and Hamme, R. C.: Chemical Oceanography, Cambridge University Press, ISBN 978-1-107-17989-9, 2022. a
Falkowski, P., Scholes, R. J., Boyle, E., Canadell, J., Canfield, D., Elser, J., Gruber, N., Hibbard, K., Högberg, P., Linder, S., Mackenzie, F. T., Moore III, B., Pedersen, T., Rosenthal, Y., Seitzinger, S., Smetacek, V., and Steffen, W.: The Global Carbon Cycle: A Test of Our Knowledge of Earth as a System, Science, 290, 291–296, https://doi.org/10.1126/science.290.5490.291, 2000. a
Flandrin, P., Rilling, G., and Goncalves, P.: Empirical Mode Decomposition as a Filter Bank, IEEE Signal Proc. Let., 11, 112–114, https://doi.org/10.1109/LSP.2003.821662, 2004. a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Bakker, D. C. E., Hauck, J., Landschützer, P., Le Quéré, C., Luijkx, I. T., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Barbero, L., Bates, N. R., Becker, M., Bellouin, N., Decharme, B., Bopp, L., Brasika, I. B. M., Cadule, P., Chamberlain, M. A., Chandra, N., Chau, T.-T.-T., Chevallier, F., Chini, L. P., Cronin, M., Dou, X., Enyo, K., Evans, W., Falk, S., Feely, R. A., Feng, L., Ford, D. J., Gasser, T., Ghattas, J., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Heinke, J., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jacobson, A. R., Jain, A., Jarníková, T., Jersild, A., Jiang, F., Jin, Z., Joos, F., Kato, E., Keeling, R. F., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Lan, X., Lefèvre, N., Li, H., Liu, J., Liu, Z., Ma, L., Marland, G., Mayot, N., McGuire, P. C., McKinley, G. A., Meyer, G., Morgan, E. J., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K. M., Olsen, A., Omar, A. M., Ono, T., Paulsen, M., Pierrot, D., Pocock, K., Poulter, B., Powis, C. M., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Séférian, R., Smallman, T. L., Smith, S. M., Sospedra-Alfonso, R., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tans, P. P., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., van Ooijen, E., Wanninkhof, R., Watanabe, M., Wimart-Rousseau, C., Yang, D., Yang, X., Yuan, W., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2023, Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, 2023. a, b
Frisch, U.: Turbulence: The Legacy of A. N. Kolmogorov, Cambridge University Press, ISBN 978-0-521-45713-2, 1995. a
Gao, Y., Schmitt, F. G., Hu, J., and Huang, Y.: Scaling Analysis of the China France Oceanography Satellite Along-Track Wind and Wave Data, J. Geophys. Res.-Oceans, 126, e2020JC017119, https://doi.org/10.1029/2020JC017119, 2021. a, b
Gao, Z., Liu, H., Arntzen, E., Mcfarland, D. P., Chen, X., and Huang, M.: Uncertainties in Turbulent Statistics and Fluxes of CO2 Associated With Density Effect Corrections, Geophys. Res. Lett., 47, e88859, https://doi.org/10.1029/2020GL088859, 2020. a, b
Henson, S., Le Moigne, F., and Giering, S.: Drivers of Carbon Export Efficiency in the Global Ocean, Global Biogeochem. Cy., 33, 891–903, https://doi.org/10.1029/2018GB006158, 2019. a
Hernández-Carrasco, I., Sudre, J., Garçon, V., Yahia, H., Garbe, C., Paulmier, A., Dewitte, B., Illig, S., Dadou, I., González-Dávila, M., and Santana-Casiano, J. M.: Reconstruction of super-resolution ocean pCO2 and air–sea fluxes of CO2 from satellite imagery in the southeastern Atlantic, Biogeosciences, 12, 5229–5245, https://doi.org/10.5194/bg-12-5229-2015, 2015. a
Hernández-Carrasco, I., Garçon, V., Sudre, J., Garbe, C., and Yahia, H.: Increasing the Resolution of Ocean pCO2 Maps in the South Eastern Atlantic Ocean Merging Multifractal Satellite-Derived Ocean Variables, IEEE T. Geosci. Remote, 56, 6596–6610, https://doi.org/10.1109/TGRS.2018.2840526, 2018. a
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H.: The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, P. Roy. Soc. Lond. A-Math., 454, 903–995, https://doi.org/10.1098/rspa.1998.0193, 1998. a
Huang, N. E., Shen, Z., and Long, S. R.: A new view of nonlinear water waves: The Hilbert Spectrum1, Annu. Rev. Fluid Mech., 31, 417–457, https://doi.org/10.1146/annurev.fluid.31.1.417, 1999. a
Huang, Y. and Schmitt, F. G.: Time Dependent Intrinsic Correlation Analysis of Temperature and Dissolved Oxygen Time Series Using Empirical Mode Decomposition, J. Marine Syst., 130, 90–100, https://doi.org/10.1016/j.jmarsys.2013.06.007, 2014. a
Huang, Y. X., Schmitt, F. G., Lu, Z. M., and Liu, Y. L.: An Amplitude-Frequency Study of Turbulent Scaling Intermittency Using Empirical Mode Decomposition and Hilbert Spectral Analysis, Europhys. Lett., 84, 40010, https://doi.org/10.1209/0295-5075/84/40010, 2008. a, b
Huang, Y. X., Schmitt, F. G., Hermand, J.-P., Gagne, Y., Lu, Z. M., and Liu, Y. L.: Arbitrary-Order Hilbert Spectral Analysis for Time Series Possessing Scaling Statistics: Comparison Study with Detrended Fluctuation Analysis and Wavelet Leaders, Phys. Rev. E, 84, 016208, https://doi.org/10.1103/PhysRevE.84.016208, 2011. a, b, c
Katul, G. G., Chu, C. R., Parlange, M. B., Albertson, J. D., and Ortenburger, T. A.: Low-Wavenumber Spectral Characteristics of Velocity and Temperature in the Atmospheric Surface Layer, J. Geophys. Res.-Atmos., 100, 14243–14255, https://doi.org/10.1029/94JD02616, 1995. a
Keenan, T., Baker, I., Barr, A., Ciais, P., Davis, K., Dietze, M., Dragoni, D., Gough, C. M., Grant, R., Hollinger, D., Hufkens, K., Poulter, B., McCaughey, H., Raczka, B., Ryu, Y., Schaefer, K., Tian, H., Verbeeck, H., Zhao, M., and Richardson, A. D.: Terrestrial Biosphere Model Performance for Inter-Annual Variability of Land-Atmosphere CO2 Exchange, Glob. Change Biol., 18, 1971–1987, https://doi.org/10.1111/j.1365-2486.2012.02678.x, 2012. a
Kolmogorov, A. N.: On Degeneration (Decay) of Isotropic Turbulence in an Incompressible Viscous Liquid, in: Dokl. Akad. Nauk SSSR, vol. 31, 538–540, 1941. a
Kolmogorov, A. N.: A Refinement of Previous Hypotheses Concerning the Local Structure of Turbulence in a Viscous Incompressible Fluid at High Reynolds Number, J. Fluid Mech., 13, 82–85, https://doi.org/10.1017/S0022112062000518, 1962. a
Kwiatkowski, L., Torres, O., Aumont, O., and Orr, J. C.: Modified Future Diurnal Variability of the Global Surface Ocean CO2 System, Glob. Change Biol., 29, 982–997, https://doi.org/10.1111/gcb.16514, 2023. a
Landschützer, P., Gruber, N., and Bakker, D. C. E.: Decadal Variations and Trends of the Global Ocean Carbon Sink, Global Biogeochem. Cy., 30, 1396–1417, https://doi.org/10.1002/2015GB005359, 2016. a
Liu, Z., Deng, Z., Davis, S. J., Giron, C., and Ciais, P.: Monitoring Global Carbon Emissions in 2021, Nat. Rev. Earth Environ., 3, 217–219, https://doi.org/10.1038/s43017-022-00285-w, 2022. a
Obukhov, A. M.: The Structure of the Temperature Field in a Turbulent Flow, IZv. Akad. Nauk SSSR. Ser. Geogr. Geofia, 13, 1949. a
Ohtaki, E.: On the Similarity in Atmospheric Fluctuations of Carbon Dioxide, Water Vapor and Temperature over Vegetated Fields, Bound.-Lay. Meteorol., 32, 25–37, https://doi.org/10.1007/BF00120712, 1985. a
Ohtaki, E., Tsukamoto, O., Iwatani, Y., and Mitsuta, Y.: Measurements of the Carbon Dioxide Flux over the Ocean, J. Meteorol. Soc. Jpn. Ser. II, 67, 541–554, https://doi.org/10.2151/jmsj1965.67.4_541, 1989. a
Pathak, M., Slade, R., Shukla, P., Skea, J., Pichs-Madruga, R., and Ürge-Vorsatz, D.: Technical Summary, Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Shukla, P. R., Skea, J., Slade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., Belkacemi, M., Hasija, A., Lisboa, G., Luz, S., and Malley, J., Cambridge University Press, Cambridge, https://doi.org/10.1017/9781009157926.002, 2022. a
Pritchard, D. T. and Currie, J. A.: Diffusion of Coefficients of Carbon Dioxide, Nitrous Oxide, Ethylene and Ethane in Air and Their Measurement, J. Soil Sci., 33, 175–184, https://doi.org/10.1111/j.1365-2389.1982.tb01757.x, 1982. a
Richardson, L. F.: Weather Prediction by Numerical Process, Cambridge, The University press, 1922. a
Roland, M., Serrano-Ortiz, P., Kowalski, A. S., Goddéris, Y., Sánchez-Cañete, E. P., Ciais, P., Domingo, F., Cuezva, S., Sanchez-Moral, S., Longdoz, B., Yakir, D., Van Grieken, R., Schott, J., Cardell, C., and Janssens, I. A.: Atmospheric turbulence triggers pronounced diel pattern in karst carbonate geochemistry, Biogeosciences, 10, 5009–5017, https://doi.org/10.5194/bg-10-5009-2013, 2013. a
Sabine, C. L., Feely, R. A., Gruber, N., Key, R. M., Lee, K., Bullister, J. L., Wanninkhof, R., Wong, C. S., Wallace, D. W. R., Tilbrook, B., Millero, F. J., Peng, T.-H., Kozyr, A., Ono, T., and Rios, A. F.: The Oceanic Sink for Anthropogenic CO2, Science, 305, 367–371, https://doi.org/10.1126/science.1097403, 2004. a
Sahlée, E., Smedman, A.-S., Rutgersson, A., and Högström, U.: Spectra of CO2 and Water Vapour in the Marine Atmospheric Surface Layer, Bound.-Lay. Meteorol., 126, 279–295, https://doi.org/10.1007/s10546-007-9230-5, 2008. a
Sarmiento, J. L. and Gruber, N.: Sinks for Anthropogenic Carbon, Phys. Today, 55, 30–36, https://doi.org/10.1063/1.1510279, 2002. a
Schmitt, F., Schertzer, D., Lovejoy, S., and Brunet, Y.: Empirical study of multifractal phase transitions in atmospheric turbulence, Nonlin. Processes Geophys., 1, 95–104, https://doi.org/10.5194/npg-1-95-1994, 1994. a
Schmitt, F., Schertzer, D., Lovejoy, S., and Brunet, Y.: Multifractal Temperature and Flux of Temperature Variance in Fully Developed Turbulence, Europhys. Lett., 34, 195, https://doi.org/10.1209/epl/i1996-00438-4, 1996. a
Schmitt, F. G.: Linking Eulerian and Lagrangian Structure Functions' Scaling Exponents in Turbulence, Phys. A, 368, 377–386, https://doi.org/10.1016/j.physa.2005.12.028, 2006. a
Schmitt, F. G.: Gusts in Intermittent Wind Turbulence and the Dynamics of Their Recurrent Times, in: Wind Energy, edited by: Peinke, J., Schaumann, P., and Barth, S., Springer Berlin Heidelberg, Berlin, Heidelberg, 73–79, ISBN 978-3-540-33865-9 978-3-540-33866-6, 2007. a
Schmitt, F. G. and Huang, Y.: Stochastic Analysis of Scaling Time Series: From Turbulence Theory to Applications, Cambridge University Press, https://doi.org/10.1017/CBO9781107705548, 2016. a, b, c, d
Schmitt, F. G., Dur, G., Souissi, S., and Brizard Zongo, S.: Statistical Properties of Turbidity, Oxygen and pH Fluctuations in the Seine River Estuary (France), Phys. A, 387, 6613–6623, https://doi.org/10.1016/j.physa.2008.08.026, 2008. a, b, c
Seuront, L., Schmitt, F., Lagadeuc, Y., Schertzer, D., Lovejoy, S., and Frontier, S.: Multifractal Analysis of Phytoplankton Biomass and Temperature in the Ocean, Geophys. Res. Lett., 23, 3591–3594, https://doi.org/10.1029/96GL03473, 1996. a, b
Sutton, A. J., Sabine, C. L., Maenner-Jones, S., Lawrence-Slavas, N., Meinig, C., Feely, R. A., Mathis, J. T., Musielewicz, S., Bott, R., McLain, P. D., Fought, H. J., and Kozyr, A.: A high-frequency atmospheric and seawater pCO2 data set from 14 open-ocean sites using a moored autonomous system, Earth Syst. Sci. Data, 6, 353–366, https://doi.org/10.5194/essd-6-353-2014, 2014. a
Sutton, A. J., Feely, R. A., Maenner Jones, S., Musielewicz, S., Osborne, J., Dietrich, C., Monacci, N. M., Cross, J. N., Bott, R., and Kozyr, A.: Autonomous Seawater Partial Pressure of Carbon Dioxide (pCO2) and pH Time Series from 40 Surface Buoys between 2004 and 2017 (NCEI Accession 0173932), NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5DB8043, 2018. a, b, c
Sutton, A. J., Feely, R. A., Maenner-Jones, S., Musielwicz, S., Osborne, J., Dietrich, C., Monacci, N., Cross, J., Bott, R., Kozyr, A., Andersson, A. J., Bates, N. R., Cai, W.-J., Cronin, M. F., De Carlo, E. H., Hales, B., Howden, S. D., Lee, C. M., Manzello, D. P., McPhaden, M. J., Meléndez, M., Mickett, J. B., Newton, J. A., Noakes, S. E., Noh, J. H., Olafsdottir, S. R., Salisbury, J. E., Send, U., Trull, T. W., Vandemark, D. C., and Weller, R. A.: Autonomous seawater pCO2 and pH time series from 40 surface buoys and the emergence of anthropogenic trends, Earth Syst. Sci. Data, 11, 421–439, https://doi.org/10.5194/essd-11-421-2019, 2019. a, b, c, d, e, f, g
Thorpe, S. A.: The Turbulent Ocean, Cambridge University Press, Cambridge, ISBN 978-0-521-83543-5, 2005. a
Thorpe, S. A.: An Introduction to Ocean Turbulence, Cambridge University Press, Cambridge, ISBN 978-0-521-85948-6, 2007. a
Torres, O., Kwiatkowski, L., Sutton, A. J., Dorey, N., and Orr, J. C.: Characterizing Mean and Extreme Diurnal Variability of Ocean CO2 System Variables Across Marine Environments, Geophys. Res. Lett., 48, e2020GL090228, https://doi.org/10.1029/2020GL090228, 2021. a
Turk, D., Zappa, C. J., Meinen, C. S., Christian, J. R., Ho, D. T., Dickson, A. G., and McGillis, W. R.: Rain Impacts on CO2 Exchange in the Western Equatorial Pacific Ocean, Geophys. Res. Lett., 37, L23610, https://doi.org/10.1029/2010GL045520, 2010. a
Wanninkhof, R.: Relationship between Wind Speed and Gas Exchange over the Ocean Revisited, Limnol. Oceanogr. Meth., 12, 351–362, https://doi.org/10.4319/lom.2014.12.351, 2014. a
Yamamoto, A., Abe-Ouchi, A., and Yamanaka, Y.: Long-term response of oceanic carbon uptake to global warming via physical and biological pumps, Biogeosciences, 15, 4163–4180, https://doi.org/10.5194/bg-15-4163-2018, 2018. a
Yue, X.-L. and Gao, Q.-X.: Contributions of Natural Systems and Human Activity to Greenhouse Gas Emissions, Adv. Clim. Change Res., 9, 243–252, https://doi.org/10.1016/j.accre.2018.12.003, 2018. a
Zhang, M., Cheng, Y., Bao, Y., Zhao, C., Wang, G., Zhang, Y., Song, Z., Wu, Z., and Qiao, F.: Seasonal to Decadal Spatiotemporal Variations of the Global Ocean Carbon Sink, Glob. Change Biol., 28, 1786–1797, https://doi.org/10.1111/gcb.16031, 2022. a
Zongo, S. B. and Schmitt, F. G.: Scaling properties of pH fluctuations in coastal waters of the English Channel: pH as a turbulent active scalar, Nonlin. Processes Geophys., 18, 829–839, https://doi.org/10.5194/npg-18-829-2011, 2011. a, b, c
Short summary
In this work, the multiscale dynamics of 38 oceanic and atmospheric pCO2 time series, sea surface temperature data, and salinity data from fixed buoys recorded with 3 h resolution are considered. The Fourier scaling exponents are estimated. The differences found for three ecosystems – coastal shelf, coral reefs and open ocean – are discussed. Multifractal properties of pCO2 difference between ocean and atmosphere are found and characterized over the scale range from 3 h to 1 year.
In this work, the multiscale dynamics of 38 oceanic and atmospheric pCO2 time series, sea...
