Articles | Volume 11, issue 4
https://doi.org/10.5194/os-11-591-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-11-591-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Exploring the isopycnal mixing and helium–heat paradoxes in a suite of Earth system models
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
M.-A. Pradal
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USA
R. Abernathey
Department of Earth and Environmental Sciences, Columbia University, New York, NY, USA
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Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
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Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
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Geosci. Model Dev., 15, 1595–1617, https://doi.org/10.5194/gmd-15-1595-2022, https://doi.org/10.5194/gmd-15-1595-2022, 2022
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Christopher Holder and Anand Gnanadesikan
Biogeosciences, 18, 1941–1970, https://doi.org/10.5194/bg-18-1941-2021, https://doi.org/10.5194/bg-18-1941-2021, 2021
Short summary
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A challenge for marine ecologists in studying phytoplankton is linking small-scale relationships found in a lab to broader relationships observed on large scales in the environment. We investigated whether machine learning (ML) could help connect these small- and large-scale relationships. ML was able to provide qualitative information about the small-scale processes from large-scale information. This method could help identify important relationships from observations in future research.
S. Sedigh Marvasti, A. Gnanadesikan, A. A. Bidokhti, J. P. Dunne, and S. Ghader
Biogeosciences, 13, 1049–1069, https://doi.org/10.5194/bg-13-1049-2016, https://doi.org/10.5194/bg-13-1049-2016, 2016
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This study examines challenges in modeling phytoplankton blooms in Northwestern Arabian Sea and Gulf of Oman. Blooms in the region show strong modulation both by seasons and in the wintertime by eddies. However getting both of these correct is a challenge in a set of state-of-the-art global Earth System models. It is argued that maintaining a sharp pycnocline may be the key for preventing the wintertime bloom from being too strong and for allowing eddies to modulate upward mixing of nutrients.
A. Goswami, P. L. Olson, L. A. Hinnov, and A. Gnanadesikan
Geosci. Model Dev., 8, 2735–2748, https://doi.org/10.5194/gmd-8-2735-2015, https://doi.org/10.5194/gmd-8-2735-2015, 2015
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A methodology is presented for reconstructing past global ocean bathymetry using a plate cooling model for the oceanic lithosphere, the age distribution of the oceanic crust, global oceanic sediment thicknesses, plus shelf-slope-rise structures calibrated at modern active and passive continental margins. The final product is a globally complete ocean bathymetry at arbitrary resolution with an isostatically adjusted, multicomponent sediment layer.
G. E. Kim, M.-A. Pradal, and A. Gnanadesikan
Biogeosciences, 12, 5119–5132, https://doi.org/10.5194/bg-12-5119-2015, https://doi.org/10.5194/bg-12-5119-2015, 2015
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Light absorption by colored detrital material (CDM) was included in a fully coupled Earth system model. Chlorophyll and biomass increased near the surface but decreased at greater depths when CDM was included. Concurrently, total biomass decreased leaving more nutrients in the water. Regional changes were analyzed by comparing the competing factors of diminished light availability and increased nutrient availability on phytoplankton growth.
N. Towles, P. Olson, and A. Gnanadesikan
Clim. Past, 11, 991–1007, https://doi.org/10.5194/cp-11-991-2015, https://doi.org/10.5194/cp-11-991-2015, 2015
Short summary
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In this paper we find scaling relationships for perturbations to atmosphere and ocean variables from large transient CO2 emissions. We use a carbon cycle box model to calculate peak perturbations to a variety of ocean and atmosphere variables resulting from idealized emission events. As these scaling relationships depend on the physical setup, they represent a compact way of characterizing how different climates respond to large transient perturbations.
Cited articles
Abernathey, R. and Marshall, J.: Global surface eddy diffusivities derived from satellite altimetry, J. Geophys. Res.-Oceans, 118, 901–916, 2013.
Abernathey, R., Marshall, J., Shuckburgh, E., and Mazloff, M.: Enhancement of mesoscale eddy stirring at steering levels in the Southern Ocean, J. Phys. Oceanogr., 40, 170–185, 2010.
Abernathey, R., Ferreira, D., and Klocker, A.: Diagnostics of isopycnal mixing in a circumpolar channel, Ocean Model., 72, 1–16, 2013.
Anderson, D.: The helium paradoxes, P. Natl. Acad. Sci. USA, 95, 4822–4827, 1998.
Bachman, S., Fox-Kemper, B., and Bryan, F. C.: A tracer-based inversion method for diagnosing eddy-induced diffusivity and advection. Ocean Model., 86, 1–14, https://doi.org/10.1016/j.ocemod.2014.11.006, 2015.
Bauer, S., Swenson, M. S., Griffa, A., Mariano, A. J., and Owens, K.: Eddy-mean flow decomposition and eddy-diffusivity estimates in the tropical Pacific Ocean, 1. Methodology, J. Geophys. Res., 103, 30855–30871. 1998.
Baynte, D., Visbeck, M., Tanhua, T., Krahmann, G., and Karstensen, J.: Lateral diffusivity from tracer release experiments in the tropical thermocline, J. Geophys. Res.-Oceans, 118, 2719–2733, 2013. \bibitem [Bentsen et al.(2013)]Bentsen13 Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013.
Bianchi, D., Sarmiento, J. L., Gnanadesikan, A., Key, R. M., Schlosser, P., and Newton, R.: Low helium flux from the mantle inferred from simulations of oceanic helium isotope data, Earth Planet. Sc. Lett., 297, 379–386, https://doi.org/10.1016/j.epsl.2010.06.037, 2010.
Craig, H., Clarke, W. B., and Beg, M. A.: Excess 3He in deep water on the East Pacific rise, Earth Planet. Sc. Lett., 26, 125–132, 1975.
Danabasoglu, G., McWilliams, J. C., and Gent, P.: The role of mesoscale tracer transports in the global ocean circulation, Science, 264, 1123–1126, 1994.
Danabasoglu, G., Bates, S. C., Brieglieb, B. P., Jayne, S. R., Jochum, M., Large, W. G., Peacock, S., and Yeager, S. G.: The CCSM4 ocean component, J. Climate, 25, 1361–1389, 2012.
Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E. N., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., Krasting, J. P., Levy, H.,Malyshev, S. L., Milly, P. C. D., Phillips, P. J., Sentman, L. A., Samuels, B. L., Spelman, M. J., Winton, M.,Wittenberg, A. T., and Zadeh, N.: GFDL's ESM2 global coupled climate-carbon Earth System Models Part I: Physical formulation and baseline simulation characteristics, J. Climate, 25, 6646–6665, 2012.
Dutay, J.-C., Jean-Baptiste, P., Campin, J.-M., Ishida, A., Maier-Reimer, E., Matear, R. J., Mouchet, A., Totterdell, I. J., Yamanaka, Y., Rodgers, K., Madec, G., and Orr, J. C.: Evaluation of OCMIP-2 ocean models' deep circulation with mantle helium-3, J. Marine Syst., 48, 15–36, 2004.
Eady, E.: Long waves and cyclone waves, Tellus, 1, 33–52, 1949.
Emerson, S. and Hedges, J. I.: Chemical Oceanography and the Marine Carbon Cycle, Cambridge Univ. Press, New York, 2008.
Farley, K. A., Maier-Reimer, E., Schlosser, P., and Broecker, W. S.: Constraints on mantle He-3 fluxes and deep-sea circulation from an ocean general circulation model, J. Geophys. Res., 100, 3829–3839, 1995.
Fogli, P., Manzini, E., Vichi, M., Alessandri, A., Patara, L., Gualdi, S., Scoccimoarro, E., Masina, S., and Navarra, A.: INGV-CMCC Carbon (ICC): a carbon cycle earth system model, Technical Rep. 61, Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna, 2009.
Fox-Kemper, B, Lumpkin, R., and Bryan, F. O.: Lateral transport in the ocean interior, in: Ocean Circulation and Climate – Observing and Modelling the Global Ocean, edited by: Siedler, G., Church, J., Gould, J., and Griffies, S., Elsevier, New York, 2012.
Galbraith, E. D., Gnanadesikan, A., Dunne, J. P., and Hiscock, M. R.: Regional impacts of iron-light colimitation in a global biogeochemical model, Biogeosciences, 7, 1043–1064, https://doi.org/10.5194/bg-7-1043-2010, 2010.
Galbraith, E. D., Kwon, E.-Y., Gnanadesikan, A., Rodgers, K. B., Griffies, S. M., Bianchi, D., Dunne, J. P., Sarmiento, J. L., Simeon, J., Slater, R. D., Wittenberg, A. T., and Held, I. M.: Climate variability and radiocarbon in the CM2Mc Earth System Model, J. Climate, 24, 4230–4254, https://doi.org/10.1175/2011JCLI3919.1, 2011.
Gent, P. and McWilliams, J. C.: Isopycnal mixing in ocean models, J. Phys. Oceanogr., 20, 150–155, 1990.
Gnanadesikan, A.: A simple model for the structure of the oceanic pycnocline, Science, 283, 2077–2079, 1999.
Gnanadesikan, A., Dixon, K. W., Griffies, S. M., Balaji, V., Barreiro, M, Beesley, J. A., Cooke, W. F., Delworth, T. L., Gerdes, R., Harrison, M. J., Held, I., Hurlin, W. J., Lee, H. C., Liang, Z., Nong, G., Pacanowski, R. C., Rosati, A., Russell, J. L., Samuels, B. L., Song, Q., Spelman, M. J., Stouffer, R. J., Sweeney, C., Vecchi, G. A., Winton, M., Wittenberg, A. T., Zeng, F., Zhang, R., and Dunne, J. P.: GFDL's CM2 Global coupled climate models: Part II: The baseline ocean simulation, J. Climate, 19, 675–697, 2006.
Gnanadesikan, A., Bianchi, D., and Pradal, M. A.: Critical role for mesoscale eddy diffusion in supplying oxygen to hypoxic ocean waters, Geophys. Res. Lett., 40, 5194–5198, https://doi.org/10.1002/grl.50998, 2013.
Gnanadesikan, A., Pradal, M. A., and Abernathey, R. P.: Isopycnal mixing by mesoscale eddies significantly impacts oceanic anthropogenic carbon uptake, Geophys. Res. Lett., 42, 4249–4255, https://doi.org/10.1002/2015GL064100, 2015.
Gordon, J., O'Farrell, S., Collier, M., Dix, M., and Rotstayn, L., Kowalczyk, Hirst, T., and Watterson, I.: The CSIRO Mk3.5 Climate model, Tech. Rep. 21, Center for Australian Weather and Climate Research, Melbourne, 2010.
Green, J. S.: Transfer properties of the large-scale eddies and the general circulation of the atmosphere, Q. J. Roy. Meteor. Soc., 96, 157–185, 1970.
Griffies, S. M.: The Gent–McWilliams skew flux, J. Phys. Oceanogr., 28, 831–841, 1998.
Griffies, S. M., Gnanadesikan, A., Pacanowski, R. C., Larichev, V. D., Dukowicz, J. K., and Smith, R. D.: Isoneutral diffusion in a z-coordinate ocean model, J. Phys. Oceanogr., 28, 805–830, 1998.
Harrison, D. and Ballentine, C. J.: Noble gas models of mantle structure and reservoir mass transfer, Earth's Deep Mantle: structure, composition and evolution, Geophys. Monogr. Ser., 160, 9–26, 2003.
Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S., Knight, J., Liddicoat, S., O'Connor, F. M., Andres, R. J., Bell, C., Boo, K.-O., Bozzo, A., Butchart, N., Cadule, P., Corbin, K. D., Doutriaux-Boucher, M., Friedlingstein, P., Gornall, J., Gray, L., Halloran, P. R., Hurtt, G., Ingram, W. J., Lamarque, J.-F., Law, R. M., Meinshausen, M., Osprey, S., Palin, E. J., Parsons Chini, L., Raddatz, T., Sanderson, M. G., Sellar, A. A., Schurer, A., Valdes, P., Wood, N., Woodward, S., Yoshioka, M., and Zerroukat, M.: The HadGEM2-ES implementation of CMIP5 centennial simulations, Geosci. Model Dev., 4, 543–570, https://doi.org/10.5194/gmd-4-543-2011, 2011.
Key, R. M., Kozyr, A., Sabine, C. L., Lee, K., Wanninkhof, R., Bullister, J., Feely, R. A., Millero, F., Mordy, C., and Peng. T.-H.: A global ocean carbon climatology: results from GLODAP, Global Biogeochem. Cy., 18, GB4031, https://doi.org/10.1029/2004GB002247, 2004.
Khatiawala, S., Primeau, F., and Holzer, M.: Ventilation of the deep ocean constrained with tracer observations and implications for radiocarbon estimates of ideal mean age, Earth Planet. Sc. Lett., 325–326, 116–125, https://doi.org/10.1016/j.epsl.2012.01.038, 2012.
Ledwell, J. R. and Watson, A. J.: The Santa Monica Basin tracer experiment: a study of diapycnal and isopycnal mixing, J. Geophys. Res., 103, 214999–215129, 1991.
Ledwell, J. R., Watson, A. J., and Law, C. S.: Mixing of a tracer in the pycnocline, J. Geophys. Res., 103, 21499–21529, 1998.
Marshall, J., Shuckburgh, E., Jones, H., and Hill, C.: Estimates and implications of surface eddy diffusivity in the Southern Ocean derived from tracer transport, J. Phys. Oceanogr., 36, 1806–1821, 2006.
Ollitraut, M. and Colin de Verdiere, A.: SOFAR floats reveal mid-latitude intermediate North Atlantic circulation, Part II: An Eulerian statistical view, J. Phys. Oceanogr., 32, 2034–2053, 2002.
O'Nions, R. K. and Oxburgh, E. R.: Heat and helium in the earth, Nature, 306, 429–431, 1983.
Pradal, M.-A. and Gnanadesikan, A.: Impact of isopycnal stirring on global climate in an Earth System Model, J. Adv. Model. Earth Sys., 6, 586–601, https://doi.org/10.1002/2013MS000273, 2014.
Reckinger, S. J. and Fox-Kemper, B.: Anisotropic mesoscale eddy transport in ocean general circulation models, in preparation, 2015.
Redi, M. H.: Ocean isopycnal mixing by coordinate rotation, J. Phys. Oceanogr., 12, 1154–1157, 1982.
Rye, C. D., Messais, M.-J., Ledwell, J. R., Watson, A. J., Brousseau, A., and King, B. A.: Diapycnal diffusivities from a tracer release experiment in the deep sea integrated over 13 years, Geophys. Res. Lett., 39, L04603, https://doi.org/10.1029/2011GL050294, 2012.
Rypina, I., Kamenkovich, I., Berloff, P., and Pratt, L.: Eddy-induced particle dispersion in the near-surface Atlantic, J. Phys. Oceanogr., 42, 2206–2228, 2012.
Salas y Mélia D., Chauvin, F., Déqué M., Douville, H., Guérémy J. F., Marquet, P., Planton, S., Royer, J. F., and Tyteca, S.: Description and validation of CNRM-CM3 global coupled climate model, Note de centre GMGEC (internal publication), CNRM, Toulouse, 103, 2005.
Shuckburgh, E. and Haynes, P.: Diagnosing transport and mixing using a tracer-based coordinate system, Phys. Fluids, 15, 3342–3357, 2003.
Shuckburgh, E., Jones, H., Marshall, J., and Hill, C.: Robustness of effective diffusivity diagnostic in oceanic flows, J. Phys. Oceanogr., 39, 1993–2009, 2009.
Smith, K. S. and Marshall, J.: Evidence for enhanced eddy mixing at mid-depth in the Southern Ocean, J. Phys. Oceanogr., 39, 50–69, 2009.
Visbeck, M., Marshall, J., Haine, T., and Spall, M.: Specification of eddy transfer coefficients in coarse-resolution ocean circulation models, J. Phys. Oceanogr., 27, 381–402, https://doi.org/10.1175/1520-0485(1997)027<0381:SOETCI>2.0.CO;2, 1997.
Vollmer, L. and Eden, C.: A global map of mesoscale eddy diffusivities based on linear stability analysis, Ocean Model., 72, 198–209, 2013.
Short summary
Many ocean circulation models use representations of lateral mixing based on instability theories that predict weak mixing in the ocean interior, much lower than observed. We show that using more realistic mixing improves the distribution of mantle helium-3. It does not, however, resolve the paradox that models reproduce the relationship between mantle helium and radiocarbon with a flux of helium-3 lower than is consistent with the heat leaving the mantle.
Many ocean circulation models use representations of lateral mixing based on instability...