Articles | Volume 18, issue 3
https://doi.org/10.5194/os-18-729-2022
© Author(s) 2022. 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-18-729-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 May 2022
Research article |
| 30 May 2022
| 30 May 2022
Tracer and observationally derived constraints on diapycnal diffusivities in an ocean state estimate
Department of Oceanography & Coastal Sciences, Louisiana State University, Baton Rouge, USA
Center for Computation & Technology, Louisiana State University, Baton Rouge, USA
Caitlin B. Whalen
Applied Physics Laboratory, University of Washington, Seattle, USA
Thomas W. N. Haine
Department of Earth & Planetary Sciences, Johns Hopkins University, Baltimore, USA
Amy F. Waterhouse
Scripps Institution of Oceanography, University of California at San Diego, San Diego, USA
An T. Nguyen
Oden Institute for Computational Engineering & Sciences, University of Texas at Austin, Austin, USA
Arash Bigdeli
EP Analytics, Inc., Austin, USA
Matthew Mazloff
Scripps Institution of Oceanography, University of California at San Diego, San Diego, USA
Patrick Heimbach
Oden Institute for Computational Engineering & Sciences, University of Texas at Austin, Austin, USA
Jackson School of Geosciences & Institute for Geophysics, University of Texas at Austin, Austin, USA
Related authors
No articles found.
Andrew Porter and Patrick Heimbach
State Planet Discuss., https://doi.org/10.5194/sp-2024-32, https://doi.org/10.5194/sp-2024-32, 2024
Preprint under review for SP
Short summary
Short summary
Numerical ocean forecasting is a key part of accurate models of the earth system. However, they require powerful computing resources and the architectures of the necessary computers are evolving rapidly. Unfortunately, this is a disruptive change – an ocean model must be modified to enable it to make use of this new computing hardware. This paper reviews what has been done in this area and identifies solutions to enable operational ocean forecasts to make use of the new computing hardware.
Patrick Heimbach, Fearghal O'Donncha, Jose Maria Garcia-Valdecasas, Alain Arnaud, and Liying Wan
State Planet Discuss., https://doi.org/10.5194/sp-2024-18, https://doi.org/10.5194/sp-2024-18, 2024
Preprint under review for SP
Short summary
Short summary
Operational ocean prediction relies on computationally expensive numerical models and complex workflows known as data assimilation, in which models are fit to observations to produce optimal initial conditions for prediction. Machine learning has the potential to vastly accelerate ocean prediction, improve uncertainty quantification through massive surrogate model-based ensembles, and render simulations more accurate through better model calibration. We review developments and challenges.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet Discuss., https://doi.org/10.5194/sp-2024-24, https://doi.org/10.5194/sp-2024-24, 2024
Preprint under review for SP
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 days ahead – and an outlook of their upcoming developments.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Ian Fenty, Matthew Mazloff, Köhl Armin, and Dimitris Menemenlis
EGUsphere, https://doi.org/10.5194/egusphere-2024-727, https://doi.org/10.5194/egusphere-2024-727, 2024
Short summary
Short summary
Global and basin-scale ocean reanalyses are becoming easily accessible. Yet, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluations. We conduct intercomparison analyses of Massachusetts Institute of Technology general circulation model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open ocean temporal variability and Antarctic continental shelves require improvements.
Linghan Li, Forest Cannon, Matthew R. Mazloff, Aneesh C. Subramanian, Anna M. Wilson, and Fred Martin Ralph
The Cryosphere, 18, 121–137, https://doi.org/10.5194/tc-18-121-2024, https://doi.org/10.5194/tc-18-121-2024, 2024
Short summary
Short summary
We investigate how the moisture transport through atmospheric rivers influences Arctic sea ice variations using hourly atmospheric ERA5 for 1981–2020 at 0.25° × 0.25° resolution. We show that individual atmospheric rivers initiate rapid sea ice decrease through surface heat flux and winds. We find that the rate of change in sea ice concentration has significant anticorrelation with moisture, northward wind and turbulent heat flux on weather timescales almost everywhere in the Arctic Ocean.
Stefania A. Ciliberti, Enrique Alvarez Fanjul, Jay Pearlman, Kirsten Wilmer-Becker, Pierre Bahurel, Fabrice Ardhuin, Alain Arnaud, Mike Bell, Segolene Berthou, Laurent Bertino, Arthur Capet, Eric Chassignet, Stefano Ciavatta, Mauro Cirano, Emanuela Clementi, Gianpiero Cossarini, Gianpaolo Coro, Stuart Corney, Fraser Davidson, Marie Drevillon, Yann Drillet, Renaud Dussurget, Ghada El Serafy, Katja Fennel, Marcos Garcia Sotillo, Patrick Heimbach, Fabrice Hernandez, Patrick Hogan, Ibrahim Hoteit, Sudheer Joseph, Simon Josey, Pierre-Yves Le Traon, Simone Libralato, Marco Mancini, Pascal Matte, Angelique Melet, Yasumasa Miyazawa, Andrew M. Moore, Antonio Novellino, Andrew Porter, Heather Regan, Laia Romero, Andreas Schiller, John Siddorn, Joanna Staneva, Cecile Thomas-Courcoux, Marina Tonani, Jose Maria Garcia-Valdecasas, Jennifer Veitch, Karina von Schuckmann, Liying Wan, John Wilkin, and Romane Zufic
State Planet, 1-osr7, 2, https://doi.org/10.5194/sp-1-osr7-2-2023, https://doi.org/10.5194/sp-1-osr7-2-2023, 2023
Carl Wunsch, Sarah Williamson, and Patrick Heimbach
Ocean Sci., 19, 1253–1275, https://doi.org/10.5194/os-19-1253-2023, https://doi.org/10.5194/os-19-1253-2023, 2023
Short summary
Short summary
Data assimilation methods that couple observations with dynamical models are essential for understanding climate change. Here,
climateincludes all sub-elements (ocean, atmosphere, ice, etc.). A common form of combination arises from sequential estimation theory, a methodology susceptible to a variety of errors that can accumulate through time for long records. Using two simple analogs, examples of these errors are identified and discussed, along with suggestions for accommodating them.
Rui Sun, Alison Cobb, Ana B. Villas Bôas, Sabique Langodan, Aneesh C. Subramanian, Matthew R. Mazloff, Bruce D. Cornuelle, Arthur J. Miller, Raju Pathak, and Ibrahim Hoteit
Geosci. Model Dev., 16, 3435–3458, https://doi.org/10.5194/gmd-16-3435-2023, https://doi.org/10.5194/gmd-16-3435-2023, 2023
Short summary
Short summary
In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS. We then performed a case study using the newly implemented model to study Tropical Cyclone Mekunu, which occurred in the Arabian Sea. We found that the coupled model better simulates the cyclone than the uncoupled model, but the impact of waves on the cyclone is not significant. However, the waves change the sea surface temperature and mixed layer, especially in the cold waves produced due to the cyclone.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
Short summary
Short summary
Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
Short summary
Short summary
The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Liz C. Logan, Sri Hari Krishna Narayanan, Ralf Greve, and Patrick Heimbach
Geosci. Model Dev., 13, 1845–1864, https://doi.org/10.5194/gmd-13-1845-2020, https://doi.org/10.5194/gmd-13-1845-2020, 2020
Short summary
Short summary
A new capability has been developed for the ice sheet model SICOPOLIS (SImulation COde for POLythermal Ice Sheets) that enables the generation of derivative code, such as tangent linear or adjoint models, by means of algorithmic differentiation. It relies on the source transformation algorithmic (AD) differentiation tool OpenAD. The reverse mode of AD provides the adjoint model, SICOPOLIS-AD, which may be applied for comprehensive sensitivity analyses as well as gradient-based optimization.
Rui Sun, Aneesh C. Subramanian, Arthur J. Miller, Matthew R. Mazloff, Ibrahim Hoteit, and Bruce D. Cornuelle
Geosci. Model Dev., 12, 4221–4244, https://doi.org/10.5194/gmd-12-4221-2019, https://doi.org/10.5194/gmd-12-4221-2019, 2019
Short summary
Short summary
A new regional coupled ocean–atmosphere model, SKRIPS, is developed and presented. The oceanic component is the MITgcm and the atmospheric component is the WRF model. The coupler is implemented using ESMF according to NUOPC protocols. SKRIPS is demonstrated by simulating a series of extreme heat events occurring in the Red Sea region. We show that SKRIPS is capable of performing coupled ocean–atmosphere simulations. In addition, the scalability test shows SKRIPS is computationally efficient.
Andrey Pnyushkov, Igor V. Polyakov, Laurie Padman, and An T. Nguyen
Ocean Sci., 14, 1329–1347, https://doi.org/10.5194/os-14-1329-2018, https://doi.org/10.5194/os-14-1329-2018, 2018
Short summary
Short summary
A total of 4 years of velocity and hydrography records from moored profilers over the Laptev Sea slope reveal multiple events of eddies passing through the mooring site. These events suggest that the advection of mesoscale eddies is an important component of ocean dynamics in the Eurasian Basin of the Arctic Ocean. Increased vertical shear of current velocities found within eddies produces enhanced diapycnal mixing, suggesting their importance for the redistribution of heat in the Arctic Ocean.
Nat Wilson, Fiammetta Straneo, and Patrick Heimbach
The Cryosphere, 11, 2773–2782, https://doi.org/10.5194/tc-11-2773-2017, https://doi.org/10.5194/tc-11-2773-2017, 2017
Short summary
Short summary
We estimate submarine melt rates from ice tongues in northern Greenland using WorldView satellite imagery. At Ryder Glacier, melt is strongly concentrated around regions where subglacier channels likely enter the fjord. At the 79 North Glacier, we find a large volume imbalance in which melting removes a greater quantity of ice than is replaced by inflow over the grounding line. This leads us to suggest that a reduction in the spatial extent of the ice tongue is possible over the coming decade.
Cited articles
Abernathey, R. P. and Marshall, J.: Global surface eddy diffusivities derived from satellite altimetry, J. Geophys. Res.-Oceans, 118, 901–916, https://doi.org/10.1002/jgrc.20066, 2013. a
Adcroft, A. and Campin, J.-M.: Rescaled height coordinates for accurate representation of free-surface flows in ocean circulation models, Ocean Model., 7, 269–284, 2004. a
Adcroft, A., Hill, C., and Marshall, J.: The representation of topography by shaved cells in a height coordinate model, Mon. Weather Rev., 125, 2293–2315, 1997. a
Alford, M. H., MacKinnon, J. A., Simmons, H. L., and Nash, J. D.: Near-inertial internal gravity waves in the ocean, Annu. Rev. Mar. Sci., 8, 95–123, 2016. a
Arbic, B. K., Garner, S. T., Hallberg, R. W., and Simmons, H .L.: The accuracy of surface elevations in forward global barotropic and baroclinic tide models, Deep-Sea Res. Pt. II, 51, 3069–3101, https://doi.org/10.1016/j.dsr2.2004.09.014, 2004. a
Atamanchuk, D., Koelling, J., Send, U., and Wallace, D. W. R.: Rapid transfer of oxygen to the deep ocean mediated by bubbles, Nat. Geosci., 13, 232– 237, https://doi.org/10.1038/s41561-020-0532-2, 2020. a, b
Bachman, S. D., Fox-Kemper, B., and Bryan, F. O.: A diagnosis of anisotropic eddy diffusion from a high-resolution global ocean model, J. Adv. Model. Earth Sy., 12, e2019MS001904, https://doi.org/10.1029/2019MS001904, 2020. a
Bates, M., Tulloch, R., Marshall, J., and Ferrari, R.: Rationalizing the spatial distribution of mesoscale eddy diffusivity in terms of mixing length theory, J. Phys. Oceanogr., 44, 1523–1540, https://doi.org/10.1175/jpo-d-13-0130.1, 2014. a
Bigdeli, A., Loose, B., Nguyen, A. T., and Cole, S. T.: Numerical investigation of the Arctic ice–ocean boundary layer and implications for air–sea gas fluxes, Ocean Sci., 13, 61–75, https://doi.org/10.5194/os-13-61-2017, 2017. a
Billheimer, S. J., Talley, L. D., and Martz, T. R.: Oxygen seasonality, utilization rate, and impacts of vertical mixing in the eighteen degree water region of the sargasso sea as observed by profiling biogeochemical floats, Global Biogeochem. Cy., 35, e2020GB006824, https://doi.org/10.1029/2020GB006824, 2021. a
Borovikov, A., Cullather, R., Kovach, R., Marshak, J., Vernieres, G., Vikhliaev, Y., Zhao, B., and Li, Z.: GEOS-5 seasonal forecast system, Clim. Dynam., 53, 7335–7361, https://doi.org/10.1007/s00382-017-3835-2, 2019. a
Brandt, P., Bange, H. W., Banyte, D., Dengler, M., Didwischus, S.-H., Fischer, T., Greatbatch, R. J., Hahn, J., Kanzow, T., Karstensen, J., Körtzinger, A., Krahmann, G., Schmidtko, S., Stramma, L., Tanhua, T., and Visbeck, M.: On the role of circulation and mixing in the ventilation of oxygen minimum zones with a focus on the eastern tropical North Atlantic, Biogeosciences, 12, 489–512, https://doi.org/10.5194/bg-12-489-2015, 2015. a
Brandt, P., Hahn, J., Schmidtko, S., Tuchen, F. P., Kopte, R., Kiko, R., Bourlés, B., Czeschel, R., and Dengler, M.: Atlantic equatorial undercurrent intensification counteracts warming-induced deoxygenation, Nat. Geosci., 14, 278–282, https://doi.org/10.1038/s41561-021-00716-1, 2021. a
Busecke, J. J. M. and Abernathey, R. P.: Ocean mesoscale mixing linked to climate variability, Science Advances, 5, eaav5014, https://doi.org/10.1126/sciadv.aav5014, 2019. a
Campin, J.-M., Adcroft, A., Hill, C., and Marshall, J.: Conservation of properties in a free surface model, Ocean Model., 6, 221–244, 2004. a
Chaudhuri, A. H., Ponte, R. M., Forget, G., Heimbach, P.: A comparison of atmospheric reanalysis surface products over the ocean and implications for uncertainties in air-sea boundary forcing, J. Climate, 26, 153–170, 2013. a
Chin, M., Ginoux, P., Kinne, S., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sunphotometer measurements, J. Atmos. Sci., 59, 461–483, 2002. a
Cole, S. T., Wortham, C., Kunze, E., and Owens, W. B.: Eddy stirring and horizontal diffusivity from argo float observations: Geographic and depth variability, Geophys. Res. Lett., 42, 3989–3997, https://doi.org/10.1002/2015GL063827, 2015. a, b
Couespel, D., Lévy, M., and Bopp, L.: Major contribution of reduced upper ocean oxygen mixing to global ocean deoxygenation in an earth system model, Geophys. Res. Lett., 46, 12239–12249, https://doi.org/10.1029/2019GL084162, 2019. a
Dalan, F., Stone, P. H., and Sokolov, A. P.: Sensitivity of the ocean's climate to diapycnal diffusivity in an EMIC. Part II: Global warming scenario, J. Climate, 18, 2482–2496, 2005. a
Danabasoglu, G. and McWilliams, J. C.: Sensitivity of the global ocean circulation to parameterizations of mesoscale tracer transports, J. Climate, 8, 2967–2987, 1995. a
D'Asaro, E.: Turbulence in the upper-ocean mixed layer, Annu. Rev. Mar. Sci., 6, 101–115, https://doi.org/10.1146/annurev-marine-010213-135138, 2014. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Virtart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011. a
de Lavergne, C., Vic, C., Madec, G., Roquet, F., Waterhouse, A. F., Whalen, C. B., Cuypers, Y., Bouruet-Aubertot, P., Ferron, B., and Hibiya, T.: A parameterization of local and remote tidal mixing, J. Adv. Model. Earth Sy., 12, e2020MS002065, https://doi.org/10.1029/2020MS002065, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m
DeVries, T. and Holzer, M.: Radiocarbon and helium isotope constraints on deep ocean ventilation and mantle-3He sources, J. Geophys. Res.-Oceans, 124, 3036–3057, https://doi.org/10.1029/2018JC014716, 2019. a
Duteil, O. and Oschlies, A.: Sensitivity of simulated extent and future evolution of marine suboxia to mixing intensity, Geophys. Res. Lett., 38, L06607, https://doi.org/10.1029/2011GL046877, 2011. a
Ehlert, D., Zickfeld, K., Eby, M., and Gillett, N.: The sensitivity of the proportionality between temperature change and cumulative CO2 emissions to ocean mixing, J. Climate, 30, 2921–2935, 2017. a
Ferrari, R., McWilliams, J. C., Canuto, V. M., and Dubovikov, M.: Parameterization of eddy fluxes near oceanic boundaries, J. Climate, 21, 2770–2789, 2008. a
Ferrari, R., Griffies, S. M., Nurser, A. J. G., and Vallis, G. K.: A boundary-value problem for the parameterized mesoscale eddy transport, Ocean Model., 32, 143–156, 2010. a
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015a. a, b, c, d
Forget, G., Ferreira, D., and Liang, X.: On the observability of turbulent transport rates by Argo: supporting evidence from an inversion experiment, Ocean Sci., 11, 839–853, https://doi.org/10.5194/os-11-839-2015, 2015b. a, b, c
Fox-Kemper, B., Danabasoglu, G., Ferrari, R., Griffies, S. M., Hallberg, R. W., Holland, M. M., Maltrud, M. E., Peacock, S., and Samuels, B. L.: Parameterization of mixed layer eddies. III: Implementation and impact in global ocean climate simulations, Ocean Model., 39, 61–78, 2011. a
Galbraith, E. D., Dunne, J. P., Gnanadesikan, A., Slater, R. D., Sarmiento, J. L., Dufour, C. O., de Souza, G. F., Bianchi, D., Claret, M., Rodgers, K. B., and Marvasti, S. S.: Complex functionality with minimal computation: Promise and pitfalls of reduced-tracer ocean biogeochemistry models, J. Adv. Model. Earth Sy., 7, 2012–2028, https://doi.org/10.1002/2015MS000463, 2015. a, b
Garcia, H. E., Boyer, T. P., Locarnini, R. A., Antonov, J. I., Mishonov, A. V., Baranova, O. K., Zweng, M. M., Reagan, J. R., Johnson, D. R., and Levitus, S.: World ocean atlas 2013, Vol. 3, Dissolved oxygen, apparent oxygen utilization, and oxygen saturation, NOAA Atlas NESDIS 75, https://doi.org/10.7289/V55X26VD, 2013.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G. K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gent, P. R. and McWilliams, J. C.: Isopycnal mixing in ocean circulation models, J. Phys. Oceanogr., 20, 150–155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a, b, c
Gerdes, R., Köberle, C., and Willebrand, J.: The influence of numerical advection schemes on the results of ocean general circulation models, Clim. Dynam., 5, 211–226, 1990. a
Giering, R. and Kaminski, T.: Recipes for adjoint code construction, ACM T. Math. Software, 24, 437–474, 1998. a
Gilbert, J. C. and Lemarechal, C.: Some numerical experiments with variable-storage quasi-newton algorithms, Math. Program., 45, 407–435, 1989. a
Gnanadesikan, A.: A simple predictive model for the structure of the oceanic pycnocline, Science, 283, 2077, 1999. a
Gnanadesikan, A., Pradal, M.-A., and Abernathey, R.: Isopycnal mixing by mesoscale eddies significantly impacts oceanic anthropogenic carbon uptake, Geophys. Res. Lett., 42, 4249–4255, https://doi.org/10.1002/2015GL064100, 2015. a
Gregg, M. C.: Diapycnal mixing in the thermocline: A review, J. Geophys. Res., 92, 5249–5286, https://doi.org/10.1029/JC092iC05p05249, 1987. a
Griewank, A.: Achieving logarithmic growth of temporal and spatial complexity in reverse automatic differentiation, Optim. Method. Softw., 1, 35–54, https://doi.org/10.1080/10556789208805505, 1992. a
Griffies, S. M., Winton, M., Anderson, W. G., Benson, R., Delworth, T. L., Dufour, C. O., Dunne, J. P., Goddard, P., Morrison, A. K., Rosati, A., Wittenberg, A. T., Yin, J., and Zhang, R.: Impacts on ocean heat from transient mesoscale eddies in a hierarchy of climate models, J. Climate, 28, 952–977, 2015. a
Groeskamp, S., Sloyan, B. M., Zika, J. D., and McDougall, T. J.: Mixing inferred from an ocean climatology and surface fluxes, J. Phys. Oceanogr., 47, 667–687, https://doi.org/10.1175/jpo-d-16-0125.1, 2017. a, b
Groeskamp, S., LaCasce, J. H., McDougall, T. J., Rogé, M.: Full-depth global estimates of ocean mesoscale eddy mixing from observations and theory, Geophys. Res. Lett., 47, e2020GL089425. https://doi.org/10.1029/2020GL089425, 2020. a, b
Heimbach, P., Menemenlis, D., Losch, M., Campin, J.-M., and Hill, C.: On the formulation of sea-ice models. Part 2: Lessons from multi-year adjoint sea ice export sensitivities through the Canadian Arctic archipelago, Ocean Model., 33, 145–158, https://doi.org/10.1016/j.ocemod.2010.02.002, 2010. a
Heimbach, P., Fukumori, I., Hill, C. N., Ponte, R. M., Stammer, D., Wunsch, C., Campin, J.-M., Cornuelle, B., Fenty, I., Forget, G., Köhl, A., Mazloff, M., Menemenlis, D., Nguyen, A. T., Piecuch, C., Trossman, D., Verdy, A., Wang, O., and Zhang, H.: Putting it all together: Adding value to the global ocean and climate observing systems with complete self-consistent ocean state and parameter estimates, Frontiers in Marine Science, 6, 55, https://doi.org/10.3389/fmars.2019.00055, 2019. a
Henyey, F. S., Wright, J., and Flatté, S. M.: Energy and action flow through the internal wave field: an Eikonal approach, J. Geophys. Res., 91, 8487–8495, 1986. a
Hieronymus, M., Nycander, J., Nilsson, J., Döös, K., and Hallberg, R.: Oceanic overturning and heat transport: the role of background diffusivity, J. Climate, 32, 701–716, 2019. a
Holmes, R. M., Zika,
J. D., Griffies, S. M., Hogg, A. M., Kiss, A. E., and England, M. H.: The geography of numerical mixing in a suite of global ocean models, J. Adv. Model. Earth Sy., 13, e2020MS002333, https://doi.org/10.1029/2020MS002333, 2021. a
Holzer, M., DeVries, T.,
and de Lavergne, C.: Diffusion controls the ventilation of a Pacific
Shadow Zone above abyssal overturning, Nat. Commun., 12, 4348, https://doi.org/10.1038/s41467-021-24648-x, 2021. a
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.: CICE: the Los Alamos sea ice model documentation and software user's manual version 5.0, Los Alamos National Laboratory, LA-CC-06-012, 2013. a
Ito, T., Takano, Y., Deutsch, C., and Long, M. C.: Sensitivity of global ocean deoxygenation to vertical and isopycnal mixing in an ocean biogeochemistry model, Global Biogeochem. Cy., 36, e2021GB007151, https://doi.org/10.1029/2021GB007151, 2022. a
Jenkins, W. J.: 3H and 3He in the Beta triangle: observations of gyre ventilation and oxygen utilization rates, J. Phys. Oceanogr., 27, 763–783, 1987. a
Katsumata, K.: Eddies observed by Argo floats. Part I: Eddy transport in the upper 1000 dbar, J. Phys. Oceanogr., 46, 3471–3486, https://doi.org/10.1175/JPO-D-16-0150.1, 2016. a, b, c
Khatiwala, S., Primeau, F., and Hall, T. M.: Reconstruction of the history of anthropogenic CO2 concentrations in the ocean, Nature, 462, 346–349, https://doi.org/10.1038/nature08526, 2009. a
Krasting, J., Stouffer, R., Griffies, S., Hallberg, R., Malyshev, S., Samuels, B., and Sentman, L.: Role of ocean model formulation in climate response uncertainty, J. Climate, 31, 9313–9332, 2018. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually varying air-sea flux data set, Clim. Dynam., 33, 341–364, 2009. a
Ledwell, J. R. and Watson, A. J.: The Santa Monica basin tracer experiment: A study of diapycnal and isopycnal mixing, J. Geophys. Res., 96, 8695–8718, https://doi.org/10.1029/91JC00102, 1991. a
Lévy, M., Resplandy, L., Palter, J. B., Couespel, D., and Lachkar, Z.: The crucial contribution of mixing to present and future ocean oxygen distribution, in: Ocean Mixing, edited by: Naveira Garabato, A. C. and Meredith, M. P., Elsevier, https://doi.org/10.1016/B978-0-12-821512-8.00020-7, 2021. a
Liu, C., Kohl, A., and Stammer, D.: Adjoint-based estimation of eddy-induced tracer mixing parameters in the global ocean, J. Phys. Oceanogr., 42, 1186–1206, 2012. a
Loose, N. and Heimbach, P.: Leveraging uncertainty quantification to design ocean climate observing systems, J. Adv. Model. Earth Sy., 13, e2020MS002386, https://doi.org/10.1029/2020MS002386, 2021. a
Losch, M., Menemenlis, D., Campin, J.-M., Heimbach, P., and Hill, C.: On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations, Ocean Model., 33, 129–144, https://doi.org/10.1016/j.ocemod.2009.12.008, 2010. a
Lueck, R. G., Huang, D., Newman, D., and Box, J.: Turbulence measurement with a moored instrument, J. Atmos. Ocean. Tech., 14, 143–161, https://doi.org/10.1175/1520-0426(1997)014<0143:TMWAMI>2.0.CO;2, 1997. a
MacKinnon, J., Zhao, Z., Whalen, C. B., Waterhouse, A. F., Trossman, D. S., Sun, O. M., St. Laurent, L. C., Simmons, H. L., Polzin, K., Pinkel, R., Pickering, A., Norton, N. J., Nash, J. D., Musgrave, R., Merchant, L. M., Melet, A. V., Mater, B., Legg, S., Large, W. G., Kunze, E., Klymak, J. M., Jochum, M., Jayne, S. R., Hallberg, R. W., Griffies, S. M., Diggs, S., Danabasoglu, G., Chassignet, E. P. Buijsman, M. C., Bryan, F. O., Briegleb, B. P., Barna, A., Arbic, B. K., Ansong, J. K., and Alford, M. H.: Climate process team on internal-wave driven ocean mixing, B. Am. Meteorol. Soc., 98, 2429–2454, https://doi.org/10.1175/BAMS-D-16-0030.1, 2017. a, b, c, d
Markus, T. and Cavalieri, D. J.: The AMSR-E NT2 sea ice concentration algorithm: its basis and implementation, Journal of The Remote Sensing Society of Japan, 29, 216–225, https://doi.org/10.11440/rssj.29.216, 2009. a
Marshall, J. and Speer, K.: Closure of the meridional overturning circulation through Southern Ocean upwelling, Nat. Geosci, 5, 171–180, https://doi.org/10.1038/ngeo1391, 2012.
Masuda, S. and Osafune, S.: Ocean state estimations for synthesis of ocean-mixing observations, J. Oceanogr., 77, 359–366, https://doi.org/10.1007/s10872-020-00587-x, 2021. a
Mecking, S., Warner, M. J., Greene, C. E., Hautala, S. L., and Sonnerup, R. E.: Influence of mixing on CFC uptake and CFC ages in the North Pacific thermocline, J. Geophys. Res., 109, C07014, https://doi.org/10.1029/2003JC001988, 2004. a
Melet, A., Nikurashin, M., Muller, C., Falahat, S., Nycander, J., Timko, P. G., Arbic, B. K., and Goff, J. A.: Internal tide generation by abyssal hills using analytical theory, J. Geophys. Res.-Oceans, 118, 6303–6318, 2013. a
Melet, A., Hallberg, R., Legg, S., and Nikurashin, M.: Sensitivity of the ocean state to lee wave-driven mixing, J. Phys. Oceanogr., 44, 900–921, 2014. a
Melet, A., Legg, S., and Hallberg, R.: Climatic impacts of parameterized local and remote tidal mixing, J. Climate, 29, 3473–3500, https://doi.org/10.1175/JCLI-D-15-0153.1, 2016. a
Menemenlis, D., Fukumori, I., and Lee, T.: Using Green's functions to calibrate and ocean general circulation model, Mon. Weather Rev., 133, 1224–1240, https://doi.org/10.1175/MWR2912.1, 2005. a
Messias, M.-J., Watson, A. J., Johannessen, T., Oliver, K. I. C., Olsson, K. A., Fogelqvist, E., Olafsson, J., Bacon, S., Balle, J., Bergman, N., Budéus, G., Danielsen, M., Gascard, J.-C., Jeansson, E., Olafsdottir, S. R., Simonsen, K., Tanhua, T., Van Scoy, K., and Ledwell, J. R.: The Greenland sea tracer experiment 1996–2002: Horizontal mixing and transport of Greenland sea intermediate water, Prog. Oceanogr., 78, 85–105, https://doi.org/10.1016/j.pocean.2007.06.005, 2008. a
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I. S., and Eichmann, A.: The GEOS-5 atmospheric general circulation model: Mean climate and development from MERRA to Fortuna, Technical Report Series on Global Modeling and Data Assimilation, 28, NASA/TM-2012-104606-VOL-28, 2012 (data available at: ftp://gmaoftp.gsfc.nasa.gov/pub/data/kovach/S2S_OceanAnalysis/, last access: 23 August 2019). a, b
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015. a
Molod, A., Hackert, E., Vikhliaev, Y., Zhao, B., Barahona, D., Vernieres, G., Borovikov, A., Kovach, R. M., Marshak, J., Schubert, S., Li, Z., Lim, Y.-K., Andrews, L. C., Cullather, R., Koster, R., Achuthavarier, D., Carton J., Coy, L., Friere, J. L. M., Longo, K. M., Nakada, K., and Pawson, S.: GEOS-S2S version 2: The GMAO high-resolution coupled model and assimilation system for seasonal prediction, J. Geophys. Res.-Atmos., 125, e2019JD031767, https://doi.org/10.1029/2019JD031767, 2020. a
Moum, J. N., Caldwell, D. R., Nash, J. D., and Gunderson, G. D.: Observations of boundary mixing over the continental slope, J. Phys. Oceanogr., 32, 2113–2130, https://doi.org/10.1175/1520-0485(2002)032<2113:OOBMOT>2.0.CO;2, 2002. a
Munk, W. and Wunsch, C.: Abyssal recipes II: Energetics of tidal and wind mixing, Deep-Sea Res. Pt. I, 45, 1977–2010, 1998. a
Naveira Garabato, A. C., Nurser, A. G., Scott, R. B., and Goff, J. A.: The impact of small-scale topography on the dynamical balance of the ocean, J. Phys. Oceanogr., 43, 647–668, 2013. a
Nikurashin, M. and Ferrari, R.: Global energy conversion rate from geostrophic flows into internal lee waves in the deep ocean, Geophys. Res. Lett., 38, L08610, https://doi.org/10.1029/2011GL046576, 2011. a
Nocedal, J.: Updating quasi-newton matrices with limited storage, Math. Comput., 35, 773–782, 1980. a
Nycander, J.: Generation of internal waves in the deep ocean by tides, J. Geophys. Res., 110, C10028, https://doi.org/10.1029/2004JC002487, 2005. a
Palter, J. B. and Trossman, D. S.: The sensitivity of future ocean oxygen to changes in ocean circulation, Global Biogeochem. Cy., 32, 738–751, https://doi.org/10.1002/2017GB005777, 2018. a, b
Penny, S. G., Kalnay, E., Carton, J. A., Hunt, B. R., Ide, K., Miyoshi, T., and Chepurin, G. A.: The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model, Nonlin. Processes Geophys., 20, 1031–1046, https://doi.org/10.5194/npg-20-1031-2013, 2013. a
Piecuch, C. G. and Ponte, R. M.: Mechanisms of interannual steric sea level variability, Geophys. Res. Lett., 38, L15605, https://doi.org/10.1029/2011GL048440, 2011. a
Polzin, K. L., Toole, J. M., and Schmitt, R. W.: Finescale parameterizations of turbulent dissipation, J. Phys. Oceanogr., 25, 306–328, 1995. a
Polzin, K. L., Toole, J. M., Ledwell, J. R., and Schmitt, R. W.: Spatial variability of turbulent mixing in the abyssal ocean, Science, 276, 93–96, https://doi.org/10.1126/science.276.5309.93, 1997. a
Polzin, K. L., Naveira Garabato, A. C., Huussen, T. N., Sloyan, B. N., and Waterman, S.: Finescale parameterizations of turbulent dissipation, J. Geophys. Res.-Oceans, 119, 1383–1419, https://doi.org/10.1002/2013JC008979, 2014. a, b, c
Reichl, B. G. and Hallberg, R.: A simplified energetics based planetary boundary layer (ePBL) approach for ocean climate simulations, Ocean Model., 132, 112–129, https://doi.org/10.1016/j.ocemod.2018.10.004, 2018. a
Reichle, R., Koster, R., De Lannoy, G., Forman, B., Liu, Q., Mahanama, S., and Touré, A.: Assessment and enhancement of MERRA land surface hydrology estimates, J. Climate, 24, 6322–6338, https://doi.org/10.1175/JCLI-D-10-05033.1, 2011. a
Roach, C. J., Balwada, D., and Speer, K.: Global observations of horizontal mixing from Argo float and surface drifter trajectories, J. Geophys. Res.-Oceans, 123, 4560–4575, https://doi.org/10.1029/2018JC013750, 2018. a
Scott, J. R. and Marotzke, J.: The location of diapycnal mixing and the meridional overturning circulation, J. Phys. Oceanogr., 32, 3578–3595, 2002. a
Scott, R. B., Goff, J. A., Naveira-Garabato, A. C., and Nurser, A. J. G.: Global rate and spectral characteristics of internal gravity wave generation by geostrophic flow over topography, J. Geophys. Res.-Oceans, 116, C09029, https://doi.org/10.1029/2011JC007005, 2011. a
Shao, A. E., Mecking, S., Thompson, L., and Sonnerup, R. E.: Evaluating the use of 1-D transit time distributions to infer the mean state and variability of oceanic ventilation, J. Geophys. Res.-Oceans, 121, 6650–6670, https://doi.org/10.1002/2016JC011900, 2016. a
Simmons, H. L., Jayne, S. R., St. Laurent, L. C., and Weaver, A. J.: Tidally driven mixing in a numerical model of the general circulation, Ocean Model., 6, 245–263, 2004. a
Sinha, B., Sévellec, F., Robson, J., and Nurser, G.: Surging of global surface temperature due to decadal legacy of ocean heat uptake, J. Climate, 33, 8025–8045, https://doi.org/10.1175/JCLI-D-19-0874.1, 2020. a
Stammer, D., Wunsch, C., Giering, R., Eckert, C., Heimbach, P., Marotzke, J., Adcroft, A., Hill, C. N., and Marshall, J.: Global ocean circulation during 1992–1997, estimated from ocean observations and a general circulation model, J. Geophys. Res., 107, 3118, https://doi.org/10.1029/2001JC000888, 2002. a
Stammer, D., Balmaseda, M., Heimbach, P., Köhl, A., and Weaver, A.: Ocean data assimilation in support of climate applications: status and perspectives, Annu. Rev. Mar. Sci., 8, 491–518, https://doi.org/10.1146/annurev-marine-122414-034113, 2016. a, b, c, d
St. Laurent, L. and Schmitt, R.: The contribution of salt fingers to vertical mixing in the north Atlantic tracer release experiment, J. Phys. Oceanogr., 29, 1404–1424, 1999. a
Talley, L. D.: Closure of the global overturning circulation through the Indian, Pacific, and Southern Oceans: Schematics and transports, Oceanography, 26, 80–97, https://doi.org/10.5670/oceanog.2013.07, 2013. a
Thorpe, S.: In An introduction to ocean turbulence, Cambridge University Press, https://doi.org/10.1017/CBO9780511801198, 2007. a
Treguier, A. M.: Kinetic energy analysis of an eddy resolving, primitive equation model of the North Atlantic, J. Geophys. Res., 97, 687–701, 1992. a
Trossman, D. S., Arbic, B. K., Garner, S. T., Goff, J. A., Jayne, S. R., Metzger, E. J., and Wallcraft, A. J.: Impact of parameterized lee wave drag on the energy budget of an eddying global ocean model, Ocean Model., 72, 119–142, 2013. a
Trossman, D. S., Thompson, L., Mecking, S., Warner, M. J., Bryan, F., and Peacock, S.: Evaluation of oceanic transport parameters using transient tracers from observations and model output, Ocean Model., 74, 1–21, 2014. a
Trossman, D. S., Arbic, B. K., Richman, J. G., Garner, S. T., Jayne, S. R., and Wallcraft, A. J.: Impact of topographic internal lee wave drag on an eddying global ocean model, Ocean Model., 97, 109–128, 2016. a
Trossman, D. S., Whalen, C., Haine, T. W. N., Waterhouse, A. F., Bigdeli, A., Nguyen, A. T., Mazloff, M., and Heimbach, P.: Tracer and Observationally-Derived Constraints on Diapycnal Diffusivities in an Ocean State Estimate, Zenodo [data set], https://doi.org/10.5281/zenodo.6576835, 2022. a
Verdy, A. and Mazloff, M. R.: A data assimilating model for estimating Southern Ocean biogeochemistry, J. Geophys. Res.-Oceans, 122, 6968–6988, https://doi.org/10.1002/2016JC012650, 2017. a, b
Waterhouse, A. F., MacKinnon, J. A., Nash, J. D., Alford, M. H., Kunze, E., Simmons, H. L., Polzin, K. L., St. Laurent, L. C., Sun, O. M., Pinkel, R., Talley, L. D., Whalen, C. B., Huussen, T. N., Carter, G. S., Fer, I., Waterman, S., Naveira Garabato, A. C., Sanford, T. B., and Lee, C. M.: Global patterns of diapycnal mixing from measurements of the turbulent dissipation rate, J. Phys. Oceanogr., 44, 1854–1872, 2014 (data available at: https://microstructure.ucsd.edu/, last access: 19 August 2019). a, b, c, d, e, f, g, h, i, j, k, l
Waugh, D. W., Hall, T. M., and Haine, T. W. N.: Relationships among tracer ages, J. Geophys. Res., 108, 3138, https://doi.org/10.1029/2002JC001325, 2003. a
Weaver, A. T. and Courtier, P.: Correlation modeling on a sphere using a generalized diffusion equation, Q. J. Roy. Meteor. Soc., 127, 1815–1846, 2001. a
Whalen, C. B., de Lavergne, C., Naveira Garabato, A. C., Klymak, J. M., MacKinnon, J. A., and Sheen, K. L.: Internal wave-driven mixing: governing processes and consequences for climate, Nature Reviews Earth and Environment, 1, 606–621, https://doi.org/10.1038/s43017-020-0097-z, 2020. a
Wright, C. J., Scott, R. B., Ailliot, P., and Furnival, D.: Lee wave generation rates in the deep ocean, Geophys. Res. Lett., 41, 2434–2440, https://doi.org/10.1002/2013GL059087, 2014. a
Wunsch, C.: In The ocean circulation inverse problem, Cambridge University Press, https://doi.org/10.1017/CBO9780511629570, 2006. a
Wunsch, C. and Heimbach, P.: Practical global oceanic state estimation, Physica D, 230, 197–208, https://doi.org/10.1016/j.physd.2006.09.040, 2007. a
Yang, L., Nikurashin, M., Hogg, A. M., and Sloyan, B. M.: Energy loss from transient eddies due to lee wave generation in the Southern Ocean, J. Phys. Oceanogr., 48, 2867–2885, 2018. a
Zanna, L., Khatiwala, S., Gregory, J. M., Ison, J., and Heimbach, P.: Global reconstruction of historical ocean heat storage and transport, P. Natl. Acad. Sci. USA, 116, 1126–1131, https://doi.org/10.1073/pnas.1808838115, 2019. a
Short summary
How the ocean mixes is not yet adequately represented by models. There are many challenges with representing this mixing. A model that minimizes disagreements between observations and the model could be used to fill in the gaps from observations to better represent ocean mixing. But observations of ocean mixing have large uncertainties. Here, we show that ocean oxygen, which has relatively small uncertainties, and observations of ocean mixing provide information similar to the model.
How the ocean mixes is not yet adequately represented by models. There are many challenges with...
