Articles | Volume 16, issue 1
https://doi.org/10.5194/esd-16-75-2025
© 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/esd-16-75-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Jan 2025
Research article |
| 09 Jan 2025
| 09 Jan 2025
Chaotic oceanic excitation of low-frequency polar motion variability
Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany
Michael Schindelegger
Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany
Mengnan Zhao
Atmospheric and Environmental Research (AER), Lexington, MA, USA
Rui M. Ponte
Atmospheric and Environmental Research (AER), Lexington, MA, USA
Anno Löcher
Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany
Bernd Uebbing
Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany
Jean-Marc Molines
Université Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, Institut des Géosciences de l'Environnement (IGE), Grenoble, France
Thierry Penduff
Université Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, Institut des Géosciences de l'Environnement (IGE), Grenoble, France
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Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, https://doi.org/10.5194/hess-28-2259-2024, 2024
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Ocean Sci., 17, 487–507, https://doi.org/10.5194/os-17-487-2021, https://doi.org/10.5194/os-17-487-2021, 2021
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Sylvain Watelet, Jean-Marie Beckers, Jean-Marc Molines, and Charles Troupin
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Revised manuscript not accepted
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Christina Lück, Jürgen Kusche, Roelof Rietbroek, and Anno Löcher
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A. M. Treguier, J. Deshayes, J. Le Sommer, C. Lique, G. Madec, T. Penduff, J.-M. Molines, B. Barnier, R. Bourdalle-Badie, and C. Talandier
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From running climate models to using their outputs to identify impacts, modeling the integrated human–Earth system is expensive. This work presents a method to identify a smaller subset of models from the full set that preserves the uncertainty characteristics of the full set. This results in a smaller number of runs that an impact modeler can use to assess how uncertainty propagates from the Earth to the human system, while still capturing the range of outcomes provided by climate models.
Cited articles
A, G., Wahr, J., and Zhong, S.: Computations of the Viscoelastic Response of a 3-D Compressible Earth to Surface Loading: An Application to Glacial Isostatic Adjustment in Antarctica and Canada, Geophys. J. Int., 192, 557–572, https://doi.org/10.1093/gji/ggs030, 2013. a, b
Adhikari, S. and Ivins, E. R.: Climate-Driven Polar Motion: 2003–2015, Sci. Adv., 2, e1501693, https://doi.org/10.1126/sciadv.1501693, 2016. a, b, c, d
Afroosa, M., Rohith, B., Paul, A., Durand, F., Bourdallé-Badie, R., Sreedevi, P. V., de Viron, O., Ballu, V., and Shenoi, S. S. C.: Madden-Julian Oscillation Winds Excite an Intraseasonal See-Saw of Ocean Mass That Affects Earth's Polar Motion, Communications Earth & Environment, 2, 139, https://doi.org/10.1038/s43247-021-00210-x, 2021. a, b
Altamimi, Z., Rebischung, P., Métivier, L., and Collilieux, X.: ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions, J. Geophys. Res.-Sol. Ea., 121, 6109–6131, https://doi.org/10.1002/2016JB013098, 2016. a
Arbic, B. K., Müller, M., Richman, J. G., Shriver, J. F., Morten, A. J., Scott, R. B., Sérazin, G., and Penduff, T.: Geostrophic turbulence in the frequency–wavenumber domain: Eddy-driven low-frequency variability, J. Phys. Oceanogr., 44, 2050–2069, https://doi.org/10.1175/JPO-D-13-054.1, 2014. a, b
Barnes, R., Hide, R., White, A., and Wilson, C.: Atmospheric Angular Momentum Fluctuations, Length-of-Day Changes and Polar Motion, P. Roy. Soc. Lond. A Mat., 387, 31–73, https://doi.org/10.1098/rspa.1983.0050, 1983. a, b, c
Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G.: Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, https://doi.org/10.5194/gmd-10-1091-2017, 2017. a
Bizouard, C. and Seoane, L.: Atmospheric and Oceanic Forcing of the Rapid Polar Motion, J. Geodesy, 84, 19–30, https://doi.org/10.1007/s00190-009-0341-2, 2010. a
Börger, L., Schindelegger, M., Dobslaw, H., and Salstein, D.: Are ocean reanalyses useful for Earth rotation research?, Earth and Space Science, 10, e2022EA002700, https://doi.org/10.1029/2022EA002700, 2023. a, b, c
Börger, L., Schindelegger, M., Zhao, M., Ponte, R. M., Löcher, A., Uebbing, B., Molines, J.-M., and Penduff, T.: Angular momentum estimates for global geophysical fluids, 1995–2015, Zenodo [data set], https://doi.org/10.5281/zenodo.12664036, 2024. a
Brankart, J.-M., Candille, G., Garnier, F., Calone, C., Melet, A., Bouttier, P.-A., Brasseur, P., and Verron, J.: A generic approach to explicit simulation of uncertainty in the NEMO ocean model, Geosci. Model Dev., 8, 1285–1297, https://doi.org/10.5194/gmd-8-1285-2015, 2015. a
Brzeziński, A.: Polar motion excitation by variations of the effective angular momentum function: Considerations concerning deconvolution problem, Manuscr. Geodaet., 17, 3–20, 1992. a
Carret, A., Llovel, W., Penduff, T., and Molines, J.-M.: Atmospherically Forced and Chaotic Interannual Variability of Regional Sea Level and Its Components Over 1993–2015, J. Geophys. Res.-Oceans, 126, e2020JC017123, https://doi.org/10.1029/2020JC017123, 2021. a, b, c
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of nonlinear mesoscale eddies, Prog. Oceanogr., 91, 167–216, https://doi.org/10.1016/j.pocean.2011.01.002, 2011. a
Chen, J., Wilson, C. R., Kuang, W., and Chao, B. F.: Interannual Oscillations in Earth Rotation, J. Geophys. Res.-Sol. Ea., 124, 13404–13414, https://doi.org/10.1029/2019JB018541, 2019. a, b
Chen, J. L., Wilson, C. R., Ries, J. C., and Tapley, B. D.: Rapid Ice Melting Drives Earth's Pole to the East, Geophys. Res. Lett., 40, 2625–2630, https://doi.org/10.1002/grl.50552, 2013. a, b
Chen, J. L., Wilson, C. R., Li, J., and Zhang, Z.: Reducing Leakage Error in GRACE-observed Long-Term Ice Mass Change: A Case Study in West Antarctica, J. Geodesy, 89, 925–940, https://doi.org/10.1007/s00190-015-0824-2, 2015. a, b
Clarke, P. J., Lavallée, D. A., Blewitt, G., van Dam, T. M., and Wahr, J. M.: Effect of gravitational consistency and mass conservation on seasonal surface mass loading models, Geophys. Res. Lett., 32, L08306, https://doi.org/10.1029/2005GL022441, 2005. a
Cravatte, S., Serazin, G., Penduff, T., and Menkes, C.: Imprint of chaotic ocean variability on transports in the southwestern Pacific at interannual timescales, Ocean Sci., 17, 487–507, https://doi.org/10.5194/os-17-487-2021, 2021. a, b
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, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Deng, S., Liu, S., Mo, X., Jiang, L., and Bauer-Gottwein, P.: Polar Drift in the 1990s Explained by Terrestrial Water Storage Changes, Geophys. Res. Lett., 48, e2020GL092114, https://doi.org/10.1029/2020GL092114, 2021. a
Desai, S. D.: Observing the pole tide with satellite altimetry, J. Geophys. Res.-Oceans, 107, 3186, https://doi.org/10.1029/2001JC001224, 2002. a
Dickman, S. R.: Evaluation of “effective angular momentum function” formulations with respect to core-mantle coupling, J. Geophys. Res.-Sol. Ea., 108, 2150, https://doi.org/10.1029/2001JB001603, 2003. a, b
Ding, H., Pan, Y., Xu, X. Y., Shen, W., and Li, M.: Application of the AR-z spectrum to polar motion: A possible first detection of the Inner Core Wobble and its implications for the density of Earth's core, Geophys. Res. Lett., 46, 13765–13774, https://doi.org/10.1029/2019GL085268, 2019. a, b, c
Dobslaw, H. and Dill, R.: Predicting Earth orientation changes from global forecasts of atmosphere-hydrosphere dynamics, Adv. Space Res., 61, 1047–1054, https://doi.org/10.1016/j.asr.2017.11.044, 2018. a, b
Dobslaw, H., Bergmann-Wolf, I., Dill, R., Poropat, L., and Flechtner, F.: Product Description Document for AOD1B Release 06, Rev. 6.1, GFZ Potsdam, Potsdam, Germany, 2017. a
Dussin, R., Barnier, B., Brodeau, L., and Molines, J.-M.: Drakkar Forcing Set DFS5, Tech. rep., DRAKKAR/MyOcean Report 01-04-16, 2016. a
Farrell, W. E. and Clark, J. A.: On Postglacial Sea Level, Geophys. J. Int., 46, 647–667, https://doi.org/10.1111/j.1365-246X.1976.tb01252.x, 1976. a, b
Göttl, F., Groh, A., Schmidt, M., Schröder, L., and Seitz, F.: The Influence of Antarctic Ice Loss on Polar Motion: An Assessment Based on GRACE and Multi-Mission Satellite Altimetry, Earth Planets Space, 73, 99, https://doi.org/10.1186/s40623-021-01403-6, 2021. a
Gross, R. S.: Correspondence between theory and observations of polar motion, Geophys. J. Int., 109, 162–170, https://doi.org/10.1111/j.1365-246X.1992.tb00086.x, 1992. a
Gross, R. S.: The Excitation of the Chandler Wobble, Geophys. Res. Lett., 27, 2329–2332, https://doi.org/10.1029/2000GL011450, 2000. a
Harker, A. A., Schindelegger, M., Ponte, R. M., and Salstein, D. A.: Modeling ocean-induced rapid Earth rotation variations: An update, J. Geodesy, 95, 110, https://doi.org/10.1007/s00190-021-01555-z, 2021. a, b, c
Kiani Shahvandi, M., Schartner, M., and Soja, B.: Neural ODE differential learning and its application in polar motion prediction, J. Geophys. Res.-Sol. Ea., 127, e2022JB024775, https://doi.org/10.1029/2022JB024775, 2022. a
Kuang, W., Chao, B. F., and Chen, J.: Reassessment of electromagnetic core-mantle coupling and its implications to the Earth's decadal polar motion, Geodesy and Geodynamics, 10, 356–362, https://doi.org/10.1016/j.geog.2019.06.003, 2019. a, b
Kuhlmann, J., Dobslaw, H., and Thomas, M.: Improved modeling of sea level patterns by incorporating self-attraction and loading, J. Geophys. Res.-Oceans, 116, C11036, https://doi.org/10.1029/2011JC007399, 2011. a
Kusche, J., Schmidt, R., Petrovic, S., and Rietbroek, R.: Decorrelated GRACE Time-Variable Gravity Solutions by GFZ, and Their Validation Using a Hydrological Model, J. Geodesy, 83, 903–913, https://doi.org/10.1007/s00190-009-0308-3, 2009. a
Leroux, S., Penduff, T., Bessières, L., Molines, J.-M., Brankart, J.-M., Sérazin, G., Barnier, B., and Terray, L.: Intrinsic and Atmospherically Forced Variability of the AMOC: Insights from a Large-Ensemble Ocean Hindcast, J. Climate, 31, 1183–1203, https://doi.org/10.1175/JCLI-D-17-0168.1, 2018. a, b
Llovel, W., Penduff, T., Meyssignac, B., Molines, J.-M., Terray, L., Bessières, L., and Barnier, B.: Contributions of Atmospheric Forcing and Chaotic Ocean Variability to Regional Sea Level Trends Over 1993–2015, Geophys. Res. Lett., 45, 13405–13413, https://doi.org/10.1029/2018GL080838, 2018. a
Löcher, A. and Kusche, J.: A Hybrid Approach for Recovering High-Resolution Temporal Gravity Fields from Satellite Laser Ranging, J. Geodesy, 95, 6, https://doi.org/10.1007/s00190-020-01460-x, 2021. a, b, c
Maher, N., Milinski, S., and Ludwig, R.: Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble, Earth Syst. Dynam., 12, 401–418, https://doi.org/10.5194/esd-12-401-2021, 2021. a
Meyrath, T. and Van Dam, T.: A Comparison of Interannual Hydrological Polar Motion Excitation from GRACE and Geodetic Observations, J. Geodyn., 99, 1–9, https://doi.org/10.1016/j.jog.2016.03.011, 2016. a
Munk, W. H. and MacDonald, G. J. F.: The Rotation of the Earth: A Geophysical Discussion, Cambridge University Press, New York, https://doi.org/10.1017/S0016756800060726, 1960. a
Nakiboglu, S. M. and Lambeck, K.: Deglaciation effects on the rotation of the Earth, Geophys. J. Int., 62, 49–58, https://doi.org/10.1111/j.1365-246X.1980.tb04843.x, 1980. a
Nastula, J. and Salstein, D.: Regional atmospheric angular momentum contributions to polar motion excitation, J. Geophys. Res.-Sol. Ea., 104, 7347–7358, https://doi.org/10.1029/1998JB900077, 1999. a
Nastula, J., Wińska, M., Śliwińska, J., and Salstein, D.: Hydrological Signals in Polar Motion Excitation – Evidence after Fifteen Years of the GRACE Mission, J. Geodyn., 124, 119–132, https://doi.org/10.1016/j.jog.2019.01.014, 2019. a
O'Rourke, A. K., Arbic, B. K., and Griffies, S. M.: Frequency-domain analysis of atmospherically forced versus intrinsic ocean surface kinetic energy variability in GFDL's CM2-O model hierarchy, J. Climate, 31, 1789–1810, https://doi.org/10.1175/JCLI-D-17-0024.1, 2018. a
Ponte, R. M. and Piecuch, C. G.: Interannual bottom pressure signals in the Australian–Antarctic and Bellingshausen Basins, J. Phys. Oceanogr., 44, 1456–1465, https://doi.org/10.1175/JPO-D-13-0223.1, 2014. a
Ponte, R. M., Stammer, D., and Marshall, J.: Oceanic Signals in Observed Motions of the Earth's Pole of Rotation, Nature, 391, 476–479, https://doi.org/10.1038/35126, 1998. a
Provost, C., Renault, A., Barré, N., Sennéchael, N., Garçon, V., Sudre, J., and Huhn, O.: Two repeat crossings of Drake Passage in austral summer 2006: Short-term variations and evidence for considerable ventilation of intermediate and deep waters, Deep-Sea Res. Pt. II, 58, 2555–2571, https://doi.org/10.1016/j.dsr2.2011.06.009, 2011. a
Quinn, K. J., Ponte, R. M., and Tamisiea, M. E.: Impact of self-attraction and loading on Earth rotation, J. Geophys. Res.-Sol. Ea., 120, 4510–4521, https://doi.org/10.1002/2015JB011980, 2015. a, b, c
Ratcliff, T. and Gross, R. S.: Combinations of Earth Orientation Measurements: SPACE2018, COMB2018, and POLE2018, Jet Propulsion Laboratory, California Institute of Technology, Publication 19-7, https://hdl.handle.net/2014/46964 (last access: 18 December 2024), 2019. a
Rekier, J., Chao, B. F., Chen, J., Dehant, V., Rosat, S., and Zhu, P.: Earth's rotation: Observations and relation to deep interior, Surv. Geophys., 43, 149–175, https://doi.org/10.1007/s10712-021-09669-x, 2022. a
Rhines, P. B.: Mesoscale Eddies, in: Encyclopedia of Ocean Sciences, 3rd edn., edited by: Cochran, J. K., Bokuniewicz, H. J., and Yager, P. L., Academic Press, Oxford, 115–127, https://doi.org/10.1016/B978-0-12-409548-9.11642-2, 2019. a
Rietbroek, R., Brunnabend, S.-E., Kusche, J., Schröter, J., and Dahle, C.: Revisiting the Contemporary Sea-Level Budget on Global and Regional Scales, P. Natl. Acad. Sci. USA, 113, 1504–1509, https://doi.org/10.1073/pnas.1519132113, 2016. a
Rochester, M. and Crossley, D.: Earth's long-period wobbles: A Lagrangean description of the Liouville equations, Geophys. J. Int., 176, 40–62, https://doi.org/10.1111/j.1365-246X.2008.03991.x, 2009. a
Schindelegger, M., Salstein, D., and Böhm, J.: Recent Estimates of Earth-atmosphere Interaction Torques and Their Use in Studying Polar Motion Variability, J. Geophys. Res.-Sol. Ea., 118, 4586–4598, https://doi.org/10.1002/jgrb.50322, 2013. a
Schindelegger, M., Green, J. A. M., Wilmes, S.-B., and Haigh, I. D.: Can We Model the Effect of Observed Sea Level Rise on Tides?, J. Geophys. Res.-Oceans, 123, 4593–4609, https://doi.org/10.1029/2018JC013959, 2018. a
Sérazin, G., Penduff, T., Grégorio, S., Barnier, B., Molines, J.-M., and Terray, L.: Intrinsic variability of sea level from global ocean simulations: Spatiotemporal scales, J. Climate, 28, 4279–4292, https://doi.org/10.1175/JCLI-D-14-00554.1, 2015. a
Sérazin, G., Penduff, T., Barnier, B., Molines, J.-M., Arbic, B. K., Müller, M., and Terray, L.: Inverse cascades of kinetic energy as a source of intrinsic variability: A global OGCM study, J. Phys. Oceanogr., 48, 1385–1408, https://doi.org/10.1175/JPO-D-17-0136.1, 2018. a, b, c
Swenson, S., Chambers, D., and Wahr, J.: Estimating geocenter variations from a combination of GRACE and ocean model output, J. Geophys. Res.-Sol. Ea., 113, B08410, https://doi.org/10.1029/2007JB005338, 2008. a
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S., Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D. P., Reager, J. T., Gardner, A. S., Save, H., Ivins, Erik R., Swenson, S. C., Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E., and Velicogna, I.: Contributions of GRACE to understanding climate change, Nat. Clim. Change, 9, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019. a
Uebbing, B., Kusche, J., Rietbroek, R., and Landerer, F. W.: Processing Choices Affect Ocean Mass Estimates From GRACE, J. Geophys. Res.-Oceans, 124, 1029–1044, https://doi.org/10.1029/2018JC014341, 2019. a
Venaille, A., Vallis, G. K., and Smith, K. S.: Baroclinic turbulence in the ocean: Analysis with primitive equation and quasigeostrophic simulations, J. Phys. Oceanogr., 41, 1605–1623, https://doi.org/10.1175/JPO-D-10-05021.1, 2011. a
Vinogradova, N. T., Ponte, R. M., Quinn, K. J., Tamisiea, M. E., Campin, J.-M., and Davis, J. L.: Dynamic adjustment of the ocean circulation to self-attraction and loading effects, J. Phys. Oceanogr., 45, 678–689, https://doi.org/10.1175/JPO-D-14-0150.1, 2015. a, b
Wahr, J. M.: The effects of the atmosphere and oceans on the Earth's wobble – I. Theory, Geophys. J. Int., 70, 349–372, https://doi.org/10.1111/j.1365-246X.1982.tb04972.x, 1982. a
Welch, P.: The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging over Short, Modified Periodograms, IEEE T. Audio Electroacoust., 15, 70–73, https://doi.org/10.1109/TAU.1967.1161901, 1967. a
Wińska, M.: A comparative study of interannual oscillation models for determining geophysical polar motion excitations, Remote Sens., 14, 147, https://doi.org/10.3390/rs14010147, 2022. a
Woodward, R. S.: On the form and position of the sea level with special references to its dependence on superficial masses symmetrically disposed about a normal to the Earth's surface, Tech. rep., Govt. Print. Off., Bulletin 48, 88 pp., https://doi.org/10.3133/b48, 1888. a
Zhao, M., Ponte, R. M., Penduff, T., Close, S., Llovel, W., and Molines, J.-M.: Imprints of Ocean Chaotic Intrinsic Variability on Bottom Pressure and Implications for Data and Model Analyses, Geophys. Res. Lett., 48, e2021GL096341, https://doi.org/10.1029/2021GL096341, 2021. a, b, c
Short summary
Flows in the ocean are driven either by atmospheric forces or by small-scale internal disturbances that are inherently chaotic. We use computer simulation results to show that these chaotic oceanic disturbances can attain spatial scales large enough to alter the motion of Earth's pole of rotation. Given their size and unpredictable nature, the chaotic signals are a source of uncertainty when interpreting observed year-to-year polar motion changes in terms of other processes in the Earth system.
Flows in the ocean are driven either by atmospheric forces or by small-scale internal...
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