Abstract
An intraseasonal see-saw has been observed in the Indo-Pacific barotropic sea level anomaly during boreal winters. This see-saw carries a significant amount of energy and is crucial for the tropical sea level and angular momentum budget. Here, we evaluate the performance of several state-of-the-art ocean general circulation models (OGCMs), including the Modular Ocean Model (MOM), the Nucleus for European Modeling of the Ocean (NEMO), Massachusetts Institute of Technology general circulation model (MITgcm), and the HYbrid Coordinate Ocean Model (HYCOM) in reproducing the see-saw. Regardless of differences in model physics, forcings, setup, and resolution, all OGCMs simulate see-saw in the Indo-Pacific oceanic mass, making it a robust oceanic phenomenon. The models with horizontal resolutions ranging from 25 to 9 km, particularly those with higher resolution, are successful at simulating the qualitative characteristics of the see-saw. We show that a proper representation of the Indonesian straits is vital for a reasonable simulation of the see-saw. Furthermore, we show that the inclusion of the polar ocean in the models has little impact on the see-saw structure, implying that OGCMs with semi-global domain are appropriate tools for capturing the see-saw dynamics.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
ECCO v4r4 model data is available from Earth Data (https://search.earthdata.nasa.gov/projects?p=!C1990404797-POCLOUD&pg[1][v]=t&pg[1][m]=download&tl=1629883998!3!!). ECCO2 can be downloaded from APDRC website (http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=4922). The BPR data are obtained from NDBC (http://www.ndbc.noaa.gov/dart.shtml) and INCOIS/NIOT (http://do.incois.gov.in). NEMO, MOM5.1, MOM4p1, and HYCOM data are available on request.
References
Afroosa M, Rohith B, Paul A et al (2021) Madden-Julian oscillation winds excite an intraseasonal see-saw of ocean mass that affects Earth’s polar motion. Commun Earth Environ 2:139. https://doi.org/10.1038/s43247-021-00210-x
Amante C, Eakins BW (2009) ETOPO1 1 Arc-minute global relief model: procedures, data sources and analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center Marine Geology and Geophysics Division Boulder, Colorado, 19 pp. https://doi.org/10.7289/V5C8276M
Andersen OB, Knudsen P (2009) DNSC08 mean sea surface and mean dynamic topography models. J Geophys Res 114:C11001. https://doi.org/10.1029/2008JC005179
Antonov JI, Seidov D, Boyer TP, et al (2010) World Ocean Atlas 2009. Vol. 2: Salinity. S Levitus, Ed; NOAA Atlas NESDIS 69:184. https://doi.org/10.13140/2.1.1115.4889
Arnold NP, Branson M, Kuang Z, et al (2015) MJO intensification with warming in the superparameterized CESM. J Clim 28. https://doi.org/10.1175/JCLI-D-14-00494.1
Azaneu M, Kerr R, Mata MM (2014) Assessment of the representation of Antarctic Bottom Water properties in the ECCO2 reanalysis. Ocean Sci 10:923–946. https://doi.org/10.5194/os-10-923-2014
Becker JJ, Sandwell DT, Smith WHF et al (2009) Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Mar Geod 32:355–371. https://doi.org/10.1080/01490410903297766
Bleck R (2002) An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Model 4:55–88. https://doi.org/10.1016/S1463-5003(01)00012-9
Carnes MR, Helber RW, Barron CN, Dastugue JM (2010) Validation Test Report for GDEM4. https://doi.org/10.21236/ada530343
Chambers DP (2006) Evaluation of new GRACE time-variable gravity data over the ocean. Geophys Res Lett 33:L17603. https://doi.org/10.1029/2006GL027296
Chatterjee A, Shankar D, Shenoi SSC, et al (2012) A new atlas of temperature and salinity for the North Indian Ocean. J Earth Syst Sci 121. https://doi.org/10.1007/s12040-012-0191-9
Chen J, Tapley B, Tamisiea ME, et al (2021) Error assessment of GRACE and GRACE Follow‐On Mass Change. J Geophys Res Solid Earth 126(9). https://doi.org/10.1029/2021JB022124
Cummings JA (2005) Operational multivariate ocean data assimilation. Q J R Meteorol Soc 131:3583–3604. https://doi.org/10.1256/qj.05.105
Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. https://doi.org/10.1002/qj.828
Duchon CE (1979) Lanczos filtering in one and two dimensions. J Appl Meteorol 18:1016–1022. https://doi.org/10.1175/1520-0450(1979)018%3c1016:LFIOAT%3e2.0.CO;2
ECCO Consortium, Fukumori, Ichiro, Wang, Ou et al (2021) Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate, Version 4 Release 4. https://doi.org/10.5281/ZENODO.4533349
Egbert GD, Ray RD (2000) Significant dissipation of tidal energy in the deep ocean inferred from satellite altimeter data. Nature 405:775–778. https://doi.org/10.1038/35015531
Ferrari R, Wunsch C (2009) Ocean circulation kinetic energy: reservoirs, sources, and sinks. Annu Rev Fluid Mech 41:253–282. https://doi.org/10.1146/annurev.fluid.40.111406.102139
Forget G, Ponte RM (2015) The partition of regional sea level variability. Prog Oceanogr 137:173–195. https://doi.org/10.1016/j.pocean.2015.06.002
Forget G, Campin J-M, Heimbach P et al (2015) 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
Fu L-L, Davidson RA (1995) A note on the barotropic response of sea level to time-dependent wind forcing. J Geophys Res 100:24955. https://doi.org/10.1029/95JC02259
Fu L-L, Smith RD (1996) Global ocean circulation from satellite altimetry and high-resolution computer simulation. Bull Am Meteorol Soc 77:2625–2636. https://doi.org/10.1175/1520-0477(1996)077%3c2625:GOCFSA%3e2.0.CO;2
Fukumori I, Wang O, Llovel W et al (2015) A near-uniform fluctuation of ocean bottom pressure and sea level across the deep ocean basins of the Arctic Ocean and the Nordic Seas. Prog Oceanogr 134:152–172. https://doi.org/10.1016/j.pocean.2015.01.013
Fukumori I, Wang O, Fenty I (2021) Causal Mechanisms of Sea-level and Freshwater Content Change in the Beaufort Sea. J Phys Oceanogr 51:3217-3234. https://doi.org/10.1175/JPO-D-21-0069.1
Gordon A (2005) Oceanography of the Indonesian seas and their throughflow. Oceanography 18:14–27. https://doi.org/10.5670/oceanog.2005.01
Griffies SM (2012) Elements of the modular ocean model (MOM). GFDL Ocean Group Technical Report No. 7, Princeton
Griffies SM, Schmidt M, Herzfeld M (2010) Elements of MOM4p1. Gfdl Ocean Gr Tech Rep 6:444. https://www.gfdl.noaa.gov/wp-content/uploads/files/model_development/ocean/guide4p1.pdf
Gross RS, Fukumori I, Menemenlis D (2003) Atmospheric and oceanic excitation of the earth’s wobbles during 1980–2000. J Geophys Res Solid Earth 108(B8):2370
Gross RS, Fukumori I, Menemenlis D, Gegout P (2004) Atmospheric and oceanic excitation of length-of-day variations during 1980-2000. J Geophys Res Solid Earth 109:1–15. https://doi.org/10.1029/2003JB002432
Haertel P (2020) Prospects for erratic and intensifying Madden-Julian oscillations. Climate 8:24. https://doi.org/10.3390/cli8020024
Halliwell GR, Bleck R, Chassignet EP (1998) Atlantic Ocean simulations performed using a new hybrid-coordinate ocean model. In: EOS, American Geophysical Union (AGU), Fall 1998 Meeting, San Francisco
Harker AA, Schindelegger M, Ponte RM, Salstein DA (2021) Modeling ocean-induced rapid Earth rotation variations: an update. J Geod 95:110. https://doi.org/10.1007/s00190-021-01555-z
Hautala SL, Sprintall J, Potemra JT et al (2001) Velocity structure and transport of the Indonesian Throughflow in the major straits restricting flow into the Indian Ocean. J Geophys Res Ocean 106:19527–19546. https://doi.org/10.1029/2000JC000577
Hogan T, Liu M, Ridout J et al (2014) The navy global environmental model. Oceanography 27:116–125. https://doi.org/10.5670/oceanog.2014.73
Hunke EC, Lipscomb WH (2010) CICE: the Los Alamos sea ice model documentation and software user’s manual version 4.1 la-cc-06-012. In: Version 4.1, LA-CC-06-012, Los Alamos National Laboratory, Los Alamos
Jithin AK, Subeesh MP, Francis PA, Ramakrishna SSVS (2020) Intensification of tidally generated internal waves in the north-central Bay of Bengal. Sci Rep 10:6059. https://doi.org/10.1038/s41598-020-62679-4
Kwok R (1998) The RADARSAT geophysical processor system. Analysis of SAR data of the polar oceans. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 235–257
Large WG, Yeager SG (2009) The global climatology of an interannually varying air–sea flux data set. Clim Dyn 33:341–364. https://doi.org/10.1007/s00382-008-0441-3
Lellouche J-M, Greiner E, Le Galloudec O, et al (2018) Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system. Ocean Sci 14:1093–1126. https://doi.org/10.5194/os-14-1093-2018
Locarnini RA, Mishonov AV, Antonov JI et al (2010) World Ocean Atlas 2009. Vol. 1: Temperature. S Levitus, Ed; NOAA Atlas NESDIS 69:184
Locarnini RA, Mishonov AV, Antonov JI, et al (2013) World ocean atlas 2013. Vol. 1: Temperature. S Levitus, Ed; A Mishonov, Tech Ed; NOAA Atlas NESDIS 73:40
Losch M, Menemenlis D, Campin J-M et al (2010) 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
Madden RA, Julian PR (1971) Detection of a 40–50 day oscillation in the zonal wind in the Tropical Pacific. J Atmos Sci 28:702–708. https://doi.org/10.1175/1520-0469(1971)028%3c0702:DOADOI%3e2.0.CO;2
Madden RA, Julian PR (1972) Description of global-scale circulation cells in the tropics with a 40–50 day period. J Atmos Sci 29:1109–1123. https://doi.org/10.1175/1520-0469(1972)029%3c1109:DOGSCC%3e2.0.CO;2
Madec G, NEMO team (2016) NEMO Reference Mannual 3_6_STABLE: NEMO ocean engine. Note du Pôle de modélisation, Institut Pierre-Simon Laplace (IPSL), France, No. 27 ISNN, No: 1288–1619
Makowski JK, Chambers DP, Bonin JA (2015) Using ocean bottom pressure from the gravity recovery and climate experiment (GRACE) to estimate transport variability in the southern Indian Ocean. J Geophys Res Ocean 120:4245–4259. https://doi.org/10.1002/2014JC010575
Marshall J, Adcroft A, Hill C et al (1997) A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. J Geophys Res Ocean 102:5753–5766. https://doi.org/10.1029/96JC02775
Maximenko NA, Niiler PP (2005) Hybrid decade-mean global sea level with mesoscale resolution. In: Saxena NK (ed) Recent advances in marine science and technology 2004. PACON Int, Honolulu, pp 55–59
Menemenlis D, Fukumori I, Lee T (2005) Using Green’s functions to calibrate an ocean general circulation model. Mon Weather Rev 133:1224–1240. https://doi.org/10.1175/MWR2912.1
Menemenlis D, Campin JM, Heimbach P, et al (2008) ECCO2: high resolution global ocean and sea ice data synthesis. Mer Ocean Quar News 31:13–21
Merchant CJ, Mittaz J, Corlett GK (2014) Climate Data Assessment Framework. 1–24. https://doi.org/10.5281/zenodo.4700356
Metzger EJ, Smedstad OM, Thoppil P et al (2014) US Navy operational global ocean and Arctic ice prediction systems. Oceanography 27:32–43. https://doi.org/10.5670/oceanog.2014.66
Metzger EJ, Helber RW, Hogan PJ, et al (2017) Global Ocean Forecast System 3.1 validation test. Technical Report. NRL/MR/7320-17-9722. Stennis Space Center, Hancock, pp 61
Murray MT (1964) A general method for the analysis of hourly heights of tide. Int Hydrogr Rev 41(2):91–101
Tamasiunas MCN, Shinoda T, Susanto RD, et al (2021) Intraseasonal variability of the Indonesian throughflow associated with the Madden-Julian oscillation. Deep Sea Res Part II Top Stud Oceanogr 193:104985. https://doi.org/10.1016/j.dsr2.2021.104985
Onogi K, Tsutsui J, Koide H et al (2007) The JRA-25 reanalysis. J Meteorol Soc Japan Ser II 85:369–432. https://doi.org/10.2151/jmsj.85.369
Pacanowski RC, Dixon K, Rosati A (1991) The GFDL modular ocean model user guide. GFDL Ocean Group Tech . Rep. 2 , Geophysical Fluid Dynamics Laboratory, Princeton, pp 16
Ponte RM (1994) Understanding the relation between wind- and pressure-driven sea level variability. J Geophys Res 99:8033. https://doi.org/10.1029/94JC00217
Prasad VS, Mohandas S, Dutta SK et al (2014) Improvements in medium range weather forecasting system of India. J Earth Syst Sci 123:247–258. https://doi.org/10.1007/s12040-014-0404-5
Reynolds RW, Smith TM, Liu C et al (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496. https://doi.org/10.1175/2007JCLI1824.1
Rohith B, Paul A, Durand F et al (2019) Basin-wide sea level coherency in the tropical Indian Ocean driven by Madden–Julian oscillation. Nat Commun 10:1257. https://doi.org/10.1038/s41467-019-09243-5
Rousset C, Vancoppenolle M, Madec G et al (2015) The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities. Geosci Model Dev 8:2991–3005. https://doi.org/10.5194/gmd-8-2991-2015
Saha S, Moorthi S, Pan H-L et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1058. https://doi.org/10.1175/2010BAMS3001.1
Saha S, Moorthi S, Wu X et al (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1
Schindelegger M, Harker AA, Ponte RM et al (2021) Convergence of daily GRACE solutions and models of submonthly ocean bottom pressure variability. J Geophys Res Ocean 126(2). https://doi.org/10.1029/2020JC017031
Sindhu B, Suresh I, Unnikrishnan AS et al (2007) Improved bathymetric datasets for the shallow water regions in the Indian Ocean. J Earth Syst Sci 116:261–274. https://doi.org/10.1007/s12040-007-0025-3
Sprintall J, Wijffels SE, Molcard R, Jaya I (2009) Direct estimates of the Indonesian Throughflow entering the Indian Ocean: 2004–2006. J Geophys Res 114:C07001. https://doi.org/10.1029/2008JC005257
Tapley BD, Bettadpur S, Watkins M, Reigber C (2004) The gravity recovery and climate experiment: mission overview and early results: GRACE mission overview and early results. Geophys Res Lett 31(9). https://doi.org/10.1029/2004GL019920
Thacker WC, Long RB (1988) Fitting dynamics to data. J Geophys Res 93:1227. https://doi.org/10.1029/JC093iC02p01227
Treguier AM, Deshayes J, Le Sommer J et al (2014) Meridional transport of salt in the global ocean from an eddy-resolving model. Ocean Sci 10:243–255. https://doi.org/10.5194/os-10-243-2014
Vancoppenolle M, Fichefet T, Goosse H et al (2009) Simulating the mass balance and salinity of Arctic and Antarctic sea ice. 1 Model Description and Validation. Ocean Model 27:33–53. https://doi.org/10.1016/j.ocemod.2008.10.005
Winton M (2000) A reformulated three-layer sea ice model. J Atmos Ocean Technol 17:525–531. https://doi.org/10.1175/1520-0426(2000)017%3c0525:ARTLSI%3e2.0.CO;2
Wunsch C, Heimbach P (2007) Practical global oceanic state estimation. Phys D Nonlinear Phenom 230:197–208. https://doi.org/10.1016/j.physd.2006.09.040
Zweng MM, Reagan JR, Antonov JI et al (2013) World ocean atlas 2013. Vol 2: Salinity. S Levitus, Ed; A Mishonov, Tech Ed; NOAA atlas NESDIS 74:39
Acknowledgements
The lead author is grateful to the Council of Scientific and Industrial Research (CSIR) for providing the Research Fellowship grant. This work is supported by INCOIS, MoES, and by the project “Barotropic Influence on Global Ocean (BINGO); grant no 41-DS-GMMC-BINGO-CNRS195918.” This is INCOIS contribution number 461.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Responsible Editor: Andrea Pinones
Rights and permissions
About this article
Cite this article
Afroosa, M., Rohith, B., Paul, A. et al. Investigating the robustness of the intraseasonal see-saw in the Indo-Pacific barotropic sea level across models. Ocean Dynamics 72, 523–538 (2022). https://doi.org/10.1007/s10236-022-01518-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10236-022-01518-8