Abstract
Tropical instability waves (TIWs) are a major source of internally-generated oceanic variability in the equatorial Pacific Ocean. These non-linear phenomena play an important role in the sea surface temperature (SST) budget in a region critical for low-frequency modes of variability such as the El Niño–Southern Oscillation (ENSO). However, the direct contribution of TIW-driven stochastic variability to ENSO has received little attention. Here, we investigate the influence of TIWs on ENSO using a \(1/4^\circ\) ocean model coupled to a simple atmosphere. The use of a simple atmosphere removes complex intrinsic atmospheric variability while allowing the dominant mode of air−sea coupling to be represented as a statistical relationship between SST and wind stress anomalies. Using this hybrid coupled model, we perform a suite of coupled ensemble forecast experiments initiated with wind bursts in the western Pacific, where individual ensemble members differ only due to internal oceanic variability. We find that TIWs can induce a spread in the forecast amplitude of the Niño 3 SST anomaly 6-months after a given sequence of WWBs of approximately \(\pm \,45\%\) the size of the ensemble mean anomaly. Further, when various estimates of stochastic atmospheric forcing are added, oceanic internal variability is found to contribute between about \(20\%\) and \(70\%\) of the ensemble forecast spread, with the remainder attributable to the atmospheric variability. While the oceanic contribution to ENSO stochastic forcing requires further quantification beyond the idealized approach used here, our results nevertheless suggest that TIWs may impact ENSO irregularity and predictability. This has implications for ENSO representation in low-resolution coupled models.
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Notes
A form of TIWs can exist in these low-resolution models, and a correct representation of them within \(1^\circ\) models may be possible with an appropriate choice of viscosity (Jochum et al. 2008).
SST could also be used for this purpose. However, this is more strongly influenced by atmospheric variability that is not present in our control simulations.
References
Abellán E, McGregor S (2015) The role of the southward wind shift in both, the seasonal synchronization and duration of ENSO events. Clim Dyn 47(1-2): 509–527.https://doi.org/10.1007/s00382-015-2853-1
An S (2008) Interannual variations of the tropical ocean instability wave and ENSO. J Clim 21(15):3680–3686. https://doi.org/10.1175/2008JCLI1701.1
An SI (2009) A review of interdecadal changes in the nonlinearity of the El Niño–Southern Oscillation. Theor Appl Climatol 97(1):29–40. https://doi.org/10.1007/s00704-008-0071-z
Arbic BK, Müller M, Richman JG, Shriver JF, Morten AJ, Scott RB, Sérazin G, Penduff T (2014) Geostrophic turbulence in the frequency-wavenumber domain: Eddy-driven low-frequency variability. J Phys Oceanogr 44(8):2050–2069. https://doi.org/10.1175/JPO-D-13-054.1
Balmaseda MA, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 139(674):1132–1161. https://doi.org/10.1002/qj.2063
Blanke B, Neelin JD, Gutzler D (1997) Estimating the effect of stochastic wind stress forcing on ENSO irregularity. J Clim 10(7):1473–1486. https://doi.org/10.1175/1520-0442(1997)010<1473:ETEOSW>2.0.CO;2
Chelton DB, Esbensen SK, Schlax MG, Thum N, Freilich MH, Wentz FJ, Gentemann CL, McPhaden MJ, Schopf PS (2001) Observations of coupling between surface wind stress and sea surface temperature in the eastern tropical Pacific. J Clim 14(7):1479–1498. https://doi.org/10.1175/1520-0442(2001)014%3c1479:OOCBSW%3e2.0.CO;2
Chiodi AM, Harrison DE, Vecchi GA (2014) Subseasonal atmospheric variability and El Niño waveguide warming: observed effects of the Madden-Julian oscillation and westerly wind events. J Clim 27(10):3619–3642. https://doi.org/10.1175/JCLI-D-13-00547.1
Contreras RF (2002) Long-term observations of tropical instability waves. J Phys Oceanogr 32(9):2715–2722
Cox MD (1980) Generation and propagation of 30-day waves in a numerical model of the Pacific. J Phys Oceanogr 10(8):1168–1186. https://doi.org/10.1175/1520-0485(1980)010%3c1168:GAPODW%3e2.0.CO;2
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597. https://doi.org/10.1002/qj.828
Deremble B, Wienders N, Dewar WK (2013) CheapAML: a simple, atmospheric boundary layer model for use in ocean-only model calculations. Mon Weather Rev 141(2):809–821. https://doi.org/10.1175/MWR-D-11-00254.1
Dommenget D (2010) The slab ocean El Niño. Geophys Res Lett 37(20):L20–701. https://doi.org/10.1029/2010GL044888
Dueing W, Hisard P, Katz E, Meincke J, Miller L, Moroshkin KV, Philander G, Ribnikov AA, Voigt K, Weisberg R (1975) Meanders and long waves in the equatorial atlantic. Nature 257:280–284. https://doi.org/10.1038/257280a0
Eisenman I, Yu L, Tziperman E (2005) Westerly wind bursts: ENSO’s tail rather than the dog? J Clim 18(24):5224–5238. https://doi.org/10.1175/JCLI3588.1
Fairall CW, Bradley EF, Rogers DP, Edson JB, Young GS (1996) Bulk parameterization of air–sea fluxes for tropical ocean–global atmosphere coupled-ocean atmosphere response experiment. J Geophys Res 101(C2):3747–3764. https://doi.org/10.1029/95JC03205
Fedorov AV, Hu S, Lengaigne M, Guilyardi E (2015) The impact of westerly wind bursts and ocean initial state on the development, and diversity of El Niño events. Clim Dyn 44(5):1381–1401. https://doi.org/10.1007/s00382-014-2126-4
Flament P, Kennan S, Knox R, Niiler P, Bernstein R (1996) The three-dimensional structure of an upper ocean vortex in the tropical Pacific ocean. Nature 383(6601):610–613. https://doi.org/10.1038/383610a0
Frauen C, Dommenget D (2010) El Niño and La Niña amplitude asymmetry caused by atmospheric feedbacks. Geophys Res Lett 37(18):L18–801. https://doi.org/10.1029/2010GL044444
Gebbie G, Eisenman I, Wittenberg A, Tziperman E (2007) Modulation of westerly wind bursts by sea surface temperature: a semistochastic feedback for ENSO. J Atmos Sci 64(9):3281–3295. https://doi.org/10.1175/JAS4029.1
Graham NE, Barnett TP (1987) Sea surface temperature, surface wind divergence, and convection over tropical oceans. Science 238(4827):657–659. https://doi.org/10.1126/science.238.4827.657
Graham T (2014) The importance of eddy permitting model resolution for simulation of the heat budget of tropical instability waves. Ocean Model 79:21–32. https://doi.org/10.1016/j.ocemod.2014.04.005
Ham YG, Kang IS (2011) Improvement of seasonal forecasts with inclusion of tropical instability waves on initial conditions. Clim Dyn 36(7–8):1277–1290. https://doi.org/10.1007/s00382-010-0743-0
Hayashi M, Watanabe M (2017) ENSO complexity induced by state dependence of westerly wind events. J Clim 30(9):3401–3420. https://doi.org/10.1175/JCLI-D-16-0406.1
Holmes RM, Thomas LN (2016) Modulation of tropical instability wave intensity by equatorial Kelvin waves. J Phys Oceanogr 46:2623–2643. https://doi.org/10.1175/JPO-D-16-0064.1
Hu S, Fedorov AV, Lengaigne M, Guilyardi E (2014) The impact of westerly wind bursts on the diversity and predictability of El Niño events: an ocean energetics perspective. Geophys Res Lett 41(13):4654–4663. https://doi.org/10.1002/2014GL059573
Imada Y, Kimoto M (2012) Parameterization of tropical instability waves and examination of their impact on ENSO characteristics. J Clim 25(13):4568–4581. https://doi.org/10.1175/JCLI-D-11-00233.1
Jochum M, Murtugudde R (2004) Internal variability of the tropical Pacific ocean. Geophys Res Lett. https://doi.org/10.1029/2004GL020488
Jochum M, Murtugudde R (2005) Internal variability of Indian ocean SST. J Clim 18(18):3726–3738. https://doi.org/10.1175/JCLI3488.1
Jochum M, Murtugudde R (2006) Temperature advection by tropical instability waves. J Phys Oceanogr 36(4):592–605. https://doi.org/10.1175/JPO2870.1
Jochum M, Cronin M, Kessler W, Shea D (2007a) Observed horizontal temperature advection by tropical instability waves. Geophys Res Lett. https://doi.org/10.1029/2007GL029416
Jochum M, Deser C, Phillips A (2007b) Tropical atmospheric variability forced by oceanic internal variability. J Clim 20(4):765–771. https://doi.org/10.1175/JCLI4044.1
Jochum M, Danabasoglu G, Holland M, Kwon YO, Large WG (2008) Ocean viscosity and climate. J Geophys Res 113(C6):C06017. https://doi.org/10.1029/2007JC004515
Johnson NC, Xie SP (2010) Changes in the sea surface temperature threshold for tropical convection. Nat Geosci 3(12):842–845. https://doi.org/10.1038/ngeo1008
Keen RA (1982) The role of cross-equatorial tropical cyclone pairs in the Southern Oscillation. Mon Weather Rev 110(10):1405–1416. https://doi.org/10.1175/1520-0493(1982)110%3c1405:TROCET%3e2.0.CO;2
Kirtman BP (1997) Oceanic rossby wave dynamics and the ENSO period in a coupled model. J Clim 10(7):1690–1704. https://doi.org/10.1175/1520-0442(1997)010%3c1690:ORWDAT%3e2.0.CO;2
Large W, Yeager S (2004) Diurnal to decadal global forcing for ocean and sea-ice models: the data sets and flux climatologies. National Center for Atmospheric Research
Large WG, McWilliams JC, Doney SC (1994) Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev Geophys 32(4):363–403. https://doi.org/10.1029/94RG01872
Latif M, Sperber K, Arblaster J, Braconnot P, Chen D, Colman A, Cubasch U, Cooper C, Delecluse P, Dewitt D, Fairhead L, Flato G, Hogan T, Ji M, Kimoto M, Kitoh A, Knutson T, Le Treut H, Li T, Manabe S, Marti O, Mechoso C, Meehl G, Power S, Roeckner E, Sirven J, Terray L, Vintzileos A, Voß R, Wang B, Washington W, Yoshikawa I, Yu J, Zebiak S (2001) ENSIP: the El Niño simulation intercomparison project. Clim Dyn 18(3):255–276. https://doi.org/10.1007/s003820100174
Legeckis R (1977) Long waves in the eastern equatorial Pacific ocean: a view from a geostationary satellite. Science 197(4309):1179–1181
Levine A, Jin F, McPhaden M (2016) Extreme noise-extreme El Niño: How state-dependent noise forcing creates El Niño-la Niña asymmetry. J Clim. https://doi.org/10.1175/JCLI-D-16-0091.1
Levine AFZ, Jin FF (2017) A simple approach to quantifying the noise-ENSO interaction. Part I: deducing the state-dependency of the windstress forcing using monthly mean data. Clim Dyn 48(1):1–18. https://doi.org/10.1007/s00382-015-2748-1
Locarnini RA, Mishonov AV, Antonov JI, Boyer TP, Garcia HE, Baranova OK, Zweng MM, Paver CR, Reagan JR, Johnson DR, Hamilton M, Seidov D (2013) World Ocean Atlas 2013, vol 1: temperature. In: Levitus S (ed) NOAA Atlas NESDIS 73:40. A. Mishonov Technical Ed
Lyman J, Johnson G, Kessler W (2007) Distinct 17- and 33-day tropical instability waves in subsurface observations. J Phys Oceanogr 37(4):855–872. https://doi.org/10.1175/JPO3023.1
Marchesiello P, Capet X, Menkes C, Kennan S (2011) Submesoscale dynamics in tropical instability waves. Ocean Model 39(1–2):31–46. https://doi.org/10.1016/j.ocemod.2011.04.011
Masina S, Philander S, Bush A (1999) An analysis of tropical instability waves in a numerical model of the Pacific ocean 2. Generation and energetics of the waves. J Geophys Res 104(29):637–29. https://doi.org/10.1029/1999JC900226
McGregor S, Ramesh N, Spence P, England MH, McPhaden MJ, Santoso A (2013) Meridional movement of wind anomalies during ENSO events and their role in event termination. Geophys Res Lett. https://doi.org/10.1002/grl.50136
Meinen CS, McPhaden MJ (2001) Interannual variability in warm water volume transports in the equatorial pacific during 1993–1999. J Phys Oceanogr 31(5):1324–1345. https://doi.org/10.1175/1520-0485(2001)031%3c1324:IVIWWV%3e2.0.CO;2
Menkes C, Vialard J, Kennan S, Boulanger J, Madec G (2006) A modeling study of the impact of tropical instability waves on the heat budget of the eastern equatorial Pacific. J Phys Oceanogr 36(5):847–865. https://doi.org/10.1175/JPO2904.1
Moore AM, Kleeman R (1999) Stochastic forcing of ENSO by the intraseasonal oscillation. J Clim 12(5):1199–1220. https://doi.org/10.1175/1520-0442(1999)012%3c1199:SFOEBT%3e2.0.CO;2
Narapusetty B, Kirtman B (2014) Sensitivity of near-surface atmospheric circulation to tropical instability waves. Clim Dyn 42(11–12):3139–3150. https://doi.org/10.1007/s00382-014-2167-8
Neelin JD (1990) A hybrid coupled general circulation model for El Niño studies. J Atmos Sci 47(5):674–693. https://doi.org/10.1175/1520-0469(1990)047%3c0674:AHCGCM%3e2.0.CO;2
Penduff T, Juza M, Barnier B, Zika J, Dewar WK, Treguier AM, Molines JM, Audiffren N (2011) Sea level expression of intrinsic and forced ocean variabilities at interannual time scales. J Clim 24(21):5652–5670. https://doi.org/10.1175/JCLI-D-11-00077.1
Pezzi LP, Vialard J, Richards KJ, Menkes C, Anderson D (2004) Influence of ocean–atmosphere coupling on the properties of tropical instability waves. Geophys Res Lett 31(16): https://doi.org/10.1029/2004GL019995
Philander S (1976) Instabilities of zonal equatorial currents. J Geophys Res 81(21):3725–3735. https://doi.org/10.1029/JC081i021p03725
Puy M, Vialard J, Lengaigne M, Guilyardi E, Voldoire A, Madec G (2016) Modulation of equatorial pacific sea surface temperature response to westerly wind events by the oceanic background state. Clim Dyn, pp 1–25. https://doi.org/10.1007/s00382-016-3480-1
Santoso A, McPhaden MJ, Cai W (2017) The defining characteristics of ENSO extremes and the strong 2015/2016 El Niño. Rev Geophys 55(4):1079–1129. https://doi.org/10.1002/2017RG000560
von Schuckmann K, Brandt P, Eden C (2008) Generation of tropical instability waves in the Atlantic Ocean. J Geophys Res 113(C8):C08–034. https://doi.org/10.1029/2007JC004712
Seager R, Blumenthal MB, Kushnir Y (1995) An advective atmospheric mixed layer model for ocean modeling purposes: Global simulation of surface heat fluxes. J Clim 8(8):1951–1964. https://doi.org/10.1175/1520-0442(1995)008%3c1951:AAAMLM%3e2.0.CO;2
Shchepetkin A, McWilliams J (2005) The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model 9(4):347–404. https://doi.org/10.1016/j.ocemod.2004.08.002
Small RJ, Richards KJ, Xie SP, Dutrieux P, Miyama T (2009) Damping of tropical instability waves caused by the action of surface currents on stress. J Geophys Res 114(C4): https://doi.org/10.1029/2008JC005147
Small RJ, Curchitser E, Hedstrom K, Kauffman B, Large WG (2015) The Benguela upwelling system: quantifying the sensitivity to resolution and coastal wind representation in a global climate model. J Clim 28(23):9409–9432. https://doi.org/10.1175/JCLI-D-15-0192.1
Stein K, Timmermann A, Schneider N, Jin FF, Stuecker MF (2014) ENSO seasonal synchronization theory. J Clim 27:5285–5310. https://doi.org/10.1175/JCLI-D-13-00525.1
Syu HH, Neelin JD, Gutzler D (1995) Seasonal and interannual variability in a hybrid coupled GCM. J Clim 8(9):2121–2143. https://doi.org/10.1175/1520-0442(1995)008%3c2121:SAIVIA%3e2.0.CO;2
Tziperman E, Zebiak SE, Cane MA (1997) Mechanisms of seasonal-ENSO interaction. J Atmos Sci 54(1):61–71. https://doi.org/10.1175/1520-0469(1997)054%3c0061:MOSEI%3e2.0.CO;2
Willett CS, Leben RR, Lavín MF (2006) Eddies and tropical instability waves in the eastern tropical Pacific: a review. Prog Oceanogr 69:218–238. https://doi.org/10.1016/j.pocean.2006.03.010
Zavala-Garay J, Moore A, Perez C, Kleeman R (2003) The response of a coupled model of ENSO to observed estimates of stochastic forcing. J Clim 16(17):2827–2842. https://doi.org/10.1175/1520-0442
Zelle H, Appeldoorn G, Burgers G, van Oldenborgh GJ (2004) The relationship between sea surface temperature and thermocline depth in the eastern equatorial pacific. J Phys Oceanogr 34(3):643–655. https://doi.org/10.1175/2523.1
Zhang C (2005) Madden-Julian Oscillation. Rev Geophys 43(2):RG2003. https://doi.org/10.1029/2004RG000158
Zhang RH (2014) Effects of tropical instability wave (TIW)-induced surface wind feedback in the tropical pacific ocean. Clim Dyn 42(1):467–485. https://doi.org/10.1007/s00382-013-1878-6
Zhang RH (2015) A hybrid coupled model for the Pacific ocean–atmosphere system. Part I: description and basic performance. Adv Atmos Sci 32(3):301–318. https://doi.org/10.1007/s00376-014-3266-5
Zhang RH (2016) A modulating effect of Tropical Instability Wave (TIW)-induced surface wind feedback in a hybrid coupled model of the tropical Pacific. J Geophys Res. https://doi.org/10.1002/2015JC011567
Zhang RH, Busalacchi AJ (2008) Rectified effects of tropical instability wave (TIW)-induced atmospheric wind feedback in the tropical Pacific. Geophys Res Lett. https://doi.org/10.1029/2007GL033028
Zweng M, Reagan J, Antonov J, Locarnini R, Mishonov A, Boyer T, Garcia H, Baranova O, Johnson D, DSeidov, Biddle M (2013) World Ocean Atlas 2013, vo 2: salinity. In: Levitus S (ed) NOAA Atlas NESDIS 74:39. A. Mishonov Technical Ed
Acknowledgements
This study benefited from discussions with Vishal Dixit and comments from two anonymous reviewers. A.S. and M.H.E. are supported by the Earth Science and Climate Change Hub of the Australian Government’s National Environmental Science Programme (NESP) and the Centre for Southern Hemisphere Oceans Research (CSHOR), a joint research centre for Southern Hemisphere oceans between QNLM, CSIRO, UNSW and UTAS. S.M. was supported by the Australian Research Council. The altimeter products were produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (http://www.marine.copernicus.eu). We thank the TAO Project Office of NOAA/PMEL for providing the TAO data. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.
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Appendix: The atmospheric boundary layer model
Appendix: The atmospheric boundary layer model
As discussed in Sect. 2, we use an Atmospheric Boundary Layer Model (ABLM) to freely determine the air temperature \(T_{air}\) and air humidity \(q_{air}\). Our implementation is based on the cheapAML model of Deremble et al. (2013), following earlier work by Seager et al. (1995). The model solves single layer advection-diffusion equations for \(T_{air}\) and \(q_{air}\),
where \(\varvec{U}\) is the prescribed 10m wind field, \(\kappa\) is an isotropic horizontal diffusivity, \(\rho _a\) is the density of air, \(C_p\) is the heat capacity of air, h is the spatially variable depth of the atmospheric boundary layer, \(r_T\) is a restoring time-scale that is non-zero only over land (where it takes the value 0.1 days) and \(T_b\) and \(q_b\) are background restoring fields for air temperature and humidity.
As discussed in Deremble et al. (2013), the imbalance of heat loss from the top of the boundary layer, \(F^+\), and heat gain from the ocean \(F^-\) are parameterized using long-wave radiative fluxes and the air–sea sensible heat flux (solar radiation and the latent heat flux both pass through the boundary layer at first order). Heat is lost via long-wave radiation from the top of the boundary layer using an average lapse rate of \(0.0098\,^\circ\)C m\(^{-1}\). The upper and lower fluxes of moisture, \(F_Q^+\) and \(F_Q^-\) are represented by evaporation and entrainment at the top of the boundary layer. The advecting wind-velocities \(\varvec{U}\), the boundary and over-land air temperature and air humidity and the spatially variable boundary layer depth h are taken from the ERA Interim 1980–2014 July–December average discussed above. All air–sea fluxes are determined using the ROMS bulk flux routines, based on Fairall et al. (1996). Due to the constant wind speeds and lack of storm systems, we use a large diffusivity of \(\kappa =5 \times 10^5\) m\(^2\)s\(^{-1}\).
In regions with high SST, the air temperature determined by the ABLM has a tendency to warm too much due to the absence of convection. This excessive warming in convective regions was also noted by Deremble et al. (2013), but they did not suggest a solution other than restoring. In order to avoid this unphysical warming we include a simple threshold on the surface air temperature, chosen as \(28\,^\circ\)C. This crudely models the effects of convection, which above this threshold mixes the air column vertically until the surface air temperature is once again below the threshold, returning the system to marginal stability. The presence of a threshold SST of around 27–28 °C above which convection occurs is well supported in the literature (e.g. Graham and Barnett 1987; Johnson and Xie 2010). Wind convergence also plays an important role in modulating convection (Graham and Barnett 1987). However, as we have a temporally constant wind field and do not resolve any synoptic scale variability we do not include a parameterization for this effect.
Our implementation of the ABLM includes several tuning parameters, such as the effective height of upwards long-wave radiation out of the boundary layer, the convective air temperature threshold and the constant of proportionality \(\alpha\) relating the entrainment of humidity at the top of the boundary layer to the surface fluxes. The best parameter set was found to be \(\alpha =0.3\) [compared to the value of 0.25 used by Deremble et al. (2013)], a \(28\,\,^\circ\)C threshold and long-wave radiation from the top of the boundary layer. The remaining biases include a tendency to be too warm and wet in the warm and wet regions and too cool and dry in the cool regions [as also noted by Deremble et al. (2013) in a fixed SST experiment]. This bias is likely due to the absence of low-cloud feedbacks.
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Holmes, R.M., McGregor, S., Santoso, A. et al. Contribution of tropical instability waves to ENSO irregularity. Clim Dyn 52, 1837–1855 (2019). https://doi.org/10.1007/s00382-018-4217-0
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DOI: https://doi.org/10.1007/s00382-018-4217-0