Skip to main content

Advertisement

Log in

How well do coupled models replicate ocean energetics relevant to ENSO?

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Accurate replication of the processes associated with the energetics of the tropical ocean is necessary if coupled GCMs are to simulate the physics of ENSO correctly, including the transfer of energy from the winds to the ocean thermocline and energy dissipation during the ENSO cycle. Here, we analyze ocean energetics in coupled GCMs in terms of two integral parameters describing net energy loss in the system using the approach recently proposed by Brown and Fedorov (J Clim 23:1563–1580, 2010a) and Fedorov (J Clim 20:1108–1117, 2007). These parameters are (1) the efficiency γ of the conversion of wind power into the buoyancy power that controls the rate of change of the available potential energy (APE) in the ocean and (2) the e-folding rate α that characterizes the damping of APE by turbulent diffusion and other processes. Estimating these two parameters for coupled models reveals potential deficiencies (and large differences) in how state-of-the-art coupled GCMs reproduce the ocean energetics as compared to ocean-only models and data assimilating models. The majority of the coupled models we analyzed show a lower efficiency (values of γ in the range of 10–50% versus 50–60% for ocean-only simulations or reanalysis) and a relatively strong energy damping (values of α−1 in the range 0.4–1 years versus 0.9–1.2 years). These differences in the model energetics appear to reflect differences in the simulated thermal structure of the tropical ocean, the structure of ocean equatorial currents, and deficiencies in the way coupled models simulate ENSO.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. We use conventional abbreviation APE in the text and notation E in the equations.

  2. Fedorov (2007) used a slightly different notation, with 2α as the coefficient in front of E in Eq. (4).

References

  • AchutaRao K, Sperber KR (2006) ENSO simulation in coupled ocean-atmosphere models: are the current models better? Clim Dyn 27:1–15

    Article  Google Scholar 

  • Barnier B, Madec G, Penduff J-MM, Treguier AM, Le Sommer J, Beckmann A, Biastoch A, Boning C, Dengg J, Derval C, Durand E, Gulev S, Remy E, Talandier C, Theetten S, Maltrud M, McClean JL, De Cuevas B (2006) Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution. Ocean Dyn 56(5–6):543–567

    Google Scholar 

  • Behringer DW (2007) The global ocean data assimilation system at NCEP, 11th symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface, AMS 87th annual meeting. Henry B. Gonzales Convention Center, San Antonio, Texas

  • Boccaletti G, Pacanowski RC, Philander SG, Fedorov AV (2004) The thermal structure of the upper ocean. J Phys Oceanogr 34:888–902

    Article  Google Scholar 

  • Brierley C, Fedorov AV, Liu Z, Herbert T, Lawrence K, LaRiviere J (2009) Greatly expanded tropical warm pool and weaker Hadley circulation in the early Pliocene. Science 323:1714–1717

    Article  Google Scholar 

  • Brown JN, Fedorov AV (2008) Mean energy balance in the tropical Pacific Ocean. J Mar Res 66(1):1–23

    Article  Google Scholar 

  • Brown JN, Fedorov AV (2010a) How much energy is transferred from the winds to the thermocline on ENSO timescales? J Clim 23:1563–1580

    Article  Google Scholar 

  • Brown J, Fedorov AV (2010b) Estimating the diapycnal transport contribution to warm water volume variations in the tropical Pacific ocean. J Clim 23:221–237

    Article  Google Scholar 

  • Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS, Carton JA, Chang P, Doney SC, Hack JJ, Henderson TB, Kiehl JT, Large WG, McKenna DS, Santer BD, Smith RD (2006) The community climate system model version 3 (CCSM3). J Clim 19(11):2122–2143

    Article  Google Scholar 

  • Collins M, S-I An, Cai W, Ganachaud A, Guilyardi E, Jin F-F, Jochum M, Lengaigne M, Power S, Timmermann A, Vecchi G, Wittenberg A (2010) The impact of global warming on the tropical Pacific and El Niño. Nat Geosci 3:391–397

    Article  Google Scholar 

  • Dawe JT, Thompson L (2006) Effect of ocean surface currents on wind stress, heat flux, and wind power input to the ocean. Geophys Res Lett 33(9)

  • Delworth TL et al (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19(5):643–674

    Article  Google Scholar 

  • Dewitte B, Cibot C, Périgaud C, An S-I, Terray L (2007) Interaction between near-annual and ENSO modes in a CGCM simulation: role of equatorial background mean state. J Clim 20:1035–1052

    Google Scholar 

  • Fedorov AV (2007) Net energy dissipation rates in the tropical ocean and ENSO dynamics. J Clim 20:1108–1117

    Article  Google Scholar 

  • Fedorov AV (2010) Ocean response to wind variations, warm water volume, and simple models of ENSO in the low-frequency approximation. J Clim 23:3855–3873

    Article  Google Scholar 

  • Fedorov AV, Brown JN (2009) Equatorial waves. In: Steele J (ed) Encyclopedia of ocean sciences, 2nd edn. Academic Press, Dublin

    Google Scholar 

  • Fedorov AV, Philander SG (2000) Is El Niño changing? Science 288:1997–2002

    Google Scholar 

  • Fedorov AV, Philander SGH (2001) A stability analysis of the tropical ocean-atmosphere interactions: bridging measurements and theory for El Niño. J Clim 14:3086–3101

    Google Scholar 

  • Fedorov AV, Harper SL, Philander SG, Winter B, Wittenberg AT (2003) How predictable is El Niño? Bull Amer Meteor Soc 84:911–919

    Article  Google Scholar 

  • Fedorov AV, Pacanowski RC, Philander SGH, Boccaletti G (2004) The effect of salinity on the wind-driven circulation and the thermal structure of the upper ocean. J Phys Oceanogr 34:1949–1966

    Article  Google Scholar 

  • Fedorov AV, Brierley C, Emanuel K (2010) Tropical cyclones and permanent El Nino in the early Pliocene epoch. Nature 463:1066–1070

    Article  Google Scholar 

  • Gleckler P, Taylor KE, Dutriaux C (2008) Performance metrics for climate models. J Geophys Res 113:D06104

    Article  Google Scholar 

  • Goddard L, Philander SG (2000) The energetics of El Nino and La Nina. J Clim 13:1496–1516

    Article  Google Scholar 

  • Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA, O’Farrell SP, Waterman LJ, Hirst AC, Wilson SG, Collier MA, Watterson IG, Elliott TI (2002) The CSIRO Mk3 climate system model [electronic publication]. Aspendale: CSIRO Atmospheric Research (CSIRO Atmospheric Research technical paper; no. 60), pp 130

  • Griffies SM, Gnanadesikan A, Dixon KW, Dunne JP, Gerdes R, Harrison MJ, Rosati A, Russell J, Samuels B, Spelman MJ, Winton M, Zhang R (2005) Formulation of an ocean model for global climate simulations. Ocean Sci 1:45–79

    Article  Google Scholar 

  • Gualdi S, Scoccimarro E, Navarra A (2008) Changes in tropical cyclone activity due to global warming: results from a high-resolution coupled general circulation model. J Clim 21:5204–5228

    Article  Google Scholar 

  • Guilyardi E (2006) El Nino-mean state-seasonal cycle interactions in a multi-model ensemble. Clim Dyn 26:329–348

    Article  Google Scholar 

  • Guilyardi E, Wittenberg A, Fedorov AV et al (2009a) Understanding El Niño in ocean-atmosphere general circulation models. Bull Amer Meteorol Soc 90:325–340

    Article  Google Scholar 

  • Guilyardi E, Braconnot P, Li T, Jin F-F, Kim P, Kolasinski M, Musat I (2009b) Mechanisms for ENSO suppression in a coupled GCM with a modified atmospheric convection scheme. J Clim 22:5698–5718

    Article  Google Scholar 

  • Jin F-F, Kim ST, Bejarano L (2006) A coupled-stability index for ENSO. Geophys Res Lett 33:L23708

    Article  Google Scholar 

  • Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ, Ridley JK, Senior CA, Williams KD, Jones A, Rickard GJ, Cusack S, Ingram WJ, Crucifix M, Sexton DMH, Joshi MM, Dong B-W, Spencer H, Hill RSR, Gregory JM, Keen AB, Pardaens AK, Lowe JA, Bodas-Salcedo A, Stark S, Searl Y (2006) The new Hadley centre climate model HadGEM1: evaluation of coupled simulations. J Clim 19(7):1327–1353

    Article  Google Scholar 

  • K-1 model developers (2004) K-1 coupled model (MIROC) description, K-1 technical report, 1. In: Hasumi H, Emori S (eds) Center for climate system research. University of Tokyo, pp 34

  • Kim ST, Jin F-F (2010) An ENSO stability analysis. Part II: results from the twentieth and twenty-first century simulations of the CMIP3 models. Clim Dyn. doi:10.1007/s00382-010-0872-5

  • Kim S-J, Flato GM, Boer GJ, McFarlane NA (2002) A coupled climate model simulation of the last glacial maximum, part 1: transient multi-decadal response. Clim Dyn 19:515–537

    Article  Google Scholar 

  • Large WG, McWilliams JC, Doney SC (1994) Oceanic vertical mixing: a review and model with a nonlocal boundary-layer parameterization. Rev Geophys 32:363–403

    Google Scholar 

  • Lloyd J, Guilyardi E, Weller H, Slingo J (2009) The role of atmosphere feedbacks during ENSO in the CMIP3 models. Atmos Sci Lett 10:170–176

    Google Scholar 

  • Marti O, Braconnot P, Bellier J, Benshila R, Bony S, Brockmann P, Cadulle P, Caubel A, Denvil S, Dufresne JL, Fairhead L, Filiberti M-A, Fichefet T, Friedlingstein P, Grandpeix J-Y, Hourdin F, Krinner G, L′evy C, Musat I, Talandier C. IPSL Global Climate Modeling Group 2005. The new IPSL climate system model: IPSL-CM4c

  • McPhaden MJ (1999) Genesis and evolution of the 1997–98 El Niño. Science 283:950–954

    Article  Google Scholar 

  • McPhaden MJ, Zebiak SE, Glantz MH (2006) ENSO as an integrating concept in earth science. Science 314:1740–1745

    Article  Google Scholar 

  • Meehl GA, Gent PR, Arblaster JM, Otto-Bliesner BL, Brady EC, Craig A (2001) Factors that affect the amplitude of El Niño in global coupled climate models. Clim Dyn 17:515

    Article  Google Scholar 

  • Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394

    Article  Google Scholar 

  • Neale RB, Richter JH, Jochum M (2008) The impact of convection on ENSO: from a delayed oscillator to a series of events. J Clim 21:5904–5924

    Article  Google Scholar 

  • Oort AH, Ascher SC, Levitus S, Peixoto JH (1989) New estimates of the available potential energy in the world ocean. J Geophys Res 94:3187–3200

    Article  Google Scholar 

  • Philip S, van Oldenborgh GJ (2006) Shifts in ENSO coupling processes under global warming. Geophys Res Lett 33:L11704

    Article  Google Scholar 

  • Philip S, van Oldenborgh GJ (2009) Significant atmospheric nonlinearities in the ENSO cycle. J Clim 22:4014–4028

    Article  Google Scholar 

  • Philip S, van Oldenborgh GJ, Collins M (2009) The role of atmosphere and ocean physical processes in ENSO. Ocean Sci Discuss (submitted)

  • Roberts WGH, Battisti DS (2010) A new tool for evaluating the physics of coupled atmosphere-ocean variability in nature and in general circulation models. Clim Dyn. doi:10.1007/s00382-010-0762-x

  • Salas-Mélia D (2002) A global coupled sea ice-ocean model. Ocean Model 4:137–172

    Article  Google Scholar 

  • Schmidt GA, Ruedy R, Hansen JE, Aleinov I, Bell N, Bauer M, Bauer S, Cairns B, Canuto V, Cheng Y, Del Genio A, Faluvegi G, Friend AD, Hall TM, Hu Y, Kelley M, Kiang NY, Koch D, Lacis AA, Lerner J, Lo KK, Miller RL, Nazarenko L, Oinas V, Perlwitz Ja, Perlwitz Ju, Rind D, Romanou A, Russell GL, Sato M, Shindell DT, Stone PH, Sun S, Tausnev N, Thresher D, Yao M-S (2006) Present day atmospheric simulations using GISS ModelE: comparison to in-situ, satellite and reanalysis data. J Clim 19:153–192

    Google Scholar 

  • Sun D, Yu Y, Zhang T (2008) Tropical water vapor and cloud feedbacks in climate models: a further assessment using coupled simulations. J Clim 22:1287–1304

    Article  Google Scholar 

  • Thompson CJ, Battisti DS (2000) A linear stochastic dynamical model of ENSO, part I: development. J Clim 13:2818–2883

    Article  Google Scholar 

  • Thompson CJ, Battisti DS (2001) A linear stochastic dynamical model of ENSO. Part II: analysis. J Clim 14:445–466

    Article  Google Scholar 

  • Van Oldenborgh GJ, Philip SY, Collins M (2005) El Niño in a changing climate: a multi-model study. Ocean Sci 1:81–95. Sref:1812-0792/os/2005-1-81

    Google Scholar 

  • Wang C, Xie SP, Carton JA (2004) Earth’s climate. The ocean-atmosphere interaction, vol 147. Geophysical Monograph Series, American Geophysical Union

  • Wunsch C, Heimbach P (2007) Practical global ocean state estimation. Phys D 230(1–2):197–208

    Article  Google Scholar 

  • Zhai XM, Greatbatch RJ (2007) Wind work in a model of the northwest Atlantic Ocean. Geophys Res Lett 34(4)

Download references

Acknowledgments

We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. We would like to thank Mat Maltrud for his tireless assistance setting up and running the POP model at Yale University. In addition, we thank Brian Dobbins for his help running and processing model data. We also thank Gurvan Madec for supply the ORCA data and advice, John Dunne for the MOM4 ocean model output, and George Philander, Lisa Goddard and Remi Tailleux for discussions of this topic. We are grateful to Mat Collins and anonymous reviewers for carefully reviewing the paper. ECMWF ERA-40 data used in this study have been obtained from the ECMWF data server, http://data.ecmwf.int/data/d/era40_mnth. The ECCO data assimilation was provided by the Consortium for Estimating the Circulation and Climate of the Ocean funded by the National Oceanographic Partnership Program (NOPP). We also thank NCAR for providing the NCEP Global Ocean Data Assimilation (GODAS) product. This research was supported in part by grants to AVF from NSF (OCE-0901921), Department of Energy Office of Science (DE-FG02-06ER64238, DE-FG02-08ER64590), the David and Lucile Packard Foundation, and CNRS (France) and by grants to EG from the European Community ENSEMBLES (GOCE-CT-2003-505539) under the Sixth Framework Programme and by the CNRS PICS CORDIAL project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alexey V. Fedorov.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brown, J.N., Fedorov, A.V. & Guilyardi, E. How well do coupled models replicate ocean energetics relevant to ENSO?. Clim Dyn 36, 2147–2158 (2011). https://doi.org/10.1007/s00382-010-0926-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-010-0926-8

Keywords

Navigation