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Zonal structure and variability of the Western Pacific dynamic warm pool edge in CMIP5

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Abstract

The equatorial edge of the Western Pacific Warm Pool is operationally identified by one isotherm ranging between 28° and 29 °C, chosen to align with the interannual variability of strong zonal salinity gradients and the convergence of zonal ocean currents. The simulation of this edge is examined in 19 models from the World Climate Research Program Coupled Model Intercomparison Project Phase 5 (CMIP5), over the historical period from 1950 to 2000. The dynamic warm pool edge (DWPE), where the zonal currents converge, is difficult to determine from limited observations and biased models. A new analysis technique is introduced where a proxy for DWPE is determined by the isotherm that most closely correlates with the movements of the strong salinity gradient. It can therefore be a different isotherm in each model. The DWPE is simulated much closer to observations than if a direct temperature-only comparison is made. Aspects of the DWPE remain difficult for coupled models to simulate including the mean longitude, the interannual excursions, and the zonal convergence of ocean currents. Some models have only very weak salinity gradients trapped to the western side of the basin making it difficult to even identify a DWPE. The model’s DWPE are generally 1–2 °C cooler than observed. In line with theory, the magnitude of the zonal migrations of the DWPE are strongly related to the amplitudes of the Nino3.4 SST index. Nevertheless, a better simulation of the mean location of the DWPE does not necessarily improve the amplitude of a model’s ENSO. It is also found that in a few models (CSIROMk3.6, inmcm and inmcm4-esm) the warm pool displacements result from a net heating or cooling rather than a zonal advection of warm water. The simulation of the DWPE has implications for ENSO dynamics when considering ENSO paradigms such as the delayed action oscillator mechanism, the Advective-Reflective oscillator, and the zonal-advective feedback. These are also discussed in the context of the CMIP5 simulations.

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References

  • An SI, Jin FF (2001) Collective role of thermocline and zonal advective feedbacks in the ENSO mode. J Clim 14(16):3421–3432

    Article  Google Scholar 

  • An SI, Jin FF et al (1999) The role of zonal advection feedback in phase transition and growth of ENSO in the Cane-Zebiak model. J Meteorol Soc Japan 77(6):1151–1160

    Google Scholar 

  • Barnston AG, Chelliah M (1997) Documentation of a highly ENSO-related SST region in the Equatorial Pacific. Atmos Ocean 35(3):367–383

    Article  Google Scholar 

  • Battisti DS, Hirst AC (1989) Interannual variability in a Tropical Atmosphere Ocean model—influence of the basic state, Ocean geometry and nonlinearity. J Atmos Sci 46(12):1687–1712

    Article  Google Scholar 

  • Belmadani A, Dewitte B et al (2010) ENSO feedbacks and associated time scales of variability in a multimodel ensemble. J Clim 23(12):3181–3204. doi:10.1175/2010jcli2830.1

    Article  Google Scholar 

  • Bonjean F, Lagerloef GSE (2002) Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. J Phys Oceanogr 32(10):2938–2954. doi:10.1175/1520-0485(2002)032<2938:dmaaot>2.0.co;2

    Article  Google Scholar 

  • Bosc C, Delcroix T et al (2009) Barrier layer variability in the Western Pacific Warm Pool from 2000 to 2007. J Geophys Res Oceans 114:C06023. doi:10.1029/2008jc005187

    Article  Google Scholar 

  • Brown JN, Fedorov AV et al (2010) How well do coupled models replicate ocean energetics relevant to ENSO? Clim Dyn. doi:10.1007/s00382-010-0926-8

  • Brown JN, Maes C et al (2013a) Reinvigorating research on the Western Pacific Warm Pool—first workshop. CLIVAR Exchanges No 63, vol 18, No 3

  • Brown JN, Sen Gupta A et al (2013b) Implications of CMIP3 model biases and uncertainties for climate projections in the western Tropical Pacific. Clim Change. doi:10.1007/s10584-012-0603-5

  • Burgers G, Stephenson DB (1999) The “normality” of El Niño. Geophys Res Lett 26(8):1027. doi:10.1029/1999gl900161

    Article  Google Scholar 

  • Clarke AJ, Wang J et al (2000) A simple warm-pool displacement ENSO MODEL. J Phys Oceanogr 30:1679–1691

    Article  Google Scholar 

  • Cravatte S, Delcroix T et al (2009) Observed freshening and warming of the Western Pacific Warm Pool. Clim Dyn 33:565–589

    Article  Google Scholar 

  • Delcroix T, Alory G et al (2011) A gridded sea surface salinity data set for the tropical Pacific with sample applications (1950–2008). Deep Sea Res Part I Oceanogr Res Papers 58(1):38–48. doi:10.1016/j.dsr.2010.11.002

    Article  Google Scholar 

  • Durack PJ, Wijffels SE (2010) Fifty-year trends in Global Ocean Salinities and their relationship to broad-scale warming. J Clim 23(16):4342–4362. doi:10.1175/2010jcli3377.1

    Article  Google Scholar 

  • Grose MR, Brown JN et al (under review) Assessment of the CMIP5 global climate model simulations of the western tropical Pacific climate system and comparison to CMIP3. Submitted to International Journal of Climatology

  • Guilyardi E, Bellenger H et al (2012) A first look at ENSO in CMIP5. Clivar Exch 17(58):29–32

    Google Scholar 

  • Hoyos CD, Webster PJ (2011) Evolution and modulation of tropical heating from the last glacial maximum through the twenty-first century. Clim Dyn 38(7–8):1501–1519. doi:10.1007/s00382-011-1181-3

    Google Scholar 

  • Huang B, Xue Y et al (2010) The NCEP GODAS Ocean analysis of the tropical pacific mixed layer heat budget on seasonal to interannual time scales. J Clim 23(18):4901–4925. doi:10.1175/2010jcli3373.1

    Article  Google Scholar 

  • Irving DB, Perkins SE et al (2011) Evaluating global climate models for the Pacific island region. Clim Res 49:169–187. doi:10.3354/cr01028

    Article  Google Scholar 

  • Jin F–F, Kim ST et al (2006) A coupled-stability index for ENSO. Geophys Res Lett 33(23):L23708. doi:10.1029/2006gl027221

    Article  Google Scholar 

  • 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 36(7–8):1609–1627. doi:10.1007/s00382-010-0872-5

    Google Scholar 

  • Maes C, Picaut J et al (2004) Characteristics of the convergence zone at the eastern edge of the Pacific warm pool. Geophys Res Lett 31(11). doi: L1130410.1029/2004gl019867

  • Maes C, Ando K et al (2006) Observed correlation of surface salinity, temperature and barrier layer at the eastern edge of the Western Pacific Warm Pool. Geophys Res Lett 33(6):L06601. doi:10.1029/2005gl024772

    Google Scholar 

  • Maes C, Sudre J et al (2010) Detection of the Eastern edge of the Equatorial Pacific warm pool using satellite-based Ocean color observations. SOLA 6:129–132. doi:10.2151/sola.2010-033

    Article  Google Scholar 

  • Maximenko N, Hafner J et al (2012) Pathways of marine debris from trajectories of Lagrangian drifters. Mar Poll Bull 65(1–3):51–62

    Article  Google Scholar 

  • McPhaden MJ, Picaut J (1990) El-Nino Southern Oscillation displacements of the Western Equatorial Pacific warm pool. Science 250(4986):1385–1388

    Article  Google Scholar 

  • Picaut J, Ioualalen M et al (1996) Mechanism of the zonal displacements of the Pacific warm pool: implications for ENSO. Science 274(5292):1486–1489

    Article  Google Scholar 

  • Picaut J, Masia F et al (1997) An advective-reflective conceptual model for the Oscillatory nature of the ENSO. Science 277(5326):663–666. doi:10.1126/science.277.5326.663

    Article  Google Scholar 

  • Picaut J, Ioualalen M et al (2001) The oceanic zone of convergence on the eastern edge of the Pacific warm pool: a synthesis of results and implications for El Nino-Southern Oscillation and biogeochemical phenomena. J Geophys Res Oceans 106(C2):2363–2386

    Article  Google Scholar 

  • Rayner NA, Parker DE et al (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108:4407. doi:10.1029/2002JD002670

    Article  Google Scholar 

  • Shu L, Clarke AJ (2002) Using an Ocean model to examine ENSO dynamics. J Phys Oceanogr 32:903–923

    Article  Google Scholar 

  • Sudre J, Maes C et al (2013) On the global estimates of geostrophic and Ekman surface currents. Limnol Oceanogr Fluids Environ (LOFE) 3:1–20. doi:10.1215/21573689-2071927

    Article  Google Scholar 

  • Taylor KE, Stouffer RJ et al (2011) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498

    Article  Google Scholar 

  • Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans A Math Phys Eng Sci 365(1857):2053–2075. doi:10.1098/rsta.2007.2076

    Article  Google Scholar 

  • Wittenberg AT (2009) Are historical records sufficient to constrain ENSO simulations? Geophys Res Lett 36(12). doi:10.1029/2009gl038710

  • Wyrtki K (1989) Some thoughts about the West Pacific warm pool. Western Pacific international meeting and workshop on TOGA-COARE, Noumea, ORSTOM editions

  • Xiang B, Wang B et al (2011) Reduction of the thermocline feedback associated with mean SST bias in ENSO simulation. Clim Dyn 39(6):1413–1430. doi:10.1007/s00382-011-1164-4

    Article  Google Scholar 

  • Xie S-P, Deser C et al (2010) Global warming pattern formation: sea surface temperature and rainfall*. J Clim 23(4):966–986. doi:10.1175/2009jcli3329.1

    Article  Google Scholar 

  • Zhang X, Church JA (2012) Sea level trends, interannual and decadal variability in the Pacific Ocean. Geophys Res Lett 39(701). doi:10.1029/2012GL053240

  • Zheng Y, Lin J-L et al (2012) The equatorial Pacific cold tongue simulated by IPCC AR4 coupled GCMs: Upper ocean heat budget and feedback analysis. J Geophys Res 117(C5). doi:10.1029/2011jc007746

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Acknowledgments

We thank the participants of the Western Pacific Warm Pool Workshop (Hobart, March 2013) for helpful discussions of the warm pool and ENSO (Brown et al. 2013a), particularly Felicity Graham. As well as the CSIRO Frohlich Fellowship for supporting Christophe’s visit to the CSIRO. This research was conducted with the support of the Pacific-Australia Climate Change Science and Adaptation Planning Program funded by AusAID in collaboration with the Department of Climate Change and Energy Efficiency, and delivered by the Bureau of Meteorology and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). We thank the PCMDI and the World Climate Research Programs Working Group on Coupled Modelling for their roles in making available the CMIP3 and CMIP5 multi-model datasets. Support of this dataset is provided by the Office of Science, US Department of Energy. More details on model documentation are available at the PCMDI website (www-pcmdi.llnl.gov).

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Authors and Affiliations

  1. Centre for Australian Weather and Climate Research, CSIRO, Hobart, Australia

    Jaclyn N. Brown & Clothilde Langlais

  2. LEGOS, IRD, Université de Toulouse, Toulouse, France

    Christophe Maes

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  1. Jaclyn N. Brown
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  2. Clothilde Langlais
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Correspondence to Jaclyn N. Brown.

Electronic supplementary material

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382_2013_1931_MOESM1_ESM.tif

Supplementary Figure 1 Hovmoller diagrams of zonal equatorial SSS anomaly from 1950 to 2000 in each GCM. A linear trend has been removed from each. (TIFF 31624 kb)

382_2013_1931_MOESM2_ESM.tif

Supplementary Figure 2 Difference between mean GCM a) SST and b) SSS and what is observed over the period 1950-2000. Mean observed SSS and SST are plotted in bottom right corner. (TIFF 4516 kb)

Supplementary material 3 (TIFF 4749 kb)

382_2013_1931_MOESM4_ESM.tif

Supplementary Figure 3 The DJF zonal SST structure for individual GCMs (blue lines) compared to observations (grey lines). The filled squares represent the longitude of the DWPE according to our method of selection (Table 1). Three contours represent the mean SST in El Niño (thick line), La Niña (thin line) and neutral conditions (dashed line). El Niño and La Niña phases are identified as when the model Nino3.4 index is more than two-thirds of the models Nino3.4 standard deviation. This metric corresponds to a threshold of 0.5°C in the observations. (TIFF 23131 kb)

Appendix 1

Appendix 1

The Nino3.4 box (170°W to 120°W, 5°S to 5°N) is the traditional ENSO metric for eastern Pacific SST. During El Niño events, SST is quite uniform within the box in observations (Fig. 9a) and in models (not shown). In La Niña years this is not the case as the upwelled cool water is trapped to the equator with warmer water along 5°N and S (Fig. 9b).

Fig. 9
figure 9

Observed SST for (a) a composite of El Niño years and (b) a composite of La Niña years. Black box shows Nino3.4 region (170°W–120°W, 5°S–5°N). c Standard deviation of the Nino3.4 index compared to the EE-SST index (170°W–120°W, 0.5°S–0.5°N) for observations and the CMIP5 models. Observations are taken from HadISST over the period 1950–2000. CMIP5 model output is analysed from 1950 to 2000 in the historical runs

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Some coupled models have a meridional SST signature that extends too far north and south so appears to be a stronger La Niña event than in observations, even if the SST at the equator is exactly the same. To reduce the effect of this bias in Nino3.4 magnitude, we use an EE-SST index of the same longitudinal extent, but only 0.5°S to 0.5°N.

The EE-SST index results in a stronger value during La Nina events in observations but also for many models (Fig. 9c). The models that do not show a change (e.g., IPSL), are doing so because their meridional band of SST anomaly is too wide.

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Brown, J.N., Langlais, C. & Maes, C. Zonal structure and variability of the Western Pacific dynamic warm pool edge in CMIP5. Clim Dyn 42, 3061–3076 (2014). https://doi.org/10.1007/s00382-013-1931-5

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