On the dynamics of interdecadal sea surface temperature variability in the tropical Pacific Ocean

1. On the dynamics of interdecadal sea surface temperature variability in the tropical Pacific Ocean Shayne McGregor Dr Neil J. Holbrook (Macquarie University, Sydney, Australia). Dr Scott B. Power (Bureau of Meteorology Research Centre, Melbourne, Australia)

2. What is forcing the Pacific Decadal Variability (PDV)? • Randomness of ENSO (e.g., Newman et al. 2003;Power and Coleman 2006). • Non-linear processes in the tropical Pacific (e.g., Jin et al. 1994; Tziperman et al. 1994; Timmermann et al. 2003; Rodgers et al. 2004 ) • Exchanges between the extra-tropics and the tropics: • via the atmosphere (Barnett et al. 1999). • via the ocean: • The v primeTbar mechanism (Kleeman et al. 1999; Nonaka et al. 2003) • The off-equatorial Rossby wave theory (e.g., Lysne et al. 1997; Liu et al. 1999; Hazeleger et al. 2001; Capotondi and Alexander 2001, 2003; Galanti and Tziperman, 2003; Wang et al. 2003a, 2003b)

3. Aims and models • The aim of this research project is to identify the relative role of off-equatorial exchanges with the tropical Pacific, via the extra-tropical Rossby waves, on the tropical Pacific Ocean’s interdecadal SST variability. • The BMRC coupled GCM • Incorporates a R21 atmosphere GCM with 17 vertical levels. • A ocean GCM component consisting of a modified version of the MOM . • The model was run for 100 yrs to to simulate real world variability. • Exhibits realistic interannual variability in the tropical Pacific (ENSO), and evidence of Rossby and Kelvin wave propagation (Power et al. 1998; Power et al. 2006)

4. CGCM decadal SST variability First mode CGCM SST spatial pattern • Meridionally broad positive weighting in the tropical Pacific. • Areas of zero and negative weighting in the mid-latitude regions • Slow decadal scale oscillation. First mode CGCM SST time series

5. The Shallow Water Model • We then use a linear ocean shallow water model (SWM) forced with wind stresses from the CGCM to analyze and further investigate the role of Ocean Dynamics (Rossby and Kelvin waves) on the Pacific Ocean interdecadal SST variability as represented to the sophisticated CGCM of BMRC. • The linear SWM model • The SWM is effectively a one and a half layer model of the stratified ocean where the two density layers are separated by an interface that approximates the thermocline. • The solution of this model permits the surface wind stress forcing to instigate Rossby and Kelvin wave propagation of the pycnocline (thermocline). Ocean Surface Thermocline

6. SWM Mode 1 CGCM VAT Mode 1 CGCMVAT SWM EOF Analysis comparison • We use the robust relationship between upper ocean heat content (CGCM output) and thermocline depth (SWM output) to compare the SWM results with the results from the CGCM simulation. • Clear from the similarity between EOF mode spatial patterns and their accompanying time series that even with the limited physics of the SWM it produces decadal variability similar to the CGCM. • These similarities lead us to conclude that at least a portion of the decadal variability of the Pacific Ocean (as represented by the CGCM) relies on ocean dynamic processes.

7. Regional wind stress forcing tests Equatorial region forcing • Now, to test the relative role of extra-tropical Rossby waves we divide the basin into two forcing regions; the equatorial region (between +/- 12.5 degrees); and the off-equatorial region (those latitudes polewards of +/- 12.5 degrees). • Here, we use the SWM to test the role of wind stress forcing (and Rossby waves) in both regions on the equatorial region thermocline depth and SST. Off-equatorial region forcing

8. Regional wind stress forcing tests Nino 3 region thermocline depth Off-equatorial Control (solid) Equatorial (dotted) Nino 4 region thermocline depth Equatorial (dotted) Off-equatorial Control

9. Regional wind stress forcing tests • Off-equatorial wind stress forcing accounts for approx 10% of the Nino 4 region variability and approx 2% of the Nino 3 region variability. • Results leave us with two unanswered questions: • Is the off-equatorial forced variability transferred by Rossby waves as prominent theory suggests? • If so, what lead times for predictability does this give us? • Do these off-equatorial forced changes in equatorial thermocline depth alter the frequency and magnitude of ENSO events?

10. Non-reflecting boundary experiments Off-equatorial wind stress forcing region Off-equatorial wind stress forcing region

11. Nino 3 region thermocline depth NRBC all NRBC 40-10 Control NRBC 40-30 NRBC 40-20 Nino 4 region thermocline depth NRBC all NRBC 40-10 Control NRBC 40-30 NRBC 40-20

12. Predictable lead times Peak correlation 0.24 when leading Nino 3 variability by 22 months This correlation jumps to 0.6 for both locations when considering the five year running mean of each time series Nino 3 region Peak correlation 0.39 when leading Nino 3 variability by 28 months

13. Equatorial region coupling • Supports prominent Rossby wave theory as a mechanism for generating interdecadal SST variability, meaning at least a proportion of the Pacific Ocean decadal variability is predictable. • It’s small but is it an important proportion? • Theory proposes that off-equatorial forced changes in equatorial thermocline depth alter the frequency and magnitude of ENSO events. • To answer this we need to address the role of ocean atmosphere coupling in the equatorial region.

14. The Hybrid Coupled Model (HCM) • Ocean Component • Shallow Water Model (SWM) presented earlier • Atmospheric Component • Statistical Atmosphere developed by carrying out an SVD on the CGCM equatorial region SST and overlying wind stresses. • The SVD was carried out on the monthly SST and wind stress values giving us a different estimate of the “coupled” atmospheric component for each calendar month. • The use of monthly SVDs allow us to develop a coupled statistical atmosphere response which has a seasonal dependence built in similar to the real world ENSO (although highly simplified).

16. The Hybrid Coupled Model (HCM) • Only a 1 mode estimate of coupling as we are recoupling with SWM SST (thermodynamics of Zebiak and Cane, 1987) which is only a good approximation in the equatorial wave guide SST.

17. The Hybrid Coupled Model (HCM) • Two hybrid coupled model simulations • Simulation 1:Forced only with equatorial noise • Simulation 2:Forced with equatorial noise + off-equatorial wind stress forcing +

18. Hybrid coupled model Results Nino 3 region filtered thermocline depth • Filtered Data • Equatorial region wind stress forcing with the coupled response explains most but not all of the coupled systems variability. • However, the addition of off-equatorial wind stress forcing in the dual region forcing experiment has a significant effect on the magnitude of the perturbation, but has no real effect on the phase. Dual region (solid) Equatorial (dotted) Nino 4 region filtered thermocline depth Dual region (solid) Equatorial (dotted)

19. Equatorial region coupling Nino 3 region thermocline depth Nino 3 region sea surface temp Equatorial (dotted) • Theory proposes that these small off-equatorial forced changes in equatorial thermocline depth alter the frequency and magnitude of ENSO events. • It is clear that these off-equatorial exchanges alter the magnitude of interannual variability • The simplicity of the model coupling means that we cant effectively test the effect on frequency. • This off-equatorially forced component effects the amplitude of model interannual variability and can account for differences in the equatorial region of up to ~ 2.7m of thermocline depth and ~0.4 of a degree. Dual region (solid)

20. Conclusions • So what does all this mean? • Our results indicate that the likely forcing mechanism of the IPO lies in the equatorial region (at least from an ocean dynamic point of view). • Supports the theories of decadal variability proposed by e.g., Jin et al. 1994; Tziperman et al. 1994; Timmermann et al. 2003; Rodgers et al. 2004; Newman et al. 2003; Power and Coleman 2006. • Theoretically this result also limits the possible predicable lead times to approximately 1 ½ years (the time it takes a Rossby wave to cross the Pacific Ocean basin in the region). • However, our results also indicate that off-equatorial wind stress forcing is responsible for a small but significant amount of the variability. • Supports the Rossby wave theories proposed by e.g., Lysne et al. 1997; Liu et al. 1999; Hazeleger et al. 2001; Capotondi and Alexander 2001, 2003; Galanti and Tziperman, 2003; Wang et al. 2003a, 2003b. • Small off-equatorial forced component is potentially predictable up to three years in advance, this result could be used to give a probabilistic measure of the amplitude of tropical Pacific Ocean interannual variability amplitude with significant lead times. • However, it must be noted that we have not been able to test the role of Off-equatorial forced variability on the frequency of ENSO events due to the simplicity of the statistical atmosphere.

21. References • Capotondi, A., and M.A. Alexander, 2001: Rossby waves in the tropical Pacific and their role in decadal thermocline variability. Journal of Physical Oceanography31, 3496-3515. • Capotondi, A., M.A. Alexander, and C. Deser, 2003: Why are there Rossby wave maxima in the Pacific at 10S and 13N? J. Phys. Oceanogr., 33, 1549-1563. • Capotondi, A., M.A. Alexander, C. Deser, and M.J. McPhaden, 2005: Anatomy and decadal evolution of the Subtropical-Tropical Cells (STCs). J. Climate, 18, 3739-3758. • Galanti, E. and E. Tziperman, 2003; A midlatitudes-ENSO teleconnection mechanism via baroclinically unstable long Rossby waves. J.Phys. Oceanogr., 33, 1877-1888. • Hazeleger, W., M. Visbeck, M. Cane, A. Karspeck, and N. Naik, 2001: Decadal upper ocean temperature variability in the tropical Pacific. J. Geophys. Res., 106, 8971-8988. • Jin, F. F., J.D. Neelin and M. Ghil, 1994: El Niño on the Devil's Staircase: Annual subharmonic steps to chaos. Science, 264, 70-72. • Kleeman, R., J.J.P. McCreary and B.A. Klinger, 1999; A mechanism for generating ENSO decadal variability. Geophys.Res. Lett., 26, 1743-1746. • Liu, Z., L. Wu, and E. Bayler, 1999: Rossby wave-coastal Kelvin wave interaction in the extratropics, part I, Low-frequency adjustment in a closed basin. J. Phys. Oceanogr., 29, 2382-2404. • Lysne, J., P. Chang, and B. Giese, 1997: Impact of extratropical Pacific on equatorial variability. Geophys. Res. Lett., 24, 2589-2592. • McPhaden, M.J., and D. Zhang, 2002: Slowdown of the meridional overturning circulation in the upper Pacific Ocean, Nature, 415, 603-608.

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23. “Open” western boundary Normal boundary conditions Non-reflecting boundary condition imposed

24. The correlation coefficients at each spatial location between the CGCM SST and the SST of the SWM control simulation.

26. filt uncoupled eq forcing 29.78%

27. Filt rand eq scm forcing 37.58% variance

28. offeq + uncoupled eq scm 13.56%

29. offeq rand eq scm forcing 12.5% variance