Ocean Science Journal

The Korean Society of Oceanography (한국해양학회)
권호 표지

The Characteristics of Signal versus Noise SST Variability in the North Pacific and the Tropical Pacific Ocean

  • Yeh, Sang-Wook (Ocean Circulation and Climate Research Division, KORDI) ;
  • Kirtman, Ben P. (Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society, George Mason University)
  • Published : 2006.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Total sea surface temperature (SST) in a coupled GCM is diagnosed by separating the variability into signal variance and noise variance. The signal and the noise is calculated from multi-decadal simulations from the COLA anomaly coupled GCM and the interactive ensemble model by assuming both simulations have a similar signal variance. The interactive ensemble model is a new coupling strategy that is designed to increase signal to noise ratio by using an ensemble of atmospheric realizations coupled to a single ocean model. The procedure for separating the signal and the noise variability presented here does not rely on any ad hoc temporal or spatial filter. Based on these simulations, we find that the signal versus the noise of SST variability in the North Pacific is significantly different from that in the equatorial Pacific. The noise SST variability explains the majority of the total variability in the North Pacific, whereas the signal dominates in the deep tropics. It is also found that the spatial characteristics of the signal and the noise are also distinct in the North Pacific and equatorial Pacific.

Keywords

References

  1. Alexander, M.A. 1992. Midlatitude atmosphere-ocean interaction during El Nino, I. The North Pacific Ocean. J. Climate, 5, 944-958 https://doi.org/10.1175/1520-0442(1992)005<0944:MAIDEN>2.0.CO;2
  2. Barnett, T.P., D.W. Pierce, R. Saravanan, N. Schneider, D. Dommenget, and M. Latif. 1999. Origins of the midlatitude Pacific decadal variability. Geophys. Res. Lett., 26, 1454-1456
  3. Battisti, D.S., U.S. Bhatt, and M.A. Alexander. 1995. A modeling study of interannual variability in the wintertime North Atlantic Ocean. J. Climate, 8, 3067-3083 https://doi.org/10.1175/1520-0442(1995)008<3067:AMSOTI>2.0.CO;2
  4. Briegleb, B.P. 1992. Delta-Eddington approximation for solar radiation in the NCAR community climate model. J. Geopys. Res., 97, 7603-7612 https://doi.org/10.1029/92JD00291
  5. Davis, R.E. 1976. Predictability of sea surface temperature anomalies and sea level pressure anomalies over the North Pacific ocean. J. Phys. Oceanogr., 6, 249-266 https://doi.org/10.1175/1520-0485(1976)006<0249:POSSTA>2.0.CO;2
  6. Delworth, T. 1996. North Atlantic interannual variability in a coupled ocean-atmosphere model. J. Climate, 9, 2356-2375 https://doi.org/10.1175/1520-0442(1996)009<2356:NAIVIA>2.0.CO;2
  7. DeWitt, D.G. 1996. The effect of the cumulus convection on the climate of the COLA general circulation model. COLA Tech. Rep. 27. 69 p
  8. DeWitt, D.G. and E.K. Schneider. 1996. The Earth radiation budget as simulated by the COLA GCM. COLA Tech. Rep. 35. 39 p
  9. Frankignoul, C. and K. Hasselmann. 1977. Stochastic climate models: Part I. Application to sea surface temperature anomalies and thermocline variability. Tellus, 29, 289-305 https://doi.org/10.1111/j.2153-3490.1977.tb00740.x
  10. Gent, P.R. and J.C. McWilliams. 1990. Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr., 20, 150-155 https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2
  11. Hannachi, A. 2001. Toward a nonlinear identification of the atmospheric response to ENSO. J. Climate, 14, 2138-2149 https://doi.org/10.1175/1520-0442(2001)014<2138:TANIOT>2.0.CO;2
  12. Hasselmann, K. 1976. Stochastic climate models: Part I. Theory. Tellus, 28, 473-485 https://doi.org/10.1111/j.2153-3490.1976.tb00696.x
  13. Harshvardhan, R.R. Davis, D.A. Randall, and T.G. Corsetti. 1987. A fast radiation parameterization for general circulation models. J. Geophys. Res., 92, 1009-1016 https://doi.org/10.1029/JD092iD01p01009
  14. Harzallah, A. and R. Sadourny. 1995. Internal versus SST-forced atmospheric variability as simulated by an atmospheric general circulation model. J. Climate, 8, 474-495 https://doi.org/10.1175/1520-0442(1995)008<0474:IVSFAV>2.0.CO;2
  15. Hoerling, M.P., A. Kumar, and M. Zhong. 1977. El Nino, La Nina, and the nonlinearity of their teleconnections. J. Climate, 10, 1769-1786 https://doi.org/10.1175/1520-0442(1997)010<1769:ENOLNA>2.0.CO;2
  16. Kiehl, J.T., J.J. Hack, and B.P. Briegleb. 1994. The simulated earth radiation budget of the National Center for Atmospheric Research community climate model CCM2 and comparisons with the Earth Radiation Budget Experiment (ERBE). J. Geophy. Res., 99, 20815-20827 https://doi.org/10.1029/94JD00941
  17. Kinter, J.L. III, J. Shukla, L. Marx, and E.K. Schneider. 1988. A simulation of winter and summer circulations with the NMC global spectral model. J. Atmos. Sci., 45, 2468-2522
  18. Kirtman, B.P. and J. Shukla. 2002. Interactive coupled ensemble: A new coupling strategy for CGCMs. Geophys. Res. Lett., 29, 1029-1032 https://doi.org/10.1029/2001GL013866
  19. Kirtman, B.P., Y. Fan, and E.K. Schneider. 2002. The COLA global coupled and anomaly coupled ocean-atmosphere GCM. J. Climate, 15, 2301-2320 https://doi.org/10.1175/1520-0442(2002)015<2301:TCGCAA>2.0.CO;2
  20. Kirtman, B.P. and S. Zebiak. 1997. ENSO simulation and prediction with a hybrid coupled model. Mon. Wea. Rev., 125, 2620- 2641 https://doi.org/10.1175/1520-0493(1997)125<2620:ESAPWA>2.0.CO;2
  21. Large, W.G., J.C. McWilliams, and S.C. Doney. 1994. Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys., 32, 363-403 https://doi.org/10.1029/94RG01872
  22. Latif, M. and T.P. Barnett. 1994. Causes of decadal climate variability over the North Pacific and North America. Science, 266, 634-637 https://doi.org/10.1126/science.266.5185.634
  23. Miller, A.J., D.R. Cayan, and W.B. White. 1998. A westwardintensified decadal change in the North Pacific thermocline and gyre-scale circulation. J. Climate, 11, 3112-3127 https://doi.org/10.1175/1520-0442(1998)011<3112:AWIDCI>2.0.CO;2
  24. Miyakoda, K. and J. Sirutis. 1977. Comparative integrations of global spectral models with various parameterized processes of sub-grid scale vertical transport. Beitr. Phys. Atmos., 50, 445-480
  25. Moorthi, S. and M.J. Suarez. 1992. Relaxed Arakawa-Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 978-1002 https://doi.org/10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2
  26. Nakamura, H., G. Lin, and T. Yamagata. 1997. Decadal climate variability in the North Pacific during the recent decades. Bull. Amer. Meteor. Soc., 78, 2215-2225 https://doi.org/10.1175/1520-0477(1997)078<2215:DCVITN>2.0.CO;2
  27. Pacanowski, R.C., K. Dixon, and A. Rosati. 1993. The GFDL modular ocean model users guide, version 1.0. GFDL Ocean Group Tech Rep. No. 2. 77 p
  28. Pacanowski, R.C. and S.M. Griffies. 1998. MOM 3.0 manual. NOAA/Geophysical Fluid Dynamics Laboratory. 638 p
  29. Redi, M.H. 1982. Oceanic isopycnal mixing by coordinate rotation. J. Phys. Oceanogr., 12, 1155-1158
  30. Reynolds, R. and T. M. Smith. 1994. Improved global sea surface temperature analysis using optimum interpolation. J. Climate, 7, 929-948 https://doi.org/10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2
  31. Robertson, A.W. 1996. Interdecadal variability over the North Pacific in a multi-century climate simulation. Climate Dyn., 12, 227-241 https://doi.org/10.1007/BF00219498
  32. Rosati, A. and K. Miyakoda. 1988. A general circulation model for upper ocean circulation. J. Phys. Oceanogr., 18, 1601-1626 https://doi.org/10.1175/1520-0485(1988)018<1601:AGCMFU>2.0.CO;2
  33. Rowell, D.P. 1998. Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11, 109-120 https://doi.org/10.1175/1520-0442(1998)011<0109:APSPWA>2.0.CO;2
  34. Saravanan, R. 1998. Atmospheric low-frequency variability and its relationship to midlatitude SST variability: Studies and the NCAR Climate System Model. J. Climate, 11, 1386- 1404 https://doi.org/10.1175/1520-0442(1998)011<1386:ALFVAI>2.0.CO;2
  35. Saravanan, R. and J.C. McWilliams. 1995. Multiple equilibria, natural variability, and climate transitions in an idealized ocean-atmosphere model. J. Climate, 8, 2296-2323 https://doi.org/10.1175/1520-0442(1995)008<2296:MENVAC>2.0.CO;2
  36. Schneider, E.K. 2002. Causes of differences between the equatorial Pacific as simulated by two coupled GCMs. J. Climate, 15, 2301-2320 https://doi.org/10.1175/1520-0442(2002)015<2301:TCGCAA>2.0.CO;2
  37. Seager, R., Y. Kushnir, N.H. Naik, M.A. Cane, and J. Miller. 2001. Wind-driven shifts in the latitude of the Kuroshio- Oyashio extension and generation of SST anomalies on decadal timescales. J. Climate, 15, 4249-4265
  38. Shukla, J. and co-authors. 2000. Dynamical seasonal prediction. Bull. Amer. Meteor. Soc., 81, 2593-2606 https://doi.org/10.1175/1520-0477(2000)081<2593:DSP>2.3.CO;2
  39. Smagorinsky, J. 1963. General circulation experiments with the primitive equations. I. The basic experiment. Mon. Wea. Rev., 91, 99-164 https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2
  40. Straus D.M. and J. Shukla. 2000. Distinguishing between the SST-forced variability and internal variability in mid-latitudes: Analysis of observation and GCM simulations. Quart. J. Roy. Meteorol. Soc., 126, 2323-2350 https://doi.org/10.1256/smsqj.56715
  41. Tanimoto, Y., N. Iwasaka, and K. Hanawa. 1997. Relationships between sea surface temperature, the atmospheric circulation and air-sea fluxes on multiple timescales. J. Meteor. Soc. Jpn., 75, 831-849 https://doi.org/10.2151/jmsj1965.75.4_831
  42. Yeh, S.-W. and Ben P. Kirtman. 2004. The impact of internal atmospheric variability on the North Pacific SST variability. Climate Dyn., 22, 721-732 https://doi.org/10.1007/s00382-004-0399-8
  43. Zhang, Y., J.M. Wallace, and D.S. Battisti. 1997. ENSO-like interdecadal variability: 1900-93. J. Climate, 10, 1004-1020 https://doi.org/10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2
  44. Zwiers, F.W. 1996. Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2. Climate Dyn., 12, 825-847 https://doi.org/10.1007/s003820050146