Editorial Type:
Article
Article Type:
Research Article

Changes in the Spread of the Variability of the Seasonal Mean Atmospheric States Associated with ENSO

Arun Kumar Climate Modeling Branch, EMC, NCEP/NWS/NOAA, Washington, District of Columbia

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Anthony G. Barnston Climate Prediction Center, NCEP/NWS/NOAA, Washington, District of Columbia

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Peitao Peng Climate Prediction Center, NCEP/NWS/NOAA, Washington, District of Columbia

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Martin P. Hoerling Climate Diagnostics Center, NOAA/CIRES, Boulder, Colorado

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Lisa Goddard Experimental Forecast Division, IRI/SIO, La Jolla, California

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Print Publication:
01 Sep 2000
DOI:
https://doi.org/10.1175/1520-0442(2000)013<3139:CITSOT>2.0.CO;2
Page(s):
3139–3151
Restricted access

Abstract

For a fixed sea surface temperature (SST) forcing, the variability of the observed seasonal mean atmospheric states in the extratropical latitudes can be characterized in terms of probability distribution functions (PDFs). Predictability of the seasonal mean anomalies related to interannual variations in the SSTs, therefore, entails understanding the influence of SST forcing on various moments of the probability distribution that characterize the variability of the seasonal means. Such an understanding for changes in the first moment of the PDF for the seasonal means with SSTs is well documented. In this paper the analysis is extended to include also the impact of SST forcing on the second moment of the PDFs.

The analysis is primarily based on ensemble atmospheric general circulation model (AGCM) simulations forced with observed SSTs for the period 1950–94. To establish the robustness of the results and to ensure that they are not unduly affected by biases in a particular AGCM, the analysis is based on simulations from four different AGCMs.

The analysis of AGCM simulations indicates that over the Pacific–North American region, the impact of interannual variations in SSTs on the spread of the seasonal mean atmospheric states (i.e., the second moment of the PDFs) may be small. This is in contrast to their well-defined impact on the first moment of the PDF for the seasonal mean atmospheric state that is manifested as an anomalous wave train over this region. For seasonal predictions, the results imply that the dominant contribution to seasonal predictability comes from the impact of SSTs on the first moment of the PDF, with the impact of SSTs on the second moment of the PDFs playing a secondary role.

Corresponding author address: Dr. Arun Kumar, Climate Modeling Branch, EMC/NCEP, 5200 Auth Road, Rm. 807, Camp Springs, MD 20746.

Email: arun.kumar@noaa.gov

Abstract

For a fixed sea surface temperature (SST) forcing, the variability of the observed seasonal mean atmospheric states in the extratropical latitudes can be characterized in terms of probability distribution functions (PDFs). Predictability of the seasonal mean anomalies related to interannual variations in the SSTs, therefore, entails understanding the influence of SST forcing on various moments of the probability distribution that characterize the variability of the seasonal means. Such an understanding for changes in the first moment of the PDF for the seasonal means with SSTs is well documented. In this paper the analysis is extended to include also the impact of SST forcing on the second moment of the PDFs.

The analysis is primarily based on ensemble atmospheric general circulation model (AGCM) simulations forced with observed SSTs for the period 1950–94. To establish the robustness of the results and to ensure that they are not unduly affected by biases in a particular AGCM, the analysis is based on simulations from four different AGCMs.

The analysis of AGCM simulations indicates that over the Pacific–North American region, the impact of interannual variations in SSTs on the spread of the seasonal mean atmospheric states (i.e., the second moment of the PDFs) may be small. This is in contrast to their well-defined impact on the first moment of the PDF for the seasonal mean atmospheric state that is manifested as an anomalous wave train over this region. For seasonal predictions, the results imply that the dominant contribution to seasonal predictability comes from the impact of SSTs on the first moment of the PDF, with the impact of SSTs on the second moment of the PDFs playing a secondary role.

Corresponding author address: Dr. Arun Kumar, Climate Modeling Branch, EMC/NCEP, 5200 Auth Road, Rm. 807, Camp Springs, MD 20746.

Email: arun.kumar@noaa.gov

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  • Barnston, A. G., M. Chelliah, and S. B. Goldenberg, 1997: Documentation of highly ENSO-related SST region in the equatorial Pacific. Atmos.–Ocean,35, 367–383.

  • Blade, I., 1999: The influence of midlatitude ocean–atmosphere coupling on the low-frequency variability of a GCM. Part II: Interannual variability induced by tropical SST forcing. J. Climate,12, 21–45.

  • Chen, W. Y., and H. M. van den Dool, 1997: Asymmetric impact of tropical SST anomalies on atmospheric internal variability over North Pacific. J. Atmos. Sci.,54, 725–740.

  • Chervin, R. M., 1986: Interannual variability and seasonal climate variability. J. Atmos. Sci.,43, 233–251.

  • Hoerling, M. P., A. Kumar, and M. Zhong, 1997: El Niño, La Niña, and the nonlinearity of their teleconnections. J. Climate,10, 1769–1786.

  • Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev.,109, 813–829.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc.,77, 437–471.

  • Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, D. L. Williamson, and P. J. Rasch, 1998: The National Center for Atmospheric Research Community Climate Model: CCM3. J. Climate,11, 1131–1149.

  • Kumar, A., and M. P. Hoerling, 1995: Prospects and limitations of seasonal atmospheric GCM predictions. Bull. Amer. Meteor. Soc.,76, 335–345.

  • ——, and ——, 1997: Interpretation and implications of the observed inter-El Niño variability. J. Climate,10, 83–91.

  • ——, and ——, 1998: Annual cycle of Pacific–North American seasonal predictability associated with different phases of ENSO. J. Climate,11, 3295–3308.

  • ——, ——, M. Ji, A. Leetmaa, and P. Sardeshmukh, 1996: Assessing a GCM’s suitability for making seasonal predictions. J. Climate,9, 115–129.

  • Lau, N.-C., 1985: Modeling the seasonal dependence of the atmospheric response to observed El Ni&ntilde☆ in 1962–76. Mon. Wea. Rev.,113, 1970–1996.

  • ——, 1997: Interactions between global SST anomalies and midlatitude atmospheric circulation. Bull. Amer. Meteor. Soc.,78, 21– 34.

  • Palmer, T. N., 1988: Medium and extended range predictability and stability of the Pacific/North American mode. Quart. J. Roy. Meteor. Soc.,114, 691–713.

  • Peng, P., A. Kumar, A. G. Barnston, and L. Goddard, 2000: Simulation skills of the SST-forced global climate variability of the NCEP-MRF9 and the Scripps–MPI ECHAM3 models. J. Climate, in press.

  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analysis using optimum interpolation. J. Climate,7, 929–948.

  • Roeckner, E., and Coauthors, 1992: Simulation of the present-day climate with ECHAM model: Impact of model physics and resolution. Max-Planck-Institut für Meteorologie Rep. 93, 171 pp. [Available from MPI für Meteorologie, Bundesstr. 55, D-20146, Hamburg, Germany.].

  • Rowell, D. P., 1998: Assessing potential seasonal predictability with an ensemble of multi-decadal GCM simulations. J. Climate,11, 109–120.

  • Smith, T. M., R. W. Reynolds, R. E. Livezey, and D. Stokes, 1996: Reconstruction of historical sea surface temperatures using empirical orthogonal functions. J. Climate,9, 1403–1420.

  • Trenberth, K. E., G. W. Branstrator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J. Geophys. Res.,103 (C7), 14291– 14324.

  • Zwiers, F. W., 1996: Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with CCC GCM2. Climate Dyn.,12, 825–847.

  • ——, X. L. Wang, and J. Sheng, 2000: Effects of specifying bottom boundary conditions in an ensemble of atmospheric GCM simulations. J. Geophys. Res.,105, 7295–7315.

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