On the origins of temporal power-law behavior in the global atmospheric circulation

Geophysical Research Letters

Volumn 36, Issue 14, 2009, Pages

  Vyushin, Dmitry I ^a   Kushner, Paul J ^a   Mayer, Josh ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

20TH CENTURY; ATMOSPHERIC CIRCULATION; CLIMATE FORCINGS; CLIMATE MODEL; CLIMATE PREDICTION MODEL; CLIMATE VARIATION; CURRENT GENERATION; DATA SETS; HURST EXPONENTS; LOW FREQUENCY; LOW FREQUENCY VARIABILITY; NOISE BEHAVIOR; PERFORMANCE TESTS; PHYSICAL MECHANISM; POWER LAW EXPONENT; POWER-LAW; POWER-LAW BEHAVIOR; SCALING BEHAVIOR; SPATIAL DISTRIBUTION; SPECTRAL POWER; STATISTICAL TESTING; TEMPORAL SCALING; TIME-SCALES;

ATMOSPHERICS; MATHEMATICAL MODELS; SIZE DISTRIBUTION; SULFUR COMPOUNDS;

CLIMATOLOGY;

ATMOSPHERIC CIRCULATION; CLIMATE CHANGE; CLIMATE FORCING; CLIMATE MODELING; MODEL TEST; SPATIAL DISTRIBUTION; STRATOSPHERE; TROPOSPHERE; TWENTIETH CENTURY;

EID: 70349609311     PISSN: 00948276     EISSN: None     Source Type: Journal    
DOI: 10.1029/2009GL038771     Document Type: Article

References (23)

1. Bengtsson, L., S. Hagemann, and K. I. Hodges (2004), Can climate trends be calculated from reanalysis data?, J. Geophys. Res., 109, D11111, doi: 10.1029/2004 JD004536. (Pubitemid 39103541)
2. Berton, R. P. H. (2004), Influence of a discontinuity on the spectral and fractal analysis of one-dimensional data, Nonlinear Processes Geophys., 11, 659-682.
3. Blender, R., and K. Fraedrich (2003), Long time memory in global warming simulations, Geophys. Res. Lett, 30(14), 1769, doi: 10.1029/ 2003GL017666. (Pubitemid 37250264)
4. Fraedrich, K., and R. Blender (2003), Scaling of atmosphere and ocean temperature correlations in observations and climate models, Phys. Rev. Lett, 90, 108501, doi:10.1103/PhysRevLett.90.108501.
5. GFDL Global Atmospheric Model Development Team (2004), The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations, J. Clim., 17, 4641-4673.
6. Govindan, R. B., D. Vyushin, S. Brenner, A. Bunde, S. Havlin, and H.-J. Schellnhuber (2002), Global climate models violate scaling of the observed atmospheric variability, Phys. Rev. Lett, 89, 028501, doi:10.1103/ PhysRevLett.89.028501.
7. Hall, A., and S. Manabe (1997), Can local linear stochastic meory explain sea surface temperature and salinity variability?, Clint. Dyn., 13, 167-180, doi:10.1007/s003820050158. (Pubitemid 127546040)
8. Hasselmann, K. (1976), Stochastic climate models, part 1: Theory, Tellus, 28, 473-485.
9. Huybers, P., and W. Curry (2006), Links between annual, Milankovitch, and continuum temperature variability, Nature, 441, 329-332, doi:10.1038/ nature04745. (Pubitemid 44050194)
10. Kantelhardt, J. W., E. Koscielny-Bunde, H. H. A. Rego, S. Havlin, and A. Bunde (2001), Detecting long-range correlations with detrended fluctuation analysis, Physica A, 295, 441-454. (Pubitemid 32495759)
11. Onogi, K., et al. (2007), The JRA-25 reanalysis, J. Meteorol. Soc. Jpn., 85, 369-432.
12. Pelletier, J. D. (2002), Natural variability of atmospheric temperatures and geomagnetic intensity over a wide range of time scales, Proc. Natl. Acad. Sci. U. S. A., 99, 2546-2553.
13. Percival, D. B., J. E. Overland, and H. O. Mofjeld (2001), Interpretation of North Pacific variability as a short- and long-memory process, J. Clim., 14, 4545-4559.
14. Rust, H. W., O. Mestre, and V. K. C. Venema (2008), Fewer jumps, less memory: Homogenized temperature records and long memory, J. Geophys. Res, 113, D19110, doi:10.1029/2008JD009919.
15. Santer, B. D., T. M. L. Wigley, C. Mears, F. J. Wentz, and S. A. Klein (2005), Amplification of surface temperature trends and variability in the tropical atmosphere, Science, 309, 1551-1556, doi:10.1126/ science.1114867.
16. Simmons, A. J., P. D. Jones, V. da Costa Bechtold, A. C. M. Beljaars, P. W. Kllberg, S. Saarinen, S. M. Uppala, P. Viterbo, and N. Wedi (2004), Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature, J. Geophys. Res., 109, D24115, doi:10.1029/2004JD005306.
17. Smith, R. L. (1993), Long-range dependence and global warming in Statistics for the Environment edited by V. Barnett and F. Turkman pp. 141-161, John Wiley, Chichester, U. K.
18. Tsonis, A. A., P. J. Roebber, and J. B. Eisner (1999), Long-range correlations in the extratropical atmospheric circulation: Origins and implications, J. Clim., 12, 1534-1541.
19. Uppala, S. M., et al. (2005), The ERA-40 re-analysis, Q. J. R. Meteorol. Soc., 131, 2961-3012, doi:10.1256/qj.04.176.
20. Vyushin, D. I., and P. J. Kushner (2009), Power-law and long-memory characteristics of the atmospheric general circulation, J. Clim., 22, 2890-2904, doi: 10.1175/2008JCLI2528.1.
21. Vyushin, D., I. Zhidkov, S. Havlin, A. Bunde, and S. Brenner (2004), Volcanic forcing improves Atmosphere-Ocean Coupled General Circulation Model scaling performance, Geophys. Res. Lett., 31, L10206, doi: 10.1029/2004GL019499. (Pubitemid 39102713)
22. Vyushin, D. I., V. E. Fioletov, and T. G. Shepherd (2007), Impact of long-range correlations on trend detection in total ozone, J. Geophys. Res., 112, D14307, doi:10.1029/2006JD008168.
23. Wigley, T. M. L., and S. C B. Raper (1990), Natural variability of the climate system and detection of the greenhouse effect, Nature, 344, 324-327, doi:10.1038/344324a0. (Pubitemid 20406194)