Advertisement

Meteorology and Atmospheric Physics

, Volume 69, Issue 3–4, pp 231–237 | Cite as

Climatic noise and potential predictability of monthly mean temperature over China

  • K. Ma
  • J. Cao
Article

Summary

In this paper, based on the data at 162 stations selected over China from 1960 to 1991 the climatic noise and potential predictability of monthly mean temperature have been studied. The method of estimating climatic noise is based on the idea of Yamamoto et al. (1985) and the potential predictability is expressed by the ratio of the estimated inter-annual variation to the estimated natural variation (or climatic noise). Generally the climatic noise of monthly mean temperature increases with latitude and altitude and varies with season. The continental air from Siberia and Mongolia plays a significant role and the ocean acts as an adjustor and a reductor in the climatic noise except for the tropical Pacific ocean in transitional season. The potential predictability is diversified from month to month and one station to another, but generally the monthly mean temperature over China is potentially predictable at statistical significance level 0.10. The results suggest that we could not ask a climate model to predict the climate with satisfactory results worldwide in all seasons and that the regional model could be a hopeful way to predict the climate.

Keywords

Climate Change Waste Water Significant Role Water Management Water Pollution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Leith, C. E., 1975: The design of a statistical-dynamical climate model and statistical constraints on the predictability of climate.The Physical Basis of Climate and climate modeling. GARP Pub. Ser., No. 16, WMO-ICSU, 137–141.Google Scholar
  2. Madden, R. A., 1976: Estimates of the natural variability of time-averaged sea-level pressure.Mon. Wea. Rev.,104, 942–952.Google Scholar
  3. Papoulis, A., 1965:Probability, Random Variability and Stochastic Processes. New York: McGraw-Hill, 583 pp.Google Scholar
  4. Shukla, J., 1983: Comments on “natural variability and predictability”.Mon. Wea. Rev.,111, 583–585.Google Scholar
  5. Shukla, J., Gutzler, D., 1983: Interannual variability and predictability of 500hPa geopotential heights over the Northern Hemisphere.Mon. Wea. Rev.,111, 1273–1279.Google Scholar
  6. Straus, D. M., Halen, M., 1981: A stochastic dynamical approach to the study of the natural variability of climate.Mon. Wea. Rev.,109, 407–421.Google Scholar
  7. Trenberth, K. E., 1984a: Some effects of finite sample size and persistence on meteorological statistics, part I: Autocorrelation.Mon. Wea. Rev.,112, 2359–2368.Google Scholar
  8. Trenberth, K. E., 1984b: Some effects of finite sample size and persistence on meteorological statistics, part II: potential predictability.Mon. Wea. Rev.,112, 2369–2379.Google Scholar
  9. Trenberth, K. E., 1985: Potential predictability of geopotential heights over the southern Hemisphere.Mon. Wea. Rev.,113, 54–64.Google Scholar
  10. Yamamoto, R. et al., 1985: An estimate of climate noise.J. Meteor. Soc. Japan,63, 1147–1156.Google Scholar

Copyright information

© Springer-Verlag 1999

Authors and Affiliations

  • K. Ma
    • 1
  • J. Cao
    • 1
  1. 1.Department of Atmospheric ScienceNanjing UniversityNanjingChina

Personalised recommendations