The atmosphere is known to be forced by a variety of energy sources, including radiation and heat fluxes emanating from the boundary layer associated with sea-surface temperature anomalies and land-surface features. The atmosphere is also subject to internal variability which is essentially unforced and is thought to be a basic characteristic of fluids. Whereas much work has been done in quantifying the links between external forcing of the atmosphere and its long-term response as well as the influence of boundary layer forcing in determining organized, large-scale modes of planetary-scale circulation, less is known about the importance of internal variability or chaos in determining the evolution of weather and climate. General circulation models (GCMs) now provide for this possibility. Multiple evolutions of the climate system may be computed in GCM simulations. Where these simulations are identical except for the conditions by which the model is initialized, the degree of departure in the evolution of climate from one model run to the next corresponds precisely to the degree of internal variability or chaos present in the model atmosphere. A methodology for quantifying this chaotic forcing is considered and is applied to century-long integrations of the UK Meteorological Office model HADAM2A.
Airey, M. J., Hulme, M. and Johns, T. C.1996: Evaluation of simulations of terrestrial precipitation in UK Met. Office/Hadley Centre climate change experiments. Geophysical Research Letters23, 1657-1660.
2.
Basnett, T. A. and Parker, D. E.1997: Development of the global mean sea level pressure data set GMSLP2. Hadley Centre Climate Researc h Technical Note79. Hadley: UK Meteorological Office.
3.
Bottomley, M., Folland, C. K., Hsiung, J., Newell, R. E. and Parker, D. E.1990: Global ocean surface temperature atlas. London: HMSO.
4.
Charney, J. G. and Shukla, J.1981: Predictability of monsoons. In Lighthill, J. and Pearce, R. J., editors, Monsoon dynamics, Cambridge: Cambridge University Press.
5.
Colman, A. W.1992: Development of worldwide marine data eigenvectors since 1985. Climate Research Technical Note 28. Hadley: UK Meteorological Office.
6.
Folland, C. K. and Parker, D. E.1995: Correction of instrumental biases in historical sea surface temperature data. Quarterly Journal of the Royal Meteorological Society121, 319-367.
7.
Folland, C. K., Sexton, D. M. H., Karoly, D. J., Johnson, C. E., Rowell, D. P. and Parker, D. E.1998: Influences of anthropogenic and oceanic forcing on recent climate change. Geophysical Research Letters25, 353-356.
8.
Fraedrich, K.1987: Estimating weather and climate predictability on attractors. Journal of Atmospheric Science44, 722-728.
9.
Gallani, M. L.1994: Comparison between 1st and 2nd versions of the climate model. Internal Note 58. Hadley: UK Meteorologiical Office.
10.
Gates, W. L.1992: AMIP: the Atmospheric Model Intercomparison Project. Bulletin of the American Meteorological Society73, 1962-1970.
11.
Gates, W. L., Henderson-Sellers, A., Boer, G. J., Folland, C. K., Kitoh, A., McAvaney, B. J., Semazzi, F., Smith, N., Weaver, A. J. and Zeng, Q.-C.1996: Climate models -evaluation. In: Houghton, J. T., Meira Filho, L. G., Callander, B. A., Harris, N., Kattenberg, A. and Maskell, K., editors, Climate change 1995. The science of climate change, Cambridge: Cambridge University Press.
12.
Grotjahn, R.1993: Global atmospheric circulations. Oxford: Oxford University Press.
13.
Harrison, M.S.J. 1986: A synoptic climatology of South African rainfall variations. PhD thesis, University of Witwatersrand.
14.
Hulme, M.1994: Validation of large-scale precipitation fields in general circulation models. In Debois, M. and Desalmand, F., editors, Global p recipitation and climate change, Heidelberg: Springer-Verlag.
15.
Joubert, A.1997: Simulation by the Atmospheric Model Intercomparison Project of the atmospheric circulation over southern Africa. International Journal of Climatology17, 1129-1154.
16.
Legates, D. R. and Willmott, C. J.1990: Mean seasonal and spatial variability in gauge corrected global precipitation. International Journal of Climatology10, 111-127.
17.
Lorenz, E. N.1956: Empirical orthogonal functions and statistical weather prediction. Scientific Report 1, Contract AF19 (604) 1566. Meteorological Department, MIT.
18.
Lorenz, E. N.1963: Deterministic non-periodic flow. Journal of Atmospheric Science20, 130-141.
19.
Lorenz, E. N. 1986: The Index Cycle is alive and well. In Roads, J. O., editor, Namias symposium. Scripps Institution of Oceanography Reference Series86-17, San Diego, CA: University of California.
20.
Lorenz, E. N.1993: The essence of chaos. Seattle, WA: University of Washington Press.
21.
Mandlebrot, B.B.1967: How long is the coastline of Britain? Statistical self-similarity and fractal dimensions. Science56, 636-638.
22.
Mitchell, J. F. B., Johns, T. C., Gregory, J. M. and Tett, S. F. B.1995: Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature376, 501-504.
23.
Mitchell, T. D. and Hulme, M.1999: Predicting regional climate change: living with uncertainty. Progress in Physical Geography23, 57-78.
24.
Palmer, T. N.1998: Nonlinear dynamics and climate change: Rossby’s legacy. Bulletin of the American Meteorological Society79, 1411-1423.
25.
Palmer, T. N. and Anderson, D. L. T.1994: The prospects for seasonal forecasting -a review paper. Quarterly Journal of the Royal Meteorological Society120, 755-793.
26.
Parker, D. E., Folland, C. K. and Jackson, M.1995: Marine surface temperature: observed variations and data requirements. Climatic Change31, 559-600.
27.
Phillips J. D.1993a: Chaotic evolution of some coastal plain soils. Physical Geography14, 566-580.
28.
Phillips J. D.1993b: Instability and chaos in hillslope evolution. American Journal of Science293, 25-48.
29.
Phillips, T. J.1994: A summary documentation of the AMIP models. PCMDI Report1 8. University of California, Lawrence Livermore Laboratory.
30.
Rayner, N. A., Horton, E. B., Parker, D. E., Folland, C. K. and Hackett, R. B.1996: Version 2.2 of the global sea-ice and sea surface temperature data set, 1903-1994. Climate Research Technical Note74. Hadley: UK Meteorological Office.
31.
Rodwell, M. R., Rowell, D. P. and Folland, C. K.1999: Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature398, 320-323.
32.
Rossby, C.-G.1941: The scientific basis of modern meteorology. US Yearbook of Agriculture, Climate and Man, 656-661.
33.
Rossby, C.-G.et al.1939: Relation between variations in the intensity of the zonal circulation of the atmosphere and the displacement of the semi-permanent centres of actions. Tellus2, 275-301.
34.
Rowell, D. P.1996: Using an ensemble of multi-decadal GCM simulations to assess potential seasonal predictability. Climate Research Technical Note69. Hadley: UK Meterological Office.
35.
Rowell, D. P.1998: Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. Journal of Climate11, 109-120.
36.
Rowell, D. P., Folland, C. K., Maskell, K. and Ward, M. N.1995: Variability of summer rainfall over tropical north Africa (1906-92): observations and modelling. Quarterly Journal of the Royal Meteorological Society121, 669-704.
37.
Ruosteenoja, K.1988: Factors affecting the occurrence and lifetime of 500 mb height analogs: a study based on a large amount of data. Monthly Weather Review116, 368-376.
38.
Srinivasan, G., Hulme, M. and Jones, C. G.1995: An evaluation of the spatial and interannual variability of tropical precipitation as simulated by GCMs. Geophysical Research Letters22, 2139-2142.
39.
Sutton, R. T. and Allen, M. R.1997: Decadal predictability of North Atlantic sea surface temperature and climate. Nature388, 563-567.
40.
Tyson, P. D.1986: Climatic change and variability in southern Africa. Cape Town: Oxford University Press.
41.
Van den Dool, H. M.1994: Searching for analogues, how long must we wait?Tellus46A, 314-324.
42.
Ward, M. N. and Navarra, A.1997: Pattern analysis of SST-forced variability in ensemble GCM simulations: examples over Europe and the tropical Pacific. Journal of Climate10, 2210-2220.
43.
Webster, P. J. and Yang, S.1992: Monsoon and ENSO: selectively interactive systems. Quaterly Journal of the Royal Meteorological Society118, 877-926.
44.
Woodruff, S. D., Sutz, R. J.,. Jenne, R. L. and Steurer, P. M.1987: A comprehensive ocean-atmosphere data set. Bulletin American Meteorological Society68, 1239-1250.
45.
Xie, P. and Arkin, P. A.1996: Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. Journal of Climate9, 840-858.
