Natural Decadal Climate Variability
One of the central problems in understanding man-made climate changes is to improve our knowledge of the natural variability of the atmospheric climate and its causes. Flows observed in the atmosphere consist of a hierarchy of structures with different spatial and time scales ranging from small scale turbulence to the planetary scale patterns. There is a great demand to understand climate variability on the time scale of decades up to centuries. In order to estimate the natural variability on this mentioned time scales we use a hierarchy of climate models.
There is evidence from both theoretical and observational investigations, that the long-term behaviour of the atmosphere is controlled by few degrees of freedom. This gives the justification to investigate the long-term behaviour of the atmosphere by nonlinear models with a limited number of variables, so called low-order models (LOM). We performed long-term runs over 1,000 years with a 1-layer, a baroclinic 2-layer and in future also with a 3-layer LOM. Additionally, similar investigations are made with a Simple General Circulation Model (SGCM) using the full set of the thermo-hydrodynamical equations of the atmosphere and ocean.
From the results of these model runs we determine characteristic circulation patterns and their temporal behaviour by means of statistical methods. The figure shows the most dominant spatial structure of the barotropic stream function of the 2-layer model. It reflects the mean zonal flow influenced by the model's orography.
Our analyses of the power spectra of LOM results reveal evident maxima of internal variability on the decadal as well as on the seasonal time scale. These maxima on the decadal scale are confirmed by first investigations with the SGCM.
This multiscale natural variability of the atmosphere is caused by deterministic nonlinear interactions between the model components without any external forcing. The dominant mechanisms generating the long-term climate variability are internally driven by short time scale instabilities and nonlinearities connected with large scale atmospheric processes.
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