Abstract
The systematic development of reduced low-dimensional stochastic
climate models from observations or comprehensive highdimensional
climate models is an important topic for atmospheric
low-frequency variability, climate sensitivity, and improved
extended range forecasting. Here techniques from applied mathematics
are utilized to systematically derive normal forms for
reduced stochastic climate models for low-frequency variables. The
use of a few Empirical
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