You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Effects of Stochastic Parametrization on Conceptual Climate Models
Daniel S. Wilks
Philosophical Transactions: Mathematical, Physical and Engineering Sciences
Vol. 366, No. 1875, Stochastic Physics and Climate Modelling (Jul. 28, 2008), pp. 2477-2490
Published by: Royal Society
Stable URL: http://www.jstor.org/stable/25190850
Page Count: 14
You can always find the topics here!Topics: Climate models, Stochastic models, Parametric models, Harmonic functions, Energy, Global climate models, Statistical models, Probability distributions, Headway, Simulations
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
Conceptual climate models are very simple mathematical representations of climate processes, which are especially useful because their workings can be readily understood. The usual procedure of representing effects of unresolved processes in such models using functions of the prognostic variables (parametrizations) that include no randomness generally results in these models exhibiting substantially less variability than do the phenomena they are intended to simulate. A viable yet still simple alternative is to replace the conventional deterministic parametrizations with stochastic parametrizations, which can be justified theoretically through the central limit theorem. The result is that the model equations are stochastic differential equations. In addition to greatly increasing the magnitude of variability exhibited by these models, and their qualitative fidelity to the corresponding real climate system, representation of unresolved influences by random processes can allow these models to exhibit surprisingly rich new behaviours of which their deterministic counterparts are incapable.
Philosophical Transactions: Mathematical, Physical and Engineering Sciences © 2008 Royal Society