Statistical optimization for passive scalar transport: maximum entropy production versus maximum Kolmogorov–Sinai entropy

• Main Authors: M. Mihelich, D. Faranda, B. Dubrulle, D. Paillard
• Format: Article
• Language: English
• Published: Copernicus Publications 2015-03-01
• Series: Nonlinear Processes in Geophysics
• Online Access: http://www.nonlin-processes-geophys.net/22/187/2015/npg-22-187-2015.pdf
• ISSN: 1023-5809; 1607-7946

We derive rigorous results on the link between the principle of maximum entropy production and the principle of maximum Kolmogorov–Sinai entropy for a Markov model of the passive scalar diffusion called the Zero Range Process. We show analytically that both the entropy production and the Kolmogorov–Sinai entropy, seen as functions of a parameter <i>f</i> connected to the jump probability, admit a unique maximum denoted <i>f</i>_max_EP and <i>f</i>_max_KS. The behaviour of these two maxima is explored as a function of the system disequilibrium and the system resolution <i>N</i>. The main result of this paper is that <i>f</i>_max_EP and <i>f</i>_max_KS have the same Taylor expansion at first order in the deviation from equilibrium. We find that <i>f</i>_max_EP hardly depends on <i>N</i> whereas <i>f</i>_max_KS depends strongly on <i>N</i>. In particular, for a fixed difference of potential between the reservoirs, <i>f</i>_max_EP<i>(N)</i> tends towards a non-zero value, while <i>f</i>_max_KS<i>(N)</i> tends to 0 when <i>N</i> goes to infinity. For values of <i>N</i> typical of those adopted by Paltridge and climatologists working on maximum entropy production (<i>N</i> &asymp; 10&ndash;100), we show that <i>f</i>_max_EP and <i>f</i>_max_KS coincide even far from equilibrium. Finally, we show that one can find an optimal resolution <i>N</i>* such that <i>f</i>_max_EP and <i>f</i>_max_KS coincide, at least up to a second-order parameter proportional to the non-equilibrium fluxes imposed to the boundaries. We find that the optimal resolution <i>N</i>^* depends on the non-equilibrium fluxes, so that deeper convection should be represented on finer grids. This result points to the inadequacy of using a single grid for representing convection in climate and weather models. Moreover, the application of this principle to passive scalar transport parametrization is therefore expected to provide both the value of the optimal flux, and of the optimal number of degrees of freedom (resolution) to describe the system.