title: The path integral formulation of climate dynamics.
abstract: The chaotic nature of the atmospheric dynamics has stimulated the applications of methods and ideas derived from statistical dynamics. For instance, ensemble systems are used to make weather predictions recently extensive, which are designed to sample the phase space around the initial condition. Such an approach has been shown to improve substantially the usefulness of the forecasts since it allows forecasters to issue probabilistic forecasts. These works have modified the dominant paradigm of the interpretation of the evolution of atmospheric flows (and oceanic motions to some extent) attributing more importance to the probability distribution of the variables of interest rather than to a single representation. The ensemble experiments can be considered as crude attempts to estimate the evolution of the probability distribution of the climate variables, which turn out to be the only physical quantity relevant to practice. However, little work has been done on a direct modeling of the probability evolution itself. In this paper it is shown that it is possible to write the evolution of the probability distribution as a functional integral of the same kind introduced by Feynman in quantum mechanics, using some of the methods and results developed in statistical physics. The approach allows obtaining a formal solution to the Fokker-Planck equation corresponding to the Langevin-like equation of motion with noise. The method is very general and provides a framework generalizable to red noise, as well as to delaying differential equations, and even field equations, i.e., partial differential equations with noise, for example, general circulation models with noise. These concepts will be applied to an example taken from a simple ENSO model.
authors.affiliation: Centro Euromediterraneo sui Cambiamenti Climatici, Bologna, Italy. antonio.navarra@cmcc.it
authors.forename: Antonio
authors.initials: A
authors.lastname: Navarra
authors.forename: Joe
authors.initials: J
authors.lastname: Tribbia
authors.forename: Giovanni
authors.initials: G
authors.lastname: Conti
mesh_terms: D000465:Algorithms; D002980:Climate; D055904:Meteorology / Q000379:methods; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D009792:Oceans and Seas; D013269:Stochastic Processes; D014887:Weather