Predicting the response of statistical averages of geophysical model dynamics to changes in random forcing

Abstract

Abstract The proposal describes a mathematical approach for the response of a complex chaotic nonlinear multiscale climate model to changes in external physical parameters which control random forcing. The proposed approach is based on the PI’s prior work to create a mathematical and computational approach to predict the response of a nonlinear chaotic forced-dissipative dynamical system to an external deterministic perturbation (such as a forcing which is a predefined function of time and system’s state) with improved skill, based on a precise geometric response formula for systems with chaotic attractors. The situation with random forcing is different from the one with deterministic forcing in the following respect: with random forcing, it is impossible to know the exact future effect of it on the system, and one can only estimate the average expectation of the response over all possible random realizations of the forcing. This requires a qualitatively different approach, where the theory of stochastic processes plays a key role. Within the framework of stochastic processes, the PI will develop a geometrictype response formula akin to that previously developed for deterministic forcing. The formula will be tested analytically on simple exactly solvable stochastic models, and computationally on more realistic stochastic models which describe various geophysical processes, such as seasurface wind, mean flow – wave interaction, and stochastically forced barotropic climate model with realistic Earth topography and vorticity forcing.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 12, 2016
Source ID
N000141512036

Entities

People

  • Rafail V Abramov

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Illinois Urbana–Champaign

Tags

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Wave Propagation and Nonlinear Chaotic Dynamics.