Systematic Strategies for Stochastic Mode Reduction in Climate

By Andrew J. Majda, Ilya Timofeyev, Eric Vanden-Eijnden. 2003

236 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/ad9b1583e90b343d3eef423ebefcbdca56227d88

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Challenges in Climate Science and Contemporary Applied Mathematics (2012, methodology): “Systematic mathematical stochasticmode reduction strategies [90, 82, 83] have been utilized recently to develop normal forms for reduced, stochastic climate models [94].”
Filtering Complex Turbulent Systems (2012, background): “First, the simplest stochastic models for modeling turbulent fluctuations consist of replacing the nonlinear interaction at these modes by additional dissipation and white noise forcing to mimic rapid energy transfer [120, 104, 106, 103, 37, 97].”
Variational principles for stochastic fluid dynamics (2014, background): “[15] for the history and motivation for this approach, as well as descriptions of their own approach, called stochastic mode reduction (SMR).”
Multilevel Regression Modeling of Nonlinear Processes: Derivation and Applications to Climatic Variability (2005, methodology): “Majda et al. (1999, 2001, 2002, 2003) have developed a strategy for systematic mode reduction in deterministic models that govern geophysical flows.”
Stochastic climate theory and modeling (2015, unknown): “[92] Majda, A.”
Reconciling Non-Gaussian Climate Statistics with Linear Dynamics (2009, background): “The conditions under which the components of a dynamical system may be separable into slow and ‘‘fast enough’’ components in this sense, as well as procedures for classifying specific system components as such, have been the subject of many theoretical and empirical studies (e.g., Khas’minskii 1966;”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “Novel approaches that exploit ideas ranging from stochastic parameterization to systematic coarse-graining likely have roles to play here [e.g., Majda et al., 2003, 2008; Klein and Majda, 2006; Palmer et al., 2005; Palmer and Williams, 2010; Majda, 2012; Wouters and Lucarini, 2013; Lucarini et al.,…”