A Hierarchy of Data-Based ENSO Models

By D. Kondrashov, Sergey Kravtsov, Andrew Robertson, Michael Ghil. 2005

130 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/a4e8a59ccadbd8c438ede386a00abbb87d0d9195

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In Oceanography

Citations in the corpus

Data-driven non-Markovian closure models (2014, methodology): “More precisely, the dynamics of the auxiliary variable r1 is directly slaved to that of xm, while the other rj-variables are indirectly slaved to xm, since each variable rj in a given layer 2 ≤ j ≤ k interacts with rj−1 in the previous layer, thus sharing a multilayer structure reminiscent of the on”
FUNDAMENTAL LIMITATIONS OF AD HOC LINEAR AND QUADRATIC MULTI-LEVEL REGRESSION MODELS FOR PHYSICAL SYSTEMS (2012, unknown): “The predictability properties for ensembles of x(t) which are not the marginal equilibrium dynamics can be very different for the two level regression model compared with the original model [22]; this is an important comment to keep in mind for [16, 17] and is readily quantified in the present simpl”
Multilevel Regression Modeling of Nonlinear Processes: Derivation and Applications to Climatic Variability (2005, methodology): “We note, however, that our regression technique is entirely data driven, and can be used when no dynamical model is explicitly associated with the dataset (see also Kondrashov et al. 2005).”
Covariance and precision matrix estimation for high-dimensional time series (2013, background): “In the classical situation where p is proposed quadratic nonlinear dynamic models to accommodate the interactions between the processes which are useful for characterizing dynamic processes in geophysics [Kondrashov et al. (2005)].”
Physics constrained nonlinear regression models for time series (2012, background): “Examples of such data driven dynamical models are multi-level linear autoregressive models with external factors [2, 6] as well as ad-hoc quadratic nonlinear regression models [6, 11, 12, 13, 14]”
The physics of climate variability and climate change (2019, background): “This time series is the longest instrumentally measured record of temperatures; see, though, Kondrashov et al. (2005a) for Nile River water levels.”
A mathematical theory of climate sensitivity or, How to deal with both anthropogenic forcing and natural variability? (2015, methodology): “More generally, the empirical model reduction (EMR) approach (Kondrashov et al., 2005; Kravtsov et al., 2005, 2009) provides precisely the required kind of reduction from a highdimensional model, like a GCM, to a loworder model that is both nonlinear and noisedriven.”
Polynomial nonlinear spatio‐temporal integro‐difference equation models (2011, background): “The class of models they propose arise naturally since quadratic interactions are critical for many geophysical (Kondrashov et al., 2005; Majda et al., 2005) and ecological (Hooten and Wikle, 2008) processes.”