Reconciling Non-Gaussian Climate Statistics with Linear Dynamics
By P. Sardeshmukh, P. Sura. 2009
155 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/964d46c7659e85b008cb46808543c6f1dbf5c634
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Andrew J. Majda
- Christian L. E. Franzke
- Daan Crommelin
- Aaron F. Z. Levine
- F. Jin
- Maizar Raissi
- Steven L. Brunton
- J. Nathan Kutz
In Global and Planetary Change
- Andrew J. Majda
- Christian L. E. Franzke
- Daan Crommelin
- Aaron F. Z. Levine
- F. Jin
- Maizar Raissi
- Steven L. Brunton
- J. Nathan Kutz
Citations in the corpus
- Normal forms for reduced stochastic climate models (2009, background): “However, this argument applies for low-frequency data (measured by the leading EOFs) and does not rule out the possible usefulness of the proposed form (12) for other climate variables at high frequencies in station data (16).”
- A simple approach to quantifying the noise–ENSO interaction. Part I: deducing the state-dependency of the windstress forcing using monthly mean data (2015, background): “Either additive noise in a system with deterministic nonlinear dynamics (Jin et al. 2003; Su et al. 2010) or state-dependent noise in a linear system can generate these asymmetries (Müller 1987; Sardeshmukh and Sura 2009; Levine and Jin 2010; Lopez and Kirtman 2014).”