Stochastic climate models

By Peter Imkeller, Jin-Song von Storch. 2001

923 citations recorded by the source corpus.

Subfields: Atmospheric Science, Earth-Surface Processes, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3c1b228664e9510176dc05620e9a50cfa0404c5b

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

1. John Marshall
2. Andrew J. Majda
3. Christian L. E. Franzke
4. Michael Ghil
5. Eric Guilyardi
6. Robert Hallberg
7. M. Buckley
8. Demetris Koutsoyiannis

In Earth-Surface Processes

1. F. Jin
2. J. H. Zanten
3. Robert Hallberg
4. B. Fox‐Kemper
5. John Marshall
6. M. Buckley
7. M. Collins
8. Ilya Timofeyev

In Global and Planetary Change

1. John Marshall
2. Andrew J. Majda
3. M. Buckley
4. Demetris Koutsoyiannis
5. Eric Guilyardi
6. Michael Ghil
7. Robert Hallberg
8. Christian L. E. Franzke

Citations in the corpus

• Pacific Decadal Variability: The Tropical Pacific Mode and the North Pacific Mode* (2003, background): “The role of tropical teleconnective forcing Previous studies have shown that ENSO can affect the North Pacific via atmospheric teleconnections (e.g., Horel and Wallace 1981; Graham 1994).”
• Midlatitude Excitation of Tropical Variability in the Pacific: The Role of Thermodynamic Coupling and Seasonality* (2009, methodology): “These subtropical SST anomalies develop through boreal winter and maximize in boreal spring, consistent with the phasing implied by integration of the imposed forcing (Hasselmann 1976), then decay through the remainder of the model simulation.”
• A Comparison of the Influence of Additive and Multiplicative Stochastic Forcing on a Coupled Model of ENSO (2005, background): “In this work we have attempted to give a new perspective on how the natural tropical Pacific atmosphere and ocean might operate by expanding on the seminal work of Hasselmann (1976), who proposed additive stochastic forcing as a model and null hypothesis for climate variability.”
• Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study* (2015, background): “More specifically, the cross correlation is small at the zero lag and reaches maximum when the atmosphere leads the ocean by about the decorrelation time of the atmosphere (Hasselmann 1976; Barsugli and Battisti 1998).”
• Statistical Analysis in Climate Research (2000, neutral): “For the midlatitude sea-surface temperature (SST) the variabley is the SST and the variables x that vary on short time scales are the air–sea heat flux and the wind stress (Frankignoul and Hasselmann [133]).”