### Abstract

Linear response to external perturbation through the fluctuation-dissipation theorem has recently become a popular topic in the climate research community. It relates an external perturbation of climate dynamics to climate change in a simple linear fashion, which provides key insight into physics of the climate change phenomenon. Recently, the authors developed a suite of linear response algorithms for low-frequency response of large-scale climate dynamics to external perturbation, including the novel blended response algorithm, which combines the geometrically exact general response formula using integration of a linear tangent model at short response times and the classical quasi-Gaussian response algorithm at longer response times, overcoming numerical instability of the tangent linear model for longer times due to positive Lyapunov exponents. Here, the authors apply the linear response framework to several leading empirical orthogonal functions (EOFs) of a quasigeostrophic model of wind-driven ocean circulation. It is demonstrated that the actual nonlinear response of this system under external perturbation at leading EOFs can be predicted by the linear response algorithms with adequate skill with moderate errors; in particular, the blended response algorithm has a pattern correlation with the ideal response operator on the four leading EOFs of the mean state response of 94% after 5 yr. In addition, interesting properties of the mean flow response to large-scale changes in wind stress at the leading EOFs are observed.

Original language | English (US) |
---|---|

Pages (from-to) | 243-260 |

Number of pages | 18 |

Journal | Journal of Physical Oceanography |

Volume | 42 |

Issue number | 2 |

DOIs | |

State | Published - Feb 2012 |

### Fingerprint

### Keywords

- Climate sensitivity
- Ocean circulation
- Ocean models
- Quasigeostrophic models
- Wind stress

### ASJC Scopus subject areas

- Oceanography

### Cite this

*Journal of Physical Oceanography*,

*42*(2), 243-260. https://doi.org/10.1175/JPO-D-11-052.1

**Low-frequency climate response of quasigeostrophic Wind-Driven Ocean Circulation.** / Abramov, Rafail V.; Majda, Andrew J.

Research output: Contribution to journal › Article

*Journal of Physical Oceanography*, vol. 42, no. 2, pp. 243-260. https://doi.org/10.1175/JPO-D-11-052.1

}

TY - JOUR

T1 - Low-frequency climate response of quasigeostrophic Wind-Driven Ocean Circulation

AU - Abramov, Rafail V.

AU - Majda, Andrew J.

PY - 2012/2

Y1 - 2012/2

N2 - Linear response to external perturbation through the fluctuation-dissipation theorem has recently become a popular topic in the climate research community. It relates an external perturbation of climate dynamics to climate change in a simple linear fashion, which provides key insight into physics of the climate change phenomenon. Recently, the authors developed a suite of linear response algorithms for low-frequency response of large-scale climate dynamics to external perturbation, including the novel blended response algorithm, which combines the geometrically exact general response formula using integration of a linear tangent model at short response times and the classical quasi-Gaussian response algorithm at longer response times, overcoming numerical instability of the tangent linear model for longer times due to positive Lyapunov exponents. Here, the authors apply the linear response framework to several leading empirical orthogonal functions (EOFs) of a quasigeostrophic model of wind-driven ocean circulation. It is demonstrated that the actual nonlinear response of this system under external perturbation at leading EOFs can be predicted by the linear response algorithms with adequate skill with moderate errors; in particular, the blended response algorithm has a pattern correlation with the ideal response operator on the four leading EOFs of the mean state response of 94% after 5 yr. In addition, interesting properties of the mean flow response to large-scale changes in wind stress at the leading EOFs are observed.

AB - Linear response to external perturbation through the fluctuation-dissipation theorem has recently become a popular topic in the climate research community. It relates an external perturbation of climate dynamics to climate change in a simple linear fashion, which provides key insight into physics of the climate change phenomenon. Recently, the authors developed a suite of linear response algorithms for low-frequency response of large-scale climate dynamics to external perturbation, including the novel blended response algorithm, which combines the geometrically exact general response formula using integration of a linear tangent model at short response times and the classical quasi-Gaussian response algorithm at longer response times, overcoming numerical instability of the tangent linear model for longer times due to positive Lyapunov exponents. Here, the authors apply the linear response framework to several leading empirical orthogonal functions (EOFs) of a quasigeostrophic model of wind-driven ocean circulation. It is demonstrated that the actual nonlinear response of this system under external perturbation at leading EOFs can be predicted by the linear response algorithms with adequate skill with moderate errors; in particular, the blended response algorithm has a pattern correlation with the ideal response operator on the four leading EOFs of the mean state response of 94% after 5 yr. In addition, interesting properties of the mean flow response to large-scale changes in wind stress at the leading EOFs are observed.

KW - Climate sensitivity

KW - Ocean circulation

KW - Ocean models

KW - Quasigeostrophic models

KW - Wind stress

UR - http://www.scopus.com/inward/record.url?scp=84857757925&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84857757925&partnerID=8YFLogxK

U2 - 10.1175/JPO-D-11-052.1

DO - 10.1175/JPO-D-11-052.1

M3 - Article

AN - SCOPUS:84857757925

VL - 42

SP - 243

EP - 260

JO - Journal of Physical Oceanography

JF - Journal of Physical Oceanography

SN - 0022-3670

IS - 2

ER -