Response of a global atmospheric circulation model to spatio-temporal stochastic forcing: ensemble statistics

Pérez‐Muñuzuri, V.; Lorenzo, M. N.; Montero, Pedro; Fraedrich, Klaus; Kirk, Edilbert; Lunkeit, Frank

doi:10.5194/npg-10-453-2003

Cited by 6 publications

(8 citation statements)

References 38 publications

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“…The results of this review corroborate the importance of considering a certain level of noise for improving the performance of climate models to explain nonlinear behavior, including noise-induced transitions between different regimes. [36][37][38][39][40][41][42][43][44] Most AOGCM models currently used have not taken into account processes below certain temporal and spatial scales. 56,57 This review addresses the role of stochastic sources in climate models and claims the importance of the study of these sources to understand the fate of the climate system.…”

Section: Discussionmentioning

confidence: 99%

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The Role of Stochastic Forcing on the Behavior of Thermohaline Circulation

Lorenzo

Taboada

Iglésias

et al. 2008

Annals of the New York Academy of Sciences

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The nonlinear nature of the climate system suggests that its reactions to unexpected perturbations could be different from the expected ones. In nonlinear science it is recognized as a promising paradigm that stochastic fluctuations can generate order or other counterintuitive effects. Thus, noise sources, adequately coupled to a nonlinear system, may give rise to a rich new phenomenology not present in a deterministic noiseless scenario. In this review we focus attention on thermohaline circulation (THC). THC presents two modes of operation; one state shows active THC and the other inactive. Previous episodes of transitions between both states of THC observed in paleoclimatic records and the influence of this circulation on climate have resulted in detailed investigations on the dynamics of the THC. A weakening or a collapse of this current could trigger the onset of a new Younger Dryas. In this review the introduction of stochastic forcing in key parameters, both in a simple box model and in an earth model of intermediate complexity, provokes a weakening and even a shutdown of the THC. The consequences of this weakening are observed in different variables. The surface air temperature and the sea surface temperature are dominated by cooling of the Northern Hemisphere. Changes in the position of the Intertropical Convergence Zone and in precipitation are observed. There is also an intensification of the North Atlantic Oscillation values during winter. These results reinforce the necessity to consider stochastic sources in climate models to improve our understanding of the climate.

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Section: Discussionmentioning

confidence: 99%

“…35 Transition probabilities are known to depend sensitively on noise levels. [36][37][38] Considering that the full spectrum of spatial and temporal scales exhibited by the climate system will not be resolvable by models for decades, the stochastic techniques offer a direct, suitable, and computationally economical solution.…”

Section: Stochastic Forcingmentioning

confidence: 99%

The Role of Stochastic Forcing on the Behavior of Thermohaline Circulation

Lorenzo

Taboada

Iglésias

et al. 2008

Annals of the New York Academy of Sciences

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“…In that case, the stochastic process appears as multiplicative noise which, as is well known, can substantially change the dynamical behavior of not only nonlinear systems (Horsthemke and Léfèver 1984;Landa and McClintock 2000;Sura 2002), but also linear systems. The potentially significant role of multiplicative noise to improve the representation of subgrid-scale phenomena in models of the climate systems has been stressed in several studies (e.g., Buizza et al 1999;Palmer 2001;Sardeshmukh et al 2001;Pérez-Muñuzuri et al 2003;Sura 2002). Sardeshmukh et al (2001) introduced multiplicative noise in the linearized barotropic vorticity equation, and found that the mean stationary wave response to steady forcing was amplified when the damping parameter fluctuated, but was weakened (in a scale-dependent manner) when the ambient flow fluctuated.…”

Section: Stochastically Perturbed Rossby Wavesmentioning

confidence: 99%

Multiplicative Noise and Non-Gaussianity: A Paradigm for Atmospheric Regimes?

Sura

Newman

Penland

et al. 2005

Journal of the Atmospheric Sciences

121

123

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Atmospheric circulation statistics are not strictly Gaussian. Small bumps and other deviations from Gaussian probability distributions are often interpreted as implying the existence of distinct and persistent nonlinear circulation regimes associated with higher-than-average levels of predictability. In this paper it is shown that such deviations from Gaussianity can, however, also result from linear stochastically perturbed dynamics with multiplicative noise statistics. Such systems can be associated with much lower levels of predictability. Multiplicative noise is often identified with state-dependent variations of stochastic feedbacks from unresolved system components, and may be treated as stochastic perturbations of system parameters. It is shown that including such perturbations in the damping of large-scale linear Rossby waves can lead to deviations from Gaussianity very similar to those observed in the joint probability distribution of the first two principal components (PCs) of weekly averaged 750-hPa streamfunction data for the past 52 winters. A closer examination of the Fokker-Planck probability budget in the plane spanned by these two PCs shows that the observed deviations from Gaussianity can be modeled with multiplicative noise, and are unlikely the results of slow nonlinear interactions of the two PCs. It is concluded that the observed non-Gaussian probability distributions do not necessarily imply the existence of persistent nonlinear circulation regimes, and are consistent with those expected for a linear system perturbed by multiplicative noise.

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“…When noise is added, the random perturbations increase the likelihood of the particle to overcome the potential barrier and move to the other state. This phenomenon is known as the paradigm of stochastic resonance (figure 1) and plays an important role in the transition probabilities which are known to depend on sensitivity to noise levels (Gammaitoni et al, 1998;García-Ojalvo & Sancho, 1999;Pérez-Muñuzuri et al, 2003;Lorenzo et al, 2003). Fig.…”

Section: The Role Of Stochastic Forcingsmentioning

confidence: 99%