Multivariate singular spectrum analysis and the road to phase synchronization

Groth, Andreas; Ghil, Michael

doi:10.1103/physreve.84.036206

Cited by 91 publications

(92 citation statements)

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“…For a more reliable separation of ST-EOF pairs, we used here the recently developed variance-maximisation (varimax) rotation of the ST-EOFs. This approach reduces mixture effects between distinct oscillatory components and improves the biological interpretability of the results (Groth and Ghil 2011). …”

Section: /40mentioning

confidence: 99%

Pathogens trigger top-down climate forcing on ecosystem dynamics

et al. 2016

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; Vollestad, L. Asbjorn; Stenseth, Nils C.; Ghil, Michael. 2016. Pathogens trigger top-down climate forcing on ecosystem dynamics. Oecologia, 181 (2). 519-532. 10.1007/s00442-016-3575-8 Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. Pathogens trigger top-down climate forcing on ecosystem dynamics AbstractEvaluating the effects of climate variation on ecosystems is of paramount importance for our ability to forecast and mitigate the consequences of global change. However, the ways in which complex food webs respond to climate variations remain poorly understood. Here, we use long-term time series to investigate the effects of temperature variation on the intraguild-predation (IGP) system of Windermere (UK), a lake where pike (Esox lucius, top predator) feed on small-sized perch (Perca fluviatilis) but compete with large-sized perch for the same food sources. Spectral analyses of time series reveal that pike recruitment dynamics are temperature-controlled. In 1976, expansion of a perch pathogen into the lake severely impacted large perch and favoured pike as the IGP-dominant species. This pathogeninduced regime shift to a pike-dominated IGP apparently triggered a temperature-controlled trophic cascade passing through pike down to dissolved nutrients. In simple food chains, warming is predicted to strengthen top-down control by accelerating metabolic rates in ectothermic consumers, while pathogens of top consumers are predicted to dampen this top-down control. In contrast, the local IGP structure in Windermere made warming and pathogens synergistic in their top-down effects on ecosystem functioning. More generally, our results point to top predators as major mediators of community response to global change, and show that size-selective agents (e.g., pathogens, fishers or hunters) may change the topological architecture of food-webs and alter whole-ecosystem sensitivity to climate variation.

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Section: /40mentioning

confidence: 99%

Pathogens trigger top-down climate forcing on ecosystem dynamics

et al. 2016

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“…In this research-and-review paper, we set out to combine three strands of research: (i) the formulation and study of a non-equilibrium dynamic model (NEDyM) of endogenous business cycles (EnBCs) ; (ii) the application of this NEDyM model to the investigation of the impact of global warming and extreme events on the economy [Hallegatte et al, 2007;; and (iii) the quantitative extraction of business cycles from macroeconomic data [Groth and Ghil , 2011;Groth et al, 2012], including the validation of the FDT hypothesis on the functioning of the U.S. economy.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the transformation to PCs introduces frequency-dependent phase distortions and complicates the correct assignment of a given variance to a precise instant in time. A subsequent transformation to RCs, on the other hand, neutralizes this undesirable effect [Groth and Ghil , 2011].…”

Section: Episodes Of High Fluctuation Levelsmentioning

confidence: 99%

“…Vautard and Ghil [1989], i.e., the number of statistically significant components, commonly referred to as the signal, as opposed to the noise; or (ii) a so-called oscillatory pair K = {k1, k2}, which might capture a possibly cyclic mode of behavior of the system [Vautard and Ghil , 1989;Plaut and Vautard , 1994]. Information about oscillatory modes can help detect clusters of phase-and frequency-locked oscillators even in the presence of high observational noise [Groth and Ghil , 2011]. Summing over the whole set of RCs, K = {k : 1 ≤ k ≤ DM }, gives the complete reconstruction of the time series.…”

Section: B1 Reconstruction Of Oscillatory Behaviormentioning

confidence: 99%

“…The notation e k = {e dk (m) : 1 ≤ d ≤ D, 1 ≤ m ≤ M } reflects the special structure of the eigenvectors [Groth and Ghil , 2011]; formulas for r dk (n) near the endpoints of the time series are given in Vautard et al [1992] and Ghil et al [2002]. Given any subset K of eigenelements {(λ k , e k ) : k ∈ K}, we obtain the corresponding reconstruction r dK by summing the RCs r dk over k ∈ K, r dK (n) = k∈K r dk (n).…”

Section: B1 Reconstruction Of Oscillatory Behaviormentioning

confidence: 99%

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Impacts of Natural Disasters on a Dynamic Economy

et al. 2012

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Abstract. This paper presents a modeling framework for macroeconomic growth dynamics; it is motivated by recent attempts to formulate and study "integrated models" of the coupling between natural and socio-economic phenomena. The challenge is to describe the interfaces between human activities and the functioning of the earth system. We examine the way that this interface works in the presence of endogenous business cycle dynamics, based on a non-equilibrium dynamic model. Recent findings about the macroeconomic response to natural disasters in such a nonequilibrium setting have shown a more severe response to natural disasters during expansions than during recessions. These findings raise questions about the assessment of climate change damages or natural disaster losses that are based purely on long-term growth models. In order to compare the theoretical findings with observational data, we analyze cyclic behavior in the U.S. economy, based on multivariate singular spectrum analysis. We analyze a total of nine aggregate indicators in a 52-year interval and demonstrate that the behavior of the U.S. economy changes significantly between intervals of growth and recession, with higher volatility during expansions.

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Time scales of the European surface air temperature variability: The role of the 7–8 year cycle

Jajcay

Hlinka

Kravtsov

et al. 2016

Geophysical Research Letters

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Air temperature variability on different time scales exhibits recurring patterns and quasi‐oscillatory phenomena. Climate oscillations with the period about 7–8 years have been observed in many instrumental records in Europe. Although these oscillations are weak if considering their amplitude, they might have nonnegligible influence on temperature variability on shorter time scales due to cross‐scale interactions recently observed by Paluš (2014). In order to quantify the cross‐scale influence, we propose a simple conditional mean approach which estimates the effect of the cycle with the period close to 8 years on the amplitude of the annual cycle in surface air temperature (SAT) in the range 0.7–1.4°C and the effect on the overall variability of the SAT anomalies (SATA) leads to the changes 1.5–1.7°C in the annual SATA means. The strongest effect in the winter SATA means reaches 4–5°C in central European station and reanalysis data.

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Multivariate singular spectrum analysis and the road to phase synchronization

Cited by 91 publications

References 30 publications

Pathogens trigger top-down climate forcing on ecosystem dynamics

Pathogens trigger top-down climate forcing on ecosystem dynamics

Impacts of Natural Disasters on a Dynamic Economy

Time scales of the European surface air temperature variability: The role of the 7–8 year cycle

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