Networks in Climate

Dijkstra, Henk A.; Hernández-Garcı́a, Emilio; Masoller, Cristina; Barreiro, Marcelo

doi:10.1017/9781316275757

Cited by 57 publications

(52 citation statements)

References 359 publications

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“…The diffusion tensor σ(x) ∈ R n×m is a function of x. We assume that the potential V and the tensor σ are smooth enough to ensure the regularity of the SDE (2).…”

Section: A Uncontrolled Systemmentioning

confidence: 99%

“…Consider the case where the potential V has a finite number of disjoint local minima x 1 , x 2 , · · · , x K ∈ R n . In the absence of noise (σ ≡ 0), the local minima x k (k = 1, 2, · · · , K) are all locally stable fixed points of the ODE (2). The presence of noise (σ = 0) allows for rare transitions between these fixed points.…”

Section: A Uncontrolled Systemmentioning

confidence: 99%

“…Note that for α = 0, the Fokker-Plank equation (11) determines the evolution of the density p corresponding to the uncontrolled system (2). We are particularly interested in stationary densities p 0 which are invariant in time and describe the asymptotic evolution of the stochastic process.…”

Section: Stationary Distributionmentioning

confidence: 99%

See 2 more Smart Citations

Mitigation of tipping point transitions by time-delay feedback control

Farazmand

2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In stochastic multistable systems driven by the gradient of a potential, transitions between equilibria is possible because of noise. We study the ability of linear delay feedback control to mitigate these transitions, ensuring that the system stays near a desirable equilibrium. For small delays, we show that the control term has two effects: i) a stabilizing effect by deepening the potential well around the desirable equilibrium, and ii) a destabilizing effect by intensifying the noise by a factor of (1 − τ α) −1/2 , where τ and α denote the delay and the control gain, respectively. As a result, successful mitigation depends on the competition between these two factors. We also derive analytical results that elucidate the choice of the appropriate control gain and delay that ensure successful mitigations. These results eliminate the need for any Monte Carlo simulations of the stochastic differential equations, and therefore significantly reduce the computational cost of determining the suitable control parameters. We demonstrate the application of our results on two examples.Many complex systems, such as the climate, stock markets, population of species, and the nervous system, have multiple stable equilibrium states. Near the tipping point of the system, small amounts of stochastic disturbances can lead to transitions between these equilibria. Typically, one of the equilibria is desirable and transitions away from it lead to catastrophic consequences. Here, we analyze the ability of a delay feedback control to mitigate transitions away from a desirable equilibrium. arXiv:1911.05292v2 [math.OC]

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“…The diffusion tensor σ(x) ∈ R n×m is a function of x. We assume that the potential V and the tensor σ are smooth enough to ensure the regularity of the SDE (2).…”

Section: A Uncontrolled Systemmentioning

confidence: 99%

Section: A Uncontrolled Systemmentioning

confidence: 99%

See 1 more Smart Citation

Mitigation of tipping point transitions by time-delay feedback control

Farazmand

2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Networks provide a relatively new perspective with which to extract significant statistical relationships from the multitude of entangled interactions within the climate system, and are a useful complement to standard methods of analyzing patterns of climate variability (Tsonis and Roebber 2004;Donges et al 2009;Guez et al 2012;Radebach et al 2013;Fountalis et al 2014;Boers et al 2014;Donges et al 2015;Dijkstra et al 2019). These entangled interactions are more commonly referred to as climatological teleconnections, which in themselves have been studied for much of the last century (Walker and Bliss 1932;Wallace and Gutzler 1981;Glantz et al 1991), and also in recent years for the similar purpose of Arctic sea ice prediction (Yuan et al 2016;Comeau et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

Gregory

Stroeve

Sollich

2020

Weather and Forecasting

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Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

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“…All connections between neurons are translated into specific connection weights which quantify the strengths of positive, negative or neutral relationships between information. The greater the weights between the neurons, the stronger their correlations and the stronger their effect on the output signal [25,34,37]. This allows the model to identify and quantify associations between predictors and output variables [34].…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

Goulier

Paas

Ehrnsperger

et al. 2020

IJERPH

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Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO 2 , NH 3 , NO,NO 2 , NO x , O 3 , PM 1 , PM 2.5 , PM 10 and PN 10 ) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO 2 , NO x , and O 3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH 3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.health [12][13][14]. Especially in street canyons, both the noise levels and the NO 2 and PM 10 concentrations are high [15]. As an increased traffic density correlates with a high noise level [16], several studies have shown that urban air pollutant concentrations can be well described by metrics of sound [17,18]. Alternatively, another proxy for the traffic density and, in turn, air pollutant concentrations, can be achieved by combining the time of the day with the day of the week [19].While sources of air pollutants beyond traffic, e.g., industry, are also important for certain cities, the main drivers of local pollutant concentrations in the urban atmospheric boundary layer are traffic emissions, background concentrations and meteorological conditions that control the transport of pollutants [20]. Thus, these drivers should be considered as predictors in air pollution models. Since environmental relationships are nonlinear and reasonably complex, this context is well suited to artificial neural networks (ANNs) [18,[21][22][23]. In particular, the modelling of urban NO 2 /NO x concentrations [24] and PM/PN concentrations [18] using a Multilayer Perceptron (MLP) has shown good prediction results. The basic idea of ANNs is to mimic processes that occur in the human brain. They receive and process information and output results, whereby the network can restructure itself during processing [25]. By recognizing certain patterns within input datasets, they can learn the best way to predict the output [26].This study presents the development and evaluation of ten ANN models for predicting urban air pollutant concentrations (CO 2 , NH 3 , NO, NO 2 , NO x , O 3 , PM 1 , PM 2.5 , PM 10 and PN 10 ) using meteorological data, background concentrations and certain predictors of traffic volume, namely sound, traffic and time, as new input variables. We selected ...

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Networks in Climate

Cited by 57 publications

References 359 publications

Mitigation of tipping point transitions by time-delay feedback control

Mitigation of tipping point transitions by time-delay feedback control

Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

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