Valerie Livina

Early warning signals of ecological transitions: methods for spatial patterns.

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness

We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings.

The human physiological impact of global deoxygenation

There has been a clear decline in the volume of oxygen in Earth’s atmosphere over the past 20xa0years. Although the magnitude of this decrease appears small compared to the amount of oxygen in the atmosphere, it is difficult to predict how this process may evolve, due to the brevity of the collected records. A recently proposed model predicts a non-linear decay, which would result in an increasingly rapid fall-off in atmospheric oxygen concentration, with potentially devastating consequences for human health. We discuss the impact that global deoxygenation, over hundreds of generations, might have on human physiology. Exploring the changes between different native high-altitude populations provides a paradigm of how humans might tolerate worsening hypoxia over time. Using this model of atmospheric change, we predict that humans may continue to survive in an unprotected atmosphere for ~3600xa0years. Accordingly, without dramatic changes to the way in which we interact with our planet, humans may lose their dominance on Earth during the next few millennia.

Optimization of carbon emissions in smart grids

In this paper the carbon emissions in smart grids are optimised. This is achieved by performing dynamical modelling with uncertainty analysis of smart grids using an Ensemble Based Closed-Loop Optimisation Scheme (EnOpt). Uncertainties include the fluctuations of consumer energy demand (behavioural) and generation fuel mix (green and non-green) are estimated using the Monte Carlo method. The UK electricity grid carbon factor is calculated based on the available energy data provided by the Balancing Mechanism Reporting System (BMRS). We apply the Ensemble Kalman Filter (EnKF) for the forecasting and prediction of the energy generation, consumption and resultant carbon emissions. Then we apply EnOpt to maximize the carbon savings in the system. Finally, we present results of EnOpt optimization of carbon emissions with estimated carbon savings.

A novel scaling indicator of early warning signals helps anticipate tropical cyclones

Tipping events in dynamical systems have been studied in many contexts, often modelled by the decay of critical modes, system states which are tending towards bifurcation, characterised by increased return times to stable equilibria. Temporal scaling properties of time series data can be used to detect the presence of a critical mode by estimating the decay rate, and indicators of changes in these properties may therefore be used to provide an early warning signal (EWS) for an impending tipping event. The lag-1 autocorrelation function (ACF(1)) indicator and the detrended fluctuation analysis (DFA) indicator have previously been used in such a way; in this paper we introduce a novel scaling indicator based on the decay rate of the power spectrum (PS). We compare the ACF(1), DFA- and PS-indicators using artificial data; data from a model which includes a bifurcation point; and sea-level pressure data along the paths of 14 tropical cyclones. By using the PS-indicator with such data, we show that the new indicator may be used to provide an EWS in a context where the ACF(1)- and DFA-indicators fail.

The UK electricity demand side response: Carbon savings analysis

We quantify carbon emissions and savings in Demand Side Response (DSR) programmes, such as Short Term Operating Reserve (STOR) and Triad, substituting grid energy in the UK power networks. We model each of the DSR programmes with configurations and assumptions appropriate for the UK energy industry. This enables us to compare carbon emissions between the business-as-usual (BAU) and the smart intervention applied, thus deriving carbon savings. Standby diesel generators are the main intervention for most of the DSR programmes. Carbon emissions of standby diesel generators are compared with the BAU solution. Whether such a DSR produces carbon savings or not depends on the scale and operational policies of the interventions.