Mechanisms of variability and predictability of the tropical coupled ocean-atmosphere system

A conceptual model is proposed to explain the observed aperiodicity in the short term climate fluctuations of the tropical coupled ocean-atmosphere system. This is based on the evidence presented here that the tropical coupled ocean-atmosphere system sustains a low frequency inter-annual mode and a host of higher frequency intra-seasonal unstable modes. At long wavelengths, the low frequency mode is dominant while at short wavelengths, the high frequency modes are dominant resulting in the co-existence of a long wave low frequency mode with some short wave intra-seasonal modes in the tropical coupled system. It is argued that due to its long wavelength, the low frequency mode would behave like a linear oscillator while the higher frequency short wave modes would be nonlinear. The conceptual model envisages that an interaction between the low frequency linear oscillator and the high frequency nonlinear oscillations results in the observed aperiodicity of the tropical coupled system. This is illustrated by representing the higher frequency intra-seasonal oscillations by a nonlinear low order model which is then coupled to a linear oscillator with a periodicity of four years. The physical mechanism resulting in the aperiodicity in the low frequency oscillations and implications of these results on the predictability of the coupled system are discussed.

Lyapunov exponents and predictability of the tropical coupled ocean-atmosphere system

It has recently been proposed that the broad spectrum of interannual variability in the tropics with a peak around four years results from an interaction between the linear low-frequency oscillatory mode of the coupled system and the nonlinear higher-frequency modes of the system. In this study we determine the Lyapunov exponents of the conceptual model consisting of a nonlinear low-order model coupled to a linear oscillator for various values of the coupling constants.

A Macroscopic Model for First Return Stroke of Lightning

A lightning return stroke model for a downward flash is proposed. The model includes underlying physical phenomena governing return stroke evolution, namely, electric field due to charge distributed along the leader and cloud, transient enhancement of series channel conductance at the bridging regime, and the nonlinear variation of channel conductance, which supports the return stroke current evolution. Thermal effects of free burning arc at the stroke wave front and its impact on channel conductance are studied. A first-order arc model for determining the dynamic channel conductance along with a field-dependent conductivity for corona sheath is used in the model. The model predicts consistent current propagation along the channel with regard to current amplitude and return stroke velocity. The model is also capable of predicting the remote electromagnetic fields that are consistent with the experimental observations.

Multi-scale interactions and predictability of the Indian summer monsoon

Following the seminal work of Charney and Shukla (198 1), the tropical climate is recognised to be more predictable than extra tropical climate as it is largely forced by 'external' slowly varying forcing and less sensitive to initial conditions. However, the Indian summer monsoon is an exception within the tropics where 'internal' low frequency (LF) oscillations seem to make significant contribution to its interannual variability (IAV) and makes it sensitive to initial conditions. Quantitative estimate of contribution of 'internal' dynamics to IAV of Indian monsoon is made using long experiments with an atmospheric general circulation model (AGCM) and through analysis of long daily observations. Both AGCM experiments and observations indicate that more than 50% of IAV of the monsoon is contributed by 'internal' dynamics making the predictable signal (external component) burried in unpredictable noise (internal component) of comparable amplitude. Better understanding of the nature of the 'internal' LF variability is crucial for any improvement in predicition of seasonal mean monsoon. Nature of 'internal' LF variability of the monsoon and mechanism responsible for it are investigated and shown that vigorous monsoon intraseasonal oscillations (ISO's) with time scale between 10-70 days are primarily responsible for generating the 'internal' IAV. The monsoon ISO's do this through scale interactions with synoptic disturbances (1-7 day time scale) on one hand and the annual cycle on the other. The spatial structure of the monsoon ISO's is similar to that of the seasonal mean. It is shown that frequency of occurance of strong (weak) phases of the ISO is different in different seasons giving rise to stronger (weaker) than normal monsoon. Change in the large scale circulation during strong (weak) phases of the ISO make it favourable (inhibiting) for cyclogenesis and gives rise to space time clustering of synoptic activity. This process leads to enhanced (reduced) rainfall in seasons of higher frequency of occurence strong (weak) phases of monsoon ISO.

Genesis of return stroke current evolution at the wavefront

The channel dynamics at the wavefront is quite complex and is basically responsible for the evolution of return stroke current. The physical processes that actually contribute to the current evolution are not very clearly known. The enhancement of channel conductance at the wavefront is necessary for the current evolution and hence, return stroke. With regard to this, several questions arise like: (i) what causes the enhancement of this conductance, (ii) as the channel core temperature and electrical conductance are closely related, does one support the other and (iii) is the increase in core temperature on the nascent section of the channel is the result of free burning arc of the wavefront just below. These questions are investigated in detail in this work with appropriate transient thermal analysis and a macroscopic physical model for the lightning return stroke. Results clearly indicate that the contribution from the thermal field of the wavefront region to the adjacent nascent channel section is negligible as compared to the field enhancement brought in by the same. In other words, the whole process of return stroke evolution is dependent on the local heat generation at the nascent section caused by the enhancement of the electric field due to the arrival of the wavefront.

Electrical breakdown of carbon nanotube devices and the predictability of breakdown position

We have investigated electrical transport properties of long (>10 mu m) multiwalled carbon nanotubes (NTs) by dividing individuals into several segments of identical length. Each segment has different resistance because of the random distribution of defect density in an NT and is corroborated by Raman studies. Higher is the resistance, lower is the current required to break the segments indicating that breakdown occurs at the highly resistive segment/site and not necessarily at the middle. This is consistent with the one-dimensional thermal transport model. We have demonstrated the healing of defects by annealing at moderate temperatures or by current annealing. To strengthen our mechanism, we have carried out electrical breakdown of nitrogen doped NTs (NNTs) with diameter variation from one end to the other. It reveals that the electrical breakdown occurs selectively at the narrower diameter region. Overall, we believe that our results will help to predict the breakdown position of both semiconducting and metallic NTs. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.4720426]

TURBULENT PUMPING OF MAGNETIC FLUX REDUCES SOLAR CYCLE MEMORY AND THUS IMPACTS PREDICTABILITY OF THE SUN'S ACTIVITY

Prediction of the Sun's magnetic activity is important because of its effect on space environment and climate. However, recent efforts to predict the amplitude of the solar cycle have resulted in diverging forecasts with no consensus. Yeates et al. have shown that the dynamical memory of the solar dynamo mechanism governs predictability, and this memory is different for advection- and diffusion-dominated solar convection zones. By utilizing stochastically forced, kinematic dynamo simulations, we demonstrate that the inclusion of downward turbulent pumping of magnetic flux reduces the memory of both advection- and diffusion-dominated solar dynamos to only one cycle; stronger pumping degrades this memory further. Thus, our results reconcile the diverging dynamo-model-based forecasts for the amplitude of solar cycle 24. We conclude that reliable predictions for the maximum of solar activity can be made only at the preceding minimum-allowing about five years of advance planning for space weather. For more accurate predictions, sequential data assimilation would be necessary in forecasting models to account for the Sun's short memory.

An inquisitive analysis on the temporal variation of return stroke current essential for producing observed remote electro-magnetic fields

The gross characteristics of spatio-temporal current evolution in the return stroke phase of a cloud-to-ground lightning are rather well defined. However, they by themselves do not ensure the salient features for the resulting remote Electro- Magnetic Fields (EMFs). In spite of significant efforts in the engineering models wherein, the spatio-temporal current distribution all along the channel is specified by the design, all the salient features of remote EMFs could not be achieved. Only the current evolution that ensures the basic characteristics along with its ability to reproduce all the salient features of remote EMFs ranging from 50 m – 200 km from the lightning channel, can be considered as a realistic return stroke channel current. In view of this, the present work intends to investigate on the required fine features of the return stroke current evolution that yields all the desired features. To ensure that the current evolution is not arbitrary but obeys the involved basic physical processes, a recently developed physical model will be employed for the analysis.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system’s Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.

Predictability and chaotic nature of daily streamflow

The predictability of a chaotic series is limited to a few future time steps due to its sensitivity to initial conditions and the exponential divergence of the trajectories. Over the years, streamflow has been considered as a stochastic system in many approaches. In this study, the chaotic nature of daily streamflow is investigated using autocorrelation function, Fourier spectrum, correlation dimension method (Grassberger-Procaccia algorithm) and false nearest neighbor method. Embedding dimensions of 6-7 obtained indicates the possible presence of low-dimensional chaotic behavior. The predictability of the system is estimated by calculating the system's Lyapunov exponent. A positive maximum Lyapunov exponent of 0.167 indicates that the system is chaotic and unstable with a maximum predictability of only 6 days. These results give a positive indication towards considering streamflow as a low dimensional chaotic system than as a stochastic system.

Predictability of nonstationary time series using wavelet and EMD based ARMA models

Research has been undertaken to ascertain the predictability of non-stationary time series using wavelet and Empirical Mode Decomposition (EMD) based time series models. Methods have been developed in the past to decompose a time series into components. Forecasting of these components combined with random component could yield predictions. Using this ideology, wavelet and EMD analyses have been incorporated separately which decomposes a time series into independent orthogonal components with both time and frequency localizations. The component series are fit with specific auto-regressive models to obtain forecasts which are later combined to obtain the actual predictions. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability is checked for six and twelve months ahead forecasts across both the methodologies. Based on performance measures, it is observed that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm can be used to model events such as droughts with reasonable accuracy. Also, some modifications that can be made in the model have been discussed that could extend the scope of applicability to other areas in the field of hydrology. (C) 2013 Elesvier B.V. All rights reserved.

Return levels of hydrologic droughts under climate change

Developments in the statistical extreme value theory, which allow non-stationary modeling of changes in the frequency and severity of extremes, are explored to analyze changes in return levels of droughts for the Colorado River. The transient future return levels (conditional quantiles) derived from regional drought projections using appropriate extreme value models, are compared with those from observed naturalized streamflows. The time of detection is computed as the time at which significant differences exist between the observed and future extreme drought levels, accounting for the uncertainties in their estimates. Projections from multiple climate model-scenario combinations are considered; no uniform pattern of changes in drought quantiles is observed across all the projections. While some projections indicate shifting to another stationary regime, for many projections which are found to be non-stationary, detection of change in tail quantiles of droughts occurs within the 21st century with no unanimity in the time of detection. Earlier detection is observed in droughts levels of higher probability of exceedance. (C) 2014 Elsevier Ltd. All rights reserved.