arXiv subjects

Ashwin K. Seshadri

Publications and source records attributed to Ashwin K. Seshadri.

3 recordsLinked to original sources

arXiv2017

Detection of Spatiotemporally Coherent Rainfall Anomalies Using Markov Random Fields

Precipitation is a large-scale, spatio-temporally heterogeneous phenomenon, with frequent anomalies exhibiting unusually high or low values. We use Markov Random Fields (MRFs) to detect spatio-temporally coherent anomalies in gridded annual rainfall data across India from 1901-2005. MRFs are undirected graphical models where each node is associated with a \{location,year\} pair, with edges connecting nodes representing adjacent locations or years. Some nodes represent observations of precipitation, while the rest represent unobserved (\emph{latent}) states that can take one of three values: high/low/normal. The MRF represents a probability distribution over the variables, using \emph{node potential} and \emph{edge potential} functions defined on nodes and edges of the graph. Optimal values of latent state variables are estimated by maximizing the posterior probability of the observations, using Gibbs sampling. Edge potentials enforce spatial and temporal coherence, and node potentials influence threshold for anomalies by affecting the prior probabilities of the states. The model can be tuned to recover anomalies detected by threshold-based methods. The competing influences of spatial and temporal coherence can be adjusted through edge potentials. We study spatio-temporal properties of rainfall anomalies discovered by this method, using suitable measures. We identify nonstationarities in occurrence of positive and negative anomalies between the first and second halves of the 20th century. We find that between these periods, there has been decrease in rainfall during June-September (JJAS) and an increase during other months. These effects are highlighted prominently in the statistics of anomalies. Properties of anomalies learnt from this approach could present tests of regional-scale rainfall simulations by climate models and statistical simulators.

stat.AP↗

arXiv2016

Exploring Spatial Coherence in Inter-annual Changes and Annual Extremes of Rainfall over India

Forecasts of monsoon rainfall for India are made at national scale. But there is spatial coherence and heterogeneity that is relevant to forecasting. This paper considers year-to-year rainfall change and annual extremes at sub-national scales. We use Data Mining techniques to gridded rain-gauge data for 1901-2011 to characterize coherence and heterogeneity and identify spatially homogeneous clusters. We study the direction of change in rainfall between years (Phase), and extreme annual rainfall at both grid level and national level. Grid-level Phase is found to be spatially coherent, and significantly correlated with all-India mean rainfall (AIMR) phase. Grid-level extreme-rainfall years are not strongly associated with corresponding extremes in AIMR, although in extreme AIMR years local extremes of the same type occur with higher spatial coherence. Years of extremes in AIMR entail widespread phase of the corresponding sign. Furthermore, local extremes and phase are found to frequently co-occur in spatially contiguous clusters.

stat.AP↗

arXiv2016

Factors controlling the time-delay between peak CO2 emissions and concentrations

Carbon-dioxide (CO2) is the main contributor to anthropogenic global warming, and the timing of its peak concentration in the atmosphere is likely to govern the timing of maximum radiative forcing. It is well-known that dynamics of atmospheric CO2 is governed by multiple time-constants, and here we approximate the solutions to a linear model of atmospheric CO2 dynamics with four time-constants to identify factors governing the time-delay between peaks in CO2 emissions and concentrations, and therefore the timing of the concentration peak. The main factor affecting this time-delay is the ratio of the rate of change of emissions during its increasing and decreasing phases. If this ratio is large in magnitude then the time-delay between peak emissions and concentrations is large. Therefore it is important to limit the magnitude of this ratio through mitigation, in order to achieve an early peak in CO2 concentrations. This can be achieved with an early global emissions peak, combined with rapid decarbonization of economic activity, because the delay between peak emissions and concentrations is affected by the time-scale with which decarbonization occurs. Of course, for limiting the magnitude of peak concentrations it is also important to limit the magnitude of emissions throughout its trajectory, but that aspect has been studied elsewhere and is not examined here. The carbon cycle parameters affecting the timing of the concentration peak are primarily the long multi-century time-constant of atmospheric CO2, and the ratio of contributions to the impulse response function of atmospheric CO2 from the infinite time-constant and the long time-constant respectively. Reducing uncertainties in these parameters can reduce uncertainty in forecasts of the radiative forcing peak.

physics.ao-ph↗