Dr. Bedartha Goswami :: Research Profile
Research Domain IV
Transdisciplinary Concepts and Methods
Fundamentally, all of paleoclimate research involves the following paradigm: the measurement of paleoclimate proxies along the depth of a paleoclimate archive, and the measurement of the age of the depth levels of the archive with techniques such as radiocarbon dating, U/Th dating, layer counting, etc. The goal is then to combine these two sets of measurements to obtain a proxy versus time data set called the proxy record. Typically, the chronological uncertainty involved in the dating is not appropriately carried forward to the proxy record in a quantitative sense.
I focus on estimating the proxy records using a Bayesian approach that can provide posterior proxy estimates such as the mean or the median along with estimates of the uncertainty such as the variance or inter-quantile ranges. I am also involved in developing a toolbox that seeks to provide proxy records with uncertainty estimates using Mote Carlo procedures.
Recurrences are defined as the (approximate) return of the trajectory of a dynamical system to an earlier state. Patterns of recurrences contain information about the fundamental dynamical nature of the system itself. Over the past decades, recurrences have been shown to a be a powerful time series analysis tool that can help quantify the dynamics, behavior and even similarities of complex systems — systems that are otherwise extremely intractable.
I attempt to extend existing recurrence-based methods for the quantification of synchronization between pairs of time series to apply them to complex datasets and estimate the similarities between them.
Normally, time series obtained from real-world and natural systems are the sole realization of the dynamical black boxes that give rise to them. Thus, when we estimate measures such as cross-correlation, variance, power spectra, etc. from such time series, we have no way of comparing the obtained values of these measures to a benchmark test case in order to be able to interpret them properly. For instance, if we get a correlation value of 0.61 between two time series obtained from, let's say, a stalagmite in China and another stalagmite in India, we have no way of knowing whether this value of 0.61 could actually have been caused by a random chance between the two time series given their inherent dynamical natures. Neither can we say anything about whether 0.61 should be interpreted as a case of a strong or moderate connectivity because of the lack of a baseline to compare it to. This is why the measured values have to placed against a background distribution of values obtained using surrogate data sets — data sets generated by randomization of the observed time series.
I seek to find a proper formal framework of describing the statistical significance of measures that have been estimated from time series. This also involves an in-depth understanding on the various types of surrogate creation and their corresponding null hypotheses.
D. Traxl, N. Boers, A. Rheinwalt, B. Goswami, J. Kurths, The size distribution of spatiotemporal extreme rainfall clusters around the globe, Geophysical Research Letters 43 (2016) doi:
- B. Goswami, S. M. Shekatkar, A. Rheinwalt, G. Ambika, J. Kurths, A random interacting network model for complex networks, Scientific Reports 5 (2015) 18183 doi:10.1038/srep18183
- S. Dey, B. Goswami, A. Joshi, A possible mechanism for the attainment of out-of-phase periodic dynamics in two chaotic subpopulations coupled at low dispersal rate, Journal of Theoretical Biology 367 (2015) 100–110 doi:10.1016/j.jtbi.2014.11.028
- B. Goswami, J. Heitzig, K. Rehfeld, N. Marwan, A. Ambili, S. Prasad, J. Kurths, Estimation of sedimentary proxy records together with associated uncertainty, Nonlinear Processes in Geophysics 21 (2014) 1093–1111doi:10.5194/npg-21-1093-2014
- S. Dey, B. Goswami, A. Joshi, Effects of symmetric and asymmetric dispersal on the dynamics of heterogeneous metapopulations: Two-patch systems revisited, Journal of Theoretical Biology 345 (2014) 52–60 doi:10.1016/j.jtbi.2013.12.005
- B. Goswami, N. Marwan, G. Feulner, J. Kurths, How do global temperature drivers influence each other? A network perspective using recurrences, European Physical Journal Special Topics 222 (2013) 861–873 doi:10.1140/epjst/e2013-01889-8
- S. F. M. Breitenbach, K. Rehfeld, B. Goswami, J. U. L. Baldini, H. E. Ridley, D. J. Kennett, K. M. Prufer, V. V. Aquino, Y. Asmerom, V. J. Polyak, H. Cheng, J. Kurths, N. Marwan, COnstructing Proxy Records from Age models (COPRA), Climate of the Past 8 (2012) 1765–1779 doi:10.5194/cp-8-1765-2012
- B. Goswami, G. Ambika, N. Marwan, J. Kurths, On interrelations of recurrences and connectivity trends between stock indices, Physica A 391 (2012) 4364–4376 doi:10.1016/j.physa.2012.04.018
- A. Rheinwalt, B. Goswami, N. Boers, J. Heitzig, N. Marwan, R. Krishnan and J. Kurths, Teleconnections in Climate Networks: A Network of Networks Approach to Investigate the Influence of Sea Surface Temperature Variability on Monsoon Systems, in: Machine Learning and Data Mining Approaches to Climate Science (2015) Springer International Publishing. pp 23–33