@article{MaheswaranAgarwalSivakumaretal.2019,
  author    = {Maheswaran, Rathinasamy and Agarwal, Ankit and Sivakumar, Bellie and Marwan, Norbert and Kurths, J{\"u}rgen},
  title     = {Wavelet analysis of precipitation extremes over India and teleconnections to climate indices},
  series = {Stochastic Environmental Research and Risk Assessment},
  volume    = {33},
  journal   = {Stochastic Environmental Research and Risk Assessment},
  number    = {11-12},
  publisher = {Springer},
  address   = {New York},
  issn      = {1436-3240},
  doi       = {10.1007/s00477-019-01738-3},
  pages     = {2053 -- 2069},
  year      = {2019},
  abstract  = {Precipitation patterns and extremes are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. This study uses wavelet coherence analysis to detect significant interannual and interdecadal oscillations in monthly precipitation extremes across India and their teleconnections to three prominent climate indices, namely, Nino 3.4, Pacific Decadal Oscillation, and Indian Ocean Dipole (IOD). Further, partial wavelet coherence analysis is used to estimate the standalone relationship between the climate indices and precipitation after removing the effect of interdependency. The wavelet analysis of monthly precipitation extremes at 30 different locations across India reveals that (a) interannual (2-8 years) and interdecadal (8-32 years) oscillations are statistically significant, and (b) the oscillations vary in both time and space. The results from the partial wavelet coherence analysis reveal that Nino 3.4 and IOD are the significant drivers of Indian precipitation at interannual and interdecadal scales. Intriguingly, the study also confirms that the strength of influence of large-scale atmospheric circulation patterns on Indian precipitation extremes varies with spatial physiography of the region.},
  language  = {en}
}
@article{KurthsAgarwalShuklaetal.2019,
  author    = {Kurths, J{\"u}rgen and Agarwal, Ankit and Shukla, Roopam and Marwan, Norbert and Maheswaran, Rathinasamy and Caesar, Levke and Krishnan, Raghavan and Merz, Bruno},
  title     = {Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach},
  series = {Nonlinear processes in geophysics},
  volume    = {26},
  journal   = {Nonlinear processes in geophysics},
  number    = {3},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1023-5809},
  doi       = {10.5194/npg-26-251-2019},
  pages     = {251 -- 266},
  year      = {2019},
  abstract  = {A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Ni{\~n}o-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting.},
  language  = {en}
}
@misc{AgarwalCaesarMarwanetal.2019,
  author    = {Agarwal, Ankit and Caesar, Levke and Marwan, Norbert and Maheswaran, Rathinasamy and Merz, Bruno},
  title     = {Network-based identification and characterization of teleconnections on different scales},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {731},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43052},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-430520},
  pages     = {12},
  year      = {2019},
  abstract  = {Sea surface temperature (SST) patterns can - as surface climate forcing - affect weather and climate at large distances. One example is El Ni{\~n}o-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of - at a certain timescale - similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.},
  language  = {en}
}
@article{AgarwalCaesarMarwanetal.2019,
  author    = {Agarwal, Ankit and Caesar, Levke and Marwan, Norbert and Maheswaran, Rathinasamy and Merz, Bruno},
  title     = {Network-based identification and characterization of teleconnections on different scales},
  series = {Scientific Reports},
  volume    = {9},
  journal   = {Scientific Reports},
  publisher = {Macmillan Publishers Limited},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-019-45423-5},
  pages     = {12},
  year      = {2019},
  abstract  = {Sea surface temperature (SST) patterns can - as surface climate forcing - affect weather and climate at large distances. One example is El Ni{\~n}o-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of - at a certain timescale - similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.},
  language  = {en}
}