Unraveling the spatial diversity of Indian precipitation teleconnections via nonlinear multi-scale approach

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 nonlinear, multiscale approach, based on wavelets and event synchronization, for unraveling teleconnection influences on precipitation. We consider those climate patterns with 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 time scales. We find substantial variation across India and across time scales. In particular, El Niño/Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the southeast 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 PDO stretches across the whole country, whereas 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 improving precipitation forecasting.

[1]  Ankit Agarwal,et al.  Optimal design of hydrometric station networks based on complex network analysis , 2020, Hydrology and Earth System Sciences.

[2]  Norbert Marwan,et al.  Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach. , 2019, Chaos.

[3]  J. Kurths,et al.  Network-based identification and characterization of teleconnections on different scales , 2019, Scientific Reports.

[4]  Jürgen Kurths,et al.  Complex networks reveal global pattern of extreme-rainfall teleconnections , 2019, Nature.

[5]  Norbert Marwan,et al.  A network-based comparative study of extreme tropical and frontal storm rainfall over Japan , 2019, Climate Dynamics.

[6]  J. Kurths,et al.  Climate change perception: an analysis of climate change and risk perceptions among farmer types of Indian Western Himalayas , 2018, Climatic Change.

[7]  Jürgen Kurths,et al.  Wavelet-based multiscale similarity measure for complex networks , 2018, The European Physical Journal B.

[8]  Norbert Marwan,et al.  Unfolding Community Structure in Rainfall Network of Germany Using Complex Network-Based Approach , 2018, Water Resources and Environmental Engineering II.

[9]  Bruno Merz,et al.  Quantifying the roles of single stations within homogeneous regions using complex network analysis , 2018, Journal of Hydrology.

[10]  N Marwan,et al.  Complex networks for tracking extreme rainfall during typhoons. , 2018, Chaos.

[11]  Bruno Merz,et al.  An event synchronization method to link heavy rainfall events and large‐scale atmospheric circulation features , 2018 .

[12]  Mansour Almazroui,et al.  ENSO relationship to summer rainfall variability and its potential predictability over Arabian Peninsula region , 2017, npj Climate and Atmospheric Science.

[13]  A. Agarwal Unraveling spatio-temporal climatic patterns via multi-scale complex networks , 2018 .

[14]  P. Guhathakurta,et al.  Trends and variability of meteorological drought over the districts of India using standardized precipitation index , 2017, Journal of Earth System Science.

[15]  M. Ting,et al.  A Dipole Pattern of Summertime Rainfall across the Indian Subcontinent and the Tibetan Plateau , 2017 .

[16]  Bruno Merz,et al.  Multi-scale event synchronization analysis for unravelling climate processes , 2017 .

[17]  Jurgen Kurths,et al.  Rewiring hierarchical scale-free networks: Influence on synchronizability and topology , 2017, 1707.04057.

[18]  J. Adamowski,et al.  Association between three prominent climatic teleconnections and precipitation in Iran using wavelet coherence , 2017 .

[19]  R. Khosa,et al.  Partial wavelet coherence analysis for understanding the standalone relationship between Indian Precipitation and Teleconnection patterns , 2017, 1702.06568.

[20]  A. P. Dimri,et al.  Effect of changing tropical easterly jet, low level jet and quasi‐biennial oscillation phases on Indian summer monsoon , 2017 .

[21]  Dongguo Shao,et al.  Wavelet analysis of precipitation extremes over Canadian ecoregions and teleconnections to large‐scale climate anomalies , 2016 .

[22]  R. Bhatla,et al.  Influence of North Atlantic Oscillation on Indian Summer Monsoon Rainfall in Relation to Quasi-Binneal Oscillation , 2016, Pure and Applied Geophysics.

[23]  Yang Wang,et al.  ClimateLearn: A machine-learning approach for climate prediction using network measures , 2016 .

[24]  Xiao Dong Influences of the Pacific Decadal Oscillation on the East Asian Summer Monsoon in non‐ENSO years , 2016 .

[25]  Dennis D. Baldocchi,et al.  Identifying scale‐emergent, nonlinear, asynchronous processes of wetland methane exchange , 2016 .

[26]  B. Si,et al.  Technical note : Multiple wavelet coherence for untangling scale-specific and localized multivariate relationships in geosciences , 2016 .

[27]  L. Krishnamurthy,et al.  Teleconnections of Indian monsoon rainfall with AMO and Atlantic tripole , 2016, Climate Dynamics.

[28]  Norbert Marwan,et al.  Non-linear time series analysis of precipitation events using regional climate networks for Germany , 2015, Climate Dynamics.

[29]  V. V. Srinivas,et al.  Delineation of homogeneous hydrometeorological regions using wavelet‐based global fuzzy cluster analysis , 2015 .

[30]  Vijay P. Singh,et al.  Catchment classification framework in hydrology: challenges and directions , 2015 .

[31]  M. Rajeevan,et al.  Analysis of the daily rainfall events over India using a new long period (1901–2010) high resolution (0.25° × 0.25°) gridded rainfall data set , 2015, Climate Dynamics.

[32]  S. W. Fleming,et al.  Complex network theory, streamflow, and hydrometric monitoring system design , 2014 .

[33]  Jan Adamowski,et al.  Wavelet‐based multiscale performance analysis: An approach to assess and improve hydrological models , 2014 .

[34]  Steve Harenberg,et al.  Community detection in large‐scale networks: a survey and empirical evaluation , 2014 .

[35]  Norbert Marwan,et al.  The South American rainfall dipole: A complex network analysis of extreme events , 2014 .

[36]  Jürgen Kurths,et al.  Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka , 2014 .

[37]  Diego G. Miralles,et al.  Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation , 2014 .

[38]  O. P. Sreejith,et al.  Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region , 2014 .

[39]  A. Timmermann,et al.  Inferred changes in El Niño–Southern Oscillation variance over the past six centuries , 2013 .

[40]  Jürgen Kurths,et al.  Complex networks identify spatial patterns of extreme rainfall events of the South American Monsoon System , 2013 .

[41]  B. Goswami,et al.  Opportunities and challenges in monsoon prediction in a changing climate , 2013, Climate Dynamics.

[42]  T. Ouarda,et al.  Power of teleconnection patterns on precipitation and streamflow variability of upper Medjerda Basin , 2013 .

[43]  J. Wallace,et al.  A prominent pattern of year-to-year variability in Indian Summer Monsoon Rainfall , 2012, Proceedings of the National Academy of Sciences.

[44]  R. Khosa,et al.  Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach , 2012 .

[45]  J. Kurths,et al.  Relationship between El-Niño/Southern Oscillation and the Indian monsoon , 2012, Izvestiya, Atmospheric and Oceanic Physics.

[46]  Jürgen Kurths,et al.  Analysis of spatial and temporal extreme monsoonal rainfall over South Asia using complex networks , 2012, Climate Dynamics.

[47]  Nitesh V. Chawla,et al.  Multivariate and multiscale dependence in the global climate system revealed through complex networks , 2012, Climate Dynamics.

[48]  S. Bansod Interannual variability of convective activity over the tropical Indian Ocean during the El Niño/La Niña events , 2011 .

[49]  L. D. Costa,et al.  Community structure and dynamics in climate networks , 2011 .

[50]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[51]  Amilcare Porporato,et al.  Causality across rainfall time scales revealed by continuous wavelet transforms , 2010 .

[52]  Matthieu Lengaigne,et al.  Influence of the state of the Indian Ocean Dipole on the following year’s El Niño , 2010 .

[53]  Y. Masumoto,et al.  Interaction between El Niño and extreme Indian Ocean dipole. , 2010 .

[54]  K. Mohankumar,et al.  Individual and combined influence of El Niño–Southern Oscillation and Indian Ocean Dipole on the Tropospheric Biennial Oscillation , 2010 .

[55]  Rik Sarkar,et al.  Community Detection , 2014, Encyclopedia of Machine Learning and Data Mining.

[56]  P. Swapna,et al.  Significant Influence of the Boreal Summer Monsoon Flow on the Indian Ocean Response during Dipole Events , 2009 .

[57]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[58]  Potsdam,et al.  Complex networks in climate dynamics. Comparing linear and nonlinear network construction methods , 2009, 0907.4359.

[59]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[60]  D. Peters,et al.  Do Changes in Connectivity Explain Desertification? , 2009 .

[61]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[62]  D. Percival Analysis of Geophysical Time Series Using Discrete Wavelet Transforms: An Overview , 2008 .

[63]  Ian T. Jolliffe,et al.  Empirical orthogonal functions and related techniques in atmospheric science: A review , 2007 .

[64]  Monica G. Turner,et al.  Cross–Scale Interactions and Changing Pattern–Process Relationships: Consequences for System Dynamics , 2007, Ecosystems.

[65]  M. Rajeevan,et al.  On the El Niño‐Indian monsoon predictive relationships , 2007 .

[66]  M. Hoerling,et al.  Unraveling the Mystery of Indian Monsoon Failure During El Niño , 2006, Science.

[67]  T. Delworth,et al.  Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes , 2006 .

[68]  Paul J. Roebber,et al.  What Do Networks Have to Do with Climate , 2006 .

[69]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[70]  Debasis Sengupta,et al.  A physical mechanism for North Atlantic SST influence on the Indian summer monsoon , 2006 .

[71]  Roger Guimerà,et al.  Cartography of complex networks: modules and universal roles , 2005, Journal of statistical mechanics.

[72]  E. Cook,et al.  On the variability of ENSO over the past six centuries , 2005 .

[73]  T. Delworth,et al.  Simulated Tropical Response to a Substantial Weakening of the Atlantic Thermohaline Circulation , 2005 .

[74]  Aslak Grinsted,et al.  Nonlinear Processes in Geophysics Application of the Cross Wavelet Transform and Wavelet Coherence to Geophysical Time Series , 2022 .

[75]  Brandon T Bestelmeyer,et al.  Cross-scale interactions, nonlinearities, and forecasting catastrophic events. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[76]  H. Wanner,et al.  Wet season Mediterranean precipitation variability: influence of large-scale dynamics and trends , 2004 .

[77]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[78]  Paul J. Roebber,et al.  The architecture of the climate network , 2004 .

[79]  Masato Sugi,et al.  Pacific decadal oscillation and variability of the Indian summer monsoon rainfall , 2003 .

[80]  Q. Hu,et al.  Interannual Rainfall Variations in the North American Summer Monsoon Region: 1900-98* , 2002 .

[81]  R Quian Quiroga,et al.  Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[82]  R. Quiroga,et al.  Reply to ``Comment on `Performance of different synchronization measures in real data: A case study on electroencephalographic signals' '' , 2001, nlin/0109023.

[83]  T. Yamagata,et al.  Impact of the Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO , 2001 .

[84]  Michael R. Chernick,et al.  Wavelet Methods for Time Series Analysis , 2001, Technometrics.

[85]  S. Dugam,et al.  The simultaneous effect of NAO and SO on the monsoon activity over India , 2000 .

[86]  T. Yamagata,et al.  Unusual ocean‐atmosphere conditions in the tropical Indian Ocean during 1994 , 1999 .

[87]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .