49131
2019
2019
eng
12
6
29
article
American Institute of Physics
Melville
1
--
--
--
Disentangling the multi-scale effects of sea-surface temperatures on global precipitation
The oceans and atmosphere interact via a multiplicity of feedback mechanisms, shaping to a large extent the global climate and its variability. To deepen our knowledge of the global climate system, characterizing and investigating this interdependence is an important task of contemporary research. However, our present understanding of the underlying large-scale processes is greatly limited due to the manifold interactions between essential climatic variables at different temporal scales. To address this problem, we here propose to extend the application of complex network techniques to capture the interdependence between global fields of sea-surface temperature (SST) and precipitation (P) at multiple temporal scales. For this purpose, we combine time-scale decomposition by means of a discrete wavelet transform with the concept of coupled climate network analysis. Our results demonstrate the potential of the proposed approach to unravel the scale-specific interdependences between atmosphere and ocean and, thus, shed light on the emerging multiscale processes inherent to the climate system, which traditionally remain undiscovered when investigating the system only at the native resolution of existing climate data sets. Moreover, we show how the relevant spatial interdependence structures between SST and P evolve across time-scales. Most notably, the strongest mutual correlations between SST and P at annual scale (8-16 months) concentrate mainly over the Pacific Ocean, while the corresponding spatial patterns progressively disappear when moving toward longer time-scales. Published under license by AIP Publishing.
Chaos : an interdisciplinary journal of nonlinear science
a coupled networks approach
10.1063/1.5095565
31266324
1054-1500
1089-7682
wos:2019
063116
WOS:000475984700023
Ekhtiari, N (reprint author), Potsdam Inst Climate Impact Res, Telegrafenberg A31, D-14473 Potsdam, Germany.; Ekhtiari, N (reprint author), Humboldt Univ, Dept Phys, Newtonstr 15, D-12489 Berlin, Germany., ekhtiari@pik-potsdam.de; reik.donner@pik-potsdam.de
German Research Foundation (DFG) via the International Research Training Group IRTG 1740German Research Foundation (DFG); German Research Foundation (DFG) via the Research Training Group GRK 2043/1German Research Foundation (DFG); German Federal Ministry for Education and Research (BMBF) via the BMBF Young Investigators Group CoSy-CC2: Complex Systems Approaches to Understanding Causes and Consequences of Past, Present and Future Climate ChangeFederal Ministry of Education & Research (BMBF) [01LN1306A]; Belmont Forum/JPI Climate project GOTHAM [01LP16MA]; German Academic Exchange Service (DAAD)Deutscher Akademischer Austausch Dienst (DAAD); Academy of Sciences of the Czech Republic under the DAAD Project [57154685]
2021-01-27T15:24:44+00:00
sword
importub
filename=package.tar
8b5b14e282e48dc73078190c537bc9b4
false
true
Nikoo Ekhtiari
Ankit Agarwal
Norbert Marwan
Reik Volker Donner
Physik
Institut für Physik und Astronomie
Referiert
Import
51162
2018
2018
eng
12
11
91
article
Springer
New York
1
--
2018-11-26
--
Wavelet-based multiscale similarity measure for complex networks
In recent years, complex network analysis facilitated the identification of universal and unexpected patterns in complex climate systems. However, the analysis and representation of a multiscale complex relationship that exists in the global climate system are limited. A logical first step in addressing this issue is to construct multiple networks over different timescales. Therefore, we propose to apply the wavelet multiscale correlation (WMC) similarity measure, which is a combination of two state-of-the-art methods, viz. wavelet and Pearson’s correlation, for investigating multiscale processes through complex networks. Firstly we decompose the data over different timescales using the wavelet approach and subsequently construct a corresponding network by Pearson’s correlation. The proposed approach is illustrated and tested on two synthetics and one real-world example. The first synthetic case study shows the efficacy of the proposed approach to unravel scale-specific connections, which are often undiscovered at a single scale. The second synthetic case study illustrates that by dividing and constructing a separate network for each time window we can detect significant changes in the signal structure. The real-world example investigates the behavior of the global sea surface temperature (SST) network at different timescales. Intriguingly, we notice that spatial dependent structure in SST evolves temporally. Overall, the proposed measure has an immense potential to provide essential insights on understanding and extending complex multivariate process studies at multiple scales.
The European physical journal : B, Condensed matter and complex systems
10.1140/epjb/e2018-90460-6
1434-6028
1434-6036
wos:2018
296
WOS:000451065900003
Agarwal, A (reprint author), Leibniz Assoc, Potsdam Inst Climate Impact Res PIK, Potsdam, Germany.; Agarwal, A (reprint author), Univ Potsdam, Inst Earth & Environm Sci, Potsdam, Germany.; Agarwal, A (reprint author), GFZ German Res Ctr Geosci, Sect Hydrol 5 4, Potsdam, Germany., agarwal@pik-potsdam.de
Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG) [GRK 2043/1]; Humboldt FoundationAlexander von Humboldt Foundation; Alexander Von Humboldt Fellowship award; DST, IndiaDepartment of Science & Technology (India)
2021-06-29T10:13:21+00:00
sword
importub
filename=package.tar
339a1f03d8e0164b21e1466c99e27fe8
Agarwal, Ankit
false
true
Ankit Agarwal
Rathinasamy Maheswaran
Norbert Marwan
Levke Caesar
Jürgen Kurths
eng
uncontrolled
Statistical and Nonlinear Physics
Physik
Institut für Physik und Astronomie
Referiert
Import
47850
2019
2019
eng
2053
2069
17
11-12
33
article
Springer
New York
1
2019-11-09
--
--
Wavelet analysis of precipitation extremes over India and teleconnections to climate indices
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.
Stochastic Environmental Research and Risk Assessment
10.1007/s00477-019-01738-3
1436-3240
1436-3259
wos:2019
WOS:000495209400001
Sivakumar, B (reprint author), Indian Inst Technol, Dept Civil Engn, Mumbai 400076, Maharashtra, India., ankitfhy@iitr.ac.in
Inspire Faculty Award, Department of Science and Technology, India [IFA-12-ENG/28]; Science and Engineering Research Board (SERB), India [ECRA/16/1721]; Deutsche Forschungsgemeinschaft (DFG) within the graduate research training group Natural risk in a changing world (NatRiskChange) at the University of PotsdamGerman Research Foundation (DFG) [GRK 2043/1]
importub
2020-10-06T12:36:44+00:00
filename=package.tar
a69f09c74cd4b61d4b216672d799fe64
false
true
Rathinasamy Maheswaran
Ankit Agarwal
Bellie Sivakumar
Norbert Marwan
Jürgen Kurths
eng
uncontrolled
Extreme precipitation
eng
uncontrolled
Teleconnection patterns
eng
uncontrolled
Wavelets
eng
uncontrolled
Partial wavelet coherence
eng
uncontrolled
India
Physik
Institut für Physik und Astronomie
Referiert
Import
47099
2020
2020
eng
2235
2251
17
5
24
article
Copernicus Publ.
Göttingen
1
2020-05-08
2020-05-08
--
Optimal design of hydrometric station networks based on complex network analysis
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Hydrology and Earth System Sciences
10.5194/hess-24-2235-2020
1027-5606
1607-7938
Universität Potsdam
PA 2020_045
1881.39
<a href="https://doi.org/10.25932/publishup-47100">Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 951</a>
false
false
CC-BY - Namensnennung 4.0 International
Ankit Agarwal
Norbert Marwan
Rathinasamy Maheswaran
Ugur Öztürk
Jürgen Kurths
Bruno Merz
eng
uncontrolled
identifying influential nodes
eng
uncontrolled
climate networks
eng
uncontrolled
rainfall
eng
uncontrolled
streamflow
eng
uncontrolled
synchronization
eng
uncontrolled
precipitation
eng
uncontrolled
classification
eng
uncontrolled
events
Geowissenschaften
open_access
Institut für Geowissenschaften
Referiert
Publikationsfonds der Universität Potsdam
Open Access
Institut für Erd- und Umweltwissenschaften
46265
2017
2017
eng
599
611
13
24
article
Copernicus
Göttingen
1
--
--
--
Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach
The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
Nonlinear processes in geophysics
10.5194/npg-24-599-2017
1023-5809
wos:2017
WOS:000412939000001
Agarwal, A (reprint author), Univ Potsdam, Inst Earth & Environm Sci, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany.; Agarwal, A (reprint author), Potsdam Inst Climate Impact Res, POB 60 12 03, D-14412 Potsdam, Germany.; Agarwal, A (reprint author), GFZ German Res Ctr Geosci, Sect Hydrol 5 4, Potsdam, Germany., aagarwal@uni-potsdam.de
Deutsche Forschungsgemeinschaft (DFG) within graduate research training group Natural risk in a changing world (NatRiskChange) at the University of Potsdam [GRK 2043/1]; RSF support (Russian Science Foundation) [16-12-10198]; Inspire Faculty Award, Department of Science and Technology, India
importub
2020-04-20T00:02:02+00:00
filename=package.tar
05d2debf906471dec759cee3a66e60f5
Ankit Agarwal
Norbert Marwan
Rathinasamy Maheswaran
Bruno Merz
Jürgen Kurths
Institut für Geowissenschaften
Referiert
Institut für Erd- und Umweltwissenschaften
Import
52318
2018
2018
eng
802
810
9
563
article
Elsevier
Amsterdam
1
2018-06-23
--
--
Quantifying the roles of single stations within homogeneous regions using complex network analysis
Regionalization and pooling stations to form homogeneous regions or communities are essential for reliable parameter transfer, prediction in ungauged basins, and estimation of missing information. Over the years, several clustering methods have been proposed for regional analysis. Most of these methods are able to quantify the study region in terms of homogeneity but fail to provide microscopic information about the interaction between communities, as well as about each station within the communities. We propose a complex network-based approach to extract this valuable information and demonstrate the potential of our approach using a rainfall network constructed from the Indian gridded daily precipitation data. The communities were identified using the network-theoretical community detection algorithm for maximizing the modularity. Further, the grid points (nodes) were classified into universal roles according to their pattern of within- and between-community connections. The method thus yields zoomed-in details of individual rainfall grids within each community.
Journal of hydrology
10.1016/j.jhydrol.2018.06.050
0022-1694
1879-2707
wos:2018
WOS:000441492700064
Agarwal, A (reprint author), Univ Potsdam, Inst Earth & Environm Sci, Potsdam, Germany., aagarwal@uni-potsdam.de
Deutsche Forschungsgemeinschaft (DFG) within the graduate research training group "Natural risk in a changing world (NatRiskChange)" at the University of PotsdamGerman Research Foundation (DFG) [GRK 2043/1]; Humboldt FoundationAlexander von Humboldt Foundation
2021-10-20T09:05:02+00:00
sword
importub
filename=package.tar
c023fcc4e0597c334a559d37a9682f9e
Agarwal, Ankit
false
true
Ankit Agarwal
Norbert Marwan
Rathinasamy Maheswaran
Bruno Merz
Jürgen Kurths
eng
uncontrolled
Complex network
eng
uncontrolled
Event synchronization
eng
uncontrolled
Rainfall network
eng
uncontrolled
Z-P approach
Geowissenschaften
Institut für Geowissenschaften
Referiert
Import
Green Open-Access
48477
2019
2019
eng
251
266
16
3
26
article
Copernicus
Göttingen
1
--
2019-08-15
--
Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach
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 Niño–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.
Nonlinear processes in geophysics
10.5194/npg-26-251-2019
1023-5809
1607-7946
wos:2019
WOS:000481731600001
Agarwal, A (reprint author), Leibniz Assoc, Potsdam Inst Climate Impact Res PIK, Potsdam, Germany.; Agarwal, A (reprint author), Univ Potsdam, Inst Earth & Environm Sci, Potsdam, Germany.; Agarwal, A (reprint author), GFZ German Res Ctr Geosci, Sect 5 4, Potsdam, Germany., agarwal.10891.ankit@gmail.com
Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [2043/1]
2020-11-30T18:20:34+00:00
sword
importub
filename=package.tar
4cf957729871a95b9e11b4db200526e2
Agarwal, Ankit
false
true
Jürgen Kurths
Ankit Agarwal
Roopam Shukla
Norbert Marwan
Rathinasamy Maheswaran
Levke Caesar
Raghavan Krishnan
Bruno Merz
Physik
Institut für Physik und Astronomie
Referiert
Import
Gold Open-Access
DOAJ gelistet
43052
2019
2019
eng
12
731
postprint
1
2019-06-21
2019-06-21
--
Network-based identification and characterization of teleconnections on different scales
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-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.
Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
10.25932/publishup-43052
urn:nbn:de:kobv:517-opus4-430520
1866-8372
8808
Scientific Reports 9 (2019) Art. 8808 DOI: 10.1038/s41598-019-45423-5
<a href="http://publishup.uni-potsdam.de/opus4-ubp/frontdoor/index/index/docId/43051">Bibliographieeintrag der Originalveröffentlichung/Quelle</a>
false
true
CC-BY - Namensnennung 4.0 International
Ankit Agarwal
Levke Caesar
Norbert Marwan
Rathinasamy Maheswaran
Bruno Merz
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
731
Naturwissenschaften und Mathematik
Technik, Technologie
open_access
Institut für Geowissenschaften
Referiert
Open Access
Institut für Erd- und Umweltwissenschaften
Universität Potsdam
https://publishup.uni-potsdam.de/files/43052/pmnr731.pdf
43051
2019
2019
eng
12
9
article
Macmillan Publishers Limited
London
1
2019-06-19
2019-06-19
--
Network-based identification and characterization of teleconnections on different scales
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-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.
Scientific Reports
10.1038/s41598-019-45423-5
2045-2322
8808
Universität Potsdam
PA 2019_51
1773.10
<a href="http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-430520">Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 731</a>
CC-BY - Namensnennung 4.0 International
Ankit Agarwal
Levke Caesar
Norbert Marwan
Rathinasamy Maheswaran
Bruno Merz
Naturwissenschaften und Mathematik
Technik, Technologie
open_access
Institut für Geowissenschaften
Referiert
Publikationsfonds der Universität Potsdam
Open Access
Institut für Erd- und Umweltwissenschaften
47100
2020
2020
eng
19
951
postprint
1
2020-06-10
2020-06-10
--
Optimal design of hydrometric station networks based on complex network analysis
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
10.25932/publishup-47100
urn:nbn:de:kobv:517-opus4-471006
1866-8372
Hydrology and Earth System Sciences 24 (2020) 5, 2235–2251 DOI: 10.5194/hess-24-2235-2020
<a href="http://publishup.uni-potsdam.de/47099">Bibliographieeintrag der Originalveröffentlichung/Quelle</a>
false
false
CC-BY - Namensnennung 4.0 International
Ankit Agarwal
Norbert Marwan
Rathinasamy Maheswaran
Ugur Öztürk
Jürgen Kurths
Bruno Merz
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
951
eng
uncontrolled
identifying influential nodes
eng
uncontrolled
climate networks
eng
uncontrolled
rainfall
eng
uncontrolled
streamflow
eng
uncontrolled
synchronization
eng
uncontrolled
precipitation
eng
uncontrolled
classification
eng
uncontrolled
events
Geowissenschaften
open_access
Institut für Geowissenschaften
Referiert
Open Access
Institut für Erd- und Umweltwissenschaften
Universität Potsdam
https://publishup.uni-potsdam.de/files/47100/pmnr951.pdf