Articles | Volume 21, issue 6
https://doi.org/10.5194/npg-21-1127-2014
https://doi.org/10.5194/npg-21-1127-2014
Research article
 | Highlight paper
 | 
27 Nov 2014
Research article | Highlight paper |  | 27 Nov 2014

Correlations between climate network and relief data

T. K. D. Peron, C. H. Comin, D. R. Amancio, L. da F. Costa, F. A. Rodrigues, and J. Kurths

Related authors

The role of atmospheric rivers in the distribution of heavy precipitation events over North America
Sara M. Vallejo-Bernal, Frederik Wolf, Niklas Boers, Dominik Traxl, Norbert Marwan, and Jürgen Kurths
Hydrol. Earth Syst. Sci., 27, 2645–2660, https://doi.org/10.5194/hess-27-2645-2023,https://doi.org/10.5194/hess-27-2645-2023, 2023
Short summary
Exploring meteorological droughts' spatial patterns across Europe through complex network theory
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023,https://doi.org/10.5194/npg-30-167-2023, 2023
Short summary
Interacting tipping elements increase risk of climate domino effects under global warming
Nico Wunderling, Jonathan F. Donges, Jürgen Kurths, and Ricarda Winkelmann
Earth Syst. Dynam., 12, 601–619, https://doi.org/10.5194/esd-12-601-2021,https://doi.org/10.5194/esd-12-601-2021, 2021
Short summary
Recurrence analysis of extreme event-like data
Abhirup Banerjee, Bedartha Goswami, Yoshito Hirata, Deniz Eroglu, Bruno Merz, Jürgen Kurths, and Norbert Marwan
Nonlin. Processes Geophys., 28, 213–229, https://doi.org/10.5194/npg-28-213-2021,https://doi.org/10.5194/npg-28-213-2021, 2021
Influence of extreme events modeled by Lévy flight on global thermohaline circulation stability
Daniel Tesfay, Larissa Serdukova, Yayun Zheng, Pingyuan Wei, Jinqiao Duan, and Jürgen Kurths
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2020-31,https://doi.org/10.5194/npg-2020-31, 2020
Publication in NPG not foreseen
Short summary

Related subject area

Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Downscaling of surface wind forecasts using convolutional neural networks
Florian Dupuy, Pierre Durand, and Thierry Hedde
Nonlin. Processes Geophys., 30, 553–570, https://doi.org/10.5194/npg-30-553-2023,https://doi.org/10.5194/npg-30-553-2023, 2023
Short summary
Superstatistical analysis of sea surface currents in the Gulf of Trieste, measured by high-frequency radar, and its relation to wind regimes using the maximum-entropy principle
Sofia Flora, Laura Ursella, and Achim Wirth
Nonlin. Processes Geophys., 30, 515–525, https://doi.org/10.5194/npg-30-515-2023,https://doi.org/10.5194/npg-30-515-2023, 2023
Short summary
Physically constrained covariance inflation from location uncertainty
Yicun Zhen, Valentin Resseguier, and Bertrand Chapron
Nonlin. Processes Geophys., 30, 237–251, https://doi.org/10.5194/npg-30-237-2023,https://doi.org/10.5194/npg-30-237-2023, 2023
Short summary
Data-driven methods to estimate the committor function in conceptual ocean models
Valérian Jacques-Dumas, René M. van Westen, Freddy Bouchet, and Henk A. Dijkstra
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023,https://doi.org/10.5194/npg-30-195-2023, 2023
Short summary
Exploring meteorological droughts' spatial patterns across Europe through complex network theory
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023,https://doi.org/10.5194/npg-30-167-2023, 2023
Short summary

Cited articles

Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D.: Complex networks: Structure and dynamics, Phys. Rep., 424, 175–308, 2006.
Clauset, A., Newman, M. E., and Moore, C.: Finding community structure in very large networks, Phys. Rev. E, 70, 066111, https://doi.org/10.1103/PhysRevE.70.066111, 2004.
Costa, L., Rodrigues, F., Travieso, G., and Boas, P.: Characterization of complex networks: A survey of measurements, Adv. Phys., 56, 167–242, 2007.
da Fontoura Costa, L., Oliveira Jr., O., Travieso, G., Rodrigues, F., Boas, P., Antiqueira, L., Viana, M., and Rocha, L.: Analyzing and modeling real-world phenomena with complex networks: a survey of applications, Adv. Phys., 60, 329–412, 2011.
Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: The backbone of the climate network, EPL-Europhys. Lett., 87, 48007, https://doi.org/10.1209/0295-5075/87/48007, 2009a.
Download
Short summary
In the past few years, complex networks have been extensively applied to climate sciences, yielding the new field of climate networks. Here, we generalize climate network analysis by investigating the influence of altitudes in network topology. More precisely, we verified that nodes group into different communities corresponding to geographical areas with similar relief properties. This new approach may contribute to obtaining more complete climate network models.