On the influence of spatial sampling on climate networks

Molkenthin, Nora; Rehfeld, Kira; Stolbova, Veronika; Tupikina, Liubov; Kurths, Jürgen

doi:10.5194/npg-21-651-2014

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…systems often needs to be taken into account as well. [17][18][19][20] The latter aspect relates to an entirely different class of spatial network characteristics. 8 At the vertex level, the spatial heterogeneity of vertex positions can be quantified by their local density.…”

Section: Introductionmentioning

confidence: 99%

Edge anisotropy and the geometric perspective on flow networks

Molkenthin

Kutza

Tupikina

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Spatial networks have recently attracted great interest in various fields of research. While the traditional network-theoretic viewpoint is commonly restricted to their topological characteristics (often disregarding the existing spatial constraints), this work takes a geometric perspective, which considers vertices and edges as objects in a metric space and quantifies the corresponding spatial distribution and alignment. For this purpose, we introduce the concept of edge anisotropy and define a class of measures characterizing the spatial directedness of connections. Specifically, we demonstrate that the local anisotropy of edges incident to a given vertex provides useful information about the local geometry of geophysical flows based on networks constructed from spatiotemporal data, which is complementary to topological characteristics of the same flow networks. Taken both structural and geometric viewpoints together can thus assist the identification of underlying flow structures from observations of scalar variables. Published by AIP Publishing

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Section: Introductionmentioning

confidence: 99%

Edge anisotropy and the geometric perspective on flow networks

Molkenthin

Kutza

Tupikina

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…Specifically, spatial sampling has an effect on network measures. This important issue is discussed in Molkenthin et al (2014), where the effect of spatial sampling on the network measures is illustrated on the ISM climate network derived from NCEP/NCAR daily temperature anomalies using the method discussed in Sect. 2.2.2.…”

Section: Thresholdingmentioning

confidence: 99%

Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

Stolbova

Martin

Bookhagen

et al. 2014

Nonlin. Processes Geophys.

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109

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Abstract. This paper employs a complex network approach to determine the topology and evolution of the network of extreme precipitation that governs the organization of extreme rainfall before, during, and after the Indian Summer Monsoon (ISM) season. We construct networks of extreme rainfall events during the ISM (June-September), post-monsoon (October-December), and pre-monsoon (March-May) periods from satellite-derived (Tropical Rainfall Measurement Mission, TRMM) and rain-gauge interpolated (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE) data sets. The structure of the networks is determined by the level of synchronization of extreme rainfall events between different grid cells throughout the Indian subcontinent. Through the analysis of various complex-network metrics, we describe typical repetitive patterns in North Pakistan (NP), the Eastern Ghats (EG), and the Tibetan Plateau (TP). These patterns appear during the pre-monsoon season, evolve during the ISM, and disappear during the post-monsoon season. These are important meteorological features that need further attention and that may be useful in ISM timing and strength prediction.

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“…The sites are still included in the analysis due to negligible bias on a hemispheric or Arctic scale. In order to measure the degree of inhomogeneous sampling and to identify the main geographical gaps with focus on the Arctic, we performed a numerical quantification of the distribution of boreholes and active layer grids in the Northern Hemisphere with the help of a Voronoi tessellation analysis (VTA) as suggested by Molkenthin et al (2014). This analysis demonstrates the potential of the GTN-P Database for assessing the monitoring of permafrost on a hemispheric scale.…”

Section: Tsp Borehole Depth Distributionmentioning

confidence: 99%

The new database of the Global Terrestrial Network for Permafrost (GTN-P)

Biskaborn

Lanckman

Lantuit

et al. 2015

Earth Syst. Sci. Data

124

112

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Abstract. The Global Terrestrial Network for Permafrost (GTN-P) provides the first dynamic database associated with the Thermal State of Permafrost (TSP) and the Circumpolar Active Layer Monitoring (CALM) programs, which extensively collect permafrost temperature and active layer thickness (ALT) data from Arctic, Antarctic and mountain permafrost regions. The purpose of GTN-P is to establish an early warning system for the consequences of climate change in permafrost regions and to provide standardized thermal permafrost data to global models. In this paper we introduce the GTN-P database and perform statistical analysis of the GTN-P metadata to identify and quantify the spatial gaps in the site distribution in relation to climate-effective environmental parameters. We describe the concept and structure of the data management system in regard to user operability, data transfer and data policy. We outline data sources and data processing including quality control strategies based on national correspondents. Assessment of the metadata and data quality reveals 63 % metadata completeness at active layer sites and 50 % metadata completeness for boreholes.Voronoi tessellation analysis on the spatial sample distribution of boreholes and active layer measurement sites quantifies the distribution inhomogeneity and provides a potential method to locate additional permafrost research sites by improving the representativeness of thermal monitoring across areas underlain by permafrost. The depth distribution of the boreholes reveals that 73 % are shallower than 25 m and 27 % are deeper, reaching a maximum of 1 km depth. Comparison of the GTN-P site distribution with permafrost zones, soil organic carbon contents and vegetation types exhibits different local to regional monitoring situations, which are illustrated with maps. Preferential slope orientation at the sites most likely causes a bias in the temperature monitoring and should be taken into account when using the data for global models. The distribution of GTN-P sites within zones of projected temperature change show a high representation of areas with smaller expected temperature rise but a lower number of sites within Arctic areas where climate models project extreme temperature increase.GTN-P metadata used in this paper are available at

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On the influence of spatial sampling on climate networks

Cited by 10 publications

References 20 publications

Edge anisotropy and the geometric perspective on flow networks

Edge anisotropy and the geometric perspective on flow networks

Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

The new database of the Global Terrestrial Network for Permafrost (GTN-P)

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