Temporal evolution of the spatial covariability of rainfall in South America

Ciemer, Catrin; Boers, Niklas; Barbosa, Henrique de Melo Jorge; Kurths, Jürgen; Rammig, Anja

doi:10.1007/s00382-017-3929-x

Cited by 22 publications

(26 citation statements)

References 31 publications

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“…On the other hand, precipitation at daily scales is known to su er from the fact that the distribution presents a combination of an occurrence and an intensity process, which implies many zero values, so that the use of correlation-based analysis methods can lead to severe problems that require sophisticated concepts to cope with. 31 However, at monthly scales, except for desert or semidesert regions as well as their oceanic counterparts, the relative fraction of zeros is greatly reduced in comparison with daily data so that the distributional features become markedly less problematic at this coarser temporal resolution. Taken together, both methodological ingredients of this work-wavelet decomposition and correlation analysis-can be considered relatively weakly a ected by the distributional features of the time series under study.…”

Section: B Analysis Strategymentioning

confidence: 99%

Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach

Ekhtiari

Agarwal

Marwan

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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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 di erent temporal scales. To address this problem, we here propose to extend the application of complex network techniques to capture the interdependence between global elds of sea-surface temperature (SST) and precipitation (P) at multiple temporal scales. For this purpose, we combine timescale 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-speci c 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 timescales. Most notably, the strongest mutual correlations between SST and P at annual scale (8-16 months) concentrate mainly over the Paci c Ocean, while the corresponding spatial patterns progressively disappear when moving toward longer timescales .

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Section: B Analysis Strategymentioning

confidence: 99%

Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach

Ekhtiari

Agarwal

Marwan

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

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“…From a climatic point of view, it is well known that monthly amounts provide valuable information regarding the precipitation of a given territory. Traditionally, the focus has been placed on means and the extreme rainfall of concrete regions around the world at global (Berghuijs et al, ) and regional scales: the Mediterranean (Caloiero et al, ; Lazoglou et al, ; Zittis, ), the rest of Europe (Panagos et al, ; Beranová and Kyselý, ; Brown, ), Africa (Abahous et al, ; Gummadi et al, ), Asia (Jiang et al, ; Kim et al, ; Ullah et al, ; Zhao et al, ), and America (Bernardino et al, ; Ciemer et al, ; Valdés‐Pineda et al, ).…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution and trends of different precipitation variability indices based on daily data in Northern Chile between 1966 and 2015

Meseguer-Ruiz

Ponce-Philimon

Guijarro

et al. 2019

Intl Journal of Climatology

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Northern Chile is one of the most arid regions in the world, as it includes the Atacama Desert. At high elevations, most precipitation is observed only during a short period of the year, from December until March. This renders water availability a major concern for policymakers. Accumulated rainfall varies considerably from one year to another, and for this reason, climate projections have a very low degree of confidence in this area. Consequently, in this region, it is more interesting to study the irregularity of precipitation itself than accumulated rainfall values, as they express in a clearer way the behaviour of precipitation. According to daily data from 161 meteorological stations, four irregularity indices of precipitation were calculated: concentration index, entropy, persistence index, and fractal dimension. These indices were measured according to observed values, and their spatial distribution was subsequently determined by interpolating following multivariate regression models that consider different geographical variables such as latitude, distance to the Amazon Basin, elevation, orientation, and curvature. The temporal trends of each index and for each meteorological station were also calculated, displaying different results depending on the latitude and elevation. These changes agree with the observed modifications on the inter‐tropical atmospheric circulation and with changes in the precipitation diurnal cycle. These results will help improve climate projections for this region, in the process facilitating the development of more accurate climate models and informing the formulation of water management policies.

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“…We emphasize, however, that our analysis has focused on specific events, while previous work on Baiu dynamics and related phenomena has almost exclusively utilized standard (linear) analysis techniques based on correlations or composites commonly applied to other atmospheric variables (among other reasons, a statistically rigorous evaluation of correlations for daily precipitation data is complicated by the commonly large number of zeros, cf. Ciemer et al (2018)).…”

Section: Climatological Controls On Baiu Dynamicsmentioning

confidence: 99%

Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season

Wolf¹,

Öztürk²,

Cheung³

et al. 2020

Preprint

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Abstract. Investigating the synchrony and interdependency of heavy rainfall occurrences is crucial to understand the underlying physical mechanisms and reduce physical and economic damages by improved forecasting strategies. In this context, studies utilizing functional network representations have recently contributed to significant advances in the understanding and prediction of extreme weather events. To thoroughly expand on previous works employing the latter framework to the East Asian Summer Monsoon (EASM) system, we focus here on changes in the spatial organization of synchronous heavy precipitation events across the monsoon season (April to August) by studying the temporal evolution of corresponding network characteristics in terms of a sliding window approach. Specifically, we utilize functional climate networks together with event coincidence analysis for identifying and characterizing synchronous activity from daily rainfall estimates between 1998 and 2018. Our results demonstrate that the formation of the Baiu front as a main feature of the EASM is reflected by a double-band structure of synchronous heavy rainfall with two centers north and south of the front. Although the two separated bands are strongly related to either low- or high-level winds which are commonly assumed to be independent, we provide evidence that it is rather their mutual interconnectivity that changes during the different phases of the EASM season in a characteristic way. Our findings shed some new light on the interplay between tropical and extratropical factors controlling the EASM intraseasonal evolution, which could potentially help to improve future forecasts of the Baiu onset in different regions of East Asia.

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Temporal evolution of the spatial covariability of rainfall in South America

Cited by 22 publications

References 31 publications

Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach

Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach

Spatial distribution and trends of different precipitation variability indices based on daily data in Northern Chile between 1966 and 2015

Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season

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