Evolving climate network perspectives on global surface air temperature effects of ENSO and strong volcanic eruptions

Abstract

Episodically occurring internal (climatic) and external (non-climatic) disruptions of normal climate variability are known to both affect spatio-temporal patterns of global surface air temperatures (SAT) at time-scales between multiple weeks and several years. The magnitude and spatial manifestation of the corresponding effects depend strongly on the specific type of perturbation and may range from weak spatially coherent yet regionally confined trends to a global reorganization of co-variability due to the excitation or inhibition of certain large-scale teleconnectivity patterns. Here, we employ functional climate network analysis to distinguish qualitatively the global climate responses to different phases of the El Niño–Southern Oscillation (ENSO) from those to the three largest volcanic eruptions since the mid-20th century as the two most prominent types of recurrent climate disruptions. Our results confirm that strong ENSO episodes can cause a temporary breakdown of the normal hierarchical organization of the global SAT field, which is characterized by the simultaneous emergence of consistent regional temperature trends and strong teleconnections. By contrast, the most recent strong volcanic eruptions exhibited primarily regional effects rather than triggering additional long-range teleconnections that would not have been present otherwise. By relying on several complementary network characteristics, our results contribute to a better understanding of climate network properties by differentiating between climate variability reorganization mechanisms associated with internal variability versus such triggered by non-climatic abrupt and localized perturbations.

Dynamical phenomena in complex networks: fundamentals and applications

“…Among the applications to neuroscience, there are promising ones using statistical physics methods to deal with stochastic processes, from intracellular calcium spikes [17] and single neuron firing [20] to sequential decision-making behavior [18]. The contributions from the Earth science application field clearly demonstrate the predictive power of climate networks to study challenging Earth processes and phenomena [21,26,28]. Among the possible further developments, the applications of complex dynamical networks to machine learning can play an important role.…”

“…Kittel et al [28] employ functional climate network analysis to distinguish the global climate responses to different phases of the El Niño-Southern Oscillation (ENSO) from those to the three largest volcanic eruptions since the mid-twentieth century as the two most prominent types of recurrent climate disruptions.…”

“…A substantial part of this special issue is also devoted to Earth sciences. Special emphasis has been placed on functional climate networks [21,26,28]. Also, here it is shown how complex climate networks can be used as a tool to study various processes and phenomena, such as equatorial wave interactions associated with the Madden-Julian oscillation [23], dominant global covariability patterns between sea surface temperatures and precipitation [24], fires in tropical forests over space and time [25], Amazon rainfalls [27], South American low-level circulation [29], climate-induced hysteresis in pan-tropical forests [32], and others.…”

Pitfalls of Climate Network Construction: A Statistical Perspective

“…Most often unweighted density-threshold graphs are constructed (Tsonis and Roebber, 2004, Yamasaki et al, 2008, Kittel et al, 2021, which means that an edge of weight 1 is formed between two grid locations v i and v j when the corresponding similarity estimate Ŝij surpasses a certain threshold. This threshold is chosen so that a desired network density is attained.…”

Teleconnection patterns of different El Niño types revealed by climate network curvature

“…Impacts of ENSO diversity have been studied by evolving climate network analyses (Radebach et al, 2013;Kittel et al, 2021). Wiedermann et al use climate networks to find a robust way to distinguish different types of El Niños and La Niñas.…”