ALBERT

Description: The climate of South America exhibits pronounced differences between rainy and dry seasons, associated with specific synoptic features such as the establishment of the South Atlantic convergence zone. Here, we analyze the spatiotemporal correlation structure and in particular teleconnections of daily rainfall associated with these features by means of evolving complex networks. A modification of Pearson’s correlation coefficient is introduced to handle the intricate statistical properties of daily rainfall. On this basis, spatial correlation networks are constructed, and new appropriate network measures are introduced in order to analyze the temporal evolution of the networks’ characteristics. We particularly focus on the identification of coherent areas of similar rainfall patterns and previously unknown teleconnection structures between remote areas. We show that the monsoon onset is characterized by an abrupt transition from erratic to organized regional connectivity that prevails during the monsoon season, while only the onset times themselves exhibit anomalous large-scale organization of teleconnections. Furthermore, we reveal that the two mega-droughts in the Amazon basin were already announced in the previous year by an anomalous behavior of the connectivity structure.

Description: Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling.

Description: The Atlantic Meridional Overturning Circulation (AMOC) and the Amazon rainforest are potential tipping elements of the Earth system, i.e., they may respond with abrupt and potentially irreversible state transitions to a gradual change in forcing once a critical forcing threshold is crossed. With progressing global warming, it becomes more likely that the Amazon will reach such a critical threshold, due to projected reductions of precipitation in tropical South America, which would in turn trigger vegetation transitions from tropical forest to savanna. At the same time, global warming has likely already contributed to a weakening of the AMOC, which induces changes in tropical Atlantic sea-surface temperature (SST) patterns that in turn affect rainfall patterns in the Amazon. A large-scale decline or even dieback of the Amazon rainforest would imply the loss of the largest terrestrial carbon sink, and thereby have drastic consequences for the global climate. Here, we assess the direct impact of greenhouse gas-driven warming of the tropical Atlantic ocean on Amazon rainfall. In addition, we estimate the effect of an AMOC slowdown or collapse, e. g. induced by freshwater flux into the North Atlantic due to melting of the Greenland Ice Sheet, on Amazon rainfall. In order to provide a clear explanation of the underlying dynamics, we use a simple, but robust mathematical approach (based on the classical Stommel two-box model), ensuring consistency with a comprehensive general circulation model (HadGEM3). We find that these two processes, both caused by global warming, are likely to have competing impacts on the rainfall sum in the Amazon, and hence on the stability of the Amazon rainforest. A future AMOC decline may thus counteract direct global-warming-induced rainfall reductions. Tipping of the AMOC from the strong to the weak mode may therefore have a stabilizing effect on the Amazon rainforest.

Description: 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.

Description: The Earth’s climate is a complex system characterized by multi-scale nonlinear interrelationships between different subsystems like atmosphere and ocean. Among others, the mutual interdependence between sea surface temperatures (SST) and precipitation (PCP) has important implications for ecosystems and societies in vast parts of the globe but is still far from being completely understood. In this context, the globally most relevant coupled ocean–atmosphere phenomenon is the El Niño–Southern Oscillation (ENSO), which strongly affects large-scale SST variability as well as PCP patterns all around the globe. Although significant achievements have been made to foster our understanding of ENSO’s global teleconnections and climate impacts, there are many processes associated with ocean–atmosphere interactions in the tropics and extratropics, as well as remote effects of SST changes on PCP patterns that have not yet been unveiled or fully understood. In this work, we employ coupled climate network analysis for characterizing dominating global co-variability patterns between SST and PCP at monthly timescales. Our analysis uncovers characteristic seasonal patterns associated with both local and remote statistical linkages and demonstrates their dependence on the type of the current ENSO phase (El Niño, La Niña or neutral phase). Thereby, our results allow identifying local interactions as well as teleconnections between SST variations and global precipitation patterns.

Description: Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months.