@article{CiemerBoersHirotaetal.2019, author = {Ciemer, Catrin and Boers, Niklas and Hirota, Marina and Kurths, J{\"u}rgen and M{\"u}ller-Hansen, Finn and Oliveira, Rafael S. and Winkelmann, Ricarda}, title = {Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall}, series = {Nature geoscience}, volume = {12}, journal = {Nature geoscience}, number = {3}, publisher = {Nature Publ. Group}, address = {New York}, issn = {1752-0894}, doi = {10.1038/s41561-019-0312-z}, pages = {174 -- 179}, year = {2019}, abstract = {With ongoing global warming, the amount and frequency of precipitation in the tropics is projected to change substantially. While it has been shown that tropical forests and savannahs are sustained within the same intermediate mean annual precipitation range, the mechanisms that lead to the resilience of these ecosystems are still not fully understood. In particular, the long-term impact of rainfall variability on resilience is as yet unclear. Here we present observational evidence that both tropical forest and savannah exposed to a higher rainfall variability-in particular on interannual scales-during their long-term past are overall more resilient against climatic disturbances. Based on precipitation and tree cover data in the Brazilian Amazon basin, we constructed potential landscapes that enable us to systematically measure the resilience of the different ecosystems. Additionally, we infer that shifts from forest to savannah due to decreasing precipitation in the future are more likely to occur in regions with a precursory lower rainfall variability. Long-term rainfall variability thus needs to be taken into account in resilience analyses and projections of vegetation response to climate change.}, language = {en} } @article{BoersGoswamiRheinwaltetal.2019, author = {Boers, Niklas and Goswami, Bedartha and Rheinwalt, Aljoscha and Bookhagen, Bodo and Hoskins, Brian and Kurths, J{\"u}rgen}, title = {Complex networks reveal global pattern of extreme-rainfall teleconnections}, series = {Nature : the international weekly journal of science}, volume = {566}, journal = {Nature : the international weekly journal of science}, number = {7744}, publisher = {Nature Publ. Group}, address = {London}, issn = {0028-0836}, doi = {10.1038/s41586-018-0872-x}, pages = {373 -- 377}, year = {2019}, abstract = {Climatic observables are often correlated across long spatial distances, and extreme events, such as heatwaves or floods, are typically assumed to be related to such teleconnections(1,2). Revealing atmospheric teleconnection patterns and understanding their underlying mechanisms is of great importance for weather forecasting in general and extreme-event prediction in particular(3,4), especially considering that the characteristics of extreme events have been suggested to change under ongoing anthropogenic climate change(5-8). Here we reveal the global coupling pattern of extreme-rainfall events by applying complex-network methodology to high-resolution satellite data and introducing a technique that corrects for multiple-comparison bias in functional networks. We find that the distance distribution of significant connections (P < 0.005) around the globe decays according to a power law up to distances of about 2,500 kilometres. For longer distances, the probability of significant connections is much higher than expected from the scaling of the power law. We attribute the shorter, power-law-distributed connections to regional weather systems. The longer, super-power-law-distributed connections form a global rainfall teleconnection pattern that is probably controlled by upper-level Rossby waves. We show that extreme-rainfall events in the monsoon systems of south-central Asia, east Asia and Africa are significantly synchronized. Moreover, we uncover concise links between south-central Asia and the European and North American extratropics, as well as the Southern Hemisphere extratropics. Analysis of the atmospheric conditions that lead to these teleconnections confirms Rossby waves as the physical mechanism underlying these global teleconnection patterns and emphasizes their crucial role in dynamical tropical-extratropical couplings. Our results provide insights into the function of Rossby waves in creating stable, global-scale dependencies of extreme-rainfall events, and into the potential predictability of associated natural hazards.}, language = {en} } @article{MaheswaranAgarwalSivakumaretal.2019, author = {Maheswaran, Rathinasamy and Agarwal, Ankit and Sivakumar, Bellie and Marwan, Norbert and Kurths, J{\"u}rgen}, title = {Wavelet analysis of precipitation extremes over India and teleconnections to climate indices}, series = {Stochastic Environmental Research and Risk Assessment}, volume = {33}, journal = {Stochastic Environmental Research and Risk Assessment}, number = {11-12}, publisher = {Springer}, address = {New York}, issn = {1436-3240}, doi = {10.1007/s00477-019-01738-3}, pages = {2053 -- 2069}, year = {2019}, abstract = {Precipitation patterns and extremes are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. This study uses wavelet coherence analysis to detect significant interannual and interdecadal oscillations in monthly precipitation extremes across India and their teleconnections to three prominent climate indices, namely, Nino 3.4, Pacific Decadal Oscillation, and Indian Ocean Dipole (IOD). Further, partial wavelet coherence analysis is used to estimate the standalone relationship between the climate indices and precipitation after removing the effect of interdependency. The wavelet analysis of monthly precipitation extremes at 30 different locations across India reveals that (a) interannual (2-8 years) and interdecadal (8-32 years) oscillations are statistically significant, and (b) the oscillations vary in both time and space. The results from the partial wavelet coherence analysis reveal that Nino 3.4 and IOD are the significant drivers of Indian precipitation at interannual and interdecadal scales. Intriguingly, the study also confirms that the strength of influence of large-scale atmospheric circulation patterns on Indian precipitation extremes varies with spatial physiography of the region.}, language = {en} } @article{KurthsAgarwalShuklaetal.2019, author = {Kurths, J{\"u}rgen and Agarwal, Ankit and Shukla, Roopam and Marwan, Norbert and Maheswaran, Rathinasamy and Caesar, Levke and Krishnan, Raghavan and Merz, Bruno}, title = {Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach}, series = {Nonlinear processes in geophysics}, volume = {26}, journal = {Nonlinear processes in geophysics}, number = {3}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1023-5809}, doi = {10.5194/npg-26-251-2019}, pages = {251 -- 266}, year = {2019}, abstract = {A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Ni{\~n}o-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting.}, language = {en} } @article{OzturkMalikCheungetal.2019, author = {Ozturk, Ugur and Malik, Nishant and Cheung, Kevin and Marwan, Norbert and Kurths, J{\"u}rgen}, title = {A network-based comparative study of extreme tropical and frontal storm rainfall over Japan}, series = {Climate dynamics : observational, theoretical and computational research on the climate system}, volume = {53}, journal = {Climate dynamics : observational, theoretical and computational research on the climate system}, number = {1-2}, publisher = {Springer}, address = {New York}, issn = {0930-7575}, doi = {10.1007/s00382-018-4597-1}, pages = {521 -- 532}, year = {2019}, abstract = {Frequent and intense rainfall events demand innovative techniques to better predict the extreme rainfall dynamics. This task requires essentially the assessment of the basic types of atmospheric processes that trigger extreme rainfall, and then to examine the differences between those processes, which may help to identify key patterns to improve predictive algorithms. We employ tools from network theory to compare the spatial features of extreme rainfall over the Japanese archipelago and surrounding areas caused by two atmospheric processes: the Baiu front, which occurs mainly in June and July (JJ), and the tropical storms from August to November (ASON). We infer from complex networks of satellite-derived rainfall data, which are based on the nonlinear correlation measure of event synchronization. We compare the spatial scales involved in both systems and identify different regions which receive rainfall due to the large spatial scale of the Baiu and tropical storm systems. We observed that the spatial scales involved in the Baiu driven rainfall extremes, including the synoptic processes behind the frontal development, are larger than tropical storms, which even have long tracks during extratropical transitions. We further delineate regions of coherent rainfall during the two seasons based on network communities, identifying the horizontal (east-west) rainfall bands during JJ over the Japanese archipelago, while during ASON these bands align with the island arc of Japan.}, language = {en} }