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Subject: climatology Ă— WGL Institute: PIK Ă—

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Now showing 1 - 4 of 4
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    A volcanically triggered regime shift in the subpolar North Atlantic Ocean as a possible origin of the Little Ice Age
    (MĂĽnchen : European Geopyhsical Union, 2013) Schleussner, C.F.; Feulner, G.
    Among the climatological events of the last millennium, the Northern Hemisphere Medieval Climate Anomaly succeeded by the Little Ice Age are of exceptional importance. The origin of these regional climate anomalies remains a subject of debate and besides external influences like solar and volcanic activity, internal dynamics of the climate system might have also played a dominant role. Here, we present transient last millennium simulations of the fully coupled model of intermediate complexity Climber 3α forced with stochastically reconstructed wind-stress fields. Our results indicate that short-lived volcanic eruptions might have triggered a cascade of sea ice–ocean feedbacks in the North Atlantic, ultimately leading to a persistent regime shift in the ocean circulation. We find that an increase in the Nordic Sea sea-ice extent on decadal timescales as a consequence of major volcanic eruptions in our model leads to a spin-up of the subpolar gyre and a weakened Atlantic meridional overturning circulation, eventually causing a persistent, basin-wide cooling. These results highlight the importance of regional climate feedbacks such as a regime shift in the subpolar gyre circulation for understanding the dynamics of past and future climate.
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    Review: Visual analytics of climate networks
    (Göttingen : Copernicus GmbH, 2015) Nocke, T.; Buschmann, S.; Donges, J.F.; Marwan, N.; Schulz, H.-J.; Tominski, C.
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    Correlations between climate network and relief data
    (Göttingen : Copernicus GmbH, 2014) Peron, T.K.D.; Comin, C.H.; Amancio, D.R.; Da F. Costa, L.; Rodrigues, F.A.; Kurths, J.
    In the last few years, the scientific community has witnessed an ongoing trend of using ideas developed in the study of complex networks to analyze climate dynamics. This powerful combination, usually called climate networks, can be used to uncover non-trivial patterns of weather changes throughout the years. Here we investigate the temperature network of the North American region and show that two network characteristics, namely degree and clustering, have marked differences between the eastern and western regions. We show that such differences are a reflection of the presence of a large network community on the western side of the continent. Moreover, we provide evidence that this large community is a consequence of the peculiar characteristics of the western relief of North America.
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    Multi-scale event synchronization analysis for unravelling climate processes: A wavelet-based approach
    (Göttingen : Copernicus GmbH, 2017) Agarwal, A.; Marwan, N.; Rathinasamy, M.; Merz, B.; Kurths, J.
    The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-)processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.