Spatiotemporal complexity and time-dependent networks in sea surface temperature from mid- to late Holocene
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Spatiotemporal complexity and time-dependent networks in sea surface temperature from mid- to late Holocene Fabrizio Falasca1,a
, Julien Crétat2,3 , Pascale Braconnot2 , Annalisa Bracco1
1 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, GA
30332, USA
2 Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université
Paris-Saclay, 91191 Gif-sur-Yvette, France
3 Biogéosciences/CRC, CNRS-UB, Université de Bourgogne, Dijon, France
Received: 20 December 2019 / Accepted: 16 April 2020 © Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In climate science regime transitions include abrupt changes in modes of climate variability and shifts in the connectivity of the whole system. While important, their identification remains challenging. This paper proposes a new framework to investigate regime transitions and connectivity patterns in spatiotemporal climate fields. Firstly, local regime shifts are quantified by means of information entropy; secondly, their spatial heterogeneity is examined by identifying the underlying spatial domains of the entropy field; finally, a weighted, direct and time-dependent network is inferred to capture the linkages between these domains. The spatiotemporal variability in sea surface temperature (SST) in two simulations of the last 6000 years is investigated with the proposed approach. The largest regional regime shifts emerge as abrupt transitions from low to high-frequency SST oscillations, or vice versa, in both simulations. Furthermore, the variability in time of the two climate networks is studied in terms of their network density. Generally, rapid and sudden transitions in the degree of connectivity of the system are observed in both simulations but, in most cases, at different times, with few exceptions. This suggests that our ability to predict the climate system may be hampered by its inherent complexity resulting from internal variability.
1 Introduction Our ability to predict Earth’s climate is limited by the nonlinear and intrinsically complex interactions between its components. Quantifiable changes in the system dynamics, i.e., dynamical transitions or climate regime shifts, are manifestations of such complexity. Global-scale major climate shifts are the Snowball Earth glaciations that may have occurred 650–700 million years ago [1–4]. In addition, the climate system has experienced a large variety of regional regime shifts, such as the Dansgaard–Oeschger events [5, 6] or the abrupt hydrological fluctuations in the African and Asian monsoon regions [7, 8], the sudden cool-
a e-mail: [email protected] (corresponding author)
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ing of the North Pacific sea surface temperature in the mid-Holocene [9] or the 4.2-kiloyear BP aridification event during the Holocene epoch [10] to cite a few.1 In recent years, methods stemming from non-equilibrium statistical me
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