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Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness
Author(s) -
Timothy M. Lenton,
Valerie Livina,
Vasilis Dakos,
Egbert H. van Nes,
Marten Scheffer
Publication year - 2012
Publication title -
philosophical transactions of the royal society a mathematical physical and engineering sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.074
H-Index - 169
eISSN - 1471-2962
pISSN - 1364-503X
DOI - 10.1098/rsta.2011.0304
Subject(s) - robustness (evolution) , warning system , tipping point (physics) , younger dryas , climatology , lag , climate change , environmental science , thermohaline circulation , autocorrelation , abrupt climate change , computer science , climate system , geology , meteorology , econometrics , global warming , mathematics , statistics , physics , effects of global warming , oceanography , telecommunications , computer network , biochemistry , chemistry , electrical engineering , gene , engineering
We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings.

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