Articles | Volume 15, issue 4
https://doi.org/10.5194/esd-15-1117-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/esd-15-1117-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review
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19 Aug 2024
Review | Highlight paper |
| 19 Aug 2024
| 19 Aug 2024
Tipping point detection and early warnings in climate, ecological, and human systems
Vasilis Dakos
CORRESPONDING AUTHOR
ISEM, Univ Montpellier, CNRS, EPHE, IRD, 34095 Montpellier, France
Chris A. Boulton
CORRESPONDING AUTHOR
Global Systems Institute, University of Exeter, Exeter, UK
Joshua E. Buxton
Global Systems Institute, University of Exeter, Exeter, UK
Jesse F. Abrams
Global Systems Institute, University of Exeter, Exeter, UK
Beatriz Arellano-Nava
Global Systems Institute, University of Exeter, Exeter, UK
David I. Armstrong McKay
Global Systems Institute, University of Exeter, Exeter, UK
Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
Sebastian Bathiany
Earth System Modelling, School of Engineering and Design, Technical University Munich, Munich, Germany
Lana Blaschke
Earth System Modelling, School of Engineering and Design, Technical University Munich, Munich, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Niklas Boers
Global Systems Institute, University of Exeter, Exeter, UK
Earth System Modelling, School of Engineering and Design, Technical University Munich, Munich, Germany
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Daniel Dylewsky
Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
Carlos López-Martínez
Signal Theory and Communications Dept., Universitat Politècnica de Catalunya UPC, Barcelona, Spain
Institut d'Estudis Espacials de Catalunya IEEC, Barcelona, Spain
Isobel Parry
Global Systems Institute, University of Exeter, Exeter, UK
Paul Ritchie
Global Systems Institute, University of Exeter, Exeter, UK
Bregje van der Bolt
Environmental Sciences Group, Water Systems and Global Change, Wageningen University and Research, Wageningen, the Netherlands
Larissa van der Laan
Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
Els Weinans
Copernicus Institute of Sustainable Development, Environmental Sciences, Faculty of Geoscience, Utrecht University, Utrecht, the Netherlands
Sonia Kéfi
ISEM, Univ Montpellier, CNRS, EPHE, IRD, 34095 Montpellier, France
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
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Kerstin Monika Lux-Gottschalk and Paul David Longden Ritchie
EGUsphere, https://doi.org/10.5194/egusphere-2024-2170, https://doi.org/10.5194/egusphere-2024-2170, 2024
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For tipping points at low levels of global warming, overshoots of the threshold are becoming increasingly likely. Importantly, for some systems, tipping can still be avoided provided the forcing is reversed sufficiently quickly. Conditions for a mitigation window, that would avoid tipping, depend on system uncertainties. We highlight the need to account for uncertainty in the threshold location and other system features when designing climate mitigation strategies that avoid tipping.
Nils Bochow, Anna Poltronieri, and Niklas Boers
EGUsphere, https://doi.org/10.5194/egusphere-2024-1597, https://doi.org/10.5194/egusphere-2024-1597, 2024
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Using the latest climate models, we update the understanding of how the Greenland ice sheet responds to climate changes. We found that precipitation and temperature changes in Greenland vary across different regions. Our findings suggest that using uniform estimates for temperature and precipitation for modelling the response of the ice sheet can overestimate ice loss in Greenland. Therefore, this study highlights the need for spatially resolved data in predicting the ice sheet's future.
Dag O. Hessen, Tom Andersen, David Armstrong McKay, Sarian Kosten, Mariana Meerhoff, Amy Pickard, and Bryan M. Spears
Earth Syst. Dynam., 15, 653–669, https://doi.org/10.5194/esd-15-653-2024, https://doi.org/10.5194/esd-15-653-2024, 2024
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Lakes worldwide are changing and under threat due to stressors such as overload of nutrients, increased input of organic carbon (“browning”), and climate change, which may cause reduced water volume, salinization, or even loss of waterbodies. Some of these changes are abrupt to the extent that they can be characterized as tipping points for that particular system. Such changes may also cause increased release of greenhouse gases, and lakes are major players in the global climate in this context.
Maya Ben-Yami, Lana Blaschke, Sebastian Bathiany, and Niklas Boers
EGUsphere, https://doi.org/10.5194/egusphere-2024-1106, https://doi.org/10.5194/egusphere-2024-1106, 2024
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Recent work has used observations to find statistical signs that the Atlantic Meridional Overturning Circulation (AMOC) may be approaching a collapse. We find that in complex climate models in which the AMOC does not collapse before 2100, the statistical signs that are present in the observations are not found in the 1850–2014 equivalent model time series. This indicates that the observed statistical signs are not prone to false positives.
Takahito Mitsui and Niklas Boers
Clim. Past, 20, 683–699, https://doi.org/10.5194/cp-20-683-2024, https://doi.org/10.5194/cp-20-683-2024, 2024
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In general, the variance and short-lag autocorrelations of the fluctuations increase in a system approaching a critical transition. Using these indicators, we identify statistical precursor signals for the Dansgaard–Oeschger cooling events recorded in two climatic proxies of three Greenland ice core records. We then provide a dynamical systems theory that bridges the gap between observing statistical precursor signals and the physical precursor signs empirically known in paleoclimate research.
Larissa van der Laan, Anouk Vlug, Adam A. Scaife, Fabien Maussion, and Kristian Förster
EGUsphere, https://doi.org/10.5194/egusphere-2024-387, https://doi.org/10.5194/egusphere-2024-387, 2024
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Usually, glacier models are supplied with climate information from long (e.g. 100 year) simulations by global climate models. In this paper, we test the feasibility of supplying glacier models with shorter simulations, to get more accurate information on 5–10 year time scales. Reliable information on these time scales is very important, especially for water management experts to know how much meltwater to expect, for rivers, agriculture and drinking water.
Nico Wunderling, Anna S. von der Heydt, Yevgeny Aksenov, Stephen Barker, Robbin Bastiaansen, Victor Brovkin, Maura Brunetti, Victor Couplet, Thomas Kleinen, Caroline H. Lear, Johannes Lohmann, Rosa Maria Roman-Cuesta, Sacha Sinet, Didier Swingedouw, Ricarda Winkelmann, Pallavi Anand, Jonathan Barichivich, Sebastian Bathiany, Mara Baudena, John T. Bruun, Cristiano M. Chiessi, Helen K. Coxall, David Docquier, Jonathan F. Donges, Swinda K. J. Falkena, Ann Kristin Klose, David Obura, Juan Rocha, Stefanie Rynders, Norman Julius Steinert, and Matteo Willeit
Earth Syst. Dynam., 15, 41–74, https://doi.org/10.5194/esd-15-41-2024, https://doi.org/10.5194/esd-15-41-2024, 2024
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This paper maps out the state-of-the-art literature on interactions between tipping elements relevant for current global warming pathways. We find indications that many of the interactions between tipping elements are destabilizing. This means that tipping cascades cannot be ruled out on centennial to millennial timescales at global warming levels between 1.5 and 2.0 °C or on shorter timescales if global warming surpasses 2.0 °C.
Takahito Mitsui, Matteo Willeit, and Niklas Boers
Earth Syst. Dynam., 14, 1277–1294, https://doi.org/10.5194/esd-14-1277-2023, https://doi.org/10.5194/esd-14-1277-2023, 2023
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The glacial–interglacial cycles of the Quaternary exhibit 41 kyr periodicity before the Mid-Pleistocene Transition (MPT) around 1.2–0.8 Myr ago and ~100 kyr periodicity after that. The mechanism generating these periodicities remains elusive. Through an analysis of an Earth system model of intermediate complexity, CLIMBER-2, we show that the dominant periodicities of glacial cycles can be explained from the viewpoint of synchronization theory.
Sina Loriani, Yevgeny Aksenov, David Armstrong McKay, Govindasamy Bala, Andreas Born, Cristiano M. Chiessi, Henk Dijkstra, Jonathan F. Donges, Sybren Drijfhout, Matthew H. England, Alexey V. Fedorov, Laura Jackson, Kai Kornhuber, Gabriele Messori, Francesco Pausata, Stefanie Rynders, Jean-Baptiste Salée, Bablu Sinha, Steven Sherwood, Didier Swingedouw, and Thejna Tharammal
EGUsphere, https://doi.org/10.5194/egusphere-2023-2589, https://doi.org/10.5194/egusphere-2023-2589, 2023
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In this work, we draw on paleoreords, observations and modelling studies to review tipping points in the ocean overturning circulations, monsoon systems and global atmospheric circulations. We find indications for tipping in the ocean overturning circulations and the West African monsoon, with potentially severe impacts on the Earth system and humans. Tipping in the other considered systems is considered conceivable but currently not sufficiently supported by evidence.
Chris A. Boulton, Joshua E. Buxton, and Timothy M. Lenton
EGUsphere, https://doi.org/10.5194/egusphere-2023-2234, https://doi.org/10.5194/egusphere-2023-2234, 2023
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Early warning signals (EWS) of tipping points (TP), are used to detect resilience loss in the incumbent regime of ICEVs, towards an electric vehicle (EV) dominated state. View share of EV ads on a UK car selling platform shows evidence of approaching a TP, with results suggesting low-mid price ranges may have passed it, likely due to achieved price parity between EVs and non-EVs in sectors of the second-hand market, showing that EWS of positive TPs are possible in social-technological systems.
Viktoria Spaiser, Sirkku Juhola, Sara M. Constantino, Weisi Guo, Tabitha Watson, Jana Sillmann, Alessandro Craparo, Ashleigh Basel, John T. Bruun, Krishna Krishnamurthy, Jürgen Scheffran, Patricia Pinho, Uche T. Okpara, Jonathan F. Donges, Avit Bhowmik, Taha Yasseri, Ricardo Safra de Campos, Graeme S. Cumming, Hugues Chenet, Florian Krampe, and Jesse F. Abrams
EGUsphere, https://doi.org/10.5194/egusphere-2023-1475, https://doi.org/10.5194/egusphere-2023-1475, 2023
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In this paper, we identify potential negative social tipping points linked to Earth system destabilisation and draw on related research to understand the drivers and likelihood of these negative social tipping dynamics, their potential effects on human societies and the Earth system, and the potential for cascading interactions and contribution to systemic risks.
Sara M. Vallejo-Bernal, Frederik Wolf, Niklas Boers, Dominik Traxl, Norbert Marwan, and Jürgen Kurths
Hydrol. Earth Syst. Sci., 27, 2645–2660, https://doi.org/10.5194/hess-27-2645-2023, https://doi.org/10.5194/hess-27-2645-2023, 2023
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Employing event synchronization and complex networks analysis, we reveal a cascade of heavy rainfall events, related to intense atmospheric rivers (ARs): heavy precipitation events (HPEs) in western North America (NA) that occur in the aftermath of land-falling ARs are synchronized with HPEs in central and eastern Canada with a delay of up to 12 d. Understanding the effects of ARs in the rainfall over NA will lead to better anticipating the evolution of the climate dynamics in the region.
Paul D. L. Ritchie, Hassan Alkhayuon, Peter M. Cox, and Sebastian Wieczorek
Earth Syst. Dynam., 14, 669–683, https://doi.org/10.5194/esd-14-669-2023, https://doi.org/10.5194/esd-14-669-2023, 2023
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Complex systems can undergo abrupt changes or tipping points when external forcing crosses a critical level and are of increasing concern because of their severe impacts. However, tipping points can also occur when the external forcing changes too quickly without crossing any critical levels, which is very relevant for Earth’s systems and contemporary climate. We give an intuitive explanation of such rate-induced tipping and provide illustrative examples from natural and human systems.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
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Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Keno Riechers, Leonardo Rydin Gorjão, Forough Hassanibesheli, Pedro G. Lind, Dirk Witthaut, and Niklas Boers
Earth Syst. Dynam., 14, 593–607, https://doi.org/10.5194/esd-14-593-2023, https://doi.org/10.5194/esd-14-593-2023, 2023
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Paleoclimate proxy records show that the North Atlantic climate repeatedly transitioned between two regimes during the last glacial interval. This study investigates a bivariate proxy record from a Greenland ice core which reflects past Greenland temperatures and large-scale atmospheric conditions. We reconstruct the underlying deterministic drift by estimating first-order Kramers–Moyal coefficients and identify two separate stable states in agreement with the aforementioned climatic regimes.
Chris Huntingford, Peter M. Cox, Mark S. Williamson, Joseph J. Clarke, and Paul D. L. Ritchie
Earth Syst. Dynam., 14, 433–442, https://doi.org/10.5194/esd-14-433-2023, https://doi.org/10.5194/esd-14-433-2023, 2023
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Emergent constraints (ECs) reduce the spread of projections between climate models. ECs estimate changes to climate features impacting adaptation policy, and with this high profile, the method is under scrutiny. Asking
What is an EC?, we suggest they are often the discovery of parameters that characterise hidden large-scale equations that climate models solve implicitly. We present this conceptually via two examples. Our analysis implies possible new paths to link ECs and physical processes.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
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Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Isobel M. Parry, Paul D. L. Ritchie, and Peter M. Cox
Earth Syst. Dynam., 13, 1667–1675, https://doi.org/10.5194/esd-13-1667-2022, https://doi.org/10.5194/esd-13-1667-2022, 2022
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Despite little evidence of regional Amazon rainforest dieback, many localised abrupt dieback events are observed in the latest state-of-the-art global climate models under anthropogenic climate change. The detected dieback events would still cause severe consequences for local communities and ecosystems. This study suggests that 7 ± 5 % of the northern South America region would experience abrupt downward shifts in vegetation carbon for every degree of global warming past 1.5 °C.
Eirik Myrvoll-Nilsen, Keno Riechers, Martin Wibe Rypdal, and Niklas Boers
Clim. Past, 18, 1275–1294, https://doi.org/10.5194/cp-18-1275-2022, https://doi.org/10.5194/cp-18-1275-2022, 2022
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In layer counted proxy records each measurement is accompanied by a timestamp typically measured by counting periodic layers. Knowledge of the uncertainty of this timestamp is important for a rigorous propagation to further analyses. By assuming a Bayesian regression model to the layer increments we express the dating uncertainty by the posterior distribution, from which chronologies can be sampled efficiently. We apply our framework to dating abrupt warming transitions during the last glacial.
Keno Riechers, Takahito Mitsui, Niklas Boers, and Michael Ghil
Clim. Past, 18, 863–893, https://doi.org/10.5194/cp-18-863-2022, https://doi.org/10.5194/cp-18-863-2022, 2022
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Building upon Milancovic's theory of orbital forcing, this paper reviews the interplay between intrinsic variability and external forcing in the emergence of glacial interglacial cycles. It provides the reader with historical background information and with basic theoretical concepts used in recent paleoclimate research. Moreover, it presents new results which confirm the reduced stability of glacial-cycle dynamics after the mid-Pleistocene transition.
Paul R. Halloran, Jennifer K. McWhorter, Beatriz Arellano Nava, Robert Marsh, and William Skirving
Geosci. Model Dev., 14, 6177–6195, https://doi.org/10.5194/gmd-14-6177-2021, https://doi.org/10.5194/gmd-14-6177-2021, 2021
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This paper describes the latest version of a simple model for simulating coastal oceanography in response to changes in weather and climate. The latest revision of this model makes scientific improvements but focuses on improvements that allow the model to be run simply at large scales and for long periods of time to explore the implications of (for example) future climate change along large areas of coastline.
Keno Riechers and Niklas Boers
Clim. Past, 17, 1751–1775, https://doi.org/10.5194/cp-17-1751-2021, https://doi.org/10.5194/cp-17-1751-2021, 2021
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Greenland ice core data show that the last glacial cycle was punctuated by a series of abrupt climate shifts comprising significant warming over Greenland, retreat of North Atlantic sea ice, and atmospheric reorganization. Statistical analysis of multi-proxy records reveals no systematic lead or lag between the transitions of proxies that represent different climatic subsystems, and hence no evidence for a potential trigger of these so-called Dansgaard–Oeschger events can be found.
David I. Armstrong McKay, Sarah E. Cornell, Katherine Richardson, and Johan Rockström
Earth Syst. Dynam., 12, 797–818, https://doi.org/10.5194/esd-12-797-2021, https://doi.org/10.5194/esd-12-797-2021, 2021
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We use an Earth system model with two new ocean ecosystem features (plankton size traits and temperature-sensitive nutrient recycling) to revaluate the effect of climate change on sinking organic carbon (the
biological pump) and the ocean carbon sink. These features lead to contrary pump responses to warming, with a combined effect of a smaller sink despite a more resilient pump. These results show the importance of including ecological dynamics in models for understanding climate feedbacks.
Denis-Didier Rousseau, Pierre Antoine, Niklas Boers, France Lagroix, Michael Ghil, Johanna Lomax, Markus Fuchs, Maxime Debret, Christine Hatté, Olivier Moine, Caroline Gauthier, Diana Jordanova, and Neli Jordanova
Clim. Past, 16, 713–727, https://doi.org/10.5194/cp-16-713-2020, https://doi.org/10.5194/cp-16-713-2020, 2020
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New investigations of European loess records from MIS 6 reveal the occurrence of paleosols and horizon showing slight pedogenesis similar to those from the last climatic cycle. These units are correlated with interstadials described in various marine, continental, and ice Northern Hemisphere records. Therefore, these MIS 6 interstadials can confidently be interpreted as DO-like events of the penultimate climate cycle.
David I. Armstrong McKay and Timothy M. Lenton
Clim. Past, 14, 1515–1527, https://doi.org/10.5194/cp-14-1515-2018, https://doi.org/10.5194/cp-14-1515-2018, 2018
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This study uses statistical analyses to look for signs of declining resilience (i.e. greater sensitivity to small shocks) in the global carbon cycle and climate system across the Palaeocene–Eocene Thermal Maximum (PETM), a global warming event 56 Myr ago driven by rapid carbon release. Our main finding is that carbon cycle resilience declined in the 1.5 Myr beforehand (a time of significant volcanic emissions), which is consistent with but not proof of a carbon release tipping point at the PETM.
Niklas Boers, Mickael D. Chekroun, Honghu Liu, Dmitri Kondrashov, Denis-Didier Rousseau, Anders Svensson, Matthias Bigler, and Michael Ghil
Earth Syst. Dynam., 8, 1171–1190, https://doi.org/10.5194/esd-8-1171-2017, https://doi.org/10.5194/esd-8-1171-2017, 2017
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We use a Bayesian approach for inferring inverse, stochastic–dynamic models from northern Greenland (NGRIP) oxygen and dust records of subdecadal resolution for the interval 59 to 22 ka b2k. Our model reproduces the statistical and dynamical characteristics of the records, including the Dansgaard–Oeschger variability, with no need for external forcing. The crucial ingredients are cubic drift terms, nonlinear coupling terms between the oxygen and dust time series, and non-Markovian contributions.
Denis-Didier Rousseau, Anders Svensson, Matthias Bigler, Adriana Sima, Jorgen Peder Steffensen, and Niklas Boers
Clim. Past, 13, 1181–1197, https://doi.org/10.5194/cp-13-1181-2017, https://doi.org/10.5194/cp-13-1181-2017, 2017
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We show that the analysis of δ18O and dust in the Greenland ice cores, and a critical study of their source variations, reconciles these records with those observed on the Eurasian continent. We demonstrate the link between European and Chinese loess sequences, dust records in Greenland, and variations in the North Atlantic sea ice extent. The sources of the emitted and transported dust material are variable and relate to different environments.
Niklas Boers, Bedartha Goswami, and Michael Ghil
Clim. Past, 13, 1169–1180, https://doi.org/10.5194/cp-13-1169-2017, https://doi.org/10.5194/cp-13-1169-2017, 2017
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We introduce a Bayesian framework to represent layer-counted proxy records as probability distributions on error-free time axes, accounting for both proxy and dating errors. Our method is applied to NGRIP δ18O data, revealing that the cumulative dating errors lead to substantial uncertainties for the older parts of the record. Applying our method to the widely used radiocarbon comparison curve derived from varved sediments of Lake Suigetsu provides the complete uncertainties of this curve.
Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebastian Sippel, and Miguel D. Mahecha
Earth Syst. Dynam., 8, 677–696, https://doi.org/10.5194/esd-8-677-2017, https://doi.org/10.5194/esd-8-677-2017, 2017
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Anomalies and extremes are often detected using univariate peak-over-threshold approaches in the geoscience community. The Earth system is highly multivariate. We compare eight multivariate anomaly detection algorithms and combinations of data preprocessing. We identify three anomaly detection algorithms that outperform univariate extreme event detection approaches. The workflows have the potential to reveal novelties in data. Remarks on their application to real Earth observations are provided.
Helge F. Goessling and Sebastian Bathiany
Earth Syst. Dynam., 7, 697–715, https://doi.org/10.5194/esd-7-697-2016, https://doi.org/10.5194/esd-7-697-2016, 2016
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Carbon dioxide, while warming the Earth's surface, cools the atmosphere beyond about 15 km (the middle atmosphere). This cooling is considered a fingerprint of anthropogenic global warming, yet the physical reason behind it remains prone to misconceptions. Here we use a simple radiation model to illustrate the physical essence of stratospheric cooling, and a complex climate model to quantify how strongly different mechanisms contribute.
Sebastian Bathiany, Bregje van der Bolt, Mark S. Williamson, Timothy M. Lenton, Marten Scheffer, Egbert H. van Nes, and Dirk Notz
The Cryosphere, 10, 1631–1645, https://doi.org/10.5194/tc-10-1631-2016, https://doi.org/10.5194/tc-10-1631-2016, 2016
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We examine if a potential "tipping point" in Arctic sea ice, causing abrupt and irreversible sea-ice loss, could be foreseen with statistical early warning signals. We assess this idea by using several models of different complexity. We find robust and consistent trends in variability that are not specific to the existence of a tipping point. While this makes an early warning impossible, it allows to estimate sea-ice variability from only short observational records or reconstructions.
Mark S. Williamson, Sebastian Bathiany, and Timothy M. Lenton
Earth Syst. Dynam., 7, 313–326, https://doi.org/10.5194/esd-7-313-2016, https://doi.org/10.5194/esd-7-313-2016, 2016
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We find early warnings of abrupt changes in complex dynamical systems such as the climate where the usual early warning indicators do not work. In particular, these are systems that are periodically forced, for example by the annual cycle of solar insolation. We show these indicators are good theoretically in a general setting then apply them to a specific system, that of the Arctic sea ice, which has been conjectured to be close to such a tipping point. We do not find evidence of it.
B. B. B. Booth, D. Bernie, D. McNeall, E. Hawkins, J. Caesar, C. Boulton, P. Friedlingstein, and D. M. H. Sexton
Earth Syst. Dynam., 4, 95–108, https://doi.org/10.5194/esd-4-95-2013, https://doi.org/10.5194/esd-4-95-2013, 2013
S. Bathiany, M. Claussen, and K. Fraedrich
Earth Syst. Dynam., 4, 63–78, https://doi.org/10.5194/esd-4-63-2013, https://doi.org/10.5194/esd-4-63-2013, 2013
S. Bathiany, M. Claussen, and K. Fraedrich
Earth Syst. Dynam., 4, 79–93, https://doi.org/10.5194/esd-4-79-2013, https://doi.org/10.5194/esd-4-79-2013, 2013
Cited articles
Abis, B. and Brovkin, V.: Alternative tree-cover states of the boreal ecosystem: A conceptual model, Global Ecol. Biogeogr., 28, 612–627, https://doi.org/10.1111/geb.12880, 2019.
Alberto, J., Stelzer, A., Padric, J., Adrian, R., and Willem, B.: Early warning signals of regime shifts for aquatic systems: Can experiments help to bridge the gap between theory and real-world application?, Ecol. Complex., 47, 100944, https://doi.org/10.1016/j.ecocom.2021.100944, 2021.
AlMomani, A. A. and Bollt, E.: An early warning sign of critical transition in the Antarctic ice sheet – a data-driven tool for a spatiotemporal tipping point, Nonlin. Processes Geophys., 28, 153–166, https://doi.org/10.5194/npg-28-153-2021, 2021.
Ashwin, P., Wieczorek, S., Vitolo, R., and Cox, P.: Tipping points in open systems: bifurcation, noise-induced and rate-dependent examples in the climate system, Philos. T. R. Soc. A, 13, 20, https://doi.org/10.1098/rsta.2011.0306, 2011.
Bathiany, S., Claussen, M., and Fraedrich, K.: Detecting hotspots of atmosphere–vegetation interaction via slowing down – Part 1: A stochastic approach, Earth Syst. Dynam., 4, 63–78, https://doi.org/10.5194/esd-4-63-2013, 2013.
Bathiany, S., van der Bolt, B., Williamson, M. S., Lenton, T. M., Scheffer, M., van Nes, E. H., and Notz, D.: Statistical indicators of Arctic sea-ice stability – prospects and limitations, The Cryosphere, 10, 1631–1645, https://doi.org/10.5194/tc-10-1631-2016, 2016.
Berdugo, M., Kéfi, S., Soliveres, S., and Maestre, F. T.: Plant spatial patterns identify alternative ecosystem multifunctionality states in global drylands, Nat. Ecol. Evol., 1, 0003, https://doi.org/10.1038/s41559-016-0003, 2017.
Berdugo, M., Gaitán, J. J., Delgado-Baquerizo, M., Crowther, T. W., and Dakos, V.: Prevalence and drivers of abrupt vegetation shifts in global drylands, P. Natl. Acad. Sci. USA, 119, 1–10, https://doi.org/10.1073/pnas.2123393119, 2022.
Bestelmeyer, B. T., A. M. Ellison, Fraser, W. R., Gorman, K. B., Holbrook, S. J., Laney, C. M., Ohman, M. D., Peters, D. P. C., Pillsbury, F. C., Rassweiler, A., Schmitt, R. J., and Sharm, S.: Analysis of abrupt transitions in ecological systems, Ecosphere, 2, 129, https://doi.org/10.1890/ES11-00216.1, 2011.
Boerlijst, M. C., Oudman, T., and de Roos, A. M.: Catastrophic Collapse Can Occur without Early Warning: Examples of Silent Catastrophes in Structured Ecological Models, PLOS ONE, 8, e62033, https://doi.org/10.1371/journal.pone.0062033, 2013.
Boers, N.: Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation, Nat. Clim. Change, 11, 680–688, https://doi.org/10.1038/s41558-021-01097-4, 2021.
Boettiger, C. and Hastings, A.: Early warning signals and the prosecutor's fallacy, P. R. Soc. B, 279, 4734–4739, https://doi.org/10.1098/rspb.2012.2085, 2012a.
Boettiger, C. and Hastings, A.: Quantifying limits to detection of early warning for critical transitions, J. R. Soc. Interface, 9, 2527–39, https://doi.org/10.1098/rsif.2012.0125, 2012b.
Boettiger, C., Ross, N., and Hastings, A.: Early warning signals: the charted and uncharted territories, Theor. Ecol., 6, 255–264, https://doi.org/10.1007/s12080-013-0192-6, 2013.
Boulton, C. A. and Lenton, T. M.: Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change, P. Natl. Acad. Sci. USA, 112, 11496–11501, https://doi.org/10.1073/pnas.1501781112, 2015.
Boulton, C. a., Good, P., and Lenton, T. M.: Early warning signals of simulated Amazon rainforest dieback, Theor. Ecol., 6, 373–384, https://doi.org/10.1007/s12080-013-0191-7, 2013.
Boulton, C. A., Booth, B. B. B., and Good, P.: Exploring uncertainty of Amazon dieback in a perturbed parameter Earth system ensemble, Glob. Change Biol., 23, 5032–5044, https://doi.org/10.1111/gcb.13733, 2017.
Boulton, C. A., Lenton, T. M., and Boers, N.: Pronounced loss of Amazon rainforest resilience since the early 2000s, Nat. Clim. Change, 12, 271–278, https://doi.org/10.1038/s41558-022-01287-8, 2022.
Brett, T. S. and Rohani, P.: Dynamical footprints enable detection of disease emergence, PLOS Biol., 18, e3000697, https://doi.org/10.1371/journal.pbio.3000697, 2020.
Burthe, S. J., Henrys, P. A., Mackay, E. B., Spears, B. M., Campbell, R., Carvalho, L., Dudley, B., Gunn, I. D. M., Johns, D. G., Maberly, S. C., May, L., Newell, M. A., Wanless, S., Winfield, I. J., Thackeray, S. J., and Daunt, F.: Do early warning indicators consistently predict nonlinear change in long-term ecological data?, 53, 666–676, https://doi.org/10.1111/1365-2664.12519, 2016.
Bury, T. M.: ewstools: A Python package for early warning signals of bifurcations in time series data, J. Open Source Softw., 8, 8–11, https://doi.org/10.21105/joss.05038, 2023.
Bury, T. M., Sujith, R. I., Pavithran, I., Scheffer, M., Lenton, T. M., Anand, M., and Bauch, C. T.: Deep learning for early warning signals of regime shifts, P. Natl. Acad. Sci. USA, 450, e2106140118, https://doi.org/10.1073/pnas.2106140118, 2021.
Carpenter, S. R. and Brock, W. A.: Rising variance: a leading indicator of ecological transition, Ecol. Lett., 9, 311–318, https://doi.org/10.1111/j.1461-0248.2005.00877.x, 2006.
Carpenter, S. R., Brock, W. A., Cole, J. J., Kitchell, J. F., and Pace, M. L.: Leading indicators of trophic cascades, Ecol. Lett., 11, 128–38, https://doi.org/10.1111/j.1461-0248.2007.01131.x, 2008.
Carpenter, S. R., Brock, W. A., Cole, J. J., and Pace, M. L.: A new approach for rapid detection of nearby thresholds in ecosystem time series, Oikos, 123, 290–297, https://doi.org/10.1111/j.1600-0706.2013.00539.x, 2014.
Carstensen, J. and Weydmann, A.: Tipping Points in the Arctic: Eyeballing or Statistical Significance?, AMBIO, 41, 34–43, https://doi.org/10.1007/s13280-011-0223-8, 2012.
Cavaliere, M., Yang, G., Danos, V., and Dakos, V.: Detecting the Collapse of Cooperation in Evolving Networks, Sci. Rep.-UK, 6, 30845, https://doi.org/10.1038/srep30845, 2016.
Clements, C. F. and Ozgul, A.: Including trait-based early warning signals helps predict population collapse, Nat. Commun., 7, 1–8, https://doi.org/10.1038/ncomms10984, 2016.
Clements, C. F. and Ozgul, A.: Indicators of transitions in biological systems, Ecol. Lett., 21, 905–919, https://doi.org/10.1111/ele.12948, 2018.
Dai, L., Vorselen, D., Korolev, K. S., and Gore, J.: Generic Indicators for Loss of Resilience Before a Tipping Point Leading to Population Collapse, Science, 336, 1175–1177, https://doi.org/10.1126/science.1219805, 2012.
Dai, L., Korolev, K. S., and Gore, J.: Relation between stability and resilience determines the performance of early warning signals under different environmental drivers, P. Natl. Acad. Sci. USA, 112, 201418415, https://doi.org/10.1073/pnas.1418415112, 2015.
Dakos, V.: Identifying best-indicator species for abrupt transitions in multispecies communities, Ecol. Indic., 94, 494–502, https://doi.org/10.1016/j.ecolind.2017.10.024, 2018.
Dakos, V. and Kéfi, S.: Ecological resilience: what to measure and how, Environ. Res. Lett., 17, 043003, https://doi.org/10.1088/1748-9326/ac5767, 2022.
Dakos, V., Scheffer, M., van Nes, E. H., Brovkin, V., Petoukhov, V., and Held, H.: Slowing down as an early warning signal for abrupt climate change., P. Natl. Acad. Sci. USA, 105, 14308–12, https://doi.org/10.1073/pnas.0802430105, 2008.
Dakos, V., van Nes, E. H., Donangelo, R., Fort, H., and Scheffer, M.: Spatial correlation as leading indicator of catastrophic shifts, Theor. Ecol., 3, 163–174, https://doi.org/10.1007/s12080-009-0060-6, 2010.
Dakos, V., Kéfi, S., Rietkerk, M., van Nes, E. H., and Scheffer, M.: Slowing down in spatially patterned ecosystems at the brink of collapse, Am. Nat., 177, E153–166, https://doi.org/10.1086/659945, 2011.
Dakos, V., Carpenter, S. R., Brock, W. A., Ellison, A. M., Guttal, V., Ives, A. R., Kéfi, S., Livina, V., Seekell, D. A., van Nes, E. H., and Scheffer, M.: Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data, PLoS ONE, 7, e41010, https://doi.org/10.1371/journal.pone.0041010, 2012.
Dakos, V., van Nes, E. H., and Scheffer, M.: Flickering as an early warning signal, Theor. Ecol., 6, 309–317, https://doi.org/10.1007/s12080-013-0186-4, 2013.
Dakos, V., Carpenter, S. R., Nes, E. H. V., and Scheffer, M.: Resilience indicators: prospects and limitations for early warnings of regime shifts, Philos. T. R. Soc. B, 370, 20130263, https://doi.org/10.1098/rstb.2013.0263, 2015.
Deb, S., Sidheekh, S., Clements, C. F., Krishnan, N. C., and Dutta, P. S.: Machine learning methods trained on simple models can predict critical transitions in complex natural systems, Roy. Soc. Open Sci., 9, 211475, https://doi.org/10.1098/rsos.211475, 2022.
Ditlevsen, P. and Ditlevsen, S.: Warning of a forthcoming collapse of the Atlantic meridional overturning circulation, Nat. Commun., 14, 4254, https://doi.org/10.1038/s41467-023-39810-w, 2023.
Drake, J. M. and Griffen, B. D.: Early warning signals of extinction in deteriorating environments, Nature, 467, 456–459, https://doi.org/10.1038/nature09389, 2010.
Drake, J. M., O'Regan, S. M., Dakos, V., Kéfi, S., Rohani, P., Drake, J. M., O'Regan, S. M., Dakos, V., Kéfi, S., and Rohani, P.: Alternative stable states, tipping points, and early warning signals of ecological transitions, in: Theoretical Ecology: Concepts and Applications, edited by: McCann, K. S. and Gellner, G., Oxford University Press, 263–284, https://doi.org/10.1093/oso/9780198824282.003.0015, 2020.
Dylewsky, D., Lenton, T. M., Scheffer, M., Bury, T. M., Fletcher, C. G., Anand, M., and Bauch, C. T.: Universal early warning signals of phase transitions in climate systems, J. R. Soc. Interface, 20, 20220562, https://doi.org/10.1098/rsif.2022.0562, 2023.
Erfanian, A., Wang, G., and Fomenko, L.: Unprecedented drought over tropical South America in 2016: significantly under-predicted by tropical SST, Sci. Rep.-UK, 7, 5811, https://doi.org/10.1038/s41598-017-05373-2, 2017.
Fanelli, D.: Negative results are disappearing from most disciplines and countries, Scientometrics, 90, 891–904, https://doi.org/10.1007/s11192-011-0494-7, 2012.
Forzieri, G., Dakos, V., McDowell, N. G., Ramdane, A., and Cescatti, A.: Emerging signals of declining forest resilience under climate change, Nature, 608, 534–539, https://doi.org/10.1038/s41586-022-04959-9, 2022.
Franco, A., Malhotra, N., and Simonovits, G.: Publication bias in the social sciences: Unlocking the file drawer, Science, 345, 1502–1505, https://doi.org/10.1126/science.1255484, 2014.
Génin, A., Majumder, S., Sankaran, S., Danet, A., Guttal, V., Schneider, F. D., and Kéfi, S.: Monitoring ecosystem degradation using spatial data and the R package spatialwarnings, Methods Ecol. Evol., 9, 2067–2075, https://doi.org/10.1111/2041-210X.13058, 2018.
Gilmore, R.: Catastrophe theory for scientists and engineers, John Wiley & Sons, ISBN 0-486-67539-4, 1981.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Guttal, V. and Jayaprakash, C.: Changing skewness: an early warning signal of regime shifts in ecosystems, Ecol. Lett., 11, 450–460, https://doi.org/10.1111/j.1461-0248.2008.01160.x, 2008.
Guttal, V. and Jayaprakash, C.: Spatial variance and spatial skewness: leading indicators of regime shifts in spatial ecological systems, Theor. Ecol., 2, 3–12, https://doi.org/10.1007/s12080-008-0033-1, 2009.
Hagstrom, G. and Levin, S.: Phase Transitions and the Theory of Early Warning Indicators for Critical Transitions, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.12287, 23 October 2021.
Hastings, A. and Wysham, D. B.: Regime shifts in ecological systems can occur with no warning, Ecol. Lett., 13, 464–472, https://doi.org/10.1111/j.1461-0248.2010.01439.x, 2010.
Held, H. and Kleinen, T.: Detection of climate system bifurcations by degenerate fingerprinting, Geophys. Res. Lett., 31, 1–4, https://doi.org/10.1029/2004GL020972, 2004.
Hirota, M., Holmgren, M., van Nes, E. H., and Scheffer, M.: Global resilience of tropical forest and savanna to critical transitions, Science, 334, 232–235, 2011.
Hu, Z., Dakos, V., and Rietkerk, M.: Using functional indicators to detect state changes in terrestrial ecosystems, Trends Ecol. Evol., 37, 1036–1045, https://doi.org/10.1016/j.tree.2022.07.011, 2022.
Hughes, T. P., Linares, C., Dakos, V., van de Leemput, I. A., and van Nes, E. H.: Living dangerously on borrowed time during slow, unrecognized regime shifts, Trends Ecol. Evol., 28, 149–55, https://doi.org/10.1016/j.tree.2012.08.022, 2013.
Ives, A. R. and Dakos, V.: Detecting dynamical changes in nonlinear time series using locally linear state-space models, Ecosphere, 3, 1–15, https://doi.org/10.1890/ES11-00347.1, 2012.
Jones, C., Lowe, J., Liddicoat, S., and Betts, R.: Committed terrestrial ecosystem changes due to climate change, Nat. Geosci., 2, 484–487, https://doi.org/10.1038/ngeo555, 2009.
Kéfi, S., Rietkerk, M., Alados, C. L., Pueyo, Y., Papanastasis, V. P., ElAich, A., de Ruiter, P. C., Alados, L., and Ruiter, P. C. D.: Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems, Nature, 449, 213–217, https://doi.org/10.1038/nature06111, 2007.
Kéfi, S., Dakos, V., Scheffer, M., Van Nes, E. H., and Rietkerk, M.: Early warning signals also precede non-catastrophic transitions, Oikos, 122, 641–648, 2013.
Kéfi, S., Guttal, V., Brock, W. A., Carpenter, S. R., Ellison, A. M., Livina, V. N., Seekell, D. A., Scheffer, M., van Nes, E. H., and Dakos, V.: Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns, PLOS ONE, 9, e92097, https://doi.org/10.1371/journal.pone.0092097, 2014.
Kleinen, T., Held, H., Petschel-held, G., and Held, Á. H.: The potential role of spectral properties in detecting thresholds in the Earth system: application to the thermohaline circulation, Ocean Dynam., 53, 53–63, https://doi.org/10.1007/s10236-002-0023-6, 2003.
Klose, A. K., Karle, V., Winkelmann, R., and Donges, J. F.: Emergence of cascading dynamics in interacting tipping elements of ecology and climate: Cascading dynamics in tipping elements, Roy. Soc. Open Sci., 7, 200599, https://doi.org/10.1098/rsos.200599, 2020.
Klose, A. K., Wunderling, N., Winkelmann, R., and Donges, J. F.: What do we mean, “tipping cascade”?, Environ. Res. Lett., 16, 1–11, https://doi.org/10.1088/1748-9326/ac3955, 2021.
Kuehn, C.: A Mathematical Framework for Critical Transitions: Normal Forms, Variance and Applications, J. Nonlinear Sci., 23, 457–510, https://doi.org/10.1007/s00332-012-9158-x, 2012.
Kuznetsov, Y. A.: Elements of Applied Bifurcation Theory, Springer-Verlag, New York, https://doi.org/10.1007/978-1-4757-3978-7, 591 pp., 1995.
Lade, S. J. and Gross, T.: Early warning signals for critical transitions: A generalized modeling approach, PLOS Comput. Biol., 8, e1002360, https://doi.org/10.1371/journal.pcbi.1002360, 2012.
Laitinen, V., Dakos, V., and Lahti, L.: Probabilistic early warning signals, Ecol. Evol., 1–14, https://doi.org/10.1002/ece3.8123, 2021.
Lapeyrolerie, M. and Boettiger, C.: Limits to ecological forecasting: Estimating uncertainty for critical transitions with deep learning, Meth. Ecol. Evol., 14, 785–798, https://doi.org/10.1111/2041-210X.14013, 2022.
Lenton, T. M.: Early warning of climate tipping points, Nat. Clim. Change, 1, 201–209, 2011.
Lenton, T. M.: Environmental Tipping Points, 38, 1–29, https://doi.org/10.1146/annurev-environ-102511-084654, 2013a.
Lenton, T. M.: What early warning systems are there for environmental shocks?, Environ. Sci. Policy, 27, S60–S75, https://doi.org/10.1016/j.envsci.2012.06.011, 2013b.
Lenton, T. M., Buxton, J. E., Armstrong McKay, D. I., Abrams, J. F., Boulton, C. A., Lees, K., Powell, T. W. R., Boers, N., Cunliffe, A. M., and Dakos, V.: A resilience sensing system for the biosphere, Philos. T. R. Soc. B, 377, 20210383, https://doi.org/10.1098/rstb.2021.0383, 2022.
Lever, J. J., van de Leemput, I. A., Weinans, E., Quax, R., Dakos, V., van Nes, E. H., Bascompte, J., and Scheffer, M.: Foreseeing the future of mutualistic communities beyond collapse, Ecol. Lett., 23, 2–15, https://doi.org/10.1111/ele.13401, 2020.
Lewis, S. L., Brando, P. M., Phillips, O. L., Van Der Heijden, G. M. F., and Nepstad, D.: The 2010 Amazon Drought, Science, 331, 554–554, https://doi.org/10.1126/science.1200807, 2011.
Litzow, M. A. and Hunsicker, M. E.: Early warning signals, nonlinearity, and signs of hysteresis in real ecosystems, Ecosphere, 7, e01614, https://doi.org/10.1002/ecs2.1614, 2016.
Liu, L., Shao, Z., Lv, J., Xu, F., Ren, S., Jin, Q., Yang, J., Ma, W., Xie, H., Zhang, D., and Chen, X.: Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis, Front. Bioeng. Biotechnol., 8, 530, https://doi.org/10.3389/fbioe.2020.00530, 2020.
Liu, Y., Kumar, M., Katul, G. G., and Porporato, A.: Reduced resilience as an early warning signal of forest mortality, Nat. Clim. Change, 9, 880–885, https://doi.org/10.1038/s41558-019-0583-9, 2019.
Livina, V. N. and Lenton, T. M.: A modified method for detecting incipient bifurcations in a dynamical system, Geophys. Res. Lett., 34, 1–5, https://doi.org/10.1029/2006GL028672, 2007.
Livina, V. N., Kwasniok, F., and Lenton, T. M.: Potential analysis reveals changing number of climate states during the last 60 kyr, Clim. Past, 6, 77–82, https://doi.org/10.5194/cp-6-77-2010, 2010.
Lu, Z., Yuan, N., Yang, Q., Ma, Z., and Kurths, J.: Early Warning of the Pacific Decadal Oscillation Phase Transition Using Complex Network Analysis, Geophys. Res. Lett., 48, e2020GL091674, https://doi.org/10.1029/2020GL091674, 2021.
Majumder, S., Tamma, K., Ramaswamy, S., and Guttal, V.: Inferring critical thresholds of ecosystem transitions from spatial data, Ecology, 100, e02722, https://doi.org/10.1002/ecy.2722, 2019.
Mayfield, R. J., Langdon, P. G., Doncaster, C. P., Dearing, J. A., Wang, R., Nazarova, L. B., Medeiros, A. S., and Brooks, S. J.: Metrics of structural change as indicators of chironomid community stability in high latitude lakes, Quaternary Sci. Rev., 249, 106594, https://doi.org/10.1016/j.quascirev.2020.106594, 2020.
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020.
Nijp, J. J., Temme, A. J. A. M., van Voorn, G. A. K., Kooistra, L., Hengeveld, G. M., Soons, M. B., Teuling, A. J., and Wallinga, J.: Spatial early warning signals for impending regime shifts: A practical framework for application in real-world landscapes, Glob. Change Biol., 25, 1905–1921, https://doi.org/10.1111/gcb.14591, 2019.
Nowack, P., Runge, J., Eyring, V., and Haigh, J. D.: Causal networks for climate model evaluation and constrained projections, Nat. Commun., 11, 1415, https://doi.org/10.1038/s41467-020-15195-y, 2020.
O'Brien, D. A., Deb, S., Sidheekh, S., Krishnan, N. C., Sharathi Dutta, P., and Clements, C. F.: EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models, Ecography, 2023, e06674, https://doi.org/10.1111/ecog.06674, 2023.
Rietkerk, M., Dekker, S. C., de Ruiter, P. C., and van de Koppel, J.: Self-Organized Patchiness and Catastrophic Shifts in Ecosystems, Science, 305, 1926–1929, https://doi.org/10.1126/science.1101867, 2004.
Rietkerk, M., van de Koppel, J., and Koppel, J. V. D.: Regular pattern formation in real ecosystems, Trends Ecol. Evol., 23, 169–175, https://doi.org/10.1016/j.tree.2007.10.013, 2008.
Ritchie, P. and Sieber, J.: Early-warning indicators for rate-induced tipping, Chaos, 26, 093116, https://doi.org/10.1063/1.4963012, 2016.
Saade, C., Fronhofer, E. A., Pichon, B., and Kéfi, S.: Landscape Structure Affects Metapopulation-Scale Tipping Points, Am. Nat., 202, E17–E30, https://doi.org/10.1086/724550, 2023.
Saatchi, S., Longo, M., Xu, L., Yang, Y., Abe, H., André, M., Aukema, J. E., Carvalhais, N., Cadillo-Quiroz, H., Cerbu, G. A., Chernela, J. M., Covey, K., Sánchez-Clavijo, L. M., Cubillos, I. V., Davies, S. J., De Sy, V., De Vleeschouwer, F., Duque, A., Sybille Durieux, A. M., De Avila Fernandes, K., Fernandez, L. E., Gammino, V., Garrity, D. P., Gibbs, D. A., Gibbon, L., Gowae, G. Y., Hansen, M., Lee Harris, N., Healey, S. P., Hilton, R. G., Johnson, C. M., Kankeu, R. S., Laporte-Goetz, N. T., Lee, H., Lovejoy, T., Lowman, M., Lumbuenamo, R., Malhi, Y., Albert Martinez, J.-M. M., Nobre, C., Pellegrini, A., Radachowsky, J., Román, F., Russell, D., Sheil, D., Smith, T. B., Spencer, R. G. M., Stolle, F., Tata, H. L., Torres, D. del C., Tshimanga, R. M., Vargas, R., Venter, M., West, J., Widayati, A., Wilson, S. N., Brumby, S., and Elmore, A. C.: Detecting vulnerability of humid tropical forests to multiple stressors, One Earth, 4, 988–1003, https://doi.org/10.1016/j.oneear.2021.06.002, 2021.
Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., and Sugihara, G.: Early-warning signals for critical transitions, Nature, 461, 53–59, https://doi.org/10.1038/nature08227, 2009.
Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., van de Koppel, J., van de Leemput, I. A., Levin, S. A., van Nes, E. H., Pascual, M., and Vandermeer, J.: Anticipating Critical Transitions, Science, 338, 344–348, https://doi.org/10.1126/science.1225244, 2012a.
Scheffer, M., Hirota, M., Holmgren, M., Van Nes, E. H., and Chapin, F. S.: Thresholds for boreal biome transitions., P. Natl. Acad. Sci. USA, 109, 21384–9, https://doi.org/10.1073/pnas.1219844110, 2012b.
Scheffer, M., Carpenter, S. R., Dakos, V., and van Nes, E. H.: Generic Indicators of Ecological Resilience: Inferring the Chance of a Critical Transition, Annu. Rev. Ecol. Evol. S., 46, 145–167, https://doi.org/10.1146/annurev-ecolsys-112414-054242, 2015.
Scheffer, M., Bolhuis, J. E., Borsboom, D., Buchman, T. G., Gijzel, S. M. W., Goulson, D., Kammenga, J. E., Kemp, B., van de Leemput, I. A., Levin, S., Martin, C. M., Melis, R. J. F., van Nes, E. H., Romero, L. M., and Olde Rikkert, M. G. M.: Quantifying resilience of humans and other animals, P. Natl. Acad. Sci. USA, 115, 201810630, https://doi.org/10.1073/pnas.1810630115, 2018.
Seekell, D. A. and Dakos, V.: Heteroskedasticity as a leading indicator of desertification in spatially explicit data, Ecol. Evol., 5, 2185–2192, https://doi.org/10.1002/ece3.1510, 2015.
Seekell, D. A., Carpenter, S. R., Cline, T. J., and Pace, M. L.: Conditional Heteroskedasticity Forecasts Regime Shift in a Whole-Ecosystem Experiment, Ecosystems, 15, 741–747, https://doi.org/10.1007/s10021-012-9542-2, 2012.
Smith, T., Zotta, R.-M., Boulton, C. A., Lenton, T. M., Dorigo, W., and Boers, N.: Reliability of resilience estimation based on multi-instrument time series, Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, 2023.
Sole, R. V., Manrubia, S. C., Luque, B., Delgado, J., Bascompte, J., Solé, R. V., Manrubia, S. C., Luque, B., Delgado, J., and Bascompte, J.: Phase transitions and complex systems, Complexity, 1, 13–26, 1996.
Spanbauer, T. L., Allen, C. R., Angeler, D. G., Eason, T., Fritz, S. C., Garmestani, A. S., Nash, K. L., Stone, J. R., Stow, C. A., and Sundstrom, S. M.: Body size distributions signal a regime shift in a lake ecosystem, P. R. Soc. B, 283, https://doi.org/10.1098/rspb.2016.0249, 2016.
Spears, B. M., Futter, M. N., Jeppesen, E., Huser, B. J., Ives, S., Davidson, T. A., Adrian, R., Angeler, D. G., Burthe, S. J., Carvalho, L., Daunt, F., Gsell, A. S., Hessen, D. O., Janssen, A. B. G., Mackay, E. B., May, L., Moorhouse, H., Olsen, S., Søndergaard, M., Woods, H., and Thackeray, S. J.: Ecological resilience in lakes and the conjunction fallacy, Nat. Ecol. Evol., 1, 1616–1624, https://doi.org/10.1038/s41559-017-0333-1, 2017.
Staver, A. C., Archibald, S., and Levin, S. A.: The Global Extent and Determinants of Savanna and Forest as Alternative Biome States, Science, 334, 230–232, https://doi.org/10.1126/science.1210465, 2011.
Tang, Y., Zhu, X., He, C., Hu, J., and Fan, J.: Critical slowing down theory provides early warning signals for sandstone failure, Front. Earth Sci., 10, 934498, https://doi.org/10.3389/feart.2022.934498, 2022.
Thompson, J. M. T. and Sieber, J.: Predicting climate tipping as a noisy bifurcation: a review, Int. J. Bifurcat Chaos, 21, 399–423, https://doi.org/10.1142/s0218127411028519, 2011.
Tirabassi, G., Viebahn, J., Dakos, V., Dijkstra, H. A., Masoller, C., Rietkerk, M., and Dekker, S. C.: Interaction network based early-warning indicators of vegetation transitions, Ecol. Complex., 19, 148–157, https://doi.org/10.1016/j.ecocom.2014.06.004, 2014.
Uden, D. R., Twidwell, D., Allen, C. R., Jones, M. O., Naugle, D. E., Maestas, J. D., Allred, B. W., and Wallace, K. J.: Spatial Imaging and Screening for Regime Shifts, Front. Ecol. Evol., 7, 1–16, https://doi.org/10.3389/fevo.2019.00407, 2019.
van de Leemput, I. A., Dakos, V., Scheffer, M., and van Nes, E. H.: Slow Recovery from Local Disturbances as an Indicator for Loss of Ecosystem Resilience, Ecosystems, 21, 141–152, https://doi.org/10.1007/s10021-017-0154-8, 2018.
van Der Bolt, B., van Nes, E. H., and Scheffer, M.: No warning for slow transitions, J. R. Soc. Interface, 18, 20200935, https://doi.org/10.1098/rsif.2020.0935, 2021.
van Nes, E. H., Arani, B. M. S., Staal, A., van der Bolt, B., Flores, B. M., Bathiany, S., and Scheffer, M.: What Do You Mean, `Tipping Point'?, Trends Ecol. Evol., 31, 902–904, https://doi.org/10.1016/j.tree.2016.09.011, 2016.
Veldhuis, M. P., Martinez-Garcia, R., Deblauwe, V., and Dakos, V.: Remotely-sensed slowing down in spatially patterned dryland ecosystems, Ecography, 2022, e06139, https://doi.org/10.1111/ecog.06139, 2022.
Veraart, A. J. A. J., Faassen, E. J. E. J., Dakos, V., Van Nes, E. H. E. H., Lürling, M., Scheffer, M., and Lurling, M.: Recovery rates reflect distance to a tipping point in a living system, Nature, 481, 357–359, https://doi.org/10.1038/nature10723, 2012.
Verbesselt, J., Umlauf, N., Hirota, M., Holmgren, M., Van Nes, E. H., Herold, M., Zeileis, A., and Scheffer, M.: Remotely sensed resilience of tropical forests, Nat. Clim. Change, 6, 1028–1031, https://doi.org/10.1038/nclimate3108, 2016.
von Hardenberg, J., Meron, E., Shachak, M., Zarmi, Y., and Hardenberg, J. V.: Diversity of vegetation patterns and desertification, Phys. Rev. Lett., 8719, 198101, https://doi.org/10.1103/PhysRevLett.87.198101, 2001.
Wang, R., Dearing, J. A., Langdon, P. G., Zhang, E., Yang, X., Dakos, V., and Scheffer, M.: Flickering gives early warning signals of a critical transition to a eutrophic lake state, Nature, 492, 419–422, https://doi.org/10.1038/nature11655, 2012.
Weinans, E., Lever, J. J., Bathiany, S., Quax, R., Bascompte, J., Van Nes, E. H., Scheffer, M., and Van De Leemput, I. A.: Finding the direction of lowest resilience in multivariate complex systems, J. R. Soc. Interface, 16, https://doi.org/10.1098/rsif.2019.0629, 2019.
Weinans, E., Quax, R., van Nes, E. H., and Leemput, I. A. van de: Evaluating the performance of multivariate indicators of resilience loss, Sci. Rep.-UK, 11, 9148, https://doi.org/10.1038/s41598-021-87839-y, 2021.
Wissel, C.: A universal law of the characteristic return time near thresholds, Oecologia, 65, 101–107, 1984.
Wu, H., Hou, W., Yan, P.-C., Zhang, Z.-S., and Wang, K.: A study of the early warning signals of abrupt change in the Pacific decadal oscillation, Chinese Phys. B, 24, 089201, https://doi.org/10.1088/1674-1056/24/8/089201, 2015.
Wu, S., Liu, L., Li, D., Zhang, W., Liu, K., Shen, J., and Zhang, L.: Global desert expansion during the 21st century: Patterns, predictors and signals, Land Degrad. Dev., 34, 377–388, https://doi.org/10.1002/ldr.4466, 2023.
Yin, Z., Dekker, S. C., Rietkerk, M., van den Hurk, B. J. J. M., and Dijkstra, H. A.: Network based early warning indicators of vegetation changes in a land–atmosphere model, Ecol. Complex., 26, 68–78, https://doi.org/10.1016/j.ecocom.2016.02.004, 2016.
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The paper gives a comprehensive overview on the current state of the societally important field of Earth system tipping point early warning signals across different fields. Relevant conclusions are drawn on promising avenues for further advancing the research field.
The paper gives a comprehensive overview on the current state of the societally important field...
Short summary
Tipping points are abrupt, rapid, and sometimes irreversible changes, and numerous approaches have been proposed to detect them in advance. Such approaches have been termed early warning signals and represent a set of methods for identifying changes in the underlying behaviour of a system across time or space that might indicate an approaching tipping point. Here, we review the literature to explore where, how, and which early warnings have been used in real-world case studies so far.
Tipping points are abrupt, rapid, and sometimes irreversible changes, and numerous approaches...
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