ITS4.3/NP1.2 | Tipping Points in the Earth System
Tipping Points in the Earth System
Convener: Niklas Boers | Co-conveners: Ricarda Winkelmann, Anna von der Heydt, Timothy Lenton
Orals
| Tue, 16 Apr, 16:15–17:55 (CEST)
 
Room N2
Posters on site
| Attendance Tue, 16 Apr, 10:45–12:30 (CEST) | Display Tue, 16 Apr, 08:30–12:30
 
Hall X4
Orals |
Tue, 16:15
Tue, 10:45
Several subsystems of the Earth have been suggested to possibly react abruptly at critical levels of anthropogenic forcing. Examples of such potential Tipping Elements include the Atlantic Meridional Overturning Circulation, the polar ice sheets, tropical and boreal forests, as well as the tropical monsoon systems. Interactions between the different Tipping Elements may either have stabilizing or destabilizing effects on the other subsystems, potentially leading to cascades of abrupt transitions. The critical forcing levels at which abrupt transitions occur have recently been associated with Tipping Points.

It is paramount to determine the critical forcing levels (and the associated uncertainties) beyond which the systems in question will abruptly change their state, with potentially devastating climatic, ecological, and societal impacts. For this purpose, we need to substantially enhance our understanding of the dynamics of the Tipping Elements and their interactions, on the basis of paleoclimatic evidence, present-day observations, and models spanning the entire hierarchy of complexity. Moreover, to be able to mitigate - or prepare for - potential future transitions, early warning signals have to be identified and monitored in both observations and models.

This multidisciplinary session invites contributions that address Tipping Points in the Earth system from the different perspectives of all relevant disciplines, including

- the mathematical theory of abrupt transitions in (random) dynamical systems,
- paleoclimatic studies of past abrupt transitions,
- data-driven and process-based modelling of past and future transitions,
- methods to anticipate critical transitions from data
- the implications of abrupt transitions for climate sensitivity and response,
- ecological and socioeconomic impacts
- decision theory in the presence of uncertain Tipping Point estimates and uncertain impacts

Orals: Tue, 16 Apr | Room N2

Chairpersons: Niklas Boers, Ricarda Winkelmann
16:15–16:25
|
EGU24-18039
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ITS4.3/NP1.2
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On-site presentation
Jonathan F. Donges, Donovan P. Dennis, Sina Loriani, Boris Sakschewski, Nico Wunderling, and Ricarda Winkelmann

The Tipping Point Modelling Intercomparison Project (TIPMIP) is an international initiative that aims to systematically improve our understanding of potential tipping dynamics in different components of the Earth system and to assess the associated uncertainties (www.tipmip.org). By linking and evaluating different models through a systematic framework, TIPMIP aims to fill critical knowledge gaps in Earth system and climate risks by improving their assessment at different levels of anthropogenic forcing and associated long-term commitments and irreversibilities. The Methods and Risk Assessment Working Group of TIPMIP will further develop the methodological foundations of this systematic approach to the study of tipping dynamics in domain-specific and coupled Earth system  numerical models to support future assessment reports and comprehensive risk analyses. In this contribution, we introduce the Methods and Risk Assessment Working Group within TIPMIP, and highlight relevant lines of methodological development to be pursued, including: (i) systematic and automated detection of tipping points and critical transitions in model output and Earth observation data for TIPMIP (e.g, the TOAD framework), (ii) detection of non-linear regime shifts in time series data for TIPMIP beyond amplitude shifts, e.g. transitions between more regular and more erratic variability (e.g. the pyunicorn toolkit), and (iii) probabilistic analysis and emulator approaches of risks for triggering tipping events and cascading tipping dynamics at different levels of anthropogenic forcing (e.g. the pycascades approach).

How to cite: Donges, J. F., Dennis, D. P., Loriani, S., Sakschewski, B., Wunderling, N., and Winkelmann, R.: Methodologies for climate tipping points analysis and risk assessments in TIPMIP, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18039, https://doi.org/10.5194/egusphere-egu24-18039, 2024.

16:25–16:35
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EGU24-5870
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ITS4.3/NP1.2
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ECS
|
Virtual presentation
|
Maya Ben Yami, Andreas Morr, Sebastian Bathiany, and Niklas Boers

Observations are increasingly used to detect critical slowing down (CSD) in potentially multistable components of the Earth system in order to warn of forthcoming critical transitions in these components. In addition, it has been suggested to use the statistical changes in these historical observations to extrapolate into the future and predict the tipping time. We argue that this extrapolation is too sensitive to uncertainties to give robust results. In particular, we raise concerns regarding (1) the modelling assumptions underlying the approaches to extrapolate results obtained from analyzing historical data into the future, (2) the representativeness of individual time series representing the variability of the respective Earth system components, and (3) the effect of uncertainties and preprocessing of the employed observational datasets, with focus on non-stationary observational coverage and the way gaps are filled. We explore these uncertainties both qualitatively and quantitatively for the Atlantic Meridional Overturning Circulation (AMOC). We argue that even under the assumption that these natural systems have a tipping point that they are getting closer to, the different uncertainties are too large to be able to estimate the time of tipping based on extrapolation from historical data.

How to cite: Ben Yami, M., Morr, A., Bathiany, S., and Boers, N.: Uncertainties too large to predict tipping times of major Earth system components, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5870, https://doi.org/10.5194/egusphere-egu24-5870, 2024.

16:35–16:45
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EGU24-8618
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ITS4.3/NP1.2
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On-site presentation
Paul Ritchie and Kerstin Lux-Gottschalk

To tip or not to tip? Many subsystems of the Earth are at risk of undergoing abrupt transitions from their current stable state to a drastically different and often less desired state due to anthropogenic climate change. These so-called tipping events often present severe consequences for ecosystems and human livelihood that are difficult to reverse. One common mechanism for tipping to occur is via forcing and driving a nonlinear system beyond a critical threshold that signifies self-amplifying feedbacks inducing tipping. However, previous work has shown that it is possible to briefly overshoot a critical threshold and avoid tipping. Specifically, the peak distance of an overshoot and the time a system can spend beyond a threshold are governed by an inverse square law relationship. In the real world or complex models, critical thresholds and other system features determining the permitted overshoot are highly uncertain. In this presentation, we look at how such uncertainties affect the probability of tipping from the perspective of uncertainty quantification. We show the importance of constraining uncertainty in the location of the critical threshold and the linear restoring rate to the stable state to reduce the uncertainty in the probability of tipping. Using a simple box model for the Atlantic Meridional Overturning Circulation, we highlight the need to constrain the high uncertainty found in wind-driven fluxes represented by a diffusive time scale within the box model to reduce uncertainty in the tipping probability for overshoot scenarios. 

How to cite: Ritchie, P. and Lux-Gottschalk, K.: Uncertainty quantification for overshoots of tipping thresholds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8618, https://doi.org/10.5194/egusphere-egu24-8618, 2024.

16:45–16:55
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EGU24-12295
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ITS4.3/NP1.2
|
ECS
|
On-site presentation
Sjoerd Terpstra, Swinda K.J. Falkena, Robbin Bastiaansen, Sebastian Bathiany, Henk A. Dijkstra, and Anna von der Heydt

Many potential tipping elements have been identified in the climate system over the last decade, although some of them are surrounded by large uncertainties. We perform an updated analysis of abrupt changes in current state-of-the-art climate models to re-evaluate the evidence of these shifts—whether they are tipping points or not. We examine all CMIP6 models (59 in total) under the 1pctCO2 scenario using a Canny edge detection method—adapted for spatiotemporal dimensions—to detect abrupt shifts in climate data. We perform this semi-automatic analysis on 83 two-dimensional variables of the ocean, atmosphere, and land. We aggregate the detected shifts that are connected spatially or temporally. This results in connected regions of abrupt shifts and allows us to map areas that are most at risk of these shifts according to CMIP6 models. We report statistics on number of abrupt changes detected, surface area of abrupt changes, and critical global mean temperature at which these abrupt changes occur. This is done for various climate subsystems and potential tipping elements, such as the Arctic sea ice, Antarctic sea ice and the North Atlantic subpolar gyre. We find evidence for abrupt changes in several systems, but not all models show them equally.

How to cite: Terpstra, S., Falkena, S. K. J., Bastiaansen, R., Bathiany, S., Dijkstra, H. A., and von der Heydt, A.: Analysis of Abrupt Changes in CMIP6 Models Using Edge Detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12295, https://doi.org/10.5194/egusphere-egu24-12295, 2024.

16:55–17:05
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EGU24-18045
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ITS4.3/NP1.2
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ECS
|
On-site presentation
Victor Couplet, Marina Martínez Montero, and Michel Crucifix

A tipping cascade is a series of tipping events in the Earth system where transitions in one subsystem can trigger further transitions in other subsystems. A concern for the future is that such a cascade could lock the Earth system in a pathway towards a so-called hothouse state. We investigate this possibility with SURFER, a reduced complexity model with a process-based carbon cycle that can reliably predict CO2 concentrations, global mean temperatures, sea-level rise, and many ocean acidification metrics on timescales from decades to millions of years. We have incorporated in the model a network of interacting tipping elements and their feedback on the climate through albedo changes and additional greenhouse gas emissions. This has allowed for a systematic investigation of the effects of a family of realistic emission scenarios on the future trajectories of the Earth system. Our results show that a permanent shift to a hothouse state within the next few centuries is implausible. On longer time scales, however, tipping cascades can lead to enduring additional warming and particularly sea level rise.

How to cite: Couplet, V., Martínez Montero, M., and Crucifix, M.: Tipping cascades, future Earth system trajectories and the prospect of a hothouse: insights from the SURFER model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18045, https://doi.org/10.5194/egusphere-egu24-18045, 2024.

17:05–17:15
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EGU24-12187
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ITS4.3/NP1.2
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ECS
|
On-site presentation
Yu Huang, Sebastian Bathiany, Peter Ashwin, and Niklas Boers

Rate-induced tipping (R-tipping) occurs when the forcing rate changes too rapidly for the system to track its quasi-equilibrium state, leading to an unexpected collapse. Currently, there is a lack of valid early warning signals (EWS) for R-tipping, particularly in the presence of noise perturbations. To address this deficiency, we employ a deep learning algorithm to extract the high-order structures hidden within time series data before R-tipping occurs. Then the trained neural networks are taken to provide real-time EWS for R-tipping, demonstrating skillful forecasts with a substantially long lead time, surpassing the performance of conventional critical slowing down indicators. Our progress underscores the predictability of R-tipping, offering the potential to improve the ability to deduce the safe operating space for a wider spectrum of complex systems.

How to cite: Huang, Y., Bathiany, S., Ashwin, P., and Boers, N.: Anticipating rate-induced tipping by a deep learning framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12187, https://doi.org/10.5194/egusphere-egu24-12187, 2024.

17:15–17:25
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EGU24-5998
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ITS4.3/NP1.2
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ECS
|
On-site presentation
Joas Müller, Giuseppe Zappa, and Alessio Bellucci

Recent studies utilizing the CMIP5 and CMIP6 model ensembles reveal that the subpolar North Atlantic (NA) is prone to deep convection collapsing leading to abrupt cooling of sea surface temperatures. Consequently, the latest comprehensive study on tipping points and the first report on global tipping points include the subpolar gyre (SPG) deep convection on the list of core tipping elements of Earth’s climate system.

Here, we investigate the drivers and impacts of a collapse of deep convection in the subpolar NA and the role of internal variability using a coupled climate model large ensemble (namely, the CESM2-LE consisting of 100 ensemble members) under the SSP3-7.0 forcing scenario. We identify that freshening of surface conditions leads to the negative surface density anomaly, eventually resulting in the cessation of deep mixing and the abrupt cooling of sea surface temperatures. The ensemble shows abrupt cooling occurring approximately in 2045 with internal variability leading to a spread of ±11 years. In each ensemble member, the subpolar NA transitions to a new state without deep convection, colder sea surface temperatures, strongly reduced heat loss to the atmosphere, and large circulation changes.

Internal variability does not determine if, but when abrupt cooling occurs, suggesting a forced response to larger-scale changes and a potential tipping point to be reached decades before the prominent abrupt cooling event. We provide evidence for the collapse of deep convection being a component of a positive feedback mechanism resulting in the SPG circulation transitioning to a weaker state. Without deep convection at the center of the circulation, the density gradient-driven part of the gyre circulation vanishes and the circulation strength decreases by approximately 50 %. The tipping point of the subpolar NA is therefore reached decades prior to the abrupt cooling and abrupt cooling is an inevitable consequence of the tipping event.

This points towards a potential misconception concerning drivers of abrupt climate
change in the subpolar NA, connected tipping points, and their thresholds, highlighting
the necessity for clarifying research efforts in the future.

How to cite: Müller, J., Zappa, G., and Bellucci, A.: North Atlantic Subpolar Gyre Deep Convection: A Tipping Point Reached Decades Ago?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5998, https://doi.org/10.5194/egusphere-egu24-5998, 2024.

17:25–17:35
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EGU24-3874
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ITS4.3/NP1.2
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ECS
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On-site presentation
Frerk Pöppelmeier and Thomas F. Stocker

The Atlantic Meridional Overturning Circulation (AMOC) is projected to weaken due to the increase in buoyancy caused by anthropogenic warming and consequent freshening during the 21st century and beyond. Atmosphere-ocean general circulation models simulate this AMOC weakening due to warming of the surface ocean, changes in the hydrological cycle that shift the North Atlantic salt budget, melting sea-ice, and changes in the atmospheric circulation. However, the freshwater contribution from the melting Greenland ice sheet is often either only considered in idealized scenarios or entirely omitted due to computational constraints. This simplification contributes to the large uncertainty surrounding the possibility of the AMOC crossing a tipping point in the forthcoming centuries. Here we employ the fully coupled Earth system model of intermediate complexity Bern3D v3, which dynamically simulates all ice-ocean-atmosphere interactions. We conduct a set of simulations driven by idealized CO2 concentration paths to investigate the impact of the melting Greenland ice sheet on the stability of the AMOC over the next 3000 years. We find that for a slow CO2 increase of 0.5%/yr up to twice pre-industrial levels, the general trends of the AMOC evolution are independent of whether Greenland meltwater is taken into account, with an initial weakening, but long-term recovery. Yet, the additional meltwater results in a further weakening of about 3 Sv after 100 years, but without leading to a full collapse of the circulation. This effect is due to melt rates remaining relatively low for the initial 100 years and only reaching their peak after 500 years. In the long-term, the curtailed AMOC and hence northward heat transport substantially slows down the disintegration of the Greenland ice sheet. Only in scenarios where the melt rates are kept artificially high, the AMOC does not recover. This highlights that the meltwater-induced AMOC weakening stabilizes the Greenland ice sheet, which in turn limits further AMOC weakening. This suggests that the potential for cascading interactions may be limited.

How to cite: Pöppelmeier, F. and Stocker, T. F.: Impact of future Greenland ice sheet melt on the stability of the Atlantic Meridional Overturning Circulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3874, https://doi.org/10.5194/egusphere-egu24-3874, 2024.

17:35–17:45
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EGU24-11804
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ITS4.3/NP1.2
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ECS
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On-site presentation
Elian Vanderborght, Henk Dijkstra, and René Westen

Recent quasi-equilibrium studies performed in the Community Earth System Model (CESM) have revealed a bi-stable regime of the Atlantic Meridional Overturning Circulation (AMOC) in this model. This suggests that the present-day AMOC might exist in a bi-stable regime, emphasizing the need for accurate predictions regarding the probability of an AMOC collapse over the next decades. However, the CESM exhibits notable biases, with a critical freshwater transport bias at 34°S in the Atlantic emerging as a key determinant of AMOC stability. Specifically, this bias enhances the stability of the AMOC, rendering the CESM unable to accurately predict the likelihood of AMOC tipping.

In this study, we establish a direct connection between the freshwater transport bias in the CESM and a corresponding freshwater content bias in the Indian Ocean. By investigating the detailed freshwater balance, we identify specific regions within the Indian Ocean that exert a significant influence on the Atlantic freshwater transport bias at 34°S. This quantitative analysis enables us to construct an optimal surface-flux correction, which reduces the model biases. This physics-based surface-flux correction allows us to adjust the AMOC to its correct stability regime in the CESM without imposing unrealistic flux adjustments

How to cite: Vanderborght, E., Dijkstra, H., and Westen, R.: Origin in of the AMOC fresh water transport biases in a state-of-the-art climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11804, https://doi.org/10.5194/egusphere-egu24-11804, 2024.

17:45–17:55
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EGU24-4021
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ITS4.3/NP1.2
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ECS
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On-site presentation
Jelle Soons, Tobias Grafke, and Henk A. Dijkstra

The present-day Atlantic Meridional Overturning Circulation (AMOC) is considered to be a prominent tipping element and its collapse would have grave consequences on the global climate. Hence, it is important to determine probabilities and pathways for noise-induced tipping events. However, as there is no observational evidence for an AMOC transition over the historical period, a noise-induced transition is expected to be a rare event in models and simple Monte Carlo techniques are not suited for such low-probability events. Here, we use Large Deviation Theory to directly compute the most probable transition pathways for the collapse and recovery of the AMOC in a box model of the World Ocean calibrated to the FAMOUS-model, where we added stochastic freshwater forcing. This allows us to determine the physical mechanisms of noise-induced AMOC transitions. We show that the most likely path of an AMOC collapse starts paradoxically with a strengthening of the AMOC followed by an immediate drop within a couple of years due to a short but relatively strong freshwater pulse. The recovery on the other hand is a slow process, where the North Atlantic Ocean needs to be gradually salinified over a course of decades, and its dynamics are quite close to the recovery in a bifurcation tipping event. The proposed method provides several benefits, including an estimate of probability ratios of collapse between various freshwater noise scenarios, showing that the AMOC is most vulnerable to freshwater forcing into the Atlantic thermocline region.

How to cite: Soons, J., Grafke, T., and Dijkstra, H. A.: Optimal Transition Paths for AMOC Collapse and Recovery in a Stochastic Box Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4021, https://doi.org/10.5194/egusphere-egu24-4021, 2024.

Posters on site: Tue, 16 Apr, 10:45–12:30 | Hall X4

Display time: Tue, 16 Apr 08:30–Tue, 16 Apr 12:30
Chairpersons: Niklas Boers, Ricarda Winkelmann, Anna von der Heydt
X4.109
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EGU24-2735
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ITS4.3/NP1.2
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ECS
Teleconnections among tipping elements in the Earth system
(withdrawn)
Teng Liu, Dean Chen, Lan Yang, Jun Meng, Zanchenling Wang, Josef Ludescher, Jingfang Fan, Saini Yang, Deliang Chen, Jürgen Kurths, Xiaosong Chen, Shlomo Havlin, and Hans Joachim Schellnhuber
X4.110
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EGU24-7090
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ITS4.3/NP1.2
Eigen Micostate Theory of Complex Systems and its Application in Earth System
(withdrawn)
Xiaosong Chen, Teng Liu, Xuan Ma, Yan Xia, Jingfang Fan, and Fei Xie
X4.111
|
EGU24-19783
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ITS4.3/NP1.2
Can the time scale of glacial isostatic adjustment condition rate-induced tipping of the West-Antarctic Ice Sheet?
(withdrawn)
Marisa Montoya, Jan Swierczek-Jereczek, Alexander Robinson, and Jorge Alvarez-Solas
X4.112
|
EGU24-3802
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ITS4.3/NP1.2
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ECS
Tommaso Alberti, Fabio Florindo, Eelco J. Rohling, Valerio Lucarini, and Davide Faranda

While previous climate transitions are easily identifiable, a comprehensive understanding of their underlying mechanisms and timescales remains elusive. In this investigation, we employ dimensional analysis of benthic stable isotope records to unveil the associations between climatic fluctuations in the Cenozoic era and changes in the number of effective degrees of freedom across various timescales. The Hothouse and Warmhouse states are predominantly influenced by precession timescales, whereas the Icehouse climate is primarily shaped by obliquity and eccentricity timescales. Remarkably, the Coolhouse state lacks distinct dominant timescales. Our analytical approach effectively identifies abrupt climate shifts and extremes objectively, as evidenced by high-resolution data from the last glacial cycle, which reveals sudden climate shifts within a single climate state. These findings have profound implications for our comprehension of the inherent stability of each climate state and the assessment of (paleo-)climate models' ability to accurately replicate crucial features of past and future climate states and transitions.

How to cite: Alberti, T., Florindo, F., Rohling, E. J., Lucarini, V., and Faranda, D.: Decoding Cenozoic climate variability: dominant timescales beyond critical transitions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3802, https://doi.org/10.5194/egusphere-egu24-3802, 2024.

X4.113
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EGU24-7515
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ITS4.3/NP1.2
Juan Rocha and Anne-Sophie Crépin

Abrupt transitions in ecosystems can be interconnected, raising challenges for science and management in identifying sufficient interventions to prevent them or recover from undesirable shifts. Here we use principles of network controllability to explore how difficult it is to manage coupled regime shifts. We find that coupled regime shifts are easier to manage when they share drivers, but can become harder to manage if new feedbacks are formed when coupled. Simulation experiments showed that both network structure and coupling strength matter in our ability to manage interconnected systems. This theoretical insights calls for an empirical assessment of cascading regime shifts in ecosystems and warns about our limited ability to control cascading effects.

How to cite: Rocha, J. and Crépin, A.-S.: Structural controllability and management of cascading regime shifts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7515, https://doi.org/10.5194/egusphere-egu24-7515, 2024.

X4.114
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EGU24-12023
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ITS4.3/NP1.2
Peter Ditlevsen

 In a recent paper [2] we predicted a collapse of the AMOC as soon as mid-century at odds with assessments based on climate model scenarios. The prediction was based on the sub polar gyre fingerprint as a proxy for the AMOC as proposed by Ceasar et al. [2]. Several other fingerprints have been proposed, all showing early warning signals of a forthcoming tipping point [3]. Here we present a statistical analysis, optimally extracting the common signal in the different fingerprints in order to further solidify the assessments. 

[1] Ditlevsen, P., Ditlevsen, S. Warning of a forthcoming collapse of the Atlantic meridional overturning circulation. Nat Commun 14, 4254 (2023)

[2] Caesar, L., Rahmstorf, S., Robinson, A. et al. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature 556, 191–196 (2018)

[3] Boers, N. Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nat. Clim. Chang. 11, 680–688 (2021)

How to cite: Ditlevsen, P.: Fingerprinting the AMOC and predicting a collapse, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12023, https://doi.org/10.5194/egusphere-egu24-12023, 2024.

X4.115
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EGU24-18126
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ITS4.3/NP1.2
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ECS
|
|
Jonathan Rosser, Ricarda Winkelmann, and Nico Wunderling

The Earth's climate system is a complex system that includes key components such as the Arctic Summer Sea Ice or the El Niño Southern Oscillation as well as climate tipping elements like the continental-scale ice sheets or the Amazon rainforest. Crossing warming thresholds of these elements can lead to a qualitatively different climate state, endangering the stability of human societies. Particularly, the cryosphere elements are vulnerable at current levels of global warming (1.2°C) while also having long response times and large structural uncertainties. Investigating a network of interacting Earth system components using an established conceptual model, we systematically assess which uncertainties of key Earth system component have the largest impacts on tipping risks. We find that the cryosphere tipping elements (the Greenland and the West Antarctica ice sheets) are most decisive for tipping risks and cascading effects within our model. At a global warming level of 1.5°C, neglecting the large cryosphere tipping elements can reduce the mean number of disintegrated Earth system components by as much as 56%. This is concerning as overshooting 1.5°C of global warming is fast becoming inevitable, while current state-of-the-art IPCC-type global circulation models do not (yet) include dynamic ice sheets. Our results suggest that urgent integrated Earth system model development and Earth observation efforts including the large polar ice sheets are necessary and a precautionary measure of meeting stringent climate targets is crucial to limit tipping risks.

How to cite: Rosser, J., Winkelmann, R., and Wunderling, N.: Cryosphere tipping elements decisive for tipping risks and cascading effects in the Earth system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18126, https://doi.org/10.5194/egusphere-egu24-18126, 2024.

X4.116
|
EGU24-16007
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ITS4.3/NP1.2
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Laure Moinat, Jérôme Kasparian, and Maura Brunetti

Early Warning Signals (EWS) are indicators that can be used to anticipate tipping points i.e. abrupt changes in dynamical systems. Detecting EWS is a crucial part of climate science, especially in the context of climate change. Several methods are used to identify tipping points using time series of climate state variables (e.g. temperature, precipitation, etc), but few consider spatial correlations [1]. Spatial detection could identify the starting location of a transition process from a state to another and be directly applied to satellite observations. We consider different state variables on a numerical grid as a complex network, where grid points displaying correlation are connected and the temporal evolution of this network is studied. 
We seek for network properties that can be used as EWS when approaching the state transition. 

The network is generated and analysed using the pyUnicorn package [2], and compared to classical statistical methods. The networks are constructed using two methods: Pearson correlation coefficient and mutual information, allowing us to compare a linear and a causal approach. Multiple network indicators such as the degree of correlation, the average path length, and the area weighted connectivity are compared. To test the method robustness, we look at the network dependencies in terms of the time window, the interval over which the forcing is changed, and the effect of reducing the extent of the network (limited, for example, over polar or equatorial regions). These indicators show tipping points at the global scale, as simulated in a coupled-aquaplanet configuration with the MIT general circulation model, using as forcing parameter the atmospheric CO2 content or the input of solar energy [3] . The application of such indicators as EWS is discussed.

 

[1] van der Mheen et al. Geophysical Research Letters 40, 11  (2013)

[2] Donges et al. Chaos 25, 113101 (2015)

[3] Brunetti \& Ragon, Physical Review E 107, 054214 (2023)

How to cite: Moinat, L., Kasparian, J., and Brunetti, M.: Investigating Early Warning Signals in Climate Simulations using Complex Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16007, https://doi.org/10.5194/egusphere-egu24-16007, 2024.

X4.117
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EGU24-8927
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ITS4.3/NP1.2
Sebastian Bathiany, Lana Blaschke, Andreas Morr, and Niklas Boers

Terrestrial ecosystems are affected by climate change, deforestation and other human influences. There is concern that the resilience of these ecosystems, i.e. their ability to recover from perturbations, is thereby decreased and that their sensitivity to environmental change is increased. In the extreme case, this sensitivity could diverge at a “tipping point”, and propel systems into alternative states. A prominent example is the potential dieback of the Amazon rainforest and the transition to a savanna-like state.

The notion of resilience is a highly complex and multi-faceted concept. Ecological resilience theory and the mathematical properties of dynamical systems suggest that a number of different resilience quantifiers are related to each other, or even equivalent, which would allow improved “resilience monitoring” from space. For instance, indicators based on the phenomenon of “critical slowing down” (CSD) like variance and autocorrelation, and related indicators have been used to detect changes over time. In contrast to empirical recovery rates, these indicators do not require one to directly observe the recovery from rare extreme disturbances. Also, they do not rely on the observation or attribution of the responsible environmental drivers.

Based on the assumption that fluctuations in remotely sensed proxies of vegetation properties (like biomass or vegetation greenness) behave like iconic one-dimensional stochastic models (most importantly, the Ornstein-Uhlenbeck process), CSD-based indicators should be related to empirical recovery rates after perturbations, to the more general Kramers-Moyal coefficients rooted in statistical mechanics, and to the sensitivity of a dynamical equilibrium state to environmental change. It has been shown that in observations, the theoretically expected relationships between some of these measures roughly hold. At the same time, process-based models, as well as observations, can deviate from such simple stochastic models, e.g. when multiple plant types affect the resilience of an ecosystem but not its sensitivity to environmental change.

In our contribution, we show and discuss examples for such deviations in a global vegetation model LPJ. In addition, we compare resilience indicators across a number of state-of-the-art models from CMIP6 and compare the results to an assessment of observations, in order to separate limitations that are related to the practical measurement process (e.g. uncertainties related to retrieval algorithms) from limitations that are associated with unjustified theoretical assumptions. Our results are meant to guide resilience monitoring toward meaningful indicators and to focus on regions and observable properties that can warn of future loss of ecosystem services.

How to cite: Bathiany, S., Blaschke, L., Morr, A., and Boers, N.: Consistency of resilience indicators in terrestrial vegetation models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8927, https://doi.org/10.5194/egusphere-egu24-8927, 2024.

X4.118
|
EGU24-17709
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ITS4.3/NP1.2
Lana Blaschke, Andreas Morr, Sebastian Bathiany, Fabian Telschow, Taylor Smith, and Niklas Boers

Tropical forests are vital for climate change mitigation as carbon sinks. Yet, research suggests that climate change, deforestation and other human influences threaten these systems, potentially pushing them across a tipping point where the tropical vegetation might collapse into a low-treecover state. Signs for this trend are reductions of resilience defined as the system's capability to recover from perturbations. When resilience decreases, according to dynamic system theory, a critical slowing down (CSD) induces changes in statistical measures such as the variance and the autocorrelation. This allows to indirectly examine resilience changes in the absence of observations of strong perturbations. Yet, deriving estimates of the statistical measures indicating resilience changes based on CSD impose several assumptions on the system under observation. For tropical vegetation, it is not obvious that these assumptions are fulfilled.

Additionally, the conditions of tropical rainforests pose difficulties on the observation of the vegetation. Among other factors, cloud cover, aerosols, and the dense vegetation hinder the reliable retrieval of Vegetation Indices (Vis), especially from data gathered in the optical spectrum. Thus, such data might not be suitable for resilience analyses based on CSD, even if the theory is applicable in principle.

We investigate the different assumptions of CSD and test them on a diverse set of remotely sensed VIs. Hereby, we establish a framework that allows to decide whether a specific dataset is appropriate for resilience analyses based on CSD.

How to cite: Blaschke, L., Morr, A., Bathiany, S., Telschow, F., Smith, T., and Boers, N.: Applicability of CSD-based resilience analyses to remotely sensed Vegetation Indices in the Tropics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17709, https://doi.org/10.5194/egusphere-egu24-17709, 2024.

X4.119
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EGU24-1852
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ITS4.3/NP1.2
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ECS
Andreas Morr, Keno Riechers, Leonardo Rydin Gorjão, and Niklas Boers

When approaching a one-parameter bifurcation, the feedbacks that stabilise the initial state weaken and eventually vanish; a process referred to as critical slowing down (CSD). This motivates the use of variance and lag-1 auto-correlation as indicators of CSD in order to anticipate bifurcation-induced critical transitions. Both indicators require a prior dimension reduction to a one-dimensional time series. The use of variance is further limited to time- and state-independent driving noise, strongly constraining its generality. Here, we propose a data-driven approach based on deriving a multi-dimensional Langevin equation to detect local stability changes and anticipate bifurcation-induced transitions in systems with generally time- and state-dependent noise. Our approach substantially generalizes the conditions underlying existing early warning indicators, which we showcase in the example of a two-dimensional predator-prey model. This reduces the risk of false and missed alarms significantly and allows for a more holistic understanding of the multi-dimensional system at hand.

How to cite: Morr, A., Riechers, K., Rydin Gorjão, L., and Boers, N.: Anticipating critical transitions in multi-dimensional systems driven by time- and state-dependent noise, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1852, https://doi.org/10.5194/egusphere-egu24-1852, 2024.