ITS2.1/NP0.4 | Tipping points, resilience, and stochasticity in the Earth’s climate and ecosystems
Tipping points, resilience, and stochasticity in the Earth’s climate and ecosystems
Co-organized by CR7
Convener: Niklas Boers | Co-conveners: Balasubramanya Nadiga, Swarnendu Banerjee, Anna von der Heydt, Timothy Lenton , Marisa Montoya, Ricarda Winkelmann
Orals
| Wed, 26 Apr, 10:45–12:25 (CEST), 14:00–17:55 (CEST)
 
Room N1
Posters on site
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
Hall X4
Posters virtual
| Attendance Tue, 25 Apr, 16:15–18:00 (CEST)
 
vHall ESSI/GI/NP
Orals |
Wed, 10:45
Tue, 16:15
Tue, 16:15
Several subsystems of the Earth's climate and ecosystems have been suggested to react abruptly at critical levels of anthropogenic forcing. Well-known examples include the Atlantic Meridional Overturning Circulation, the polar ice sheets, tropical and boreal forests, but also drylands. Interactions between different Tipping Elements may either have stabilizing or destabilizing effects on the other subsystems, potentially leading to cascades of abrupt transitions.

It is paramount to determine the critical forcing levels (and the associated uncertainties) beyond which the systems in question could abruptly change their state, with potentially devastating climatic, ecological, and societal impacts. Similarly, it is crucial to understand how to help such systems to increase their resilience and evade tipping. 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, precursor signals have to be identified and monitored in both observations and models.

Given the often stochastic nature of the nonlinear and multiscale Earth system processes underlying abrupt behavior, it is important to avoid false sense of confidence that arises from perspectives that ignore the stochastic nature of such processes. This can also be the case when machine learning is used for modelling of such processes. As such this session also seeks to highlight the use of probabilistic data-driven and especially machine learning approaches.

This multidisciplinary session invites contributions 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,
- early-warning signals
- the implications of abrupt transitions for Climate sensitivity and response,
- ecological and societal impacts, as well as
- decision theory in the presence of uncertain Tipping Point estimates
- probabilistic modelling of Earth system processes
- climate change impacts on ecosystem resilience
-processes aiding ecosystem restoration and building climate resilient ecosystems

Orals: Wed, 26 Apr | Room N1

Chairpersons: Balasubramanya Nadiga, Hannah Christensen, Naiming Yuan
Stochastic modelling of nonlinear Earth system processes
10:45–10:55
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EGU23-1335
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ECS
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Virtual presentation
Changhong Mou, Leslie M. Smith, and Nan Chen

A hybrid data assimilation algorithm is developed for complex dynamical systems with partial observations. The method starts with applying a spectral decomposition to the entire spatiotemporal fields, followed by creating a machine learning model that builds a nonlinear map between the coefficients of observed and unobserved state variables for each spectral mode. A cheap low-order nonlinear stochastic parameterized extended Kalman filter (SPEKF) model is employed as the forecast model in the ensemble Kalman filter to deal with each mode associated with the observed variables. The resulting ensemble members are then fed into the machine learning model to create an ensemble of the corresponding unobserved variables. In addition to the ensemble spread, the training residual in the machine learning-induced nonlinear map is further incorporated into the state estimation that advances the quantification of the posterior uncertainty. The hybrid data assimilation algorithm is applied to a precipitation quasi-geostrophic (PQG) model, which includes the effects of water vapor, clouds, and rainfall beyond the classical two-level QG model. The complicated nonlinearities in the PQG equations prevent traditional methods from building simple and accurate reduced-order forecast models. In contrast, the SPEKF model is skillful in recovering the intermittent observed states, and the machine learning model effectively estimates the chaotic unobserved signals. Utilizing the calibrated SPEKF and machine learning models under a moderate cloud fraction, the resulting hybrid data assimilation remains reasonably accurate when applied to other geophysical scenarios with nearly clear skies or relatively heavy rainfall, implying the robustness of the algorithm for extrapolation.

How to cite: Mou, C., Smith, L. M., and Chen, N.: Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1335, https://doi.org/10.5194/egusphere-egu23-1335, 2023.

10:55–11:05
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EGU23-8645
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ECS
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On-site presentation
Francesco Guardamagna, Henk Dijkstra, and Claudia Weiners

On average every 4 years, the sea-surface temperature in the Eastern Equatorial Pacific is a few degrees higher than normal. This phenomenon, which reaches its maximum usually around Christmas is known as El Niño. This event has a strong influence on the climate all around the globe through well-known tele-connections. The occurrence of El Niño is related to extreme weather events, that affect people and properties. For these reasons is important to better understand the behavior of this climatic phenomenon. The property of EL Niño we have focused on during our project is related to the following research question: Is El Niño only due to external noise, or it is a self-sustained phenomenon, which amplitude is amplified by the noise?

To answer to this question, we have applied a Machine Learning tool called Reservoir Computer. After the training procedure, through feedback connections, the Reservoir model can be transformed into an autonomous evolving system. Our results show that the autonomous evolving Reservoir can delete the noise from the training data. The signal produced in output by the autonomous evolving Reservoir reflects the patterns of the training data, without noise. This method can therefore be used to understand if the EL Niño oscillations is only due to random noise, that excites a steady state, or it is a periodic phenomenon, which amplitude is randomly increased by external noise. To understand its limitations, our approach has been first applied to data produced by different models, that simulate EL Niño (Jin Timmerman, Zebiak Cane and CESM). After these first experiments, performed in a controlled scenario, our method has been applied to real data, to see what the self-evolving Reservoir model can tell us about the real EL Niño phenomenon.

How to cite: Guardamagna, F., Dijkstra, H., and Weiners, C.: Is El Niño only due to the noise or it is a self-sustained phenomenon?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8645, https://doi.org/10.5194/egusphere-egu23-8645, 2023.

11:05–11:15
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EGU23-9121
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On-site presentation
Ira Shokar, Peter Haynes, and Rich Kerswell

In this study, we present a deep learning approach to deriving a reduced-order model of stochastically forced atmospheric zonal jets. The approach provides a four orders of magnitude speed-up in simulating the jets, over numerical integration, together with a lower-degrees-of-freedom latent representation of the system- used to yield insight into the underlying dynamics.

We consider the behaviour of zonal jets on a beta plane as represented by a two-dimensional model driven by stochastic forcing, which parameterises the turbulence due to baroclinic instability. This idealised model gives a useful analogue for week-to-week variations in the large-scale dynamics of the tropospheric midlatitude jet - the driver of European weather. We establish that the time evolution of the jets depends both on the nonlinear two-way interaction between the mean flow and the eddies and, crucially, the time history of the stochastic forcing. As a result, the current state or recent history of the system does not predict the forward evolution but instead determines a distribution of possible time evolutions.

To model the flow, we utilise methods in manifold learning to learn a transformation to a latent representation of the system and then use a probabilistic neural network to model the stochastic latent dynamics. We verify the neural network’s performance by comparing the statistical and spectral properties of an ensemble from the neural network, obtained via sampling in the latent space, with an ensemble of numerical integrations, with different realisations of the stochastic forcing- with identical initial conditions. To study jet variability, we use ensembles of trajectories in both the latent and observation space to quantify to what extent different system states are driven by deterministic or stochastic dynamics.

 

How to cite: Shokar, I., Haynes, P., and Kerswell, R.: Learning Stochastic Dynamics with Probabilistic Neural Networks to study Zonal Jets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9121, https://doi.org/10.5194/egusphere-egu23-9121, 2023.

11:15–11:25
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EGU23-9335
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ECS
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On-site presentation
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Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl, and Kira Rehfeld

To characterize Earth’s temperature variability, it is necessary to better understand underlying mechanisms and contributions from internal and externally forced components. Here, we utilize a stochastic two-box energy balance model to emulate internal and forced global mean surface temperature (GMST) variability [1]. As target data for the emulation, we employ observations and 20 last millennium simulations from climate models of intermediate to high complexity. We infer the parameters of the stochastic EBM using the target data and a Bayesian approach, as implemented with a Markov Chain Monte Carlo algorithm in our “ClimBayes” software package [2]. This yields the best estimates of the EBM’s forced and forced + internal response. Applying spectral analysis, we contrast timescale-dependent variances of the EBM’s forced and forced + internal variance with that of the GMST target. Our findings show that the simple two-box stochastic EBM reproduces the characteristics of simulated global temperature fluctuations, even from comprehensive climate models. Minor deviations occur mainly at interannual timescales and are related to the simplistic representation of internal variability in the EBM. Furthermore, the relative contribution of internal dynamics increases with model complexity and decreases with timescale. Altogether, we demonstrate that the combined use of simple stochastic climate models and Bayesian inference provides a valuable tool to emulate climate variability across timescales.

[1] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld: “Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework,” Chaos,  https://doi.org/10.1063/5.0106123 (2022). 

[2] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld, “The ClimBayes package in R,” Zenodo, V. 0.1.1, https://doi.org/10.5281/zenodo.7317984 (2022). 

How to cite: Schillinger, M., Ellerhoff, B., Scheichl, R., and Rehfeld, K.: Emulating internal and external components of global temperature variability with a stochastic energy balance model and Bayesian approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9335, https://doi.org/10.5194/egusphere-egu23-9335, 2023.

11:25–11:35
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EGU23-10972
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On-site presentation
Fabian Romahn, Diego Loyola, Adrian Doicu, Víctor Molina García, Ronny Lutz, and Athina Argyrouli

Due to their fast computational performance, neural networks (NNs) are nowadays commonly used in the context of remote sensing. The issue of performance is especially important in the context of big data and operational processing. Classical retrieval algorithms often use a radiative transfer model (RTM) as forward model with which an optimization algorithm can then solve the inverse problem of inferring the quantities of interest from the measured spectra. However, these RTMs are usually computationally very expensive and therefore replacing them by a NN is desirable to increase performance. But the application of NNs is not straightforward and there are at least two main approaches:

1. NNs used as forward model, where a NN approximates the radiative transfer model and can thus replace it in the inversion algorithm

2. NNs for solving the inverse problem, where a NN is trained to infer the atmospheric parameters from the measurement directly

The first approach is more straightforward to apply. However, the inversion algorithm still faces many challenges, as the spectral fitting problem is generally ill-posed. Therefore, local minima are possible and the results often depend on the selection of the a-priori values for the retrieval parameters.

For the second case, some of these issues can be avoided: no a-priori values are necessary, and as the training of the NN is performed globally, i.e. for many training samples at once, this approach is potentially less affected by local minima. However, due to the black-box nature of a NN, no indication about the quality of the results is available. In order to address this issue, novel methods like Bayesian neural networks (BNNs) or invertible neural networks (INNs) have been presented in recent years. This allows the characterization of the retrieved values by an estimate of uncertainty describing a range of values that are probable to produce the observed measurement. We apply and evaluate these new BNN and INN methods for the retrieval of cloud properties from TROPOMI in order to demonstrate their potential as operational algorithms for current (Sentinel-5P) and future (Sentinel-4 and Sentinel-5) Copernicus atmospheric composition missions.

How to cite: Romahn, F., Loyola, D., Doicu, A., Molina García, V., Lutz, R., and Argyrouli, A.: Uncertainty quantification for the retrieval of cloud properties with deep neural networks for TROPOMI / Sentinel-5 Precursor, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10972, https://doi.org/10.5194/egusphere-egu23-10972, 2023.

11:35–11:45
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EGU23-8340
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On-site presentation
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Freddy Bouchet, George Milosevich, Francesco Ragone, Alessandro Lovo, Pierre Borgnat, and Patrice Abry

Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Extreme heatwaves are, and likely will be in the future, among the deadliest weather events. Forecasting their occurrence probability a few days, weeks, or months in advance is a primary challenge for risk assessment and attribution, but also for fundamental studies about processes, dataset or model validation, and climate change studies.

       Because of a lack of data related to a too short historical record, the rarity of the events, and of the difficulty to obtain rare events in climate models, uncertainty quantification is extremely difficult for extreme events. We develop a methodology to tackle this problem by combining probabilistic machine learning using deep neural network and rare event simulations.

We will first demonstrate that deep neural networks can predict the probability of occurrence of long lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height fields), and at much longer lead times for slow physical drivers (soil moisture). This forecast is made seamlessly in time and space, for fast hemispheric and slow local drivers.

A key scientific message is that training deep neural networks for predicting extreme heatwaves occurs in a regime of drastic lack of data. We suggest that this is likely the case for most other applications of machine learning to large scale atmosphere and climate phenomena. We discuss perspectives for dealing with this lack of data issue, for instance using rare event simulations.

Rare event simulations are a very efficient tool to oversample drastically the statistics of rare events. Using a climate model, with this tool we obtain several orders of magnitude more extreme heat waves compared to a control run. We will discuss the coupling of machine learning approaches, for instance the analogue method, with rare event simulations, and discuss their efficiency and their future interest for climate simulations. 

How to cite: Bouchet, F., Milosevich, G., Ragone, F., Lovo, A., Borgnat, P., and Abry, P.: Probabilistic forecast of extreme heat waves using convolutional neural networks and rare event simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8340, https://doi.org/10.5194/egusphere-egu23-8340, 2023.

Climate tipping points
11:45–11:55
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EGU23-5496
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ECS
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On-site presentation
Oisin Hamilton, Jonathan Demaeyer, Stéphane Vannitsem, and Michel Crucifix

Reduced order quasi-geostrophic ocean-atmosphere coupled models provide a platform that preserve key atmosphere behaviours, while still being simple enough to allow for analysis of the system dynamics. These models produce typical atmospheric dynamical features like atmospheric blocking and other low-frequency variability, while having a low number of degrees of freedom. For this reason, these models are well suited to investigating tipping points or bifurcations in the Earth's climate due to their simplified but insightful dynamics.

In our present work we compare the dynamics of an ocean-atmosphere coupled model, previously implemented with linearised temperature equations (Vannitsem et al., 2015), but here we solve the equations including the non-linear Stefan-Boltzmann law in the radiative temperature term. When compared with the original version of the model with linearised temperature equations, the modified version of the model is found to produce multiple stable flows in the coupled ocean-atmosphere system. We find, for increasing atmospheric emissivity, there is an increase in the number of stable attractors, and these stable attractors present distinct flows in the ocean and atmosphere and distinct Lyapunov stability properties.

Vannitsem, S., Demaeyer, J., De Cruz, L., & Ghil, M. (2015). Low-frequency variability and heat transport in a low-order nonlinear coupled ocean–atmosphere model. Physica D: Nonlinear Phenomena, 309, 71-85.

How to cite: Hamilton, O., Demaeyer, J., Vannitsem, S., and Crucifix, M.: Multistability in a Coupled Ocean-AtmosphereReduced Order Model: Non-linear TemperatureEquations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5496, https://doi.org/10.5194/egusphere-egu23-5496, 2023.

11:55–12:05
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EGU23-5250
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ECS
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On-site presentation
Andreas Morr and Niklas Boers

The observational research of tipping elements in the climate system relies largely on time series analysis via so-called Early Warning Signals. An upward trend in the estimated variance or lag-1 autocorrelation of the observable may be a sign for Critical Slowing Down (CSD), a phenomenon exhibited during the destabilization a system’s fixed point. This approach has been employed extensively both for assessing contemporary tipping risks [1] and understanding the dynamics in the advent of past abrupt climate change [2]. However, this inference of destabilization from statistical observations is in general only valid under certain model assumptions with regard to both the deterministic dynamics and the stochastic component (noise). While the assumption of additive white noise is the most canonical approach to representing unresolved dynamics, it has long been understood that certain variabilities in the climate system exhibit correlation and persistence [3]. In this case, trends in the above indicators should no longer be attributed solely to CSD, since they may also be rooted in possibly changing correlation characteristics of the driving noise. While there has been progress in the development of indicators for discrete-time models incorporating correlated noise [4], the task of assessing discrete-time data from continuous-time models has not received as much attention. We present a simple linearly restoring stochastic model with red noise as its driving force and discuss possible avenues of estimating system stability from time series data through the autocorrelation structure and power spectral density of the observable. We quantitatively compare these methods to conventional Early Warning Signals, highlighting the potential pitfalls of the latter in this setting.

 

[1] Boers, N. (2021). Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nature Climate Change 11

[2] Rypdal, M. (2016). Early-Warning Signals for the Onsets of Greenland Interstadials and the Younger Dryas–Preboreal Transition, Journal of Climate, 29(11)

[3] Mann, M.E., Lees, J.M. (1996). Robust estimation of background noise and signal detection in climatic time series. Climatic Change 33

[4] Rodal, M., Krumscheid, S., Madan,G. , LaCasce, J.H., and Vercauteren, N. (2022). Dynamical stability indicator based on autoregressive moving-average models: Critical transitions and the Atlantic meridional overturning circulation, Chaos 32

How to cite: Morr, A. and Boers, N.: Detecting Critical Slowing Down under the influence of continuous-time Red Noise, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5250, https://doi.org/10.5194/egusphere-egu23-5250, 2023.

12:05–12:15
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EGU23-8105
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ECS
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On-site presentation
Paul Ritchie, Hassan Alkhayuon, Peter Cox, and Sebastian Wieczorek

Over the last two decades, tipping points have become a hot topic due to the devastating consequences that they may have on natural and human systems. Tipping points are typically associated with a system bifurcation when external forcing crosses a critical level, causing an abrupt transition to an alternative, and often less desirable, state. However, the rate of change in forcing is arguably of even greater relevance in the human-dominated anthropocene, but is rarely examined as a potential sole mechanism for tipping points. Thus, I will introduce the related phenomenon of rate-induced tipping: an instability that occurs when external forcing varies across some critical rate, usually without crossing any bifurcations. First, I will explain when to expect rate-induced tipping. Then, using illustrating examples of differing complexity I will highlight universal and generic properties of rate-induced tipping in a range of natural and human systems.

How to cite: Ritchie, P., Alkhayuon, H., Cox, P., and Wieczorek, S.: When to Expect Rate-Induced Tipping in Natural and Human Systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8105, https://doi.org/10.5194/egusphere-egu23-8105, 2023.

12:15–12:25
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EGU23-12923
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ECS
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On-site presentation
Joe Clarke, Peter Cox, Paul Ritchie, and Chris Huntingford

Climate Change is forcing Earth System tipping elements rapidly, in some cases this forcing occurs on a similar timescale to the intrinsic timescale of the tipping element itself. This poses challenges for our ability to get good early warning signals for these tipping elements, as typical approaches require a clear timescale separation between the assumed slow forcing and the timescale of the system. We demonstrate that by calculating early warning signals ‘over space’ instead of ‘over time’ better early warning signals can be obtained for faster forcing. We compare the relative merits of these two ways of calculating early warning signals.

How to cite: Clarke, J., Cox, P., Ritchie, P., and Huntingford, C.: Spatial Early Warning Signals for Rapidly Forced Systems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12923, https://doi.org/10.5194/egusphere-egu23-12923, 2023.

Break 1
Lunch break
Chairpersons: Ricarda Winkelmann, Niklas Boers
14:00–14:10
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EGU23-12900
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solicited
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Highlight
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On-site presentation
Thomas Stocker

Multiple equilibria are found in all members of the hierarchy of climate models, ranging from simple planetary energy balance models to fully coupled general circulation models. They arise from the physical and biogeochemical coupling of different climate system components, and hence they are a general feature of planetary dynamics. Transitions from one equilibrium to another can be triggered by a temporary perturbation of the system which crosses a tipping point. Greenland ice cores and many other paleoclimate archives have abundantly demonstrated that the Earth System had limited stability during the last ice ages and that tipping has occurred in the past. A particularly dynamic period was the transition from the last ice age to the present. We present recent model simulations that reconcile different paleoceanographic indicators and so permit the quantitative reconstruction of the transient changes of the Atlantic meridional overturning circulation. This circulation may also tip in the future depending on the level and rate of increases in greenhouse gas concentrations. However, reducing the uncertainties where such tipping points lie and how close the climate system is to them, requires much better resolved climate models.

The tipping of regional systems has come into recent focus because the impacts on humans and ecosystems may be substantial. Among them are the various monsoon systems, parts of the Antarctic ice sheet, shifts in the statistics of extreme climate and weather events, the extent of the Amazon rain forest, or the grassland distribution in Eastern Africa, and hence biodiversity. Such changes would all have regional consequences that are not yet reflected in current climate change projections.

Therefore, regional tipping needs to be assessed systematically by the scientific community using a new generation of climate models at kilometer-scale resolution. A cross-working group IPCC Special Report on “Climate Tipping Points and Consequences for Habitability and Resources” in its forthcoming 7th assessment cycle would help strengthening a consensus on this topic and trigger the much needed advances in scientific understanding to more comprehensively inform adaptation and mitigation strategies.

 

How to cite: Stocker, T.: Tipping Points: A challenge for climate change projections, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12900, https://doi.org/10.5194/egusphere-egu23-12900, 2023.

14:10–14:20
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EGU23-3864
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Virtual presentation
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Stefano Pierini

A deterministic excitation (DE) paradigm is formulated, according to which the abrupt glacial-interglacial transitions occurred after the Mid-Pleistocene Transition correspond to the excitation by the orbital forcing, of nonlinear relaxation oscillations (ROs) internal to the climate system in the absence of any stochastic parameterization. Specific rules are derived from the DE paradigm: they parameterize internal climate feedbacks which, when activated by the crossing of certain tipping points, excite a RO. Such rules are then applied to the fluctuations of the glacial state simulated by a conceptual model subjected to realistic orbital forcing. The timing of the glacial terminations thus obtained in a reference simulation is found to be in good agreement with proxy records; besides, a sensitivity analysis insures the robustness of the timing. The role of noise in the glacial-interglacial transitions and the problems arising in the implementation of theories in which noise is crucial (such as stochastic resonance) are finally discussed. In conclusion, the DE paradigm provides the simplest possible dynamical systems characterization of the link between orbital forcing and glacial terminations implied by the Milankovitch hypothesis.

How to cite: Pierini, S.: The deterministic excitation paradigm, with application to the glacial-interglacial transitions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3864, https://doi.org/10.5194/egusphere-egu23-3864, 2023.

14:20–14:30
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EGU23-11899
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ECS
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On-site presentation
Keno Riechers, Georg Gottwald, and Niklas Boers

During past glacial intervals the high northern latitude’s climate was punctuated by abrupt warming events which were accompanied by a sudden loss of sea ice, a reinvigoration of the Atlantic Meridional Overturning Circulation (AMOC), and cooling of the Nordic Seas. Despite being considered the archetype of past abrupt climatic change, to date there is no consensus about the physical mechanism behind these so-called Dansgaard-Oeschger events and the subsequent milder interstadial phase. Here, we propose an excitable model system to explain the DO cycles, in which interstadials are regarded as noise-induced state space excursions. Our model comprises the mutual multi-scale interactions between four dynamical variables representing Arctic atmospheric temperatures, Nordic Seas’ temperatures and sea ice cover, and AMOC. Crucially, the model’s atmosphere-ocean heat flux is moderated by the sea ice variable, which in turn is subject to large perturbations dynamically generated by fast evolving intermittent noise. If supercritical, these perturbations trigger interstadial-like state space excursions seizing all four model variables. As a physical source for such a driving noise process we propose convective events in the ocean or atmospheric blocking events. The key characteristics of DO cycles are reproduced by our model with remarkable resemblance to the proxy record; in particular, their shape, return time, as well as the dependence of the interstadial and stadial durations on the background temperatures are reproduced accurately. In contrast to the prevailing understanding that the DO variability showcases bistability in the underlying dynamics, we conclude that multi-scale, monostable excitable dynamics provides a promising alternative candidate to explain the millennial-scale climate variability associated with the DO events.

How to cite: Riechers, K., Gottwald, G., and Boers, N.: Glacial abrupt climate change as a multi-scalephenomenon resulting from monostable excitabledynamics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11899, https://doi.org/10.5194/egusphere-egu23-11899, 2023.

14:30–14:40
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EGU23-9907
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Virtual presentation
Paul Edwin Curtis and Alexey Fedorov

In response to abruptly increasing atmospheric CO2 concentrations, general circulation model experiments typically evidence a rapid reduction or full collapse of the Atlantic Meridional Overturning Circulation (AMOC) from its current, strongly overturning state, into one characterized by weak overturning and reduced northward oceanic heat transport. This tipping point is frequently discussed in the context of present and past global climate changes. Less understood, however, is the evolution of the circulation towards a new equilibrium state, which occurs over many centuries or millennia following the initial AMOC response. To revisit this problem, we have performed multi-millennial simulations of the Community Earth System Model version 1 (CESM1) in a low-resolution configuration (T31 gx3v7), appropriate for paleoclimate studies. We consider a pre-industrial control (284.7ppm) simulation, as well as abrupt 2x, 4x, 8x, 16x, and 0.5x pre-industrial control atmospheric CO2 concentrations whereby atmospheric concentrations are increased at the start of integration and held constant for the duration of the experiment. In all global warming scenarios, we observe a rapid collapse to the AMOC within the first 250 years, attributed mechanistically to the complex interplay between surface salinity and temperature which inhibits deep-water formation in the sub-polar North Atlantic. Then, in our abrupt doubling and quadrupling of atmospheric CO2 experiments we observe a recovery to the circulation after some 1,000 years, and 3,500 years, respectively. After initially collapsing, our 8xCO2 experiment remains in this weakened state even after 10,000 years of integration have been performed, potentially indicating that a new equilibrium may have been met in this very warm climate.

 

We have further observed other intriguing bifurcations which arise stochastically in the forced system. First, in our abrupt 4xCO2 experiment, with the AMOC in a collapsed state we observe a spontaneous activation of the Pacific Meridional Overturning Circulation (PMOC) some 2,500 years following the initial forcing. The circulation persists for 1,000 years and has a notable effect on climate in the North Pacific region, for instance raising surface temperatures through the associated increase in Pacific Ocean northward heat transport. At 3,500 years the circulation collapses concomitantly with an AMOC recovery in the experiment, demonstrating a AMOC/PMOC seesaw. Secondly, in our abrupt global cooling experiment, we observe a spontaneous collapse of the AMOC after 2,000 years, which precedes a recovery over the next 1,500 years, before a secondary, rapid collapse to the circulation at 3,500 years. The behavior resembles a Dansgaard-Oeschger Event. Overall, our results highlight the rich quasi-equilibrium dynamical behavior of the Global Meridional Overturning Circulation in past climates for which atmospheric CO2 concentrations were markedly different.

How to cite: Curtis, P. E. and Fedorov, A.: The Tipping Points of the Atlantic Meridional Overturning Circulation in Warm and Cold Climates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9907, https://doi.org/10.5194/egusphere-egu23-9907, 2023.

14:40–14:50
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EGU23-4149
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ECS
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Virtual presentation
Irene Malmierca Vallet and Louise C. Sime

Greenland ice core records feature Dansgaard–Oeschger (DO) events; abrupt warming episodes followed by a gradual cooling phase during mid-glacial periods. Here, we analysis spontaneous self-sustained D-O type oscillations reproduced in three climate models: CCSM4, MPI-ESM and HadCM3. The three models show D-O type oscillatory behaviour in a remarkably similar, narrow window of atmospheric CO2 concentrations between approximately 185 to 230 parts per million (ppm). This CO2 range also compares particularly well with Marine Isotopic Stage 3 (MIS 3 - between 27.8 – 59.4 thousand of years BP, hereafter ka) atmospheric CO2 values (∼ 233-187.5 ppm), when D-O events occurred with most regularity. Outside this CO2 window of oscillatory behaviour, two different stable states are shown in the three models; warm high CO2 (strong AMOC) and cold low CO2 (weak AMOC) states. The weak state remains stable below the first critical tipping point near 185-195 ppm and the strong state remains stable above the second tipping point near 217-230 ppm. In all three models, the oscillatory experiments with higher CO2 show an increased built-up of stadial salinity in the upper ocean in the subtropics, especially in the eastern edge of the North Atlantic Current, compared with the ensemble mean: the tendency to re-invigorate the Atlantic Meridional Overturning Circulation (AMOC) is increased and so the system spend less time in the stadial phase. CO2 also affects North Atlantic and Arctic sea ice, determining interstadial and stadial duration. Similar sensitivity CO2 experiments performed with other climate models may help in further constraining the here-identified range of atmospheric CO2 (∼185-230 ppm) bounding this D-O sweet-spot. 

How to cite: Malmierca Vallet, I. and Sime, L. C.: Atmospheric CO2 impact on spontaneous Dansgaard–Oeschger type oscillations: oscillatory sweet-spot for three climate models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4149, https://doi.org/10.5194/egusphere-egu23-4149, 2023.

14:50–15:00
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EGU23-7989
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ECS
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On-site presentation
Edmund Derby and Raymond Pierrehumbert

Some simple models of Arctic sea ice show bifurcations associated with the loss of sea ice under increased surface radiative forcing (Eisenman and Wettlaufer 2009). However, experiments using GCMs typically show a smooth loss of sea ice under increasing CO2. This mismatch adds to uncertainty on the existence of tipping point behaviour in the Arctic and the processes that stabilise or destabilise it from this behaviour.

Simple models exhibiting tipping points typically omit many features of the Arctic climate system. Their bifurcations usually arise from the ice-albedo feedback. The purpose of my work is to use a bottom-up hierarchical approach to investigate how additional features of Arctic climate not included in simple models affect the existence of bifurcations in the system.

I started with a base ice model (Eisenman and Wettlaufer 2009) and investigate the role of local ice-atmosphere feedbacks using a coupled atmospheric column model. This allowed me to analyse the impact of the following on the possible states for the model to exist in:

  • Changes to the atmospheric temperature profile – particularly the transition from a stable atmosphere with a strong temperature inversion to a less stable atmosphere as the Arctic warms.
  • Explicitly resolved changes in surface heat fluxes and downwelling longwave radiation.
  • Changes in low level Arctic clouds – particularly as the atmospheric structure changes.

I also explored the sensitivity of the model to changes and variation in atmospheric heat transport.

I will present results of this work and demonstrate how local atmospheric feedbacks affect the stability of tipping points in Arctic sea ice.

How to cite: Derby, E. and Pierrehumbert, R.: Model complexity and Arctic sea ice tipping points – a single column model approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7989, https://doi.org/10.5194/egusphere-egu23-7989, 2023.

15:00–15:10
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EGU23-3074
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ECS
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Highlight
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On-site presentation
Nils Bochow, Anna Poltronieri, Martin Rypdal, Alexander Robinson, and Niklas Boers

Global sea level rise due to the melting of the Greenland ice sheet (GrIS) in response to anthropogenic global warming poses a severe threat to ecosystems and human society (IPCC, 2021). Modelling and paleoclimatic evidence suggest that rapidly increasing temperatures in the Arctic can trigger positive feedback mechanisms, and the GrIS is hypothesised to exhibit multiple stable states (Gregory et al., 2020). 
Consequently, critical transitions are expected when the global mean surface temperature crosses specific thresholds, and there is substantial hysteresis between the alternative stable states (Robinson et al., 2012). 
Here, we investigate the impact of different climate scenarios that overshoot temperature goals and then return to lower temperatures at different pace. Our results show that both the maximum GMT and the time span of overshooting given GMT targets are critical in determining GrIS stability. We find an abrupt loss of the ice sheet for a threshold temperature, preceded by several intermediate stable states. We show that even temporarily overshooting the temperature threshold may lead to catastrophic consequences in specific scenarios. On the other hand, overshoots might be tolerable if GMTs are subsequently reduced below 1.5°C GMT above pre-industrial levels within a few centuries. Even without a transition to a new ice sheet state the short-term global sea level rise can exceed several metres before returning to moderate GMTs.

Allan, R. P., Hawkins, E., Bellouin, N., & Collins, B. (2021). IPCC, 2021: Summary for Policymakers (V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, & B. Zhou, Eds.). Cambridge University Press. https://centaur.reading.ac.uk/101317/

Gregory, J. M., George, S. E., & Smith, R. S. (2020). Large and irreversible future decline of the Greenland ice sheet. The Cryosphere, 14(12), 4299–4322. https://doi.org/10.5194/tc-14-4299-2020

Robinson, A., Calov, R., & Ganopolski, A. (2012). Multistability and critical thresholds of the Greenland ice sheet. Nature Climate Change, 2(6), 429–432. https://doi.org/10.1038/nclimate1449

How to cite: Bochow, N., Poltronieri, A., Rypdal, M., Robinson, A., and Boers, N.: Overshooting the critical threshold for the Greenland ice sheet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3074, https://doi.org/10.5194/egusphere-egu23-3074, 2023.

15:10–15:20
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EGU23-2359
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ECS
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On-site presentation
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Maya Ben Yami, Niklas Boers, Vanessa Skiba, and Sebastian Bathiany

In recent years, sea-surface temperature (SST) and salinity-based indices have been used to detect critical slowing down (CSD) indicators for a possible collapse of the Atlantic Meridional Overturning Circulation (AMOC). However, these observational SST and salinity datasets have inherent uncertainties and biases which could influence the CSD analysis. Here we present an in-depth uncertainty analysis of AMOC CSD indicators. We first use uncertainties provided with the HadSST4 and HadCRUT5 datasets to generate uncertainty ensembles and estimate the uncertainty of SST-based AMOC fingerprints, and we then calculate stringent significance measures on the CSD indicators in the EN4.2.2, HadISST1 and HadCRUT5 datasets.

How to cite: Ben Yami, M., Boers, N., Skiba, V., and Bathiany, S.: Uncertainties in critical slowing down indicators of observation-based fingerprints of the AMOC, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2359, https://doi.org/10.5194/egusphere-egu23-2359, 2023.

15:20–15:30
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EGU23-8648
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ECS
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On-site presentation
Valérian Jacques-Dumas, René M. van Westen, and Henk A. Dijkstra

The Atlantic Meridional Overturning Circulation (AMOC) transports warm, saline water towards the northern North Atlantic, contributing substantially to the meridional heat transport in the climate system. Measurements of the Atlantic freshwater divergence show that the AMOC may be in a bistable state and hence subject to collapsing under anthropogenic greenhouse gas forcing. We aim at computing the probability of such a transition, focusing on time scales up to the end of this century.  

Simulating trajectories in a climate model is very expensive. To minimize the amount of data required to compute the probability of such rare AMOC transitions, we use a rare-events algorithm called TAMS (Trajectory-Adaptive Multilevel Sampling), that encourages the transition without changing the statistics. In TAMS, N trajectories are simulated and evaluated with a score function; the poorest-performing trajectories are discarded, and the best ones are re-simulated.

The optimal score function is the committor function, defined as the probability that a trajectory reaches a zone A of the phase space before another zone B. To avoid the difficulties raised by its exact computation, we estimate it using a feedforward neural network. Because of the expense of simulating data in a climate model, we also minimize the amount of data needed to train the neural network by reusing data processed through TAMS.

As a first step, using simulated data from an idealized stochastic AMOC model, where forcing and white noise are applied via a surface freshwater flux, we compute the transition probabilities versus noise and forcing amplitudes. Then, we apply the same protocol to compute these transition probabilities in the much more sophisticated climate model FAMOUS. This model is a coarse resolution Atmosphere-Ocean General Circulation Model that has been shown to exhibit a collapse of the AMOC via hosing experiments. In this new setup, we compute once again the transition probabilities of the AMOC versus noise and forcing, where the forcing amplitude is a hosing flux, and the atmosphere dynamics plays the role of the noise.

How to cite: Jacques-Dumas, V., van Westen, R. M., and Dijkstra, H. A.: Computation of the AMOC collapse probability using a rare-event algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8648, https://doi.org/10.5194/egusphere-egu23-8648, 2023.

15:30–15:40
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EGU23-3291
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ECS
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On-site presentation
Da Nian, Sebastian Bathiany, Maya Ben-Yami, Lana Blaschke, Marina Hirota, Regina Rodrigues, and Niklas Boers

The Amazon forest is at risk of dieback due to climate change, in particular decreasing mean annual precipitation (MAP) and increasing mean annual temperature (MAT). This study assesses the influence on South American vegetation under two possible future climate change scenarios: global warming, and global warming combined with an AMOC collapse. We consider MAT and MAP as control parameters and use their projected changes from climate model simulations with the Earth System Model HadGEM3. We then estimate the most probable states of vegetation based on empirical relationships between these parameters and tree cover. Our results suggest that an AMOC collapse would not contribute to further rainforest dieback over most of the Amazon basin. Instead, in parts of tropical South America, MAP increases and MAT decreases after AMOC collapse, which tends to stabilize the Amazon forest and hence delay the Amazon dieback compared to the default global warming scenario.

How to cite: Nian, D., Bathiany, S., Ben-Yami, M., Blaschke, L., Hirota, M., Rodrigues, R., and Boers, N.: The combined effect of global warming and AMOC collapse on the Amazon Forest, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3291, https://doi.org/10.5194/egusphere-egu23-3291, 2023.

Break 2
Coffee break
Chairpersons: Max Rietkerk, Mara Baudena, Susana Bautista
16:15–16:25
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EGU23-13893
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ECS
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On-site presentation
Marina Martinez Montero, Michel Crucifix, Nuria Brede, Nicola Botta, and Victor Couplet

Decisions are usually taken sequentially in climate change policy: every certain amount of years, new agreements and promises are made about greenhouse gas emission reduction etc. In the intersection of decision theory and climate science, sequential decision problems can be formulated and solved, to find optimal sequences of policies and support policy makers with some advice.

There are, however, many uncertainties affecting the outcome of these optimisations. Since these decision problems tend to be very simple in comparison with the complexity of the real world, knowing how different uncertainties affect optimal policies might be more important than what the optimal policy comes out to be. In this work, we explore how some uncertainties affect optimal policies and the possible trajectories associated with those optimal policies. 
  
For this aim we formulate a sequential decision problem with a single "global" policy maker. The decision problem starts with the world state in 2020 and decisions are taken every 10 years till 2100. The policy maker has options regarding CO2 emissions reduction, geoengineering in the form of solar radiation modification and carbon dioxide removal.

We simulate the effects of the decisions on the world’s state with SURFER. SURFER is a simple and fast model featuring a carbon cycle responsive to positive and negative emissions, it allows for geoengineering and accounts for sea level rise from ice sheets (containing tipping points) and from ocean expansion and glacier melt. SURFER has been shown to reproduce the globally averaged behavior of earth system models and models of intermediate complexity from decades to millennia. As opposed to some optimal decision problems in the context of climate change which use integrated assessment models of the climate and the economy, here, with the aim of transparency and simplicity, we consider only a climate model. 

We define a modular and transparent cost function that contains what the policy maker cares about. This function is a linear sum of costs associated with: green transition, geoengineering use and risks, temperature and ocean acidification damages and long term sea level rise commitments.

Using this decision problem we investigate how different kinds of uncertainties affect the sequence of optimal policies obtained and the optimal trajectories associated with those optimal policies. We consider three different kinds of uncertainties: uncertainties in the priorities of the decision maker (i.e., in the reward, cost or utility function), uncertainties on some physical parameters (in particular, climate sensitivity and ice sheet tipping points) and political uncertainty (policymaker’s decisions may not be implemented). 

How to cite: Martinez Montero, M., Crucifix, M., Brede, N., Botta, N., and Couplet, V.: Effects of different uncertainties on optimal policies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13893, https://doi.org/10.5194/egusphere-egu23-13893, 2023.

Ecosystem Resilience
16:25–16:35
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EGU23-11666
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solicited
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Highlight
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Virtual presentation
Miguel Berdugo, Manuel Delgado-Baquerizo, Emilio Guirado, Juan J. Gaitan, Camille Fournier, Thomas W. Crowther, and Vasilis Dakos

Drylands occupy 45% emerged lands on Earth, are home of more than 2 billion people and are extremely vulnerable to climate change. Aridity increases is expected to influence the structure and functioning of drylands in a non-linear fashion. Yet, the prevalence and drivers of these abrupt changes in ecosystem structure and function remain poorly studied. We especially lack investigations of the changes of dynamical properties of these systems and how these dynamical properties relate to aridity. Those are key to understand the real menace of experiencing abrupt shifts with aridity increases in the near future.

Here we used remote sensing tools to acquire dynamical trajectories of normalized vegetation indices (NDVI, surrogates of plant fractional cover) for more than 50,000 dryland sites. With this information we conducted analysis using machine learning processes to examine the relationship of aridity with some key dynamical properties of dryland ecosystems, including several aspects of resilience (ability to withstand fluctuations without changing the functioning of ecosystems), dynamical drivers of productivity, complexity of dynamical trajectories and abruptness of productivity changes.

By doing so we provide a comprehensive assessment of aridity thresholds on dynamical properties of dryland productivity that show clear vulnerability of certain zones of the Earth exhibiting critical aridity thresholds previously identified through space. In particular, we show accumulation of abrupt shifts on aridity values characteristic of transition areas from semiarid to arid ecosystems. Furthermore, these values exhibit also nonlinear shifts in resilience of ecosystems and on the identity of key dynamical drivers. Our work paves the way to expand the incidence of aridity threshold from spatial to temporal implications, and highlights the necessity of developing strategies to protect and monitor especially vulnerable areas affecting more than one fifth of emerged lands.

How to cite: Berdugo, M., Delgado-Baquerizo, M., Guirado, E., Gaitan, J. J., Fournier, C., Crowther, T. W., and Dakos, V.: Prevalence and drivers of abrupt shifts in global drylands: gathering dynamical evidences of aridity thresholds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11666, https://doi.org/10.5194/egusphere-egu23-11666, 2023.

16:35–16:45
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EGU23-687
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ECS
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On-site presentation
Hassan Alkhayuon, Rebecca Tyson, and Sebastian Wieczorek

Global warming is expected to lead to increase in amplitude and autocorrelation in climate variability in most locations around the world. These changes could have a great and imminent impact on ecosystems. In this work, we demonstrate that changes in climate variability can drive cyclic predator-prey ecosystems to extinction via so-called phase tipping (P-tipping), a new type of instability that occurs only from certain phases of the predator-prey cycle. We coupled a simple mathematical model of climate variability to a self-oscillating paradigmatic predator-prey model. Most importantly, we combine realistic parameter values for the Canada lynx and snowshoe hare with actual climate data from the boreal forest to demonstrate that critically important species in the boreal forest have increased likelihood of extinction under predicted changes in climate variability. The cyclic populations of these species are most vulnerable during stages of the cycle when the predator population is near its maximum. We identify stochastic resonance as the underlying mechanism for the increased likelihood extinction.

How to cite: Alkhayuon, H., Tyson, R., and Wieczorek, S.: Stochastic resonance, climate variability, and phase-tipping: The increasing risk of extinction in cyclic ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-687, https://doi.org/10.5194/egusphere-egu23-687, 2023.

16:45–16:55
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EGU23-3328
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Highlight
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On-site presentation
Johan Van de Koppel, Loreta Cornacchia, Roeland Van de Vijsel, and Daphne Van der Wal

Whether current-day ecosystems, often heavily modified by humans, can adapt to climate change is one of the most pressing scientific questions. Coastal ecosystems are at the forefront of climate impact, as salt marshes and intertidal flats may drown if these systems cannot follow sea level rise.

We developed a model to investigate how the emergence of complex creek networks during early salt marsh development affects the ability of marsh ecosystems to accumulate sediment, thereby compensating for sea level rise. This model is based on a scale-dependent feedback relation between vegetation growth and sedimentation, as plants locally block water flow, which then diverts to their surroundings. The model revealed that this self-organization process drives the emergence of a complex creek network of ever smaller creeks nested in between larger ones.

We used the model to analyze the importance of creek network complexity for the rate at which marshes accumulate sediment. The model highlights that in salt marshes, plant traits have a defining effect on the development of creek network complexity. Yet, it is the emergent effect of creek network complexity on sedimentation, rather than plant traits directly, that controlled sedimentation rates, determining the adaptive capacity of the marsh to sea level rise. Self-organized creek complexity proved a defining characteristic determining the resilience of this ecosystem to climate change.

We used our model to study whether restored coastal wetlands can be designed in such a way as to improve the adaptive capacity to sea level rise. We explored 14 realigned coastal wetlands and related the established, real-world creek network, being either entirely artificial dug-out channels or naturally formed creeks, to their potential, model-predicted sedimentation rate.

We observed that the developing channel networks in restored wetlands had much lower creek development and channel branching than natural systems, resulting in less efficient channel networks. Model simulations showed that if artificial creek networks deviated more from the creek pattern observed in natural ecosystems, or from the ones predicted from our model, they had lower sediment transport efficiency. Our findings suggest that if a more natural organization is followed when designing climate-proof coastal ecosystems, they are more resilient to climate change.

How to cite: Van de Koppel, J., Cornacchia, L., Van de Vijsel, R., and Van der Wal, D.: Using self-organization to build climate-resilient ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3328, https://doi.org/10.5194/egusphere-egu23-3328, 2023.

16:55–17:05
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EGU23-5180
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Highlight
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On-site presentation
Giovanni Forzieri, Vasilis Dakos, Nate G Mc Dowell, Ramdane Alkama, and Alessandro Cescatti

The persistence and functionality of forest ecosystems are highly dependent on their resilience to the ongoing rapid changes in climate conditions and in natural and anthropogenic pressures. Experimental evidences of a sudden increase in tree mortality across different biomes are rising concerns about the ongoing changes in forest resilience. However, how forest resilience, which is the capacity to withstand and recover from perturbations, is evolving in response to global changes is not yet explored. Here, we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators, has changed over the period 2000-2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, likely related to the increase in water limitations and climate variability. On the contrary, boreal forests show an increasing trend in resilience, likely for the benefits of climate warming and CO2 fertilization in cold biomes, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest productivity, occurring in response to a slow drifting toward a critical resilience threshold. We estimate that about 22% of intact undisturbed forests, corresponding to 3.32 Pg C of GPP, have already reached such critical threshold and are experiencing a further degradation in resilience. Altogether, these signals reveal a widespread and increasing instability of global forests and should be accounted for in the design of land-based mitigation and adaption plans.

How to cite: Forzieri, G., Dakos, V., G Mc Dowell, N., Alkama, R., and Cescatti, A.: Emerging signals of a global drift in forest resilience under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5180, https://doi.org/10.5194/egusphere-egu23-5180, 2023.

17:05–17:15
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EGU23-9297
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On-site presentation
Sebastian Bathiany, Da Nian, and Niklas Boers

The resilience of tropical forests against climate change and deforestation is vital for biodiversity and carbon drawdown. This resilience is hard to measure directly, but is suspected to be decreasing. There is particular concern that the Amazon rainforest may be approaching a “tipping point” where the large-scale loss of species and carbon pools amplifies substantially. Candidate mechanisms for such threshold effects often involve positive feedbacks and span a large range of scales. For example, individual trees can die from hydraulic failure when soil moisture decreaseses, forest fires can mediate a regional transition to a savanna state, and by synchronising remote regions, the moisture recycling feedback could cause a continental-scale forest dieback. Conceptual dynamical systems suggest that the loss of resilience that accompanies such transitions can be measured by statistical indicators like increasing autocorrelation. Satellite observations of vegetation indices related to greenness and biomass seem to support these theoretical expectations.

Here we analyse dynamic global vegetation models (DGVMs) from CMIP6, as well as idealised simulations with LPJ, in order to bridge the complexity gap between conceptual models and the real world. First, we assess how resilience of terrestrial carbon pools in the tropics depends on mean annual rainfall (MAP). We find that this relationship differs between models, and can also differ substantially from the observed positive relationship, depending on how the models capture carbon pool dynamics on the grid-cell level. Second, we show that changes in resilience do not necessarily require any atmosphere-vegetation feedbacks, fire feedback or ecological interactions, suggesting that observed relationships may capture physiological effects in individual trees rather than the stability of the entire forest. Third, we also find that the coexistence of vegetation types affects vegetation resilience in DGVMs. In particular, plant types with faster dynamics can replace slower ones (e.g., grass replacing trees), leading to decreased autocorrelation but not necessarily larger sensitivity to MAP. We conclude that suitable indicators of tropical vegetation resilience should be determined by (i) using DGVMs to understand better what mechanisms are at play, and (ii) using observations to rule out certain model approaches (e.g. area-averaged versus individual-based models).

How to cite: Bathiany, S., Nian, D., and Boers, N.: Indicators of tropical forest resilience in vegetation models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9297, https://doi.org/10.5194/egusphere-egu23-9297, 2023.

17:15–17:25
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EGU23-5449
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ECS
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On-site presentation
Taylor Smith and Niklas Boers

Spatial and temporal aggregations are common when preparing remote sensing data for analysis. Aggregations often serve to enhance the underlying signal of interest while suppressing noise, and can improve estimations of mean states and long-term trends in data. However, aggregating means that the highest-resolution parts of a signal can no longer be resolved, and rapid or fine-scale fluctuations are removed, potentially biasing analyses that rely on these parts of the signal. Further, data aggregation often goes along with gap-filling, which can further dilute the signals of interest.

In this work, we examine the impact of spatial aggregation on estimates of vegetation resilience by comparing MODIS vegetation data sets at a range of spatial resolutions (native 250 m – 25 km). We first use synthetic data to investigate various de-seasoning and de-trending schemes and their responsiveness to gaps in the underlying data. Based on these insights, we calculate two estimates of vegetation resilience at the global scale and at multiple spatial resolutions to determine the optimal level of spatial aggregation for MODIS data, considering the tradeoffs between fine-scale (gappy, noisy) and aggregated (continuous, smooth) vegetation data in terms of resilience estimation. Our results provide best practices for the aggregation, deseasoning, detrending, and analysis of vegetation resilience at the global scale.

How to cite: Smith, T. and Boers, N.: How Low Can You Go? Implications of Spatial Aggregation for the Estimation of Ecosystem Resilience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5449, https://doi.org/10.5194/egusphere-egu23-5449, 2023.

17:25–17:35
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EGU23-9072
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ECS
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On-site presentation
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Lana Blaschke, Da Nian, Sebastian Bathiany, Maya Ben-Yami, and Niklas Boers

The Amazon rainforest acts as a carbon sink and is one of the most bio-diverse ecosystems of our planet. As such, it is an important but vulnerable subsystem of the Earth System. Studies suggest that the region is bi-stable with respect to mean annual precipitation. Thus, it is considered a Tipping Element of the Earth System.

In this work, we investigate several statistics which, according to dynamical system theory, change in the advent of a Tipping Point. To assess the state of the Amazon rainforest, various remotely sensed vegetation indices (VIs) exist. Multiple single-sensor VIs are considered and analyzed if they show reasonable behavior. The results reveal an ongoing loss of resilience in several parts of the Amazon rainforest. 

 

How to cite: Blaschke, L., Nian, D., Bathiany, S., Ben-Yami, M., and Boers, N.: Loss of Amazon rainforest resilience confirmed from single-sensor satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9072, https://doi.org/10.5194/egusphere-egu23-9072, 2023.

17:35–17:45
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EGU23-1283
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On-site presentation
Simon Scheiter, Dushyant Kumar, Mirjam Pfeiffer, and Liam Langan

The projected increase of drought occurrence under future climates will affect terrestrial ecosystems, particularly by increasing drought-induced tree mortality. The capacity to simulate drought mortality in vegetation models is therefore essential to understand climate change impacts on ecosystem functions and services, as well as on functional diversity. Using the trait-based vegetation model aDGVM2, we assessed tree mortality under drought conditions in tropical Asia under future climate, and if vegetation is resilient to drought or if tipping point behavior occurs. We further assessed how drought impacts are related to pre-drought community composition and diversity. We conducted model simulations for multiple sites in tropical Asia, representing a biogeographic gradient ranging from savannas to tropical forests. Responses of vegetation attributes and mortality rates were simulated until 2099 under the RCP8.5 scenario. Repeated droughts of different length were modeled to test drought impacts and resilience. Finally, the diversity of pre-drought communities was constrained by removing different trait syndromes to test how community composition and diversity influence drought resistance and resilience. Model simulations showed substantial biomass dieback during drought which was attributed to increased mortality rates, primarily among tall and old trees. Drought response differed between current and elevated CO2 levels under RCP8.5, with higher biomass recovery under elevated CO2 due to fertilization effects. Pre-drought community composition influenced biomass dieback and mortality during drought, and the presence or absence of drought-adapted plants had the highest effect on drought impacts. Despite severe drought impacts, recovery of most vegetation attributes was possible after drought periods. We conclude that repeated droughts under future conditions will have vast impacts on vegetation attributes and mortality in tropical ecosystems. Conserving functional diversity in ecosystems buffers drought impacts. However, according to model results, vegetation is resilient, and irreversible transitions to alternative vegetation states do, for the investigated scenarios, not occur. Improved models representing lagged drought impacts, irreversible damage of individual plants, and the interactions between drought regimes, CO2 fertilization and trait diversity are required.

How to cite: Scheiter, S., Kumar, D., Pfeiffer, M., and Langan, L.: Drought mortality and resilience of savannas and forests in tropical Asia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1283, https://doi.org/10.5194/egusphere-egu23-1283, 2023.

17:45–17:55
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EGU23-16946
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ECS
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On-site presentation
Bert Wuyts and Jan Sieber

It is thought that tropical forests can exist as an alternative stable state to savanna. Therefore, perturbation by climate change or human impact may lead to crossing of a tipping point beyond which there is rapid large-scale forest dieback that is not easily reversed. Modelling studies of alternative stable tree cover states have either relied on mean-field assumptions or not included the spatiotemporal dynamics of fire, making it hard to compare their output to spatial data. In this talk, we analyse a microscopic model of tropical forest and fire and show how dynamics of forest area are linked to its emergent spatial structure. We find that the relation between forest perimeter and area determines the nonlinearity in forest growth while forest perimeter weighted by adjacent grassland area determines the nonlinearity in forest loss. Together with the linear changes, which are independent of spatial structure, these two effects lead to an emergent relation between forest area change and forest area, defining a single-variable ordinary differential equation. Such a relation between pattern and dynamics enables empiricists to assess forest stability and resilience directly from a single spatial observation of a tropical forest-grassland landscape.

How to cite: Wuyts, B. and Sieber, J.: Emergent structure, dynamics and abrupt transitions in a cellular automaton of tropical forest and fire, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16946, https://doi.org/10.5194/egusphere-egu23-16946, 2023.

Posters on site: Tue, 25 Apr, 16:15–18:00 | Hall X4

X4.58
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EGU23-17031
Balasubramanya Nadiga

Because of natural or internal variability, the behavior of processes ranging from unresolved small-scale physical and dynamical processes to the response of the climate system at the largest scales is probabilistic rather than denterministic. Indeed, it is also the case that while climate models are skilful at predicting the response of the climate system to external forcing, they are less skilful when it comes to predicting natural variability. A variety of probabilistic machine learning techniques ranging from Reservoir Computing to Generative Adversarial Networks to Bayesian Neural Networks are considered in the context of modeling natural variability. At the large scales, these models are seen to improve upon the Linear Inverse Modeling (LIM) approach which has itself been sometimes thought of as capturing the bulk of the predictable component of natural variability. 

How to cite: Nadiga, B.: Probabilistic Machine Learning of the Natural Variability of Climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17031, https://doi.org/10.5194/egusphere-egu23-17031, 2023.

X4.59
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EGU23-11696
|
ECS
Tobias Schanz and David Greenberg

Quantifying the error of predictions in earth system models is just as important as the quality of the predictions themselves. While machine learning methods become better by the day in emulating weather and climate forecasting systems, they are rarely used operationally. Two reasons for this are poor handling of extreme events and a lack of uncertainty quantification. The poor handling of extreme events can mainly be attributed to loss functions emphasizing accurate prediction of mean outcomes. Since extreme events are not frequent in climate and weather applications, capturing them accurately is not a natural consequence of minimizing such a loss. Uncertainty quantification for numerical weather prediction usually proceeds through creating an ensemble of predictions. The machine learning domain has adapted this to some extent, creating machine learning ensembles, with multiple architectures trained on the same data or the same architecture trained on altered datasets. Nevertheless, few approaches currently exist for tuning a deep learning ensemble. 

We introduce a new approach using a generative neural network, similar to those employed in adversarial learning, but we replace the discriminator with a new loss function. This gives us the control over the statistical properties the generator should learn and increases the stability of the training process immensely. By generating a prediction ensemble during training, we can tune ensemble properties such as variance or skewness in addition to the mean. Early results of this approach will be demonstrated using simple 1D experiments, showing the advantage over classically trained neural networks. Especially the task of predicting extremes and the added value of ensemble predictions will be highlighted. Additionally, predictions of a Lorenz-96 system are demonstrated to show the skill in forecasting chaotic systems.

How to cite: Schanz, T. and Greenberg, D.: A New Strategy for Training Deep Learning Ensembles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11696, https://doi.org/10.5194/egusphere-egu23-11696, 2023.

X4.60
|
EGU23-6420
|
ECS
|
Spyridon Christofilakos, Avi Putri Pertiwi, Chengfa Benjamin Lee, and Dimosthenis Traganos

With the latest advances in cloud image processing, scientists and policy makers have found an effective and robust platform to process vast satellite data in order to be able to map the extent, monitor the condition and create effective protection policies for different ecosystems across the globe. Cloud-based techniques though lack information on the spatial explicit uncertainty of the mapping algorithms. In this study, we present a novel approach on the estimation of uncertainty in a benthic habitat classification context. We explore the benefits of such information in the context of better classification results through an ensemble classifier and the visualization of the uncertain areas in an attempt to provide better maps to the policy makers. 

The study area consists of Komodo and Wakatobi islands in Indonesia while reference and satellite data come from the Allen Coral Atlas(ACA) project sampling and a six-year PlanetScope composite, free of clouds and optical deep waters Our semi-automated algorithm is divided in three sectors. The first one prepares the data in the context of sampling a number of subsets of reference points according to ACA map products and runs the first classification based on the first subset. The second one aims to help the model re-train itself in a data driven way by accepting training points of the remaining subsets that have mediocre to low uncertainty scores. The uncertainty score is calculated based on probabilistic principles and the theory of Information. The last stage consists of three ensemble classifiers with the inputs of the classification of the second sector. The ensemble classifiers produce three different map products based on mode, max likelihood and simple weighted average values, respectively.

 According to the results, our workflow is able to minimize the noise of reference points, especially when they come from mapping products and non in-situ measurements. Furthermore, accuracy scores following retraining are better than the initial ones which verifies the hypothesis of removing training data with noise in an attempt to reduce the introduced bias in the classification model. Last but not least, the bi-product of classification uncertainty map can be utilized as a tool for better in-situ sampling planning and render a better understanding to policy makers regarding the validity of scientific reports such as change detection, satellite derived bathymetry and blue carbon accounting, among others.

How to cite: Christofilakos, S., Pertiwi, A. P., Lee, C. B., and Traganos, D.: Cloud-based quantification of Spatial Explicit Uncertainty of Remote Sensing-based Benthic Habitat Classification and its utilization in the context of Active Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6420, https://doi.org/10.5194/egusphere-egu23-6420, 2023.

X4.61
|
EGU23-10528
Hamed Alemohammad

Supervised machine learning (ML) models rely on labels in the training data to learn the patterns of interest. In Earth science applications, these labels are usually collected by humans either as labels annotated on imagery (such as land cover class) or as in situ measurements (such as soil moisture). Both annotations and in situ measurements contain uncertainties resulting from factors such as class misinterpretation and device error. These training data uncertainties propagate through the ML model training and result in uncertainties in the model outputs. Therefore, it is essential to quantify these uncertainties and incorporate them in the model [1].

In this research, we will present results of inputting semantic segmentation label uncertainties into the model training and show that it improves model performance. The experiment is run using the LandCoverNet training dataset which contains global land cover labels based on time-series of Sentinel-2 multispectral imagery [2]. These labels are human annotations derived using a consensus algorithm based on the input labels from three independent annotators. The training dataset contains the consensus label and consensus score, and we treat the latter as a measure of uncertainty for each labeled pixel in the data. Our model architecture is a Convolutional Neural Network (CNN) trained on a subset of LandCoverNet with the rest of the dataset used for validation. We compare the results of this experiment with the same model trained on the dataset without the uncertainty information and show the improvement in the accuracy of the model.

 

[1] Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, J., Fishgold, L., Friedl, M., Jain, M., Kohli, D., Laso Bayas, J., Lunga, D., McCarty, J., Pontius, R., Reinmann, A., Rogan, J., Song, L., Stoynova, H., Ye, S., Yi, Z.-F., Estes, L. (2020). Accounting for Training Data Error in Machine Learning Applied to Earth Observations. Remote Sensing, 12(6), 1034. https://doi.org/10.3390/rs12061034

[2] Alemohammad, H., Booth, K. (2020). LandCoverNet: A global benchmark land cover classification training dataset. NeurIPS 2020 Workshop on AI for Earth Sciences. http://arxiv.org/abs/2012.03111

How to cite: Alemohammad, H.: Incorporating Training Data Uncertainty in Machine Learning Models for Satellite Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10528, https://doi.org/10.5194/egusphere-egu23-10528, 2023.

X4.62
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EGU23-16981
|
ECS
|
Yinling Zhang and Nan Chen

Discovering the underlying dynamics of complex systems from data is an important practical topic. Constrained optimization algorithms are widely utilized and lead to many successes. Yet, such purely data-driven methods may bring about incorrect physics in the presence of random noise and cannot easily handle the situation with incomplete data. In this paper, a new iterative learning algorithm for complex turbulent systems with partial observations is developed that alternates between identifying model structures, recovering unobserved variables, and estimating parameters. First, a causality-based learning approach is utilized for the sparse identification of model structures, which takes into account certain physics knowledge that is pre-learned from data. It has unique advantages in coping with indirect coupling between features and is robust to the stochastic noise. A practical algorithm is designed to facilitate the causal inference for high-dimensional systems. Next, a systematic nonlinear stochastic parameterization is built to characterize the time evolution of the unobserved variables. Closed analytic formula via an efficient nonlinear data assimilation is exploited to sample the trajectories of the unobserved variables, which are then treated as synthetic observations to advance a rapid parameter estimation. Furthermore, the localization of the state variable dependence and the physics constraints are incorporated into the learning procedure, which mitigate the curse of dimensionality and prevent the finite time blow-up issue. Numerical experiments show that the new algorithm succeeds in identifying the model structure and providing suitable stochastic parameterizations for many complex nonlinear systems with chaotic dynamics, spatiotemporal multiscale structures, intermittency, and extreme events.

How to cite: Zhang, Y. and Chen, N.: A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16981, https://doi.org/10.5194/egusphere-egu23-16981, 2023.

X4.63
|
EGU23-3354
|
ECS
Erwan Le Roux, Valentin Wendling, Gérémy Panthou, Paul-Alain Raynal, Abdramane Ba, Ibrahim Bouzou-Moussa, Jean-Martial Cohard, Jérome Demarty, Fabrice Gangneron, Manuela Grippa, Basile Hector, Pierre Hiernaux, Laurent Kergoat, Emmanuel Lawin, Thierry Lebel, Olivier Mora, Eric Mougin, Caroline Pierre, Jean-Louis Rajot, and Christophe Peugeot and the TipHyc Project
The Sahel (the semi-arid fringe south of the Sahara) experienced a severe drought in the 70s-90s. During this drought, an hydrological regime shift was observed for most watersheds in the Central Sahel: runoff has significantly increased despite the rainfall deficit. Did the drought cause this regime shift ? What if the drought did not happen ? To answer these questions, we introduce a simple dynamical model that represents feedbacks between soil, vegetation and runoff at the watershed scale and at the annual time step. This model is forced with annual rainfall and evaluated using long-term observations of runoff from selected watersheds. We find that the model forced with observed rainfall reproduces well the observed regime shift in runoff. For the attribution of the regime shift to the drought, we rely on two sets of historical rainfall simulations from CMIP6 global climate models: fully-coupled simulations that do not reproduce the drought, and atmosphere-only simulations (AMIP) that represent the drought. Our results show that a regime shift would have been unlikely without the drought. This approach will be extended to identify areas that are likely to experience an hydrological regime shift in the future.

How to cite: Le Roux, E., Wendling, V., Panthou, G., Raynal, P.-A., Ba, A., Bouzou-Moussa, I., Cohard, J.-M., Demarty, J., Gangneron, F., Grippa, M., Hector, B., Hiernaux, P., Kergoat, L., Lawin, E., Lebel, T., Mora, O., Mougin, E., Pierre, C., Rajot, J.-L., and Peugeot, C. and the TipHyc Project: Tipping points in hydrology: attribution of regime shifts using historical climate simulations and dynamical system modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3354, https://doi.org/10.5194/egusphere-egu23-3354, 2023.

X4.64
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EGU23-3612
Susana Barbosa, Maria Eduarda Silva, Nuno Dias, and Denis-Didier Rousseau

Greenland ice core records display abrupt transitions, designated as Dansgaard-Oeschger (DO) events, characterised by episodes of rapid warming (typically decades) followed by a slower cooling. The identification of abrupt transitions is hindered by the typical low resolution and small size of paleoclimate records, and their significant temporal variability. Furthermore, the amplitude and duration of the DO events varies substantially along the last glacial period, which further hinders the objective identification of abrupt transitions from ice core records Automatic, purely data-driven methods, have the potential to foster the identification of abrupt transitions in palaeoclimate time series in an objective way, complementing the traditional identification of transitions by visual inspection of the time series.

In this study we apply an algorithmic time series method, the Matrix Profile approach, to the analysis of the NGRIP Greenland ice core record, focusing on:

- the ability of the method to retrieve in an automatic way abrupt transitions, by comparing the anomalies identified by the matrix profile method with the expert-based identification of DO events;

- the characterisation of DO events, by classifying DO events in terms of shape and identifying events with similar warming/cooling temporal pattern

The results for the NGRIP time series show that the matrix profile approach struggles to retrieve all the abrupt transitions that are identified by experts as DO events, the main limitation arising from the diversity in length of DO events and the method’s dependence on fixed-size sub-sequences within the time series. However, the matrix profile method is able to characterise the similarity of shape patterns between DO events in an objective and consistent way.

How to cite: Barbosa, S., Silva, M. E., Dias, N., and Rousseau, D.-D.: Automatic characterisation of Dansgaard-Oeschger events in palaeoclimate ice records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3612, https://doi.org/10.5194/egusphere-egu23-3612, 2023.

X4.65
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EGU23-6885
|
ECS
Anna Poltronieri, Nils Bochow, and Martin Rypdal

The rapid loss of Arctic Sea Ice (ASI) in the last decades is one of the most evident manifestations of anthropogenic climate change. A transition to an ice-free Arctic during summer would impact climate and ecosystems, both regionally and globally. The identification of Early-Warning Signals (EWSs) for the loss of the summer ASI could provide important insights into the state of the Arctic region.

We collect and analyze CMIP6 model runs that reach ASI-free conditions (area below 106 km2) in September. Despite the high inter-model spread, with the range for the date of an ice-free summer spanning around 100 years, the evolution of the summer ASI area right before reaching ice-free conditions is strikingly similar across the CMIP6 models.

When looking for EWSs for summer ASI loss, we observe a significant increase in the variance of the ASI area before reaching ice-free conditions. This behavior is detected in the majority of the models and also averaged over the ensemble. We find no increase in the 1-year-lag autocorrelation in model data, possibly due to the multiscale characteristics of climate variability, which can mask changes in serial correlations. However, in the satellite-inferred observations, increases in both variance and 1-year-lag autocorrelation have recently been revealed. 

How to cite: Poltronieri, A., Bochow, N., and Rypdal, M.: Analysis of Early-Warning Signals for Arctic Summer Sea Ice Loss, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6885, https://doi.org/10.5194/egusphere-egu23-6885, 2023.

X4.66
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EGU23-9441
Marisa Montoya, Laura C. Jackson, Jorge Alvarez-Solas, and Alexander Robinson

The potential for the coupling between tipping elements leading to the occurrence of tipping cascades is of deep concern. One major tipping cascade that is often invoked results from coupling between the Greenland ice sheet, the Atlantic meridional overturning circulation (AMOC) and the Antarctic Ice Sheet (AIS). Melting of Greenland could contribute to a weakening of the AMOC, which would then result in a decrease in the northward heat transport in the Atlantic Ocean, causing warming of the Southern Ocean around Antarctica. This idea is supported by the evidence provided by ice-core records and models of different complexity suggesting that, during the last glacial period, the Southern Ocean acted as a heat reservoir which dampened and integrated in time the North Atlantic abrupt climatic variations through the bipolar seesaw. However, it has been argued instead that the heat reservoir to the Atlantic meridional heat transport involved does not lie in the Southern Ocean but north of the Antarctic Circumpolar Current, and transmitted via the atmosphere to the interior of Antarctica. Determining the ultimate heat reservoir in the sense of the strength of the Southern Ocean heat reservoir is critical to evaluate the risk of a tipping cascade.  Here we will investigate how model resolution affects the strength of the bipolar seesaw and the ultimate heat reservoir involved in this mechanism by using two different model horizontal resolution versions (0.25 and 1 degree, respectively) of the HadGEM3-GC3-1 model in simulations with a reduced AMOC in response to freshwater forcing in the North Atlantic.

How to cite: Montoya, M., Jackson, L. C., Alvarez-Solas, J., and Robinson, A.: Evaluating the risk of tipping cascades through the strength of the bipolar seesaw, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9441, https://doi.org/10.5194/egusphere-egu23-9441, 2023.

X4.67
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EGU23-12928
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ECS
Hannah Brown, Stephen Haddad, Aaron Hopkinson, Nigel Roberts, Steven Ramsdale, and Peter Killick

Uncertainty in numerical weather prediction (NWP) arises due to the initial state not being fully known and physical processes not being perfectly represented within the models. Precipitation is challenging to predict because it is non-linear with complex drivers from the atmosphere and so varies quickly even on a local scale. This means even advanced NWP models struggle to predict precipitation with the correct intensity at the right time or location. This study aims to explore whether machine learning (ML) can rediagnose precipitation rates based on vertical profiles of temperature, humidity and wind, thus replicating the precipitation calculated by cloud and precipitation parametrization schemes that are used in NWP models to represent the unresolved microphysical processes. A small but high-quality dataset comprised of days with widespread precipitation has been curated for developing an initial model, with in depth exploratory data analysis carried out to understand any trends in the model input data and assess the need for feature engineering. Vertical profiles of atmospheric variables (temperature, humidity, wind) taken from 6-hour forecasts of the Met Office Unified Model global ensemble (MOGREPS-G) provide input features for the ML model, and the target variable (or truth) is instantaneous precipitation intensity measured by the UK radar network at a 1km resolution. The two data sources are aligned onto the same grid by calculating the fractions of the MOGREPS-G ~20km cell containing radar precipitation in five precipitation intensity bands, with bounds informed by domain experts.

Each MOGREPS-G ensemble member is used to generate a ML prediction of the fractional precipitation coverage that exceeds each intensity threshold, then an ensemble average of these fractions is calculated for each intensity threshold. These values can be considered as ML generated ensemble probabilities. They can then be compared with the true fractional coverage from radar, as well as precipitation probabilities from MOGREPS-G to identify similarities and differences in their behaviour. Explainable AI techniques are applied to better understand the decisions made by the ML model when creating predictions.  The aim is to understand the potential of using ML for improving precipitation forecasts, either through complementing NWP outputs with ML outputs, or by using ML as a tool for improving the understanding of the drivers of errors in NWP precipitation forecasts. Initial results look promising and a number of avenues for further development have been identified following consultation with domain experts.

How to cite: Brown, H., Haddad, S., Hopkinson, A., Roberts, N., Ramsdale, S., and Killick, P.: Improving and understanding probabilistic precipitation forecasts using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12928, https://doi.org/10.5194/egusphere-egu23-12928, 2023.

X4.68
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EGU23-5409
Peter Ditlevsen and Susanne Ditlevsen

Statistical Early warning signals (EWS) indicate an approach towards a tipping point. These are increased variance (loss of resilience) and increased autocorrelation (critical slow down). The early warning is based on the significance in a linear trend above random fluctuations in the measures. Here we suggest a more rigorous evaluation of the statistics assuming a linear change with time of a control parameter towards a critical value. We calculate explicitly the uncertainty of the EWS as a function of the length of the data window and the time scales involved. This enables us to not only detect a trend but also estimate the timing of the forthcoming collapse.

 

 

Ref: Ditlevsen & Ditlevsen: Warning of a forthcoming collapse of the Atlantic meridional overturning circulation, preprint

How to cite: Ditlevsen, P. and Ditlevsen, S.: Timing the collapse of the Atlantic Meridional Overturning Circulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5409, https://doi.org/10.5194/egusphere-egu23-5409, 2023.

X4.69
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EGU23-406
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ECS
Ruth Chapman, Peter Ashwin, and Richard Wood

The Atlantic Meridional overturning Circulation is responsible for the comparatively temperate climate found in Western Europe, and its previous collapse thought to have triggered glacial periods seen in the paleo data. This is a system that has multiple stable states- referred to as ‘on’ when the circulation is strong as in the current climate, and ‘off’ when it is much weaker. The AMOC has tipping points between these states. Tipping points occur when a rapid shift in dynamics happens in response to a relatively small change in a parameter. Making future projections of AMOC response to the climate is essential for avoiding any anthropogenic caused tipping, but it is computationally expensive to calculate the full hysteresis for different scenarios. This work looks at a conceptual five box model of the AMOC [1] which is easy to understand and cheap to implement. Previous work has considered bifurcation and rate-dependent tipping [2] of this model. This current work looks to estimate a realistic amount of noise from various GCM data sets and apply this to the model. We compare the covariance of the salinity data for a variety of CMIP6 models, and we compare the amount of noise covariance found in each data set, and how this can be input back into the box model. We perform some analysis to suggest where in the model the largest noise sources should be found.

[1] Wood, R. et.al. (2019), Climate Dynamics, 53(11), 6815-6834

[2] Alkhayuon, H. et.al. (2019), Proc. R. Soc. A, 475(2225)

How to cite: Chapman, R., Ashwin, P., and Wood, R.: Stochastic data adapted AMOC box models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-406, https://doi.org/10.5194/egusphere-egu23-406, 2023.

X4.70
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EGU23-2840
Alessandro Cotronei and Martin Rypdal

It is wide scientific consensus that tipping points, in the form of rapid, large and irreversible changes in features of the climate system, are a possible scenario consequent to anthropogenic climate change. In literature there are several ways to detect the so-called Early-Warning-Signals, indicators (as increasing variance) that these changes are close to our current state and that the climate state is about to shift. We propose two novel indicators based on variance and parabolic approximations that expand the current theory to detect these EWSs. We show that the methods can produce estimations for the critical thresholds for particular systems. We finally show that our indicators predict close thresholds for the loss of ice of the Greenland ice sheet.

How to cite: Cotronei, A. and Rypdal, M.: Estimate of Critical Thresholds with Variance and Parabolic Approximations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2840, https://doi.org/10.5194/egusphere-egu23-2840, 2023.

X4.71
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EGU23-4228
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ECS
Entropy-based early detection of critical transitions in spatial vegetation fields
(withdrawn)
Giulio Tirabassi and Cristina Masoller
X4.72
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EGU23-6501
Mark Williamson

A superrotating atmosphere, one in which the angular momentum of the atmosphere exceeds the solid body rotation of the planet occurs on Venus and Titan. However, it may have occurred on the Earth in the hot house climates of the Early Cenozoic and some climate models have transitioned abruptly to a superrotating state under the more extreme global warming scenarios. Applied to the Earth, the transition to superrotation causes the prevailing easterlies at the equator to become westerlies and accompanying large changes in global circulation patterns. Although current thinking is that this scenario is unlikely, it shares features of other global tipping points in that it is a low probability, high risk event.

Using an idealized general circulation model developed for exoplanet research here at Exeter, we simulate the transition from a normal to a superrotating atmospheric state. We look at the changes in typical early warning indicators of tipping which show critical slowing down as well as oscillatory behaviour close to the transition. Inspired by the studies of phase transitions we also look at the critical spatial modes and correlation lengths close to the transition.

How to cite: Williamson, M.: Early warnings of the transition to a superrotating atmospheric state, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6501, https://doi.org/10.5194/egusphere-egu23-6501, 2023.

X4.73
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EGU23-7787
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ECS
Ignacio del Amo and Peter Ditlevsen

Inspired by the previous evidence that the DO events can be modelled as transitions driven by Lévy noise, we perform a detailed numerical study of the average transition rate in a double well potential for a Langevin equation driven by Lévy noise. The potential considered has the height and width of the potential barrier as free parameters, which allows to study their influence separately. The results show that there are two different behaviours depending on the noise intensity. For high noise intensity the transitions are dominated by gaussian diffusion and follow Kramer’s law. When noise intensity decreases the average transition time changes to the expected power law only dependent on the width on the potential and not on the height. Moreover, we find a scaling under which the transition time collapses for all heights and widths into a universal curve, only dependent on 𝛼.

How to cite: del Amo, I. and Ditlevsen, P.: Escape by jumps and diffusion by 𝛼-stable noise across the barrier in a double well potential, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7787, https://doi.org/10.5194/egusphere-egu23-7787, 2023.

X4.74
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EGU23-9117
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ECS
|
Clara Hummel

It is an ongoing debate whether the abrupt climate changes during the last glacial interval, the so-called Dansgaard-Oeschger (DO) events, are solely due to stochastic fluctuations or a result of bifurcations in the structural stability of the climate. This raises the question whether they are predictable, and thus whether early warning signals for the abrupt transitions from Greenland stadial to interstadial periods could be observed.

Here, we propose a new method to analyze the DO events between 60 ka before present and the Holocene, where we look at the ensemble of oxygen isotope ratio (δ¹⁸O) measurements from three different Greenland ice cores. For each rapid transition from a Greenland stadial to interstadial period, the three time series are normalized and scaled individually. The goal is to determine whether early warning signals in the further detrended ensemble are observable and thus to contribute to the ongoing debate whether past abrupt climate change has been purely noise-induced or a result of changed stability in the climate system.

 

How to cite: Hummel, C.: Predictability of Dansgaard-Oeschger events in the Greenland ice core ensemble, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9117, https://doi.org/10.5194/egusphere-egu23-9117, 2023.

X4.75
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EGU23-16103
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ECS
Raphael Roemer and Peter Ashwin

The fractal dimension of a nonattracting chaotic set provides information about its geometric complexity and can often be of practical use. For example in the case of a chaotic saddle on a (fractal) basin boundary between two basins of attraction where the boundary is the stable set of the chaotic saddle. Then, the fractal dimension of the saddle and of the boundary provide information about the impact of small changes to the initial conditions on the future behaviour of the system, when the system is in a state close to the boundary.
This information is highly relevant in the context of climate tipping phenomena.
Building on Edward Ott’s and David Sweet’s work from 2000, we will discuss how to rigorously construct a measure on a chaotic repellor which leads to the estimation of its fractal dimension. Further, we discuss the fractal dimension of its stable and unstable set.

How to cite: Roemer, R. and Ashwin, P.: Fractal Dimension of nonattracting chaotic sets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16103, https://doi.org/10.5194/egusphere-egu23-16103, 2023.

X4.76
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EGU23-3246
|
ECS
Swinda Falkena and Anna von der Heydt

Within the earth system several tipping elements exist. It is important to understand the links between these tipping elements, as a critical transition in one element could lead to tipping of another. Here, we study the links between some of these tipping elements in CMIP6 data. The starting point is the Atlantic Meridional Overturning Circulation (AMOC), whose collapse would have world-wide impacts and for which nearly all climate models show a decrease in the strength. In the Northern Hemisphere it would induce wide-spread cooling, impacting both sea-ice and the Greenland Ice Sheet (GIS). The corresponding changes in the global distribution of heat impact the atmospheric circulation. Where the response of the trade winds in the Atlantic is still relatively similar between models, this is not the case for the Pacific resulting in large uncertainty in the El Nino Southern Oscillation (ENSO) response.

To understand the effect of the AMOC on ENSO and other tipping elements, we consider the effect it has on the physical processes involved. For example, to study the effect of the AMOC on ENSO we consider its effect on the Pacific trade winds and other physically relevant variables. This aids in better understanding the consequences of an AMOC collapse and the potential for tipping cascades.

How to cite: Falkena, S. and von der Heydt, A.: Links between climate tipping elements: A story of ice, overturning and trade winds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3246, https://doi.org/10.5194/egusphere-egu23-3246, 2023.

X4.77
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EGU23-9078
|
ECS
|
Lucia Sophie Layritz, Prabha Neupane, and Anja Rammig

The terrestrial carbon sink plays a central role in the global carbon cycle, providing a strong negative feedback on anthropogenic climate change. However, it is also one of the more uncertain elements when simulating past and future carbon dynamics, mainly due to the challenge of modeling biological and ecological complexity across scales. One possible strategy, taken by the dynamic vegetation model LPJ-GUESS, is to use stochastic processes to describe key ecological processes, whose mechanistic modeling is still challenging (e.g. tree mortality, establishment, seed dispersal and disturbance).

Such introduced randomness can propagate through the model in various ways and may result in a final model output that is probabilistic in nature as well. Internal stochasticity can thus be seen as an additional source of model uncertainty, which so far has rarely been investigated systematically.

We perform global simulations of terrestrial carbon dynamics with LPJ-GUESS and quantify the resulting stochastic uncertainty. We find that stochasticity-induced uncertainty is a relevant share of overall uncertainty, comparable in magnitude to scenario uncertainty in some instances. When introducing stochastic processes into Earth system models, the resulting additional uncertainty should therefore be something to always be aware of.

How to cite: Layritz, L. S., Neupane, P., and Rammig, A.: The contribution of stochastic vegetation dynamics to overall model uncertainty of the global carbon sink, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9078, https://doi.org/10.5194/egusphere-egu23-9078, 2023.

X4.78
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EGU23-7898
|
ECS
|
Kolja Kypke

The  two-dimensional stochastic FitzHugh-Nagumo (sFHN) model is a popular idealization of the dynamics of the temperature of Greenland during the Last Glacial Period as measured in the ice-core record. Specifically, the sFHN model is used to simulate the Dansgaard-Oeschger (D-O) events, which are sharp changes in temperature and the most prominent example of abrupt climate change in the paleoclimate. The theory of early warning signals (EWS) has been applied to D-O events, specifically the critical slowdown corresponding to an increase in variance and autocorrelation of the climate signal right before approaching a bifurcation point where the system changes state. There is a debate in the literature on the state of these in the record of D-O events, with studies demonstrating both the absence and existence of these EWS. A desirable element of the sFHN is that it is a fast-slow system with multiple timescales. For a very large time scale separation, a quasi-steady-state in the slow variable causes the system to act as a bistable potential, where EWS do not precede an abrupt change in state. On the other hand, for a smaller time scale separation, the system displays clear EWS. The subject of this study is the case of intermediate time scale separation and its effects on EWS, along with an exploration of the physical implications of the results. 

How to cite: Kypke, K.: Dependence of Early Warning Signals on Time Scale Separation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7898, https://doi.org/10.5194/egusphere-egu23-7898, 2023.

X4.79
|
EGU23-12987
|
ECS
Swarnendu Banerjee, Mara Baudena, Paul Carter, Robbin Bastiaansen, Arjen Doelman, and Max Rietkerk

The theory of alternative stable states and tipping points has garnered a lot of attention in recent years. However, typically the ecosystem models that predict tipping behaviors do not resolve space explicitly. Ecosystems being inherently spatial, it is important to understand the implication of incorporating spatial processes in theoretical models and their applicability to real world. In this talk, I will illustrate several pattern formation phenomena that may arise when incorporating spatial dynamics in models exhibiting alternative stable state. For this, we use simple mathematical models of savannas to study the behavior of these spatial ecosystems in the face of environmental change. Model analyses presented here challenge the simplistic notion of tipping and lay down a way forward regarding studying ecosystem response to global change.

How to cite: Banerjee, S., Baudena, M., Carter, P., Bastiaansen, R., Doelman, A., and Rietkerk, M.: Rethinking tipping points in spatial ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12987, https://doi.org/10.5194/egusphere-egu23-12987, 2023.

X4.80
|
EGU23-2147
|
ECS
Paolo Bernuzzi and Christian Kuehn

Bistability is a key property of many systems arising in the nonlinear sciences. For example, it appears in many partial differential equations (PDEs). For scalar bistable reaction-diffusions PDEs, the bistable case even has taken on different names within communities such as Allee, Allen-Cahn, Chafee-Infante, Nagumo, Ginzburg-Landau, Schlögl, Stommel, just to name a few structurally similar bistable model names. One key mechanism, how bistability arises under parameter variation is a pitchfork bifurcation. In particular, taking the pitchfork bifurcation normal form for reaction-diffusion PDEs is yet another variant within the family of PDEs mentioned above. More generally, the study of this PDE class considering steady states and stability, related to bifurcations due to a parameter is well-understood for the deterministic case. For the stochastic PDE (SPDE) case, the situation is less well-understood and has been studied recently. We generalize and unify several recent results for SPDE bifurcations. Our generalisation is motivated directly by applications as we introduce in the equation a spatially heterogeneous term and relax the assumptions on the covariance operator that defines the noise. For this spatially heterogeneous SPDE, we prove a finite-time Lyapunov exponent bifurcation result. Furthermore, we extend the theory of early warning signs in our context and we explain the role of universal exponents between covariance operator warning signs and the lack of finite-time Lyapunov uniformity. Our results are accompanied and cross-validated by numerical simulations.

How to cite: Bernuzzi, P. and Kuehn, C.: Bifurcations and Early-Warning Signs for SPDEs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2147, https://doi.org/10.5194/egusphere-egu23-2147, 2023.

X4.81
|
EGU23-9554
|
ECS
|
Victor Couplet, Marina Martínez Montero, and Michel Crucifix

Tipping cascades are series of tipping events in the Earth system where transitions in one subsystem can trigger further transitions in other subsystems. In previous work, we demonstrated that the near-linear relationship predicted by GCMs between global temperature and cumulative greenhouse gas emissions for the next century can break up at millennial time scales due to cascades involving slower tipping elements such as the ice sheets. This means that we must consider tipping cascades also from a long-term perspective. Subsequently, we need fast models that encode the relevant physical processes and that we can calibrate on more comprehensive models. In this context, we present an extension of the SURFER model (Martínez Montero et al. 2022) that incorporates sediments and weathering feedbacks in the carbon cycle submodel (Archer et al. 2009), and an additional set of coupled tipping elements. This model may be used both as a surrogate for more computationally expensive models, for example in the context of decision-making problems, and as an exploratory tool to investigate the climate response's sensitivity to specific processes on long-time scales.

Archer, D. et al. (2009). “Atmospheric Lifetime of Fossil Fuel Carbon Dioxide”.en. In : Annual Review of Earth and Planetary Sciences 37.1, p. 117-134. DOI : 10.1146/annurev.earth.031208.100206.

Martínez Montero, M. et al. (2022). “SURFER v2.0 : a flexible and simple model linking anthropogenic CO2 emissions and solar radiation modification to ocean acidification and sea level rise”. en. In : Geoscientific Model Development 15.21, p. 8059-8084. DOI : 10.5194/gmd-15-8059-2022.

How to cite: Couplet, V., Martínez Montero, M., and Crucifix, M.: An extension of SURFER to study tipping cascades on multiple time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9554, https://doi.org/10.5194/egusphere-egu23-9554, 2023.

X4.82
|
EGU23-3937
|
ECS
Identifying and addressing hotspots of high climate sensitivity for global agriculture
(withdrawn)
Marta Tuninetti and Kyle Davis
X4.83
|
EGU23-14342
|
ECS
Jade Ajagun-Brauns and Peter Ditlevsen

An investigation into the dynamics of a two-parameter family of non-linear differential equations inspired by MacAyeal (1979) reveals the utility of simple conceptual models in understanding climate response to forcing. A slow-fast model is used to explain the non-linear response of the climate to insolation forcing after the Mid-Pleistocene Transition (MPT) which produces the saw-toothed glacial cycles in the paleoclimate record. Global ice volume is taken to be a function of two independently varying parameters, the solar insolation and ‘alpha’, a secondary control parameter. The pleated cusp geometry of the model, due to the addition of the second control allows the system to exhibit both smooth changes and sudden discontinuous transitions from one stable solution to another, producing the gradual increase and sudden decrease in global ice volume observed in the paleoclimate record.  The control parameter alpha is suggested to be related to internal dynamics of the climate system, proposed to be a measure of glacial-oceanic interaction, which varies due to glacial isostatic adjustments of the bedrock.  The transition in period of glacial cycles at the MPT is suggested to occur as a result of northern hemisphere glaciers exceeding a critical threshold, which allows alpha to become larger, causing the asymmetric, higher amplitude glacial cycles with quasi-period of 100kyr of the late Pleistocene.

 

Reference

R. MacAyeal, ‘A Catastrophe Model of the Paleoclimate Record’ , Journal of Glaciology , Volume 24 , Issue 90 , 1979 , pp. 245 – 257.

 

How to cite: Ajagun-Brauns, J. and Ditlevsen, P.: Investigating the dynamics of cusp bifurcations: A conceptual model for glacial-interglacial cycles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14342, https://doi.org/10.5194/egusphere-egu23-14342, 2023.

X4.84
|
EGU23-14486
|
ECS
Daniel Moreno-Parada, Jan Swierczek-Jereczek, Marisa Montoya, Jorge Alvarez-Solas, and Alexander Robinson

Marine ice-sheet behaviour and grounding line stability have been fundamental objects of study in the last two decades. In particular, the ice sheet-shelf transition deserves special attention as it determines the outflow of ice from the grounded region and, together with accumulation, governs the global mass balance. Yet, the dynamics of ice flow are strongly coupled to the climate system via surface mass balance, frontal ablation and atmospheric temperature among others. The interplay of such variables combined with the bed geometry determine the equilibrium position of a glacier terminus, which can display bistability due to the marine ice-sheet instability. These variables further define the boundary conditions of an ice-sheet model and are given by the particular climate scenario. However, a realistic representation of the climate must be described as a stochastic process (short-term variability i.e., “noise”) interacting with long-term deterministic dynamics. The response of a multi-stable system to noisy forcing can be used to predict abrupt transitions by means of so-called transition indicators. That is, a direct application of classical slowdown theory to capture the essence of shifts at tipping points. In the present work, we apply some of these indicators to a 1-D flowline model to study whether a glacier collapse can be predicted by critical slowdown theory. A key challenge with transition indicators is to determine when the system can be expected to tip given that a critical slowdown begins to occur. We explore this issue through a large ensemble of simulations.

How to cite: Moreno-Parada, D., Swierczek-Jereczek, J., Montoya, M., Alvarez-Solas, J., and Robinson, A.: Transition indicators on a flowline ice sheet model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14486, https://doi.org/10.5194/egusphere-egu23-14486, 2023.

X4.85
|
EGU23-257
|
ECS
Abhishek Chakraborty, Sekhar Muddu, and Lakshminarayana Rao

The knowledge of the long-term resilience of Indian terrestrial ecosystems is essential in the background of massive land-use conversion to croplands, intensification of irrigation, and the enhanced climate change signals over the past few decades. Previous assessments of Indian ecosystem resilience were limited by a smaller temporal span, lack of consideration for the sub-annual ecosystem transitions, and non-aridity-based stressors of the loss of resilience of ecosystems (Sharma and Goyal, 2017, Glob Chang Biol; Kumar and Sharma, 2023, J Environ Manage). This study aims towards a comprehensive understanding of the resilience of Indian terrestrial ecosystems through monthly scale assessment considering the driving role of the stressors in a standalone and compound manner.

The study utilizes ecosystem water use efficiency (WUE) as a state variable to assess the resilience of Indian ecosystems. WUE, produced from the FLUXCOM RS+METEO gross primary productivity (GPP) and evapotranspiration (ET) datasets at a monthly scale (WUEe=GPP/ET) from 1950 to 2010 (Jung et al., 2019, Sci Data; Tramontana et al., 2016, Biogeosciences), is a metric to quantify the strength of the coupling between terrestrial water and carbon cycles. Further lag-1 autocorrelation time series (AC(1)) is produced by evaluating the Kendall tau correlations for each pixel's residual component of the decomposed time series of WUE (excluding the impacts of trends and seasonal cycles). Such higher-order statistical assessments have been used earlier to quantify the loss of resilience (Smith et al., 2022, Nat Clim Change; Boulton et al., 2022, Nat Clim Change). We conduct the AC(1) analysis for resilience for India's six homogeneous meteorological regions, the eight major river basins, and the biome scale. We further consider the impacts of different forms of aridity on the loss of resilience: atmospheric aridity, hydrological aridity, and soil moisture aridity, individually and in a compound pattern. We also assess the loss of resilience at a seasonal scale (winter, summer, monsoon, post-monsoon) for the two major anthropogenic influences on Indian ecosystems: intensity of irrigation and groundwater fluctuations. This study attempts at a holistic understanding of the loss of resilience through its quantification and impacts of drivers, which could help the policymakers to identify the hotspots of loss of resilience and the significant perturbations to the resilience of Indian terrestrial ecosystems.

How to cite: Chakraborty, A., Muddu, S., and Rao, L.: Assessment of the Long-term Temporal Resilience of the Indian Terrestrial Ecosystems: Insights into the Country-scale Drivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-257, https://doi.org/10.5194/egusphere-egu23-257, 2023.

X4.86
|
EGU23-1021
|
ECS
Chahan M. Kropf, Loïc Pellissier, Lisa Vaterlaus, Christopher Fairless, and David N. Bresch

Human societies rely on the existence of functioning global ecosystems, which are threatened by a combination of gradual changes and extreme events. Among the latter, natural hazards such as wildfires or floods can play a *functional* role for ecosystems, with plant and animal species requiring regular disturbance in their life-cycle in order to thrive, but beyond a threshold, the extreme events might cause ecosystem degradation.

Here we map and project the risk of tropical cyclones on coastal ecosystems worldwide, using the probabilistic risk model CLIMADA to describe the vulnerability of global terrestrial ecosystems to tropical cyclones. First, a baseline for the current climate conditions is used to determine whether ecosystems are resilient, dependent, or vulnerable to tropical cyclones. We show that most ecosystems in the tropics are at least resilient to lower-intensity storms, but only a few ecosystems are not vulnerable to high-intensity storms. Second, the changes in tropical cyclone frequency under the high-emission scenario RCP8.5 in 2050 are used to determine which ecosystems are at risk. We show that while the global increase in the frequency of strong storms is the most threatening effect, several ecosystems with a dependency relationship are also at risk of locally decreasing frequency of low to middle-intensity storms.

Our study paves the way for a better understanding of the functional and vital relationship between extreme weather events and ecosystems at a global scale, and how regime shifts under climate change might threaten them. This can prove useful to improve ecosystem management and design appropriate nature-based protection measures in a rapidly changing climate.  

How to cite: Kropf, C. M., Pellissier, L., Vaterlaus, L., Fairless, C., and Bresch, D. N.: Impact of tropical cyclones on global ecosystems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1021, https://doi.org/10.5194/egusphere-egu23-1021, 2023.

X4.87
|
EGU23-12102
|
ECS
|
Highlight
Camille Fournier de Lauriere, Kathi Runge, Gabriel Smith, Vasilis Dakos, Sonia Kéfi, Thomas Crowther, and Miguel Berdugo
  • Context: Changes in ecosystem resilience have been recently studied on various scales using remote sensing data, revealing various regions exhibiting decreasing resilience. However, the drivers of these changes have not been identified yet. Our study aims at filling this gap by exploring the factors that have caused the resilience of ecosystems to change during the last two decades.
  • Methods: We investigate changes in vegetation resilience at the planetary scale, by quantifying two complementary aspects of resilience, namely sensitivity and autocorrelation, which are respectively associated with resistance and recovery abilities of ecosystems. We use a machine learning approach to identify the main environmental, climatic, and anthropogenic drivers of changes in resilience between two periods (the period 2000-2010 vs that of 2010-2020).
  • Results: We find that in 26% of ecosystems worldwide, vegetation exhibits signs of resilience loss, and that the changes in climate conditions as well as the ecosystem’s intrinsic properties (aridity, elevation, anthropization) affect the way vegetation resilience has changed over time. Different biomes (forest, grasslands, and savannas) exhibit similar responses to their changing environment. Regions experiencing intense warming (>0.2ºC/decade) have shown a major loss in vegetation resilience. Decreasing productivity is associated with reduced resilience, and interacts with warming, exacerbating resilience loss of degraded lands. This shows that global warming and human activities are major drivers of losses in vegetation resilience across vegetation types.
  • Conclusions: We reveal a decline in the capacity of a number of ecosystems to withstand perturbations, which should be accounted for in the management of vulnerable areas. Our results raise concerns about the persistence of ecosystems due to projected warming and expected intensification of human activities.

How to cite: Fournier de Lauriere, C., Runge, K., Smith, G., Dakos, V., Kéfi, S., Crowther, T., and Berdugo, M.: Revealing global drivers of recent losses in vegetation resilience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12102, https://doi.org/10.5194/egusphere-egu23-12102, 2023.

X4.88
|
EGU23-7885
|
ECS
Hannah Zoller, Borgþor Magnússon, Bjarni D. Sigurdsson, and Wolfgang zu Castell

In light of global changes and the need of a sustainable lifestyle, understanding the dynamics of ecological systems is steadily gaining in importance. However, with ecosystems being shaped by the complex interplay of physical, chemical, and biological processes, this remains a demanding endeavor. Addressing this challenge, we have developed a computational method to assess complex systems development, based on the abstract framework provided by Gunderson’s and Holling’s adaptive cycle metaphor [1]. The metaphor describes ecosystem development as alternating phases of stability and reorganization, being shaped by three systemic properties: the system’s potential available for future change, the connectedness among its internal variables and processes, and its resilience in the light of unpredicted perturbations. Resilience, in the sense of Gunderson and Holling, denotes the amount of disturbance that a system can absorb without changing its identity [2]. Our definitions of these three notions are based on a representation of the system as directed network of information transfer. While we consider the system’s potential and connectedness as information theoretical features of the network, we approach the system’s resilience via the spectral properties of the network’s Laplacian matrices.

In the present study, we follow this approach to provide holistic analyses of two ecosystems evolving through different successional stages. One of the systems, a vascular plant community on a volcanic island near Iceland, has been largely unspoiled since its formation and has therefore been exposed to natural perturbations, like droughts and breeding birds, only [3]. In contrast, we consider a plant community in the prairie-forest ecotone of Kansas, which has been subject to regular direct human interventions in the form of spring burns [4]. In both cases, our method reveals phases of system breakdown and reorganization, allows us to identify the corresponding drivers of change, and gives hints on the systemic role of single species in the maturation process [1,5].

The case studies illustrate the application of the R-package QtAC (Quantifying the adaptive cycle), which provides an easy access to our method [6].

 

[1] W. zu Castell, and H. Schrenk, Computing the adaptive cycle, Scientific Reports 2020(10):18175 (2020).

[2] L. H. Gunderson and C. S. Holling. Panarchy: understanding transformations in human and natural systems (Island, Washington, D.C., 2002).

[3] S. Fridriksson, Surtsey. Ecosystems formed (University of Iceland Press, 2005).

[4] Long-term studies of secondary succession and community assembly in the prairie-forest ecotone of eastern Kansas. https://foster.ku.edu/long-term-studies-secondary-succession-and-community-assembly-prairie-forest-ecotone-eastern-kansas. Accessed: 2019-05-19.

[5] H. Schrenk, B. Magnússon, B. D. Sigurdsson, and W. zu Castell, Systemic analysis of a developing plant community on the island of Surtsey, Ecology and Society 27(1):35 (2022).

[6] H. Schrenk, C. Garcia-Perez, N. Schreiber, and W. zu Castell, QtAC: an R-package for analyzing complex systems development in the framework of the adaptive cycle metaphor, Ecological Modelling 466:109860 (2022).

How to cite: Zoller, H., Magnússon, B., Sigurdsson, B. D., and zu Castell, W.: Adaptive cycles of ecosystems under natural perturbation and human intervention, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7885, https://doi.org/10.5194/egusphere-egu23-7885, 2023.

X4.89
|
EGU23-8187
|
ECS
Angelique Vermeer, Ángeles Garcia Mayor, and Saskia Förster

In this work, the ecological resilience to drought of a dryland catchment in the Moroccan High Atlas Mountains was studied. A time-series of Landsat NDVI data between 1984 and 2019 was used to determine areas of overall greening and browning. The Breaks For Additive Seasonal and Trend (BFAST) change detection methodology was used to determine breakpoints and trends in the time-series. The breakpoints were classified using a newly developed typology based on the trend before and after the breakpoint. The improved typology that is introduced, considers the statistical significance of trends, and subdivides them in categories of abrupt changes that lead to an improvement of ecosystem functioning (positive breakpoints) and abrupt changes that lead to a deterioration of ecosystem functioning (negative breakpoints). The ecological resilience in the catchment was explored using the number, sign and typology of the breakpoints detected and their relation to the various land uses and climatic zones of the catchment. In general, a small amount of change in NDVI between 1984 and 2019 was observed in the whole catchment. However, there was a large spatial variability in the number and type of breakpoints, pointing to the additional information provided by these indicators, which will be discussed in our presentation.

How to cite: Vermeer, A., Garcia Mayor, Á., and Förster, S.: Testing new indicators for ecological resilience in a dryland mountain ecosystem using a multidecadal NDVI time-series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8187, https://doi.org/10.5194/egusphere-egu23-8187, 2023.

Posters virtual: Tue, 25 Apr, 16:15–18:00 | vHall ESSI/GI/NP

vEGN.1
|
EGU23-4501
|
ECS
Gisela Daniela Charó, Michael Ghil, and Denisse Sciamarella

Random attractors are the time-evolving pullback attractors of stochastically perturbed, deterministically chaotic dynamical systems. These attractors have a structure that changes in time, and that has been characterized recently using BraMAH cell complexes and their homology groups (Chaos, 2021, doi:10.1063/5.0059461). A more complete description is obtained for their deterministic counterparts if the cell is endowed with a directed graph (digraph) that prescribes cell connections in terms of the flow direction. Such a topological description is given by a templex, which carries the information of the structure of the branched manifold, as well as information on the flow (Chaos, 2022, doi:10.1063/5.0092933). The present work (Chaos, 2023, arXiv:2212.14450 [nlin.CD]) introduces the stochastic version of a templex. Stochastic attractors in the pullback approach, like the LOrenz Random Attractor (LORA), include sharp transitions in their branched manifold. These sharp transitions can be suitably described using what we call here a random templex. In a random templex, there is one cell complex per snapshot of the random attractor and the cell complexes are such that changes can be followed in terms of how the generators of the homology groups, i.e., the “holes” of these complexes, evolve. The nodes of the digraph are the generators of the homology groups, and its directed edges indicate the correspondence between holes from one snapshot to the next. Topological tipping points can be identified with the creation, destruction, splitting or merging of holes, through a definition in terms of the nodes in the digraph.

How to cite: Charó, G. D., Ghil, M., and Sciamarella, D.: Identifying topological tipping points in noise-driven chaotic dynamics using random templexes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4501, https://doi.org/10.5194/egusphere-egu23-4501, 2023.

vEGN.2
|
EGU23-8099
|
ECS
Matteo Cini, Giuseppe Zappa, Susanna Corti, and Francesco Ragone

 Understanding the stability of the Atlantic Meridional Overturning Circulation (AMOC) and its future development under anthropogenic forcing is of key importance for advancing climate science. Previous studies have explored the stability of the AMOC by applying external perturbations in climate models, such as freshwater hosing to the North Atlantic Ocean. However, if the system is close to losing stability, the tipping of the AMOC may also spontaneously occur via internal coupled atmosphere-ocean variability. Here, we address this hypothesis - using an innovative approach - by studying the nature of a spontaneous collapse of the AMOC in an intermediate complexity climate model (PlaSIM coupled to the LSG ocean) featuring - under pre-industrial conditions - an apparently stable state. Excluding all possible external forcing elements (for example green-house gasses increase, water hosing, radiative forcing anomalies), significant AMOC slowdowns and collapses can be treated as extreme events solely driven by the chaotic internal atmospheric variability.  Facing this problem, we look for extreme AMOC slowdowns by applying a Rare Event Algorithm (Ragone, Wouters and Bouchet, 2018), which - via a selective cloning of the most interesting model trajectories -  allows a faster exploration of the model phase space in the direction of an AMOC decrease.

After exploring the parameters of the rare event algorithm, we find a regime in which PLASIM/LSG shows an abrupt AMOC slowdown over a 20-years period to a substantially weakened state, which is unprecedented in the pre-industrial run. Stability analysis reveals that part of these slowdown states are actually collapsed, i.e. states around a much lower value of the AMOC that do not recover to previous values.

This approach also enables us to isolate the atmospheric processes driving the AMOC slowdown, from the climate response to the weakened AMOC state. Interestingly, we find that the climatic response to internally-induced AMOC slowdowns shows strong similarities with the responses to externally forced AMOC slowdowns in state-of-the-art climate models  for what concerns temperature, wind, and precipitation changes. Looking at the mechanisms causing the AMOC weakening, instead, we find that zonal wind stress over the North Atlantic is the main driver of the AMOC slowdown, via changes in Ekman transport that affect salinity and deep convection in the Labrador sea. In this climate model, the repeated occurrence of this circulation anomaly for a few decades is sufficient to drive  an AMOC collapse without possibility of recovery on multi-centennial time scales.

Overall, these results show that the methodology proposed here can be generally useful for other studies in Tipping Points since it introduces the possibility of collecting a large number of critical events that cannot be sampled using traditional approaches. 

 

How to cite: Cini, M., Zappa, G., Corti, S., and Ragone, F.: Simulating spontaneous AMOC collapses with a Rare Event Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8099, https://doi.org/10.5194/egusphere-egu23-8099, 2023.

vEGN.3
|
EGU23-10199
|
ECS
Impact of different destocking strategies on theresilience of dry rangelands
(withdrawn)
Toyo Vignal, Mara Baudena, Angeles Garcia Mayor, and Jonathan A Sherratt
vEGN.4
|
EGU23-14678
|
ECS
Daniel Ohara and Michael Ghil

In the climate sciences, highly simplified nonlinear models are useful tools for understanding and discussing tipping points. However, the economic models used to study their coupling to the economy, as in Integrated Assessment Models (IAMs), are typically linear and represent an inertia-free economy in equilibrium. This representation is challenged by persistent unemployment, recessions, and changing economic institutions. 

Therefore, we investigate the non-equilibrium dynamics of the economy and the corresponding tipping from equilibrium to so-called endogenous business cycles. To this end, we build a basic Solow-type equilibrium growth model that incorporates, in a highly simplified manner, frictions and delay in the labor system. When the delay exceeds a critical value of 3.4 days, business cycles with periodic unemployment and recessions arise in our minimal business cycle (MinBC) model. Given a dynamic investment mode, the MinBC's cyclic economy responds to external forcing asymmetrically throughout the cycle. Advanced time series analysis methods are applied to macroeconomic data sets to evaluate the realism of the model's response, with encouraging results.

Our study is a step towards understanding the evolution of the sources of internal economic variability. Such an understanding is needed to represent the extent of coupling between the earth system and the economy.

How to cite: Ohara, D. and Ghil, M.: Minimal Modelling of Internal Macroeconomic Variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14678, https://doi.org/10.5194/egusphere-egu23-14678, 2023.