arXiv subjects

Maya Ben-Yami

Publications and source records attributed to Maya Ben-Yami.

8 recordsLinked to original sources

arXiv2026

Extrapolation from historical data cannot reliably predict the time of a potential AMOC collapse

Ditlevsen and Ditlevsen [Nature Communications, 2023] (DD23 hereafter) propose a statistical framework to estimate the timing of a potential collapse of the Atlantic Meridional Overturning Circulation (AMOC) based on extrapolating information from observed sea-surface temperature (SST) variability. By fitting a stochastic one-dimensional fold-bifurcation model to an SST-based fingerprint of the AMOC using Maximum Likelihood Estimation (MLE), they conclude that a collapse is most likely to occur in the middle of the 21st century, with a reported 95% confidence interval covering the time span from 2037 to 2109. Given the profound implications of such a claim for both climate and society, it is essential to thoroughly test the robustness of this result, to critically assess the underlying assumptions and uncertainties, and to estimate the extent to which the reported confidence interval reflects the true limits of current knowledge. Here we examine the sensitivity of DD23's results and argue that four types of uncertainty are insufficiently explored in their analysis: (i) structural uncertainty associated with the assumed low-order bifurcation model, (ii) statistical uncertainty in their model fit, (iii) uncertainty in the representativeness of SST-based fingerprints as proxies for the high-dimensional AMOC dynamics, and (iv) uncertainty in the underlying data, arising from non-stationary observational coverage and dataset preprocessing. Using synthetic experiments and a systematic analysis of alternative fingerprints and observational products, we show that the tipping times estimated by DD23 are highly sensitive to the uncertainties listed above, and extend several millennia into the future when these uncertainties are thoroughly propagated.

physics.geo-ph↗

arXiv2025

Anticipating AMOC transitions via deep learning

Key components of the Earth system can undergo abrupt and potentially irreversible transitions when the magnitude or rate of external forcing exceeds critical thresholds. In this study, we use the example of the Atlantic Meridional Overturning Circulation (AMOC) to demonstrate the challenges associated with anticipating such transitions when the system is susceptible to bifurcation-induced, rate-induced, and noise-induced tipping. Using a calibrated AMOC box model, we conduct large ensemble simulations and show that transition behavior is inherently probabilistic: under identical freshwater forcing scenarios, some ensemble members exhibit transitions while others do not. In this stochastic regime, traditional early warning indicators based on critical slowing down are unreliable in predicting impending transitions. To address this limitation, we develop a convolutional neural network (CNN)-based approach that identifies higher-order statistical differences between transitioning and non-transitioning trajectories within the ensemble realizations. This method enables the real-time prediction of transition probabilities for individual trajectories prior to the onset of tipping. Our results show that the CNN-based indicator provides effective early warnings in a system where transitions can be induced by bifurcations, critical forcing rates, and noise. These findings underscore the potential in identifying safe operating spaces and early warning indicators for abrupt transitions of Earth system components under uncertainty.

cs.CE↗

arXiv2025

Reconciled warning signals in observations and models imply approaching AMOC tipping point

Paleoclimate proxy records and models suggest that the Atlantic Meridional Overturning Circulation (AMOC) can transition abruptly between a strong and a weak state. Empirical warning signals in observational fingerprints indeed suggest a stability decline and raise concerns that the system may be approaching a tipping point. However, state-of-the-art Earth System Models (ESMs) do not consistently show such a stability loss, hence inconclusive in their projections of AMOC collapse under global warming. It remains unclear whether warning signals of AMOC tipping are overlooked in ESMs or overinterpreted in observations, calling for further investigations of AMOC stability. Here, based on the concept of critical slowing down, AMOC stability decline that can be interpreted as a warning signal of AMOC tipping is identified in the eastern SPNA in both observations and ESMs. This warning signal is in accordance with a physical indicator of AMOC stability, AMOC-induced freshwater convergence into the Atlantic basin. The observed signal can be reconciled with the modeled one only under warming exceeding the Paris Agreement goal, suggesting that AMOC stability is overestimated in ESMs. Our results suggest that the observed AMOC may indeed be losing stability and could thus reach a tipping point much earlier than state-of-the-art ESMs suggest.

physics.ao-ph↗

arXiv2023

Spatial correlation increase in single-sensor satellite data reveals loss of Amazon rainforest resilience

The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna-like state. Previous work suggesting declining resilience in recent decades was based only on local resilience indicators. Moreover, previous results are potentially biased by the employed multi-sensor and optical satellite data and undetected anthropogenic land-use change. Here, we show that the spatial correlation provides a more robust resilience indicator than local estimators and employ it to measure resilience changes in the ARF, based on single-sensor Vegetation Optical Depth data under conservative exclusion of human activity. Our results show an overall loss of resilience until around 2019, which is especially pronounced in the southwestern and northern Amazon for the time period from 2002 to 2011. The demonstrated reliability of spatial correlation in coupled systems suggests that in particular the southwest of the ARF has experienced pronounced resilience loss over the last two decades.

physics.geo-ph↗

arXiv2023

Uncertainties too large to predict tipping times of major Earth system components from historical data

One way to warn of forthcoming critical transitions in Earth system components is using observations to detect declining system stability. It has also been suggested to extrapolate such stability changes into the future and predict tipping times. Here, we argue that the involved uncertainties are too high to robustly predict tipping times. We raise concerns regarding (i) the modeling assumptions underlying any extrapolation of historical results into the future, (ii) the representativeness of individual Earth system component time series, and (iii) the impact of uncertainties and preprocessing of used observational datasets, with focus on nonstationary observational coverage and gap filling. We explore these uncertainties in general and specifically for the example of the Atlantic Meridional Overturning Circulation. We argue that even under the assumption that a given Earth system component has an approaching tipping point, the uncertainties are too large to reliably estimate tipping times by extrapolating historical information.

physics.geo-ph↗

arXiv2023

Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic Overturning Circulation

Observations are increasingly used to detect critical slowing down (CSD) to measure stability changes in key Earth system components. However, most datasets have non-stationary missing-data distributions, biases and uncertainties. Here we show that, together with the pre-processing steps used to deal with them, these can bias the CSD analysis. We present an uncertainty quantification method to address such issues. We show how to propagate uncertainties provided with the datasets to the CSD analysis and develop conservative, surrogate-based significance tests on the CSD indicators. We apply our method to three observational sea-surface temperature and salinity datasets and to fingerprints of the Atlantic Meridional Overturning Circulation derived from them. We find that the properties of these datasets and especially the specific gap filling procedures can in some cases indeed cause false indication of CSD. However, CSD indicators in the North Atlantic are still present and significant when accounting for dataset uncertainties and non-stationary observational coverage.

physics.ao-ph↗

arXiv2020

Theoretical vs. observational uncertainties: composition of giant exoplanets

In order to characterize giant exoplanets and better understand their origin, knowledge of how the planet's composition depends on its mass and stellar environment is required. In this work, we simulate the thermal evolution of gaseous planets and explore how various common model assumptions such as different equations of state, opacities, and heavy-element distributions affect the inferred radius and metallicity. We examine how the theoretical uncertainties translate into uncertainties in the inferred planetary radius and bulk metallicity. While we confirm the mass-metallicity trend previously reported in the literature, this correlation disappears when removing a 20 $M_{\oplus}$ heavy-element core from all the planets. We also show that using an updated hydrogen-helium equation of state leads to more compact planets. As a result, we present six planets that should be classified as inflated warm Jupiters. We next demonstrate that including the opacity-enhancement due to metal-rich envelopes of irradiated planets changes the planetary radius significantly, which can have large effects on the inferred metallicity. Even though there are other model assumptions that have not been considered in this work, we could show that the calculated theoretical uncertainties can already be comparable or even larger than the observational ones. Therefore, theoretical uncertainties are likely to be even larger. We therefore conclude that progress in theoretical models of giant planets is essential in order to take full advantage of current and future exoplanetary data.

astro-ph.EP↗

arXiv2020

Neutral Cr and V in the atmosphere of ultra hot jupiter WASP-121 b

Ultra hot jupiters (UHJs), giant exoplanets with equilibrium temperatures above 2000 K, are ideal laboratories for studying metal compositions of planetary atmospheres. At these temperatures the thermal dissociation of metal-rich molecules into their constituent elements makes these atmospheres conducive for elemental characterisation. Several elements, mostly ionized metals, have been detected in UHJs recently using high resolution transit spectroscopy. Even though a number of neutral transition metals (e.g., Fe, Ti, V, Cr) are expected to be strong sources of optical/NUV opacity and, hence, influence radiative processes in the lower atmospheres of UHJs, only Fe I has been detected to date. We conduct a systematic search for atomic species in the UHJ WASP-121 b. Using theoretical models we present a metric to predict the atomic species likely to be detectable in such planets with high resolution transmission spectroscopy. We search for the predicted species in observations of WASP-121 b and report the first detections of neutral transition metals Cr I and V I in an exoplanet at 3.6 $σ$ and 4.5 $σ$, respectively. We confirm previous detections of Fe I and Fe II. Whereas Fe II was detected previously in the NUV, we detect it in the optical. We infer that the neutral elements Fe I, V I, and Cr I are present in the lower atmosphere, as predicted by thermochemical equilibrium, while Fe II is a result of photoionisation in the upper atmosphere. Our study highlights the rich chemical diversity of UHJs.

astro-ph.EP↗