arXiv subjects

Valérian Jacques-Dumas

Publications and source records attributed to Valérian Jacques-Dumas.

7 recordsLinked to original sources

arXiv2026

Constructing efficient score functions for rare event simulation in high-dimensional ocean-climate models

Calculating transition probabilities between different states of multistable climate tipping systems is computationally challenging in high-dimensional models. Targeted algorithms, such as the Trajectory-Adaptive Multilevel Splitting (TAMS) method, require an adequate score function to be successful, i.e., to provide an estimate of a transition probability with an acceptable variance when only a relatively small ensemble of model trajectories can be computed. Here, we present a data-driven method to derive a score function based on projecting the model dynamics in a reduced state space. Using a spatially two-dimensional partial differential equation model of the Atlantic Meridional Overturning Circulation, we show that this score function performs better than currently available ones. Using the new score function, transition probabilities can be determined with low variance, even in the case of small noise amplitudes. Besides purely noise-induced transitions, we also consider the scenario of combined stochastic and time-dependent deterministic forcing, presenting a strategy to efficiently simulate AMOC tipping events in global ocean and climate models subject to transient climate change.

physics.ao-ph↗

arXiv2026

Quantification of the cascading tipping probability from the AMOC to the Amazon rainforest

The Amazon rainforest and the AMOC are considered to be tipping elements: they are important components of the Earth system, but may collapse under climate change. Moreover, an AMOC collapse may favor the transition of the rainforest to a degraded forest by influencing the precipitation patterns over the Amazon. This phenomenon is known as tipping cascade and better understanding it is key to anticipating the impact of tipping events. Here, we investigate in a coupled conceptual AMOC-Amazon model the probability that an AMOC weakening affects tree cover loss in two regions of the rainforest. To get more insight into the mechanisms behind the tipping cascade, we also analyze the dynamics of both systems and their evolution during the Amazon transition. Namely, we track the transition probability and the transition time of the Amazon, and reconstruct the distribution of AMOC strength at every stage of this transition. These tasks require a large ensemble simulation, containing in particular a large number of transitions. Since such events may be too rare to be sampled by direct numerical simulation, the collapse of both systems is studied using TAMS, a "rare-event" algorithm designed to efficiently sample rare transitions. We find that, in the northwest of Brazil, a transition of the Amazon rainforest to a degraded forest within 200 years is very unlikely. However, in this region, such transition can only occur after an AMOC collapse, which would have a large drying effect that favors the development of extreme wildfires.

physics.ao-ph↗

arXiv2024

Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm

The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called ``committor function" is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function.

physics.ao-ph↗

arXiv2024

Resilience of the Atlantic Meridional Overturning Circulation

We address the issue of resilience of the Atlantic Meridional Overturning Circulation (AMOC) given the many indications that this dynamical system is in a multi-stable regime. A novel approach to resilience based on rare event techniques is presented which leads to a measure capturing `resistance to change` and `ability to return' aspects in a probabilistic way. The application of this measure to a conceptual model demonstrates its suitability for assessing AMOC resilience but also shows its potential use in many other non-autonomous dynamical systems. This framework is then extended to compute the probability that the AMOC undergoes a transition conditioned on an external forcing. Such conditional probability can be estimated by exploiting the information available when computing the resilience of this system. This allows us to provide a probabilistic view on safe operating spaces by defining a conditional safe operating space as a subset of the parameter space of the (possibly transient) imposed forcing.

physics.ao-ph↗

arXiv2024

The Role of Sea-ice Processes on the Probability of AMOC Transitions

Recent simulations performed with the Community Earth System Model (CESM) have suggested a crucial role of sea-ice processes in AMOC hysteresis behaviour under varying surface freshwater forcing. Here, we further investigate this issue using additional CESM simulations and a novel conceptual ocean-sea-ice box model. The CESM simulations show that the presence of sea ice gives rise to the existence of statistical equilibrium states with a weak AMOC strength. This is confirmed in the conceptual model, which captures the same AMOC hysteresis behaviour as in the CESM simulation and where steady states are computed versus forcing parameters. In the conceptual model, transition probabilities between the different equilibrium states are determined using rare event techniques. The transition probabilities from a strong AMOC state to a weak AMOC state increase when considering sea-ice processes and indicate that sea ice promotes these transitions. On the other hand, sea ice strongly reduces the probabilities of the reverse transition from a weak AMOC state to a strong AMOC state and this implies that sea ice also limits AMOC recovery. The results here indicate that sea-ice processes play a dominant role in AMOC hysteresis width and influence transition probabilities between the different equilibrium states.

physics.geo-ph↗

arXiv2023

Data-driven methods to estimate the committor function in conceptual ocean models

In recent years, several climate subsystems have been identified that may undergo a relatively rapid transition compared to the changes in their forcing. Such transitions are rare events in general, and simulating long-enough trajectories in order to gather sufficient data to determine transition statistics would be too expensive. Conversely, rare events algorithms like TAMS (trajectory-adaptive multilevel sampling) encourage the transition while keeping track of the model statistics. However, this algorithm relies on a score function whose choice is crucial to ensure its efficiency. The optimal score function, called the committor function, is in practice very difficult to compute. In this paper, we compare different data-based methods (analog Markov chains, neural networks, reservoir computing, dynamical Galerkin approximation) to estimate the committor from trajectory data. We apply these methods on two models of the Atlantic Ocean circulation featuring very different dynamical behavior. We compare these methods in terms of two measures, evaluating how close the estimate is from the true committor and in terms of the computational time. We find that all methods are able to extract information from the data in order to provide a good estimate of the committor. Analog Markov Chains provide a very reliable estimate of the true committor in simple models but prove not so robust when applied to systems with a more complex phase space. Neural network methods clearly stand out by their relatively low testing time, and their training time scales more favorably with the complexity of the model than the other methods. In particular, feedforward neural networks consistently achieve the best performance when trained with enough data, making this method promising for committor estimation in sophisticated climate models.

physics.ao-ph↗

arXiv2022

Deep Learning-based Extreme Heatwave Forecast

Because of the impact of extreme heat waves and heat domes on society and biodiversity, their study is a key challenge. We specifically study long-lasting extreme heat waves, which are among the most important for climate impacts. Physics driven weather forecast systems or climate models can be used to forecast their occurrence or predict their probability. The present work explores the use of deep learning architectures, trained using outputs of a climate model, as an alternative strategy to forecast the occurrence of extreme long-lasting heatwaves. This new approach will be useful for several key scientific goals which include the study of climate model statistics, building a quantitative proxy for resampling rare events in climate models, study the impact of climate change, and should eventually be useful for forecasting. Fulfilling these important goals implies addressing issues such as class-size imbalance that is intrinsically associated with rare event prediction, assessing the potential benefits of transfer learning to address the nested nature of extreme events (naturally included in less extreme ones). We train a Convolutional Neural Network, using 1000 years of climate model outputs, with large-class undersampling and transfer learning. From the observed snapshots of the surface temperature and the 500 hPa geopotential height fields, the trained network achieves significant performance in forecasting the occurrence of long-lasting extreme heatwaves. We are able to predict them at three different levels of intensity, and as early as 15 days ahead of the start of the event (30 days ahead of the end of the event).

cs.LG↗