Machine Learning of committor functions for predicting high impact climate events

Lucente, Dario; Bouchet, Freddy; Herbert, Corentin

doi:10.5194/egusphere-egu2020-21754

Cited by 10 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instead, the tipping points or edge states can be identified with the points where tipping to B is as likely as going back to A, i.e., those states with q + (x) close to 1/2. Recently it has been argued that the forward committor is the relevant object to quantify the risk of tipping to a certain state in the future [18,38].…”

Section: Studying Tippingmentioning

confidence: 99%

“…Recently, the forward committor has been singled out as the central object for quantifying the risk of future tipping [18,38]. Several papers study how one can solve for high-dimensional committors using neural networks [31,34,35,38]. Very much related to tipping is the concept of resilience, which in its simplest form is the system's ability of returning to the original state or region after a perturbation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical analysis of tipping pathways in agent-based models

Helfmann

Heitzig

Koltai

et al. 2021

Eur. Phys. J. Spec. Top.

View full text Add to dashboard Cite

Agent-based models are a natural choice for modeling complex social systems. In such models simple stochastic interaction rules for a large population of individuals on the microscopic scale can lead to emergent dynamics on the macroscopic scale, for instance a sudden shift of majority opinion or behavior. Here we are introducing a methodology for studying noise-induced tipping between relevant subsets of the agent state space representing characteristic configurations. Due to a large number of interacting individuals, agent-based models are high-dimensional, though usually a lower-dimensional structure of the emerging collective behaviour exists. We therefore apply Diffusion Maps, a non-linear dimension reduction technique, to reveal the intrinsic low-dimensional structure. We characterize the tipping behaviour by means of Transition Path Theory, which helps gaining a statistical understanding of the tipping paths such as their distribution, flux and rate. By systematically studying two agent-based models that exhibit a multitude of tipping pathways and cascading effects, we illustrate the practicability of our approach.

show abstract

Section: Studying Tippingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical analysis of tipping pathways in agent-based models

Helfmann

Heitzig

Koltai

et al. 2021

Eur. Phys. J. Spec. Top.

View full text Add to dashboard Cite

show abstract

“…Such a direct Galerkin approximation has been used to directly compute committor functions, avoiding the burden of discretizing a high dimensional phase space [67,68]. Several computations of committor functions have been performed with applications in either geophysical fluid dynamics or in climate sciences [31,69,32,70,71], using either direct or involved approaches.…”

Section: Rare Event Algorithmmentioning

confidence: 99%

“…where δ is a Dirac delta function, and 1 {T B (Xn)≤T A (Xn)} takes value 1 if the trajectory visits set B before set A starting from X n , and 0 otherwise. Numerically, q(x) can be computed from (6) after spatial and temporal discretization of the process (see for instance [70,78,71]). Unlike the previous methods, this approach is applicable even if we do not know the equations of motion.…”

Section: Direct Sampling Of the Committor Functionmentioning

confidence: 99%

Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain

Lucente,

Rolland,

Herbert

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Rare events play a crucial role in many physics, chemistry, and biology phenomena, when they change the structure of the system, for instance in the case of multistability, or when they have a huge impact. Rare event algorithms have been devised to simulate them efficiently, avoiding the computation of long periods of typical fluctuations. We consider here the family of splitting or cloning algorithms, which are versatile and specifically suited for far-from-equilibrium dynamics. To be efficient, these algorithms need to use a smart score function during the selection stage. Committor functions are the optimal score functions.In this work we propose a new approach, based on the analogue Markov chain, for a data-based learning of approximate committor functions. We demonstrate that such learned committor functions are extremely efficient score functions when used with the Adaptive Multilevel Splitting algorithm. We illustrate our approach for a gradient dynamics in a three-well potential, and for the Charney-DeVore model, which is a paradigmatic toy model of multistability for atmospheric dynamics. For these two dynamics, we show that having observed a few transitions is enough to have a very efficient data-based score function for the rare event algorithm. This new approach is promising for use for complex dynamics: the rare events can be simulated with a minimal prior knowledge and the results are much more precise than those obtained with a user-designed score function.

show abstract

“…Tantet et al (2015) computed early warning signs of atmospheric blocking by estimating a committor (by a different name) in a reduced state space. Lucente et al (2019) and Lucente et al (2021) have also computed committor functions for simple models of El Niño, mathematically quantifying the so-called predictability barrier. Bayesian machine learning (Chen et al 2021) and kernel forecasting (Wang et al 2020) are also being investigated in the quest to forecast El Niño.…”

Section: Introductionmentioning

confidence: 99%

Exploring stratospheric rare events with transition path theory and short simulations

Finkel¹,

Webber²,

Gerber³

et al. 2021

Preprint

View full text Add to dashboard Cite

Extreme weather events are simultaneously the least likely and the most impactful features of the climate system, increasingly so as climate change proceeds. Extreme events are multi-faceted, highly variable processes which can be characterized in many ways: return time, worst-case severity, and predictability are all sought-after quantities for various kinds of rare events. A unifying framework is needed to define and calculate the most important quantities of interest for the purposes of near-term forecasting, long-term risk assessment, and benchmarking of reduced-order models. Here we use Transition Path Theory (TPT) for a comprehensive analysis of sudden stratospheric warming (SSW) events in a highly idealized wave-mean flow interaction system with stochastic forcing. TPT links together probabilities, dynamical behavior, and other risk metrics associated with rare events that represents their full statistical variability. At face value, fulfilling this promise demands extensive direct simulation to generate the rare event many times. Instead, we implement a highly parallel computational method that launches a large ensemble of short simulations, estimating long-timescale rare event statistics from short-term tendencies. We specifically investigate properties of SSW events including passage time distributions and large anomalies in vortex strength and heat flux. We visualize high-dimensional probability densities and currents, obtaining a nuanced picture of critical altitude-dependent interactions between waves and the mean flow that fuel SSW events. We find that TPT more faithfully captures the statistical variability between events as compared to the more conventional minimum action method.

show abstract

Machine Learning of committor functions for predicting high impact climate events

Cited by 10 publications

References 21 publications

Statistical analysis of tipping pathways in agent-based models

Statistical analysis of tipping pathways in agent-based models

Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain

Exploring stratospheric rare events with transition path theory and short simulations

Contact Info

Product

Resources

About