Robust intra-model teleconnection patterns for extreme heatwaves

We investigate the statistics and dynamics of extreme heat waves over different areas of Europe. We find heatwaves over France and Scandinavia to be associated with recurrent wavenumber three teleconnection patterns in surface temperature and mid-tropospheric geopotential height. For heatwaves with return times of 4 years these teleconnection patterns and their dynamics are robustly represented in a hierarchy of models of different complexity and in reanalysis data. For longer return times, reanalysis records are too short to give statistically significant results, while models confirm the relevance of these large scale patterns for the most extreme heatwaves. A time series analysis shows that heatwave indices defined at synoptic scale are fairly well described by Gaussian stochastic processes, and that these Gaussian processes reproduce well return time plots even for very rare events. These results suggest that extreme heatwaves over different areas of Europe show recurrent typical behaviours in terms of long-range spatial correlations and subseasonal-scale temporal correlations. These properties are consistently represented among models of different complexity and observations, thus suggesting their relevance for a better understanding of the drivers and causes of the occurrence of extreme midlatitude heatwaves and their predictability.Comment: 25 pages, 11 figure

Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data

Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. In this work we develop a methodology to build forecasting models for extreme heatwaves. These models are based on convolutional neural networks, trained on extremely long 8,000-year climate model outputs. Because the relation between extreme events is intrinsically probabilistic, we emphasise probabilistic forecast and validation. We demonstrate that deep neural networks are suitable for this purpose for long lasting 14-day heatwaves over France, up to 15 days ahead of time for fast dynamical drivers (500 hPa geopotential height fields), and also at much longer lead times for slow physical drivers (soil moisture). The method is easily implemented and versatile. We find that the deep neural network selects extreme heatwaves associated with a North-Hemisphere wavenumber-3 pattern. We find that the 2 meter temperature field does not contain any new useful statistical information for heatwave forecast, when added to the 500 hPa geopotential height and soil moisture fields. The main 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 to large scale atmosphere and climate phenomena. We discuss perspectives for dealing with the lack of data regime, for instance rare event simulations, and how transfer learning may play a role in this latter task.Comment: 33 pages, 12 figure

Hamiltonian description of Hall and sub-electron scales in collisionless plasmas with reduced fluid models

In MHD magnetic helicity has been shown to represent Gauss linking numbers of magnetic field lines by Moffatt and others; thus it is endowed with topological meaning. The noncanonical Hamiltonian formulation of extended MHD models (that take two-fluid effects into account) has been used to arrive at their common mathematical structure, which manifests itself via the existence of two generalized helicities and two Lie-dragged 2-forms. The helicity invariants play an important role in the second part of thesis dedicated to understanding the directionality of turbulent cascades. Generally speaking, invariants (such as energy) can flow in two directions in a turbulent cascade: forward (towards small scales, leading to dissipation) and inverse (towards large scales), leading to the formation of a condensate. This directionality in extended MHD models is estimated using analytical considerations as well as tests involving 2D numerical simulations. The cascade reversal (transition) of the square magnetic vector potential is found, viz. when the forcing wavenumber exceeds the inverse electron skin depth the square magnetic vector potential starts to flow towards large wavenumbers, as opposed to the typical MHD behavior. In addition, the numerics suggest a simultaneous transition to the inverse cascade of energy in this inertial MHD regime. This is accompanied by the appearance of large scale structures in the velocity field, as opposed to the magnetic field as in the MHD case. Final chapters of the thesis are devoted to devising the action principle for the relativistic extended MHD. First the special relativistic version is discussed, where the covariant noncanonical Poisson bracket is found. This is followed by a short recourse towards describing relativistic collisionless reconnection mediated by the electron thermal inertia (purely relativistic effect). Next, 3+1 splitting inside the Poisson bracket is performed, while only non-relativistic terms are retained. Thus one arrives at nonrelativistic extended MHD bracket with arbitrary ion to electron mass ratio. In conclusion, it is outlined how the Hamiltonian 3+1 formalism can be developed for general relativistic Hall MHD using canonical Clebsch parametrization and some comments are added on possible issues regarding the quasi-neutrality assumption in the model that is used throughout the chapter.Physic

Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning

International audienceWe present a data-driven emulator, a stochastic weather generator (SWG), suitable for estimating probabilities of prolonged heatwaves in France and Scandinavia. This emulator is based on the method of analogs of circulation to which we add temperature and soil moisture as predictor fields. We train the emulator on an intermediate complexity climate model run and show that it is capable of predicting conditional probabilities (forecasting) of heatwaves out of sample. Special attention is payed that this prediction is evaluated using a proper score appropriate for rare events. To accelerate the computation of analogs dimensionality reduction techniques are applied and the performance is evaluated. The probabilistic prediction achieved with SWG is compared with the one achieved with a Convolutional Neural Network (CNN). With the availability of hundreds of years of training data CNNs perform better at the task of probabilistic prediction. In addition, we show that the SWG emulator trained on 80 years of data is capable of estimating extreme return times of order of thousands of years for heatwaves longer than several days more precisely than the fit based on generalised extreme value distribution. Finally, the quality of its synthetic extreme teleconnection patterns obtained with SWG is studied. We showcase two examples of such synthetic teleconnection patterns for heatwaves in France and Scandinavia that compare favorably to the very long climate model control run

Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning

International audienceWe present a data-driven emulator, a stochastic weather generator (SWG), suitable for estimating probabilities of prolonged heatwaves in France and Scandinavia. This emulator is based on the method of analogs of circulation to which we add temperature and soil moisture as predictor fields. We train the emulator on an intermediate complexity climate model run and show that it is capable of predicting conditional probabilities (forecasting) of heatwaves out of sample. Special attention is payed that this prediction is evaluated using a proper score appropriate for rare events. To accelerate the computation of analogs dimensionality reduction techniques are applied and the performance is evaluated. The probabilistic prediction achieved with SWG is compared with the one achieved with a Convolutional Neural Network (CNN). With the availability of hundreds of years of training data CNNs perform better at the task of probabilistic prediction. In addition, we show that the SWG emulator trained on 80 years of data is capable of estimating extreme return times of order of thousands of years for heatwaves longer than several days more precisely than the fit based on generalised extreme value distribution. Finally, the quality of its synthetic extreme teleconnection patterns obtained with SWG is studied. We showcase two examples of such synthetic teleconnection patterns for heatwaves in France and Scandinavia that compare favorably to the very long climate model control run

Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning

International audienceWe present a data-driven emulator, a stochastic weather generator (SWG), suitable for estimating probabilities of prolonged heatwaves in France and Scandinavia. This emulator is based on the method of analogs of circulation to which we add temperature and soil moisture as predictor fields. We train the emulator on an intermediate complexity climate model run and show that it is capable of predicting conditional probabilities (forecasting) of heatwaves out of sample. Special attention is payed that this prediction is evaluated using a proper score appropriate for rare events. To accelerate the computation of analogs dimensionality reduction techniques are applied and the performance is evaluated. The probabilistic prediction achieved with SWG is compared with the one achieved with a Convolutional Neural Network (CNN). With the availability of hundreds of years of training data CNNs perform better at the task of probabilistic prediction. In addition, we show that the SWG emulator trained on 80 years of data is capable of estimating extreme return times of order of thousands of years for heatwaves longer than several days more precisely than the fit based on generalised extreme value distribution. Finally, the quality of its synthetic extreme teleconnection patterns obtained with SWG is studied. We showcase two examples of such synthetic teleconnection patterns for heatwaves in France and Scandinavia that compare favorably to the very long climate model control run

Dipolar needles in the microcanonical ensemble: Evidence of spontaneous magnetization and ergodicity breaking

We have studied needle shaped three-dimensional classical spin systems with purely dipolar interactions in the microcanonical ensemble, using both numerical simulations and analytical approximations. We have observed spontaneous magnetization for different finite cubic lattices. The transition from the paramagnetic to the ferromagnetic phase is shown to be first-order. For two lattice types we have observed magnetization flips in the phase transition region. In some cases, gaps in the accessible values of magnetization appear, a signature of the ergodicity breaking found for systems with long-range interactions. We analytically explain these effects by performing a nontrivial mapping of the model Hamiltonian onto a one-dimensional Ising model with competing antiferromagnetic nearest-neighbor and ferromagnetic mean-field interactions. These results hint at performing experiments on isolated dipolar needles in order to verify some of the exotic properties of systems with long-range interactions in the microcanonical ensemble

Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning

International audienceWe present a data-driven emulator, a stochastic weather generator (SWG), suitable for estimating probabilities of prolonged heatwaves in France and Scandinavia. This emulator is based on the method of analogs of circulation to which we add temperature and soil moisture as predictor fields. We train the emulator on an intermediate complexity climate model run and show that it is capable of predicting conditional probabilities (forecasting) of heatwaves out of sample. Special attention is payed that this prediction is evaluated using a proper score appropriate for rare events. To accelerate the computation of analogs dimensionality reduction techniques are applied and the performance is evaluated. The probabilistic prediction achieved with SWG is compared with the one achieved with a Convolutional Neural Network (CNN). With the availability of hundreds of years of training data CNNs perform better at the task of probabilistic prediction. In addition, we show that the SWG emulator trained on 80 years of data is capable of estimating extreme return times of order of thousands of years for heatwaves longer than several days more precisely than the fit based on generalised extreme value distribution. Finally, the quality of its synthetic extreme teleconnection patterns obtained with SWG is studied. We showcase two examples of such synthetic teleconnection patterns for heatwaves in France and Scandinavia that compare favorably to the very long climate model control run