09:00 - 09:50
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Petra Friederichs
(University of Bonn)
Predicting spatial weather extremes
Extremes in weather and climate do not occur 'out-of-the-blue'. Their probability of occurrence is determined by the state of the dynamical system, which in turn is to some degree predictable. However, particularly on the atmospheric mesoscale, uncertainties are large and predictions are probabilistic in nature. Thus mesoscale weather ensemble prediction systems (EPS) not only issue one future state of the atmosphere but a sample of possible future states thereby accounting for several sources of uncertainty.
We will present and discuss ensemble post-processing for spatial extremes. Statistical representation of spatial extremes naturally resorts to spatial max-stable processes. The multivariate analysis of extremes involves two tasks: first, estimating the marginals and second, characterizing the dependence structure of the spatial process. We present a Bayesian approach to estimate the spatial marginals conditional on the EPS forecasts, and discuss the Brown-Resnick process as a spatial model for observations.
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09:50 - 10:10
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Reik Donner
(Potsdam Institute for Climate Impact Research)
Functional climate network analysis for characterizing spatio-temporal patterns of climate variability
(Authors: Reik V. Donner, Marc Wiedermann, Alexander Radebach, Jonathan Donges)
As a by-product of the rising interest in the structural and dynamical characterization of networks in various scientific disciplines, complex network based methods have recently found wide use as an exploratory tool of data analysis across fields. In particular, functional networks encoding essential statistical interrelationships among multivariate time series have proven to be a versatile tool in climate sciences, neurophysiology and various other areas. In my presentation, I will present some recent applications of functional network analysis in the field of climatology to highlight the potentials as well as ongoing challenges of such “climate network” approaches.
As one particular example, I will demonstrate how the El Nino Southern Oscillation, the most prominent mode of tropical climate variability, is reflected in the global correlation structure of surface air temperatures. I will show that several climate network characteristics are able to trace the emergence of El Nino and La Nina episodes. However, there is no one-by-one correspondence between anomalies of these features and the aforementioned climate phenomena, pointing towards (i) a distinctive difference in the spatial structure of global temperature teleconnections associated with two different flavors of El Nino and La Nina and (ii) the fact that other regional-scale climate anomalies like cooling trends following strong volcanic eruptions leading to qualitatively similar effects in the network structure. Finding (i) is further corroborated by the results of a statistical assessment of the simultaneous occurrence of different event types and seasonal precipitation anomalies across the globe using the new intuitive statistical approach of event coincidence analysis.
Contributing to our understanding of coupling between different subsystems, I will present a straightforward methodological extension utilizing the idea of coupled networks and their appropriate structural quantification. Specifically, I will demonstrate the application of this idea to studying ocean-atmosphere interdependences in the Northern latitude extratropics during boreal winter, which reveals clear differences between the way correlations from the ocean (sea-surface temperatures) to the atmosphere (pressure levels) are spatially organized as compared with the opposite direction. Specifically, taking the two local measures cross-degree and local cross-clustering coefficient, the strongest correlations from the ocean towards the atmosphere exhibit a power-law between both characteristics pointing to some specific type of hierarchical structure.
While recent work has largely focused on the presented climatological applications, the utilized frameworks of functional network analysis, event coincidence analysis and coupled network analysis are generic and widely applicable to other fields as well.
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10:10 - 10:30
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Joshua Garland
(Santa Fe Institute)
Climate information production recorded in water isotopes from deep polar ice cores
Paleoclimate records are extremely rich sources of information about the past history of the Earth system. We take an information-theoretic approach to analyzing data from the WAIS Divide ice core, the longest continuous and highest-resolution water isotope record yet recovered from Antarctica. We use weighted permutation entropy to calculate the Shannon entropy rate from these isotope measurements, which are proxies for a number of different climate variables, including the temperature at the time of deposition of the corresponding layer of the core. We find that the rate of information production in these measurements reveals issues with analysis instruments, even when those issues leave no visible traces in the raw data. These entropy calculations also allow us to identify a number of intervals in the data that may be of direct relevance to paleoclimate interpretation, and to form new conjectures about what is happening in those intervals—including periods of abrupt climate change.
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10:30 - 11:00
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Coffee break
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11:00 - 11:50
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Sergio Ciliberto
(ENSL and CNRS, Lyon)
A protocol for reaching equilibrium arbitrary fast
When a control parameter of a system is suddenly changed, the accessible phase space changes too and the system needs its characteristic relaxation time to reach the final equilibrium distribution. An important and relevant question is whether it is possible to travel from an equilibrium state to another in an arbitrary time, much shorter than the natural relaxation time.
Such strategies are reminiscent of those worked out in the recent field of Shortcut to Adiabaticity, that aim at developing protocols, both in quantum and in classical regimes, allowing the system
to move as fast as possible from one equilibrium position to a new one, provided that there exist
an adiabatic transformation relating the two. Proof of principle experiments
have been carried out for isolated systems.
Instead in open system the reduction of the relaxation time, which is frequently desired and necessary, is often obtained by complex feedback processes.
In this talk, we present a protocol, named Engineered Swift Equilibration (ESE), that shortcuts time-consuming relaxations,
We tested experimentally this protocol on a Brownian particle trapped in an optical potential first and then on an AFM cantilever. We show that applying a specific driving, one can reach equilibrium in an arbitrary short time. We also estimate the energetic cost to get such a time reduction.
The ESE method paves the way for applications in micro and nano
devices, in high speed AFM, or in monitoring mesoscopic chemical or biological process.
References:
(1) Engineered Swift Equilibration, I. A. Martinez; A. Petrosyan; D. Guéry-Odelin;
E. Trizac; S. Ciliberto, Nature Physics, Vol 12, 843 (2016).
(2) Arbitrary fast modulation of an atomic force microscope, A. Le Cunuder; I. A. Martinez; A. Petrosyan; D. Guéry-Odelin; E. Trizac; S. Ciliberto, . Applied Physics Letters, 109, 113502 (2016)
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11:50 - 12:10
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Almut Gassmann
(Leibniz Institute of Atmospheric Physics, Kühlungsborn)
Entropy production due to subgrid-scale thermal fluxes with application to breaking gravity waves
Numerical formulations of turbulent heat fluxes must lead to positive energy dissipation and positive internal entropy production. Current parameterization approaches only deliver positive dissipation rates for free convection, not for forced convection. This contribution explains how positive dissipation rates are achieved by a new formulation of subgrid-scale terms in the case of stable stratification. This is of importance for the numerical realization of the breakdown of gravity waves.
A turbulent atmosphere tends to an isentropic stratification, because in addition to turbulent heat diffusion, pressure work leads to expansion when air is rising and contraction when air is sinking. This pressure work remains completely subgrid-scale when the atmosphere is unstably stratified. When the atmosphere is stably stratified, this pressure work has to be done by the outer environment. Therefore, a turbulent pressure gradient term is introduced in the vertical momentum equation and the equations account for the irreversible energy conversion from resolved kinetic energy into the model's internal energy.
Numerical experiments for breaking gravity waves in the mesosphere highlight the different behavior of new and conventional approaches. The conventional approach leads to a deepening of the wave amplitudes, whereas the new approach supports wave overturning. The observed foliate structure of very sharp inversion layers is indeed simulated with the new approach for stable stratification. In contrast to the traditional setting the new scheme does not evolve into persistent non-physical wave structures on long timescales.
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12:10 - 12:30
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Martin Pieroth
(Goethe Universität Frankfurt am Main)
Climate-dependence in SGS parameterizations of low-order climate models derived by the Fluctuation-Dissipation Theorem
The atmosphere is a complex system involving many interacting scales. Therefore, subgrid-scale (SGS) parameterizations are essential for climate simulations and numerical weather prediction. Many of those parameterizations contain tuning parameters obtained by fitting model behavior to reference data statistics. Consequently, if the atmosphere is perturbed, and hence also the statistics, these parameters might become erroneous and the SGS parameterization may no longer be able to help simulating the dynamics of the perturbed atmosphere.
Therefore, we propose a climate dependence of the tuning parameters using the Fluctuation-Dissipation Theorem (FDT). The FDT is able to predict the changes in the statistics of a system, caused by small external forcings. Those changes are then used to update the empirical components of the tuning parameters.
This procedure is tested in a toy atmosphere provided by a three-layer quasi-geostrophic model (QG3LM). The corresponding climate model is given by a low-order model, based on a reduced number of QG3LM variance patterns, with an empirical linear closure as SGS parameterization. The external perturbation is given by some local anomalous heating in the extratropics.
It is shown that the FDT is able to predict the required change in the closure parameters. The climate model with the FDT-corrected closure improves the agreement with the perturbed toy atmosphere, compared to the climate model without a corrected closure. In addition, we show that the climate model with FDT-corrected closure outperforms the direct FDT estimation of the response of the toy atmosphere, provided sufficiently many basis patterns are used.
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12:30 - 14:00
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Lunch and discussions
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14:00 - 14:50
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Paul Williams
(University of Reading)
Atmospheric flow regimes: stochasticity and noise-induced transitions
The atmospheric circulation is observed to exhibit regime behaviour. Regimes are a set of preferred flow patterns that are qualitatively different from each other. When the flow is in one regime, it will persist for an extended period of time, before undergoing a rapid transition to another regime. An example of atmospheric regimes is sudden stratospheric warmings, which occur when the stratospheric polar vortex undergoes a sudden transition from an axisymmetric state to a split or displaced state. This talk will show that useful insights into atmospheric regime transitions may be gained by studying experiments on rotating fluids in the laboratory. In this simple setting, regimes may be studied without the often ad-hoc approximations of numerical and theoretical approaches. In laboratory experiments, we have found that transitions between large-scale flow regimes may be triggered by small-scale gravity waves. These transitions may be mimicked in a numerical model via the injection of stochastic noise, motivating stochastic parameterisations in atmospheric models. The laboratory experiments have also inspired a new interpretation of sudden stratospheric warmings, as being gravity wave noise-induced transitions.
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14:50 - 15:10
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Steve Tobias
(University of Leeds)
Direct statistical simulation and the gql approximation in turbulent flows
In this talk I will summarise the progress made in determining the statistics of turbulent flows using Direct Statistical Simulation. I shall also describe recent advances in generalising the Quasilinear approximation and how this generalisation can improve the range of applicability of statistical methods.
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15:10 - 15:30
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Lenin Del Rio Amador
(McGill University, Montreal)
Exploiting macroweather symmetries for monthly to interannual predictions: StocSIPS
The atmosphere is governed by continuum mechanics and thermodynamics yet simultaneously obeys statistical turbulence laws. Up until its deterministic predictability limit (τw ≈ 10 days), only General Circulation Models (GCM’s) have been used for prediction; the turbulent laws being still too difficult to exploit. However, beyond τw – in macroweather – the GCM’s effectively become stochastic with internal variability fluctuating about the model – not the real world – climate and their predictions are poor. In contrast, the turbulent macroweather laws become advantageous notably due to a) low macroweather intermittency that allows for a Gaussian approximation, and b) thanks to a statistical space-time factorization symmetry that (for predictions) allows much decoupling of the strongly correlated spatial degrees of freedom. The laws imply new stochastic predictability limits. We show that pure macroweather – such as in GCM’s without external forcings (control runs), can be forecast nearly to these limits by the ScaLIng Macroweather Model (SLIMM) that exploits huge system memory that forces the forecasts to converge to the real world climate.
To apply SLIMM to the real world requires preprocessing to take into account anthropogenic and other low frequency external forcings. We compare the overall Stochastic Seasonal to Interannual Prediction System (StocSIPS, operational since April 2016) with a classical GCM (CanSIPS) showing that StocSIPS is superior for forecasts two months and further in the future, particularly over land. In addition, the relative advantage of StocSIPS increases with forecast lead time.
In this presentation we give some details of StocSIPS and its implementation and we evaluate its skill both absolute and relative to CanSIPS.
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15:30 - 16:00
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Coffee break
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16:00 - 16:50
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Udo Seifert
(University of Stuttgart)
Universal relations between currents, their fluctuations and entropy production in strongly driven systems
Over the last decade, stochastic thermodynamics has emerged as a
comprehensive framework for describing a large class of strongly
driven systems [1]. I will introduce its basic concepts and
discuss, as one major outcome, the fluctuation theorem for
entropy production. The more recently discovered thermodynamic
uncertainty relation gives a universal inequality relating the
mean and dispersion of any current with the overall entropy
production [2]. For molecular motors, it has been used as a
tool of thermodynamic inference to reveal hidden properties
of a system [3]. Since these results are mathematical
identities, they hold for the stationary state of any
Markovian stochastic dynamics. This fact offers the
perspective of applying them beyond biophysics to other fields
like climate science.
[1] U Seifert, Rep. Prog. Phys. 75, 126001, 2012.
[2] AC Barato and U Seifert, Phys. Rev. Lett. 114, 158101, 2015.
[3] P Pietzonka, AC Barato, and U Seifert, J Stat Mech, 124004,
2016.
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16:50 - 17:10
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Byungnam Kahng
(Seoul National University)
Entropy productions and the fluctuation theorem in transport on complex networks
Entropy production (EP) $\sigma$ has played a central role in the fluctuation theorem, so-called the Jarzynski's integral fluctuation theorem (IFT) $\lagnle e^{-\sigma}\rangle=1$ in non-equilibrium systems. The EP is obtained during a forward and backward process of a particle or system. Even though the IFT has been verified in experiments in terms of the difference of external works applied and gained to and from the system during the forward and backward processes, there is little case in which the entropy production is directly measured. Here, we check and confirm the IFT in the data packet transport process on complex networks and obtain the distribution of entropy production distribution. This work could be extended to further problems on protein folding-unfolding problems.
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17:10 - 17:30
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Richard Blender
(University of Hamburg)
Fluctuation analysis of the Lorenz energy cycle
Richard Blender, Frank Lunkeit and Denny Gohlke, Meteorological Institute and CEN, University of Hamburg
In a large number of physical systems non-equilibrium fluctuations satisfy the Fluctuation Theorem, which relates the probabilities of negative and positive entropy production. Here we analyse the fluxes in the Lorenz energy cycle in simulations with the atmospheric model PUMA (Portable University Model of the Atmosphere, University of Hamburg). The Lorenz energy cycle includes the injected power, the total dissipated energy and captures the internal conversions of available potential and kinetic energy, and the energy flow from the zonal mean to the eddy scale. PUMA is a dynamical core based on the hydrostatic primitive equations with simplified forcing and friction parameterizations. The aim is to assess the possible validity of the Fluctuation Theorem and to determine the Large Deviation rate functions for the energy fluxes. In a sub-project of the Collaborative Research Center Transregio TRR181 “Energy transfers in Atmosphere and Ocean”, we intend to use periods with negative entropy production to constrain stochastic or counter-gradient parameterizations in models.
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18:00 - 19:30
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Dinner and discussions
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