Irreversible spherical model and its stationary entropy production rate

The nonequilibrium stationary state of an irreversible spherical model is investigated on hypercubic lattices. The model is defined by Langevin equations similar to the reversible case, but with asymmetric transition rates. In spite of being irreversible, we have succeeded in finding an explicit form for the stationary probability distribution, which turns out to be of the Boltzmann-Gibbs type. This enables one to evaluate the exact form of the entropy production rate at the stationary state, which is non-zero if the dynamical rules of the transition rates are asymmetric.

Phase Transition for the Ising Model on the Critical Lorentzian Triangulation

The ferromagnetic Ising model without external field on an infinite Lorentzian triangulation sampled from the uniform distribution is considered. We prove uniqueness of the Gibbs measure in the high temperature region and coexistence of at least two Gibbs measures at low temperature. The proofs are based on the disagreement percolation method and on a variant of the Peierls contour method. The critical temperature is shown to be constant a.s.

Photoisomerization for a model protonated Schiff base in solution: Sloped/peaked conical intersection perspective

The topographical character of conical intersections (CIs)-either sloped or peaked-has played a fundamental and important role in the discussion of the efficiency of CIs as photochemical "funnels." Here this perspective is employed in connection with a recent study of a model protonated Schiff base (PSB) cis to trans photoisomerization in solution [Malhado et al., J. Phys. Chem. A 115, 3720 (2011)]. In that study, the calculated reduced photochemical quantum yield for the successful production of trans product versus cis reactant in acetonitrile solvent compared to water was interpreted in terms of a dynamical solvent effect related to the dominance, for the acetonitrile case, of S-1 to S-0 nonadiabatic transitions prior to the reaching the seam of CIs. The solvent influence on the quantum yield is here re-examined in the sloped/peaked CI topographical perspective via conversion of the model's two PSB internal coordinates and a nonequilibrium solvent coordinate into an effective branching space description, which is then used to re-analyze the generalized Langevin equation/surface hopping results. The present study supports the original interpretation and enriches it in terms of topographical detail. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4754505]

A Robust Structural PGN Model for Control of Cell-Cycle Progression Stabilized by Negative Feedbacks

The cell division cycle comprises a sequence of phenomena controlled by a stable and robust genetic network. We applied a probabilistic genetic network (PGN) to construct a hypothetical model with a dynamical behavior displaying the degree of robustness typical of the biological cell cycle. The structure of our PGN model was inspired in well-established biological facts such as the existence of integrator subsystems, negative and positive feedback loops, and redundant signaling pathways. Our model represents genes interactions as stochastic processes and presents strong robustness in the presence of moderate noise and parameters fluctuations. A recently published deterministic yeast cell-cycle model does not perform as well as our PGN model, even upon moderate noise conditions. In addition, self stimulatory mechanisms can give our PGN model the possibility of having a pacemaker activity similar to the observed in the oscillatory embryonic cell cycle.

A stochastic spatially structured epidemic model with diffusive processes

We developed a stochastic lattice model to describe the vector-borne disease (like yellow fever or dengue). The model is spatially structured and its dynamical rules take into account the diffusion of vectors. We consider a bipartite lattice, forming a sub-lattice of human and another occupied by mosquitoes. At each site of lattice we associate a stochastic variable that describes the occupation and the health state of a single individual (mosquito or human). The process of disease transmission in the human population follows a similar dynamic of the Susceptible-Infected-Recovered model (SIR), while the disease transmission in the mosquito population has an analogous dynamic of the Susceptible-Infected-Susceptible model (SIS) with mosquitos diffusion. The occurrence of an epidemic is directly related to the conditional probability of occurrence of infected mosquitoes (human) in the presence of susceptible human (mosquitoes) on neighborhood. The probability of diffusion of mosquitoes can facilitate the formation of pairs Susceptible-Infected enabling an increase in the size of the epidemic. Using an asynchronous dynamic update, we study the disease transmission in a population initially formed by susceptible individuals due to the introduction of a single mosquito (human) infected. We find that this model exhibits a continuous phase transition related to the existence or non-existence of an epidemic. By means of mean field approximations and Monte Carlo simulations we investigate the epidemic threshold and the phase diagram in terms of the diffusion probability and the infection probability.

Dynamical aspects of atmospheric data assimilation in the tropics

A faithful depiction of the tropical atmosphere requires three-dimensional sets of observations. Despite the increasing amount of observations presently available, these will hardly ever encompass the entire atmosphere and, in addition, observations have errors. Additional (background) information will always be required to complete the picture. Valuable added information comes from the physical laws governing the flow, usually mediated via a numerical weather prediction (NWP) model. These models are, however, never going to be error-free, why a reliable estimate of their errors poses a real challenge since the whole truth will never be within our grasp. The present thesis addresses the question of improving the analysis procedures for NWP in the tropics. Improvements are sought by addressing the following issues: - the efficiency of the internal model adjustment, - the potential of the reliable background-error information, as compared to observations, - the impact of a new, space-borne line-of-sight wind measurements, and - the usefulness of multivariate relationships for data assimilation in the tropics. Most NWP assimilation schemes are effectively univariate near the equator. In this thesis, a multivariate formulation of the variational data assimilation in the tropics has been developed. The proposed background-error model supports the mass-wind coupling based on convectively-coupled equatorial waves. The resulting assimilation model produces balanced analysis increments and hereby increases the efficiency of all types of observations. Idealized adjustment and multivariate analysis experiments highlight the importance of direct wind measurements in the tropics. In particular, the presented results confirm the superiority of wind observations compared to mass data, in spite of the exact multivariate relationships available from the background information. The internal model adjustment is also more efficient for wind observations than for mass data. In accordance with these findings, new satellite wind observations are expected to contribute towards the improvement of NWP and climate modeling in the tropics. Although incomplete, the new wind-field information has the potential to reduce uncertainties in the tropical dynamical fields, if used together with the existing satellite mass-field measurements. The results obtained by applying the new background-error representation to the tropical short-range forecast errors of a state-of-art NWP model suggest that achieving useful tropical multivariate relationships may be feasible within an operational NWP environment.

A dynamical system approach to data assimilation in chaotic models

The Assimilation in the Unstable Subspace (AUS) was introduced by Trevisan and Uboldi in 2004, and developed by Trevisan, Uboldi and Carrassi, to minimize the analysis and forecast errors by exploiting the flow-dependent instabilities of the forecast-analysis cycle system, which may be thought of as a system forced by observations. In the AUS scheme the assimilation is obtained by confining the analysis increment in the unstable subspace of the forecast-analysis cycle system so that it will have the same structure of the dominant instabilities of the system. The unstable subspace is estimated by Breeding on the Data Assimilation System (BDAS). AUS- BDAS has already been tested in realistic models and observational configurations, including a Quasi-Geostrophicmodel and a high dimensional, primitive equation ocean model; the experiments include both fixed and“adaptive”observations. In these contexts, the AUS-BDAS approach greatly reduces the analysis error, with reasonable computational costs for data assimilation with respect, for example, to a prohibitive full Extended Kalman Filter. This is a follow-up study in which we revisit the AUS-BDAS approach in the more basic, highly nonlinear Lorenz 1963 convective model. We run observation system simulation experiments in a perfect model setting, and with two types of model error as well: random and systematic. In the different configurations examined, and in a perfect model setting, AUS once again shows better efficiency than other advanced data assimilation schemes. In the present study, we develop an iterative scheme that leads to a significant improvement of the overall assimilation performance with respect also to standard AUS. In particular, it boosts the efficiency of regime’s changes tracking, with a low computational cost. Other data assimilation schemes need estimates of ad hoc parameters, which have to be tuned for the specific model at hand. In Numerical Weather Prediction models, tuning of parameters — and in particular an estimate of the model error covariance matrix — may turn out to be quite difficult. Our proposed approach, instead, may be easier to implement in operational models.

Coarse-grained peptide models: conformational sampling, peptide association and dynamical properties for peptides

In dieser Arbeit wird ein vergröbertes (engl. coarse-grained, CG) Simulationsmodell für Peptide in wässriger Lösung entwickelt. In einem CG Verfahren reduziert man die Anzahl der Freiheitsgrade des Systems, so dass manrngrössere Systeme auf längeren Zeitskalen untersuchen kann. Die Wechselwirkungspotentiale des CG Modells sind so aufgebaut, dass die Peptid Konformationen eines höher aufgelösten (atomistischen) Modells reproduziert werden.rnIn dieser Arbeit wird der Einfluss unterschiedlicher bindender Wechsel-rnwirkungspotentiale in der CG Simulation untersucht, insbesondere daraufhin,rnin wie weit das Konformationsgleichgewicht der atomistischen Simulation reproduziert werden kann. Im CG Verfahren verliert man per Konstruktionrnmikroskopische strukturelle Details des Peptids, zum Beispiel, Korrelationen zwischen Freiheitsgraden entlang der Peptidkette. In der Dissertationrnwird gezeigt, dass diese “verlorenen” Eigenschaften in einem Rückabbildungsverfahren wiederhergestellt werden können, in dem die atomistischen Freiheitsgrade wieder in die CG-Strukturen eingefügt werden. Dies gelingt, solange die Konformationen des CG Modells grundsätzlich gut mit der atomistischen Ebene übereinstimmen. Die erwähnten Korrelationen spielen einerngrosse Rolle bei der Bildung von Sekundärstrukturen und sind somit vonrnentscheidender Bedeutung für ein realistisches Ensemble von Peptidkonformationen. Es wird gezeigt, dass für eine gute Übereinstimmung zwischen CG und atomistischen Kettenkonformationen spezielle bindende Wechselwirkungen wie zum Beispiel 1-5 Bindungs- und 1,3,5-Winkelpotentiale erforderlich sind. Die intramolekularen Parameter (d.h. Bindungen, Winkel, Torsionen), die für kurze Oligopeptide parametrisiert wurden, sind übertragbarrnauf längere Peptidsequenzen. Allerdings können diese gebundenen Wechselwirkungen nur in Kombination mit solchen nichtbindenden Wechselwirkungspotentialen kombiniert werden, die bei der Parametrisierung verwendet werden, sind also zum Beispiel nicht ohne weiteres mit einem andere Wasser-Modell kombinierbar. Da die Energielandschaft in CG-Simulationen glatter ist als im atomistischen Modell, gibt es eine Beschleunigung in der Dynamik. Diese Beschleunigung ist unterschiedlich für verschiedene dynamische Prozesse, zum Beispiel für verschiedene Arten von Bewegungen (Rotation und Translation). Dies ist ein wichtiger Aspekt bei der Untersuchung der Kinetik von Strukturbildungsprozessen, zum Beispiel Peptid Aggregation.rn

Wet and dry model granulates under mechanical load : a confocal microscopy study

Granular matter, also known as bulk solids, consists of discrete particles with sizes between micrometers and meters. They are present in many industrial applications as well as daily life, like in food processing, pharmaceutics or in the oil and mining industry. When handling granular matter the bulk solids are stored, mixed, conveyed or filtered. These techniques are based on observations in macroscopic experiments, i.e. rheological examinations of the bulk properties. Despite the amply investigations of bulk mechanics, the relation between single particle motion and macroscopic behavior is still not well understood. For exploring the microscopic properties on a single particle level, 3D imaging techniques are required.rnThe objective of this work was the investigation of single particle motions in a bulk system in 3D under an external mechanical load, i.e. compression and shear. During the mechanical load the structural and dynamical properties of these systems were examined with confocal microscopy. Therefor new granular model systems in the wet and dry state were designed and prepared. As the particles are solid bodies, their motion is described by six degrees of freedom. To explore their entire motion with all degrees of freedom, a technique to visualize the rotation of spherical micrometer sized particles in 3D was developed. rnOne of the foci during this dissertation was a model system for dry cohesive granular matter. In such systems the particle motion during a compression of the granular matter was investigated. In general the rotation of single particles was the more sensitive parameter compared to the translation. In regions with large structural changes the rotation had an earlier onset than the translation. In granular systems under shear, shear dilatation and shear zone formation were observed. Globally the granular sediments showed a shear behavior, which was known already from classical shear experiments, for example with Jenike cells. Locally the shear zone formation was enhanced, when near the applied load a pre-diluted region existed. In regions with constant volume fraction a mixing between the different particle layers occurred. In particular an exchange of particles between the current flowing region and the non-flowing region was observed. rnThe second focus was on model systems for wet granular matter, where an additional binding liquid is added to the particle suspension. To examine the 3D structure of the binding liquid on the micrometer scale independently from the particles, a second illumination and detection beam path was implemented. In shear and compression experiments of wet clusters and bulk systems completely different dynamics compared to dry cohesive models systems occured. In a Pickering emulsion-like system large structural changes predominantly occurred in the local environment of binding liquid droplets. These large local structural changes were due to an energy interplay between the energy stored in the binding droplet during its deformation and the binding energy of particles at the droplet interface. rnConfocal microscopy in combination with nanoindentation gave new insights into the single particle motions and dynamics of granular systems under a mechanical load. These novel experimental results can help to improve the understanding of the relationship between bulk properties of granular matter, such as volume fraction or yield stress and the dynamics on a single particle level.rnrn

Thermal structure and dynamical modeling of a Mediterranean tropical-like cyclone

In the present work, a detailed analysis of a Mediterranean TLC occurred in January 2014 has been conducted. The author is not aware of other studies regarding this particular event at the publication of this thesis. In order to outline the cyclone evolution, observational data, including weather-stations data, satellite data, radar data and photographic evidence, were collected at first. After having identified the cyclone path and its general features, the GLOBO, BOLAM and MOLOCH NWP models, developed at ISAC-CNR (Bologna), were used to simulate the phenomenon. Particular attention was paid on the Mediterranean phase as well as on the Atlantic phase, since the cyclone showed a well defined precursor up to 3 days before the minimum formation in the Alboran Sea. The Mediterranean phase has been studied using different combinations of GLOBO, BOLAM and MOLOCH models, so as to evaluate the best model chain to simulate this kind of phenomena. The BOLAM and MOLOCH models showed the best performance, by adjusting the path erroneously deviated in the National Centre for Environmental Prediction (NCEP) and ECMWF operational models. The analysis of the cyclone thermal phase shown the presence of a deep-warm core structure in many cases, thus confirming the tropical-like nature of the system. Furthermore, the results showed high sensitivity to initial conditions in the whole lifetime of the cyclone, while the Sea Surface Temperature (SST) modification leads only to small changes in the Adriatic phase. The Atlantic phase has been studied using GLOBO and BOLAM model and with the aid of the same methodology already developed. After tracing the precursor, in the form of a low-pressure system, from the American East Coast to Spain, the thermal phase analysis was conducted. The parameters obtained showed evidence of a deep-cold core asymmetric structure during the whole Atlantic phase, while the first contact with the Mediterranean Sea caused a sudden transition to a shallow-warm core structure. The examination of Potential Vorticity (PV) 3-dimensional structure revealed the presence of a PV streamer that individually formed over Greenland and eventually interacted with the low-pressure system over the Spanish coast, favouring the first phase of the cyclone baroclinic intensification. Finally, the development of an automated system that tracks and studies the thermal phase of Mediterranean cyclones has been encouraged. This could lead to the forecast of potential tropical transition, against with a minimum computational investment.

Enhanced Atlantic subpolar gyre variability through baroclinic threshold in a coarse resolution model

Direct observations, satellite measurements and paleo records reveal strong variability in the Atlantic subpolar gyre on various time scales. Here we show that variations of comparable amplitude can only be simulated in a coupled climate model in the proximity of a dynamical threshold. The threshold and the associated dynamic response is due to a positive feedback involving increased salt transport in the subpolar gyre and enhanced deep convection in its centre. A series of sensitivity experiments is performed with a coarse resolution ocean general circulation model coupled to a statistical-dynamical atmosphere model which in itself does not produce atmospheric variability. To simulate the impact of atmospheric variability, the model system is perturbed with freshwater forcing of varying, but small amplitude and multi-decadal to centennial periodicities and observational variations in wind stress. While both freshwater and wind-stress-forcing have a small direct effect on the strength of the subpolar gyre, the magnitude of the gyre's response is strongly increased in the vicinity of the threshold. Our results indicate that baroclinic self-amplification in the North Atlantic ocean can play an important role in presently observed SPG variability and thereby North Atlantic climate variability on multi-decadal scales.

Daily ozone cycle in the stratosphere: global, regional and seasonal behaviour modelled with the Whole Atmosphere Community Climate Model

The Whole Atmosphere Community Climate Model (WACCM) is utilised to study the daily ozone cycle and underlying photochemical and dynamical processes. The analysis is focused on the daily ozone cycle in the middle stratosphere at 5 hPa where satellite-based trend estimates of stratospheric ozone are most biased by diurnal sampling effects and drifting satellite orbits. The simulated ozone cycle shows a minimum after sunrise and a maximum in the late afternoon. Further, a seasonal variation of the daily ozone cycle in the stratosphere was found. Depending on season and latitude, the peak-to-valley difference of the daily ozone cycle varies mostly between 3 and 5% (0.4 ppmv) with respect to the midnight ozone volume mixing ratio. The maximal variation of 15% (0.8 ppmv) is found at the polar circle in summer. The global pattern of the strength of the daily ozone cycle is mainly governed by the solar zenith angle and the sunshine duration. In addition, we find synoptic-scale variations in the strength of the daily ozone cycle. These variations are often anti-correlated to regional temperature anomalies and are due to the temperature dependence of the rate coefficients k2 and k3 of the Chapman cycle reactions. Further, the NOx catalytic cycle counteracts the accumulation of ozone during daytime and leads to an anti-correlation between anomalies in NOx and the strength of the daily ozone cycle. Similarly, ozone recombines with atomic oxygen which leads to an anti-correlation between anomalies in ozone abundance and the strength of the daily ozone cycle. At higher latitudes, an increase of the westerly (easterly) wind cause a decrease (increase) in the sunshine duration of an air parcel leading to a weaker (stronger) daily ozone cycle.

Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework

Initialising the ocean internal variability for decadal predictability studies is a new area of research and a variety of ad hoc methods are currently proposed. In this study, we explore how nudging with sea surface temperature (SST) and salinity (SSS) can reconstruct the threedimensional variability of the ocean in a perfect model framework. This approach builds on the hypothesis that oceanic processes themselves will transport the surface information into the ocean interior as seen in ocean-only simulations. Five nudged simulations are designed to reconstruct a 150 years ‘‘target’’ simulation, defined as a portion of a long control simulation. The nudged simulations differ by the variables restored to, SST or SST + SSS, and by the area where the nudging is applied. The strength of the heat flux feedback is diagnosed from observations and the restoring coefficients for SSS use the same time-scale. We observed that this choice prevents spurious convection at high latitudes and near sea-ice border when nudging both SST and SSS. In the tropics, nudging the SST is enough to reconstruct the tropical atmosphere circulation and the associated dynamical and thermodynamical impacts on the underlying ocean. In the tropical Pacific Ocean, the profiles for temperature show a significant correlation from the surface down to 2,000 m, due to dynamical adjustment of the isopycnals. At mid-tohigh latitudes, SSS nudging is required to reconstruct both the temperature and the salinity below the seasonal thermocline. This is particularly true in the North Atlantic where adding SSS nudging enables to reconstruct the deep convection regions of the target. By initiating a previously documented 20-year cycle of the model, the SST + SSS nudging is also able to reproduce most of the AMOC variations, a key source of decadal predictability. Reconstruction at depth does not significantly improve with amount of time spent nudging and the efficiency of the surface nudging rather depends on the period/events considered. The joint SST + SSS nudging applied verywhere is the most efficient approach. It ensures that the right water masses are formed at the right surface density, the subsequent circulation, subduction and deep convection further transporting them at depth. The results of this study underline the potential key role of SSS for decadal predictability and further make the case for sustained largescale observations of this field.

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a set-up of the Weather Research and Forecasting Model (WRF) model that minimises system- atic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable set-up: the horizontal resolution, the planetary boundary layer (PBL) parameterisation scheme and the way the WRF is nested to the driving data set. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model set-ups, but sharing the same driving data set. The results show that the lack of representation of the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis nudging, spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model performance is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

The impacts of volcanic aerosol on stratospheric ozone and the Northern Hemisphere polar vortex: separating radiative-dynamical changes from direct effects due to enhanced aerosol heterogeneous chemistry

After major volcanic eruptions the enhanced aerosol causes ozone changes due to greater heterogeneous chemistry on the particle surfaces (HET-AER) and from dynamical effects related to the radiative heating of the lower stratosphere (RAD-DYN). We carry out a series of experiments with an atmosphere–ocean–chemistry–climate model to assess how these two processes change stratospheric ozone and Northern Hemispheric (NH) polar vortex dynamics. Ensemble simulations are performed under present day and preindustrial conditions, and with aerosol forcings representative of different eruption strength, to investigate changes in the response behaviour. We show that the halogen component of the HET-AER effect dominates under present-day conditions with a global reduction of ozone (−21 DU for the strongest eruption) particularly at high latitudes, whereas the HET-AER effect increases stratospheric ozone due to N2O5 hydrolysis in a preindustrial atmosphere (maximum anomalies +4 DU). The halogen-induced ozone changes in the present-day atmosphere offset part of the strengthening of the NH polar vortex during mid-winter (reduction of up to −16 m s-1 in January) and slightly amplify the dynamical changes in the polar stratosphere in late winter (+11 m s-1 in March). The RAD-DYN mechanism leads to positive column ozone anomalies which are reduced in a present-day atmosphere by amplified polar ozone depletion (maximum anomalies +12 and +18 DU for present day and preindustrial, respectively). For preindustrial conditions, the ozone response is consequently dominated by RAD-DYN processes, while under present-day conditions, HET-AER effects dominate. The dynamical response of the stratosphere is dominated by the RAD-DYN mechanism showing an intensification of the NH polar vortex in winter (up to +10 m s-1 in January). Ozone changes due to the RAD-DYN mechanism slightly reduce the response of the polar vortex after the eruption under present-day conditions.