Dynamical evolution of the 2003 southern hemisphere stratospheric winter using Envisat trace-gas observations

Data from the MIPAS instrument on Envisat, supplemented by meteorological analyses from ECMWF and the Met Office, are used to study the meteorological and trace-gas evolution of the stratosphere in the southern hemisphere during winter and spring 2003. A pole-centred approach is used to interpret the data in the physically meaningful context of the evolving stratospheric polar vortex. The following salient dynamical and transport features are documented and analysed: the merger of anticyclones in the stratosphere; the development of an intense, quasi-stationary anticyclone in spring; the associated top-down breakdown of the polar vortex; the systematic descent of air into the polar vortex; and the formation of a three-dimensional structure of a tracer filament on a planetary scale. The paper confirms and extends existing paradigms of the southern hemisphere vortex evolution. The quality of the MIPAS observations is seen to be generally good. though the water vapour retrievals are unrealistic above 10 hPa in the high-latitude winter.

How well does the ECMWF Ensemble Prediction System predict blocking?

Forecasting atmospheric blocking is one of the main problems facing medium-range weather forecasters in the extratropics. The European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) provides an excellent basis for medium-range forecasting as it provides a number of different possible realizations of the meteorological future. This ensemble of forecasts attempts to account for uncertainties in both the initial conditions and the model formulation. Since 18 July 2000, routine output from the EPS has included the field of potential temperature on the potential vorticity (PV) D 2 PV units (PVU) surface, the dynamical tropopause. This has enabled the objective identification of blocking using an index based on the reversal of the meridional potential-temperature gradient. A year of EPS probability forecasts of Euro-Atlantic and Pacific blocking have been produced and are assessed in this paper, concentrating on the Euro-Atlantic sector. Standard verification techniques such as Brier scores, Relative Operating Characteristic (ROC) curves and reliability diagrams are used. It is shown that Euro-Atlantic sector-blocking forecasts are skilful relative to climatology out to 10 days, and are more skilful than the deterministic control forecast at all lead times. The EPS is also more skilful than a probabilistic version of this deterministic forecast, though the difference is smaller. In addition, it is shown that the onset of a sector-blocking episode is less well predicted than its decay. As the lead time increases, the probability forecasts tend towards a model climatology with slightly less blocking than is seen in the real atmosphere. This small under-forecasting bias in the blocking forecasts is possibly related to a westerly bias in the ECMWF model. Copyright © 2003 Royal Meteorological Society

Can 4D-Var use dynamical information from targeted observations of a baroclinic structure?

Targeted observations are generally taken in regions of high baroclinicity, but often show little impact. One plausible explanation is that important dynamical information, such as upshear tilt, is not extracted from the targeted observations by the data assimilation scheme and used to correct initial condition error. This is investigated by generating pseudo targeted observations which contain a singular vector (SV) structure that is not present in the background field or routine observations, i.e. assuming that the background has an initial condition error with tilted growing structure. Experiments were performed for a single case-study with varying numbers of pseudo targeted observations. These were assimilated by the Met Office four-dimensional variational (4D-Var) data assimilation scheme, which uses a 6 h window for observations and background-error covariances calculated using the National Meteorological Centre (NMC) method. The forecasts were run using the operational Met Office Unified Model on a 24 km grid. The results presented clearly demonstrate that a 6 h window 4D-Var system is capable of extracting baroclinic information from a limited set of observations and using it to correct initial condition error. To capture the SV structure well (projection of 0.72 in total energy), 50 sondes over an area of 1×106 km2 were required. When the SV was represented by only eight sondes along an example targeting flight track covering a smaller area, the projection onto the SV structure was lower; the resulting forecast perturbations showed an SV structure with increased tilt and reduced initial energy. The total energy contained in the perturbations decreased as the SV structure was less well described by the set of observations (i.e. as fewer pseudo observations were assimilated). The assimilated perturbation had lower energy than the SV unless the pseudo observations were assimilated with the dropsonde observation errors halved from operational values. Copyright © 2010 Royal Meteorological Society

Dynamical influences on European climate: an uncertain future

Climate science is coming under increasing pressure to deliver projections of future climate change at spatial scales as small as a few kilometres for use in impacts studies. But is our understanding and modelling of the climate system advanced enough to offer such predictions? Here we focus on the Atlantic–European sector, and on the effects of greenhouse gas forcing on the atmospheric and, to a lesser extent, oceanic circulations. We review the dynamical processes which shape European climate and then consider how each of these leads to uncertainty in the future climate. European climate is unique in many regards, and as such it poses a unique challenge for climate prediction. Future European climate must be considered particularly uncertain because (i) the spread between the predictions of current climate models is still considerable and (ii) Europe is particularly strongly affected by several processes which are known to be poorly represented in current models.

The population dynamical consequences of density-dependence in fungal plant pathogens

Almost all stages of a plant pathogen life cycle are potentially density dependent. At small scales and short time spans appropriate to a single-pathogen individual, density dependence can be extremely strong, mediated both by simple resource use, changes in the host due to defence reactions and signals between fungal individuals. In most cases, the consequences are a rise in reproductive rate as the pathogen becomes rarer, and consequently stabilisation of the population dynamics; however, at very low density reproduction may become inefficient, either because it is co-operative or because heterothallic fungi do not form sexual spores. The consequence will be historically determined distributions. On a medium scale, appropriate for example to several generations of a host plant, the factors already mentioned remain important but specialist natural enemies may also start to affect the dynamics detectably. This could in theory lead to complex (e.g. chaotic) dynamics, but in practice heterogeneity of habitat and host is likely to smooth the extreme relationships and make for more stable, though still very variable, dynamics. On longer temporal and longer spatial scales evolutionary responses by both host and pathogen are likely to become important, producing patterns which ultimately depend on the strength of interactions at smaller scales.

Towards dynamical system models of language-related brain potentials

Event-related brain potentials (ERP) are important neural correlates of cognitive processes. In the domain of language processing, the N400 and P600 reflect lexical-semantic integration and syntactic processing problems, respectively. We suggest an interpretation of these markers in terms of dynamical system theory and present two nonlinear dynamical models for syntactic computations where different processing strategies correspond to functionally different regions in the system's phase space.

Sensitivity of GCM tropical middle atmosphere variability and climate to ozone and parametrized gravity wave changes

This paper describes the impact of changing the current imposed ozone climatology upon the tropical Quasi-Biennial Oscillation (QBO) in a high top climate configuration of the Met Office U.K. general circulation model. The aim is to help distinguish between QBO changes in chemistry climate models that result from temperature-ozone feedbacks and those that might be forced by differences in climatology between previously fixed and newly interactive ozone distributions. Different representations of zonal mean ozone climatology under present-day conditions are taken to represent the level of change expected between acceptable model realizations of the global ozone distribution and thus indicate whether more detailed investigation of such climatology issues might be required when assessing ozone feedbacks. Tropical stratospheric ozone concentrations are enhanced relative to the control climatology between 20–30 km, reduced from 30–40 km and enhanced above, impacting the model profile of clear-sky radiative heating, in particular warming the tropical stratosphere between 15–35 km. The outcome is consistent with a localized equilibrium response in the tropical stratosphere that generates increased upwelling between 100 and 4 hPa, sufficient to account for a 12 month increase of modeled mean QBO period. This response has implications for analysis of the tropical circulation in models with interactive ozone chemistry because it highlights the possibility that plausible changes in the ozone climatology could have a sizable impact upon the tropical upwelling and QBO period that ought to be distinguished from other dynamical responses such as ozone-temperature feedbacks.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Low-order dynamical behavior in the martian atmosphere: Diagnosis of general circulation model results

The hypothesis of a low dimensional martian climate attractor is investigated by the application of the proper orthogonal decomposition (POD) to a simulation of martian atmospheric circulation using the UK Mars general circulation model (UK-MGCM). In this article we focus on a time series of the interval between autumn and winter in the northern hemisphere, when baroclinic activity is intense. The POD is a statistical technique that allows the attribution of total energy (TE) to particular structures embedded in the UK-MGCM time-evolving circulation. These structures are called empirical orthogonal functions (EOFs). Ordering the EOFs according to their associated energy content, we were able to determine the necessary number to account for a chosen amount of atmospheric TE. We show that for Mars a large fraction of TE is explained by just a few EOFs (with 90% TE in 23 EOFs), which apparently support the initial hypothesis. We also show that the resulting EOFs represent classical types of atmospheric motion, such as thermal tides and transient waves. Thus, POD is shown to be an efficient method for the identification of different classes of atmospheric modes. It also provides insight into the non-linear interaction of these modes.

The control of dynamical systems by neural networks

In this paper the use of neural networks for the control of dynamical systems is considered. Both identification and feedback control aspects are discussed as well as the types of system for which neural networks can provide a useful technique. Multi-layer Perceptron and Radial Basis function neural network types are looked at, with an emphasis on the latter. It is shown how basis function centre selection is a critical part of the implementation process and that multivariate clustering algorithms can be an extremely useful tool for finding centres.

Stochastic perturbations to dynamical systems: a response theory approach

Using the formalism of the Ruelle response theory, we study how the invariant measure of an Axiom A dynamical system changes as a result of adding noise, and describe how the stochastic perturbation can be used to explore the properties of the underlying deterministic dynamics. We first find the expression for the change in the expectation value of a general observable when a white noise forcing is introduced in the system, both in the additive and in the multiplicative case. We also show that the difference between the expectation value of the power spectrum of an observable in the stochastically perturbed case and of the same observable in the unperturbed case is equal to the variance of the noise times the square of the modulus of the linear susceptibility describing the frequency-dependent response of the system to perturbations with the same spatial patterns as the considered stochastic forcing. This provides a conceptual bridge between the change in the fluctuation properties of the system due to the presence of noise and the response of the unperturbed system to deterministic forcings. Using Kramers-Kronig theory, it is then possible to derive the real and imaginary part of the susceptibility and thus deduce the Green function of the system for any desired observable. We then extend our results to rather general patterns of random forcing, from the case of several white noise forcings, to noise terms with memory, up to the case of a space-time random field. Explicit formulas are provided for each relevant case analysed. As a general result, we find, using an argument of positive-definiteness, that the power spectrum of the stochastically perturbed system is larger at all frequencies than the power spectrum of the unperturbed system. We provide an example of application of our results by considering the spatially extended chaotic Lorenz 96 model. These results clarify the property of stochastic stability of SRB measures in Axiom A flows, provide tools for analysing stochastic parameterisations and related closure ansatz to be implemented in modelling studies, and introduce new ways to study the response of a system to external perturbations. Taking into account the chaotic hypothesis, we expect that our results have practical relevance for a more general class of system than those belonging to Axiom A.