Inflation and topological phase transition driven by exotic smoothness

In this paper we will discuss a model which describes the cause of inflation by a topological transition. The guiding principle is the choice of an exotic smoothness structure for the space-time. Here we consider a space-time with topology $S^{3}\times\mathbb{R}$. In case of an exotic $S^{3}\times\mathbb{R}$, there is a change in the spatial topology from a 3-sphere to a homology 3-sphere which can carry a hyperbolic structure. From the physical point of view, we will discuss the path integral for the Einstein-Hilbert action with respect to a decomposition of the space-time. The inclusion of the boundary terms produces fermionic contributions to the partition function. The expectation value of an area (with respect to some surface) shows an exponential increase, i.e. we obtain inflationary behavior. We will calculate the amount of this increase to be a topological invariant. Then we will describe this transition by an effective model, the Starobinski or $R^{2}$ model which is consistent with the current measurement of the Planck satellite. The spectral index and other observables are also calculated. Finally we obtain a realistic cosmological constant.Comment: 21 pages, no figures, iopart styla, accepted in Advances in High Energy Physics, special issue "Experimental Tests of Quantum Gravity and Exotic Quantum Field Theory Effects (QGEQ)

Anticipating climate change: knowledge use in participatory flood management in the river Meuse

Given the latest knowledge on climate change, the Dutch government wants to anticipate the increased risk of flooding. For the river Meuse in The Netherlands, the design discharge is estimated to increase from 3800m3/s to 4600m3/s. With the existing policy of “Room for the River”, this increase is to be accommodated without raising the dikes. At the same time the floodplains are often claimed for other functions, e.g. new housing or industrial estates. In 2001 the Ministry of Transport, Public Works and Water Management started the study “Integrated assessment of the river Meuse (IVM)” with the objectives of making an inventory of the probable physical effects of a design flood, assuming climate change, on the river Meuse in 2050, investigating possible spatial and technical measures to mitigate these effects, and finally combining various measures to create an integral strategy for flood protection, while at the same time increasing spatial quality. This paper presents the results of research into the decision making process that took place in order to achieve these objectives. Special attention was given to the role of scientific and technical knowledge in the decision making process, e.g. by investigating the effect of the quality of input data on acceptance by stakeholders, and the interactive use of a decision support system to visualise hydraulic effects. Conclusions on successes and pitfalls are drawn from observation and interviews with participants. It demonstrates how it is possible to integrate the necessary, technically complex knowledge in a political debate with stakeholders on how to deal with flood risk. Furthermore, the experience indicates in what area improvements could be made

Four-dimensional variational data assimilation for inverse modeling of atmospheric methane emissions: Analysis of SCIAMACHY observations

Recent observations from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument aboard ENVISAT have brought new insights in the global distribution of atmospheric methane. In particular, the observations showed higher methane concentrations in the tropics than previously assumed. Here, we analyze the SCIAMACHY observations and their implications for emission estimates in detail using a four-dimensional variational (4D-Var) data assimilation system. We focus on the period September to November 2003 and on the South American continent, for which the satellite observations showed the largest deviations from model simulations. In this set-up the advantages of the 4D-Var approach and the zooming capability of the underlying TM5 atmospheric transport model are fully exploited. After application of a latitude-dependent bias correction to the SCIAMACHY observations, the assimilation system is able to accurately fit those observations, while retaining consistency with a network of surface methane measurements. The main emission increments resulting from the inversion are an increase in the tropics, a decrease in South Asia, and a decrease at northern hemispheric high latitudes. The SCIAMACHY observations yield considerable additional emission uncertainty reduction, particularly in the (sub-)tropical regions, which are poorly constrained by the surface network. For tropical South America, the inversion suggests more than a doubling of emissions compared to the a priori during the 3 months considered. Extensive sensitivity experiments, in which key assumptions of the inversion set-up are varied, show that this finding is robust. Independent airborne observations in the Amazon basin support the presence of considerable local methane sources. However, these observations also indicate that emissions from eastern South America may be smaller than estimated from SCIAMACHY observations. In this respect it must be realized that the bias correction applied to the satellite observations does not take into account potential regional systematic errors, which - if identified in the future - will lead to shifts in the overall distribution of emission estimates

Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion

A four-dimensional variational (4D-Var) data assimilation system for inverse modelling of atmospheric methane emissions is presented. The system is based on the TM5 atmospheric transport model. It can be used for assimilating large volumes of measurements, in particular satellite observations and quasi-continuous in-situ observations, and at the same time it enables the optimization of a large number of model parameters, specifically grid-scale emission rates. Furthermore, the variational method allows to estimate uncertainties in posterior emissions. Here, the system is applied to optimize monthly methane emissions over a 1-year time window on the basis of surface observations from the NOAA-ESRL network. The results are rigorously compared with an analogous inversion by Bergamaschi et al. (2007), which was based on the traditional synthesis approach. The posterior emissions as well as their uncertainties obtained in both inversions show a high degree of consistency. At the same time we illustrate the advantage of 4D-Var in reducing aggregation errors by optimizing emissions at the grid scale of the transport model. The full potential of the assimilation system is exploited in Meirink et al. (2008), who use satellite observations of column-averaged methane mixing ratios to optimize emissions at high spatial resolution, taking advantage of the zooming capability of the TM5 model