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Research describing multiquantum well silicon and calcium fluoride heterostructures is presented. In particular a lumped element circuit is modelled for investigating the charge carrier transport.

We examine theoretically charge carrier transport across a Si/CaF[sub 2] layered heterostructure by using an equivalent lumped-element circuit. We consider a structure has been precharged by a negative pulse. The resulting charge accumulation produces a nonmonotonic current-voltage (I-V) characteristic during a subsequent positive voltage sweep. We examine the dependence of the I-V characteristic on the magnitude and duration of the precharging bias pulse. © 2001 American Institute of Physics. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate information provided by global or regional climate models ( RCMs) are often too coarse and prone to substantial biases for local assessments or use in impact models. Hence, statistical downscaling becomes necessary. For the Swiss National Climate Change Initiative ( CH2011), a delta-change approach was used to provide daily climate scenarios at the local scale. Here, we analyse a Richardson-type weather generator ( WG) as an alternative method to downscale daily precipitation, minimum and maximum temperature. The WG is calibrated for 26 Swiss stations and the reference period is 1980-2009. It is perturbed with change factors derived from RCMs ( ENSEMBLES) to represent the climate of 2070-2099 assuming the SRES A1B emission scenario. The WG can be run in multi-site mode, making it especially attractive for impact-modellers that rely on a realistic spatial structure in downscaled time-series. The results from the WG are benchmarked against the original delta-change approach that applies mean additive or multiplicative adjustments to the observations. According to both downscaling methods, the results reveal mean temperature increases and a precipitation decrease in summer, consistent with earlier studies. For the summer drying, the WG indicates primarily a decrease in wet-day frequency and correspondingly an increase in mean dry spell length of between 18 and 40% at low-elevation stations. By definition, these potential changes cannot be represented by a delta-change approach. In winter, both methods project a shortening of the frost period (−30 to −60 days) and a decrease of snow days (−20 to −100%). The WG demonstrates though, that almost present-day conditions in snow-days could still occur in the future. As expected, both methods have difficulties in representing extremes. If users focus on changes in temporal sequences and need a large number of future realizations, it is recommended to use data from a WG instead of a delta-change approach. [ABSTRACT FROM AUTHOR]

ABSTRACT Fundamental changes in the hydrological cycle are to be expected in a future warmer climate. For Switzerland, recent climate change assessments based on the ENSEMBLES regional climate models project for the A1B emission scenario summer mean precipitation to significantly decrease by the end of this century, whereas winter mean precipitation tend to rise in Southern Switzerland. From an end-user perspective, projected changes in seasonal means are often insufficient to adequately address the multifaceted challenges of climate change adaptation. In this study, we investigate the projected changes in seasonal precipitation by considering changes in frequency and intensity, precipitation type (convective vs stratiform) and temporal structure (wet and dry spells) over Switzerland. As proxies for rain-type changes, we rely on the parameterized convective and large-scale precipitation components simulated by the models. The study reveals that the projected summer drying over Switzerland at the end of the century is mainly driven by a widespread reduction in the number of precipitation days. Thereby, the drying evolves altitude-specific: over low-land regions it is associated with a decrease in both convective and large-scale precipitation. Over elevated regions it is primarily associated with a decline in large-scale precipitation only, whereas convective precipitation remains at current levels. As a consequence, almost all the models project an increase in convective fraction at elevated altitudes. The decrease in the number of wet days during summer is accompanied by decreases (increases) in the number of multi-day wet (dry) spells. This future shift in multi-day episodes also lowers down the likelihood of short dry spell occurrence in all of the models. The models further project a higher mean precipitation intensity in spring and autumn north of the Alps, whereas a similar tendency is expected for the winter season over most of Switzerland. [ABSTRACT FROM AUTHOR]

Many climate impact assessments over topographically complex terrain require high-resolution precipitation time-series that have a spatio-temporal correlation structure consistent with observations. This consistency is essential for spatially distributed modelling of processes with non-linear responses to precipitation input (e.g. soil water and river runoff modelling). In this regard, weather generators (WGs) designed and calibrated for multiple sites are an appealing technique to stochastically simulate time-series that approximate the observed temporal and spatial dependencies. In this study, we present a stochastic multi-site precipitation generator and validate it over the hydrological catchment Thur in the Swiss Alps. The model consists of several Richardson-type WGs that are run with correlated random number streams reflecting the observed correlation structure among all possible station pairs. A first-order two-state Markov process simulates intermittence of daily precipitation, while precipitation amounts are simulated from a mixture model of two exponential distributions. The model is calibrated separately for each month over the time-period 1961-2011. The WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process: the multi-site WG is capable to capture about 95% of the observed variability in daily area sums, while the summed time-series from multiple single-site WGs only explains about 13 %. Limitation of the WG have been detected in reproducing observed variability from year to year, a component that has not been considered in the WG calibration. Given the obtained performance, the presented stochastic model is expected to be a useful tool to re-sample the observed record and valuable to be used as a statistical downscaling method in a climate change context. [ABSTRACT FROM AUTHOR]

Regional projections of future climate with associated uncertainty estimates are increasingly being demanded. Generally, such scenarios rely on a finite number of model projections and are accompanied by considerable uncertainties which cannot be fully quantified. Consequently, probabilistic climate projections are conditioned on several subjective assumptions which can be treated in a Bayesian framework. In this study, a recently developed Bayesian multi-model combination algorithm is applied to regional climate model simulations from the ENSEMBLES project to generate probabilistic projections for Switzerland. The seasonal temperature and precipitation scenarios are calculated relative to 1980-2009 for three 30-year scenario periods (centred at 2035, 2060, and 2085), three regions, and the A1B emission scenario. Projections for two further emission scenarios are obtained by pattern scaling. Key to the Bayesian algorithm is the determination of prior distributions about climatic parameters. It is shown that the prior choice of model projection uncertainty ultimately determines the uncertainty in the climate change signal. Here, we assume that model uncertainty is fully sampled by the climate models available. We have extended the algorithm such that internal decadal variability is also included in all scenario calculations. The A1B scenarios show a significant rise in temperature increasing from 0.9-1.4 °C by 2035 (depending upon region and season), to 2.0-2.9 °C by 2060, and to 2.7-4.1 °C by 2085. Mean precipitation changes are subject to large uncertainties with median changes close to zero. Significant signals are seen towards the end of the century with a summer drying of 18-24% depending on region, and a likely increase of winter precipitation in Switzerland south of the Alps. The A2 scenario implies a warming of 3.2-4.8 °C, and a summer drying of 21-28% by 2085, while in case of the mitigation scenario RCP3PD, climate change could be stabilized to 1.2-1.8 °C of warming and 8-10% of drying. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Agriculture can benefit substantially from long-range weather forecasts, for the month or the season, which can help to optimize farming operations and deal more effectively with the adverse impacts of climate variability, including extreme weather events. In the context of climate change, long-range weather forecasts also represent key elements for the development of adaptation strategies. In spite of an undeniable potential, long-range forecasts issued for instance by the European Centre for Medium-Range Weather Forecasts (ECMWF) have yet to find widespread application in European agriculture. To address partially the question of why this is the case, the performance of the ECMWF monthly ensemble forecasting system was examined. It was noted that predictability is currently limited to about 3 weeks for temperature and 2 weeks for precipitation and solar radiation. This may appear deceptive at first sight, but it was noticed that precipitation forecasts over a month are, overall, at least as valuable as information obtained from observed climatology. Encouraged by this finding, the possibility of using monthly forecasts to predict soil water availability was tested. In an operational context, this could serve as a basis for scheduling irrigation. Positive skills were found for lead times of up to 1 month. It was concluded that more systematic investigations of the possibilities offered by long-range forecasts should be undertaken in the future. However, this will require additional efforts to increase the quality of the forecasts, design appropriate application tools and promote the dissemination of the outcome within the agriculture community. [ABSTRACT FROM PUBLISHER]