Accounting for global-mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

International audienceA probabilistic assessment of climate change and related impacts should consider a large range of potential future climate scenarios. State-of-the-art climate models, especially coupled atmosphere-ocean general circulation models and Regional Climate Models (RCMs) cannot, however, be used to simulate such a large number of scenarios. This paper presents a methodology for obtaining future climate scenarios through a simple scaling methodology. The projections of several key meteorological variables obtained from a few regional climate model runs are scaled, based on different global-mean warming projections drawn in a probability distribution of future global-mean warming. The resulting climate change scenarios are used to drive a hydrological and a water management model to analyse the potential climate change impacts on a water resources system. This methodology enables a joint quantification of the climate change impact uncertainty induced by the global-mean warming scenarios and the regional climate response. It is applied to a case study in Switzerland, a water resources system formed by three interconnected lakes located in the Jura Mountains. The system behaviour is simulated for a control period (1961?1990) and a future period (2070?2099). The potential climate change impacts are assessed through a set of impact indices related to different fields of interest (hydrology, agriculture and ecology). The results obtained show that future climate conditions will have a significant influence on the performance of the system and that the uncertainty induced by the inter-RCM variability will contribute to much of the uncertainty of the prediction of the total impact. These CSRs cover the area considered in the 2001?2004 EU funded project SWURVE

Protein modifications at the interface during short-term oxidation of emulsions as revealed by front-surface fluorescence

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Rainfall stochastic disaggregation models: Calibration and validation of a multiplicative cascade model

International audienceThe simulation of long time series of rainfall rates at short time steps remains an important issue for various applications in hydrology. Among the various types of simulation models, random multiplicative cascade models (RMC models) appear as an appealing solution which displays the advantages to be parameter parsimonious and linked to the multifractal theory. This paper deals with the calibration and validation of RMC models. More precisely, it discusses the limits of the scaling exponent function method often used to calibrate RMC models, and presents an hydrological validation of calibrated RMC models. A 8-year time series of 1-min rainfall rates is used for the calibration and the validation of the tested models. The paper is organized in three parts. In the first part, the scaling invariance properties of the studied rainfall series is shown using various methods (q-moments, PDMS, autocovariance structure) and a RMC model is calibrated on the basis of the rainfall data scaling exponent function. A detailed analysis of the obtained results reveals that the shape of the scaling exponent function, and hence the values of the calibrated parameters of the RMC model, are highly sensitive to sampling fluctuation and may also be biased. In the second part, the origin of the sensivity to sampling fluctuation and of the bias is studied in detail and a modified Jackknife estimator is tested to reduce the bias. Finally, two hydrological applications are proposed to validate two candidate RMC models: a canonical model based on a log-Poisson random generator, and a basic micro-canonical model based on a uniform random generator. It is tested in this third part if the models reproduce faithfully the statistical distribution of rainfall characteristics on which they have not been calibrated. The results obtained for two validation tests are relatively satisfactory but also show that the temporal structure of the measured rainfall time series at small time steps is not well reproduced by the two selected simple random cascade models. (C) 2006 Elsevier Ltd. All rights reserved

Influence of the highest values on the choice of log-Poisson random cascade model parameters

International audienceUrban drainage systems usually equip small size catchments, with a response time as short as a few tenth of minutes. Hydrological simulation of these urban catchments requires rainfall time series at a very fine temporal resolution. Most of the time, available rainfall data consist of daily or hourly measurements. It is then appealing to design rain simulation models able to disaggregate observed data into rain rate series at shorter time steps. Random cascade models are candidate tools for performing this disaggregation process. The objective of this study is to evaluate the ability of cascade model for performing realistic and robust rain series suited for needs of urban hydrology. (C) 2001 Elsevier Science Ltd, All rights reserved

Interface characterization and aging of bovine serum albumin stabilized oil-in-water emulsions as revealed by front-surface fluorescence

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Accounting for global mean warming and scaling uncertainties in climate change impact studies: application to a regulated lake system

A probabilistic assessment of climate change and related impacts should consider a large range of potential future climate scenarios. State-of-the-art climate models, especially coupled atmosphere-ocean general circulation models and Regional Climate Models (RCMs) can however not be used to simulate such a large number of scenarios. This paper presents a methodology to obtain future climate scenarios through a simple scaling methodology: The projections of several key meteorological variables obtained from a few regional climate model runs are scaled based on different global-mean warming projections drawn in a probability distribution of future global-mean warming. The resulting climate change scenarios are used to drive a hydrological and a water management model to analyse the potential climate change impacts on a water resources system. This methodology enables a joint quantification of the climate change impact uncertainty induced by the global-mean warming scenarios and the regional climate response. It is applied to a case study in Switzerland, a water resources system formed by three interconnected lakes located in the Jura Mountains. The system behaviour is simulated for a control period (1961 - 1990) and a future period (2070-2099). The potential climate change impacts are assessed through a set of impact indices related to different fields of interest (hydrology, agriculture and ecology). The obtained results show that future climate conditions have a significant influence on the system performance and that the uncertainty induced by the inter-RCM variability contributes to a large part to the total impact prediction uncertainty

The crystal and molecular structure of a diglycosylamine: the N-analogue of peracetylated α,β-trehalose

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Structural analysis of 6-S-(benzoxazol-2-yl)-6-deoxy 1,2:3,4-di-O - isopropylidene-6-thio-α-D-galactopuranose by means of X-ray diffraction, high resolution NPR spectroscopy, and molecular modelling

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