7 resultados para simulations de Monte-Carlo

em Publishing Network for Geoscientific


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This dataset characterizes the evolution of western African precipitation indicated by marine sediment geochemical records in comparison to transient simulations using CCSM3 global climate model throughout the Last Interglacial (130-115 ka). It contains (1) defined tie-points (age models), newly published stable isotopes of benthic foraminifera and Al/Si log-ratios of eight marine sediment cores from the western African margin and (2) annual and seasonal rainfall anomalies (relative to pre-industrial values) for six characteristic latitudinal bands in western Africa simulated by CCSM3 (two transient simulations: one non-accelerated and one accelerated experiment).

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Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

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A Monte Carlo based radiative transfer model has been developed for calculating the availability of solar radiation within the top 100 m of the ocean. The model is optimized for simulations of spatial high resolution downwelling irradiance Ed fluctuations that arise from the lensing effect of waves at the water surface. In a first step the accuracy of simulation results has been verified by measurements of the oceanic underwater light field and through intercomparison with an established radiative transfer model. Secondly the potential depth-impact of nonlinear shaped single waves, from capillary to swell waves, is assessed by considering the most favorable conditions for light focusing, i.e. monochromatic light at 490 nm, very clear oceanic water with a low chlorophyll a content of 0.1 mg/m**3 and high sun elevation. Finally light fields below irregular wave profiles accounting for realistic sea states were simulated. Our simulation results suggest that under open ocean conditions light flashes with 50% irradiance enhancements can appear down to 35 m depth, and light variability in the range of ±10% compared to the mean Ed is still possible in 100 m depth.