Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

Large multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse scenario–GCM–RCM matrices, these large ensembles, however, are very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, (ii) the total uncertainty of projections and its partition as a function of time, and (iii) the list of the most important contributors to the model uncertainty. For changes in total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in central–eastern Europe for instance, the difference between balanced and direct estimates is up to 0.8 ∘C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

Uncertainty assessment of open water evaporation measurements: case study in a tropical reservoir in Brazil

<p>The Brazilian semiarid region covers about 1 million km² and is characterised by irregular rainfall and high potential evaporation (above 2000 mm per year of water loss). To overcome the lack of water, the population depends on man-made reservoirs, which are the main source of water during the dry season (8 months of the year). In the federal state of Ceará there is on average one dam every 5 km, totalling more than 20,000. It is, therefore, noteworthy how the accurate estimation of evaporation would improve the management of scarce water resources. Moreover, climate change scenarios predict impacts on water availability in this region and better accuracy in evaporation assessments are fundamental to foster such simulations, which itself works at an intra-day time resolution. We suggest the most precise measurements possible, close to the lake and preferably on top of the water body. We used between October-December 2019 two direct measurement sensors in floating pans on the reservoir. A newly developed one based on the echo sounder principle and a standard differential pressure meter. Both show that at the reservoir more than 6 mm evaporate per day on average in the dry period. Uncertainty analyses for a single measurement show good performance of the acoustic sensor (± 0.11 mm) compared to the standard sensor (± 0.6 mm), sensitive enough to record the daily course of evaporation. The field measurements show 3.7% of uncertainty comparing the acoustic sensor with the standard pressuremeter. The minimum number of measurements with the acoustic sensor to obtain such uncertainty is 307. In this case, for 1h step, the uncertainty is 1.1%. The wave movement caused by wind influences the quality and frequency of the measurement. These results serve as a basis for future approaches to assess spatial variation of evaporation and wind on the lake. This might highlight the minimal number of instruments needed to solve the issue of spatial variability of evaporation.</p>

Building a Knowledge Base for the Model

In this chapter, after summarising the key conceptual challenges related to the measurement of asylum migration, we briefly outline the history of recent migration flows from Syria to Europe. This case study is intended to guide the development of a model of migration route formation, used throughout this book as an illustration of the proposed model-based research process. Subsequently, for the case study, we offer an overview of the available data types, making a distinction between the sources related to the migration processes, as well as to the context within which migration occurs. We then propose a framework for assessing different aspects of data, based on a review of similar approaches suggested in the literature, and this framework is subsequently applied to a selection of available data sources. The chapter concludes with specific recommendations for using the different forms of data in formal modelling, including in the uncertainty assessment.

Measuring uncertainty assessment of an experimental device used to determine the thermo-optico-physical properties of translucent construction materials

Studying thermo-optical (i.e., thermal conductivity, optical re ectance, optical transmittance, and optical absorbance) properties of construction materials is essential for improving human comfort within a building. Typically, these properties are measured independently using specific equipment. The emerging of new innovative construction structures, such as translucent materials, makes the experimental characterization of these properties more challenging to observe. Recently, a new device, called MultiCoefMeter (McM), which rapidly and simultaneously measures all these properties, has been created. The study described in this article covers the calculation technique for estimating measurement uncertainties linked to morphology, the component parts, and the physical formula of the experimental apparatus. The measurement uncertainty estimates are obtained from knowledge of the color of the system's walls, placement, and form of the McM components, placement of measurement sensors, and the application of measurement collection equipment. Therefore, a thorough calculation analysis was performed on the sub-systems. Calculations are divided between two categories: those based on mathematical tools and information given by the makers, and those based on experimental observations obtained during reliability testing. These uncertainties originate from statistical tools, geometric tolerance of the system, comparison with standards, and the error propagation laws of the physical models link with the device. All these uncertainties were summed up and given a global value, no more than 5%, conforming to the ASTM standard (E1225). Finally, a general method to quantify measurement uncertainty value of any experimental device was proposed.

Full-scale self-propulsion simulation with a discretized propeller

A comparison between towing tank testing and full-scale CFD simulations is presented at three different target speeds. For the current self-propulsion simulation, the self-propulsion point was obtained using polynomial interpolation. The studies of boundary layer thickness, a basic grid uncertainty assessment and verification were performed to give some confidence of grid application to current self-propulsion simulation. All simulations are performed using a commercial CFD software STAR-CCM+. It is concluded that with high-fidelity numerical methods, it’s possible to treat hull roughness and directly calculate full-scale flow characteristics, including the effects of the free surface, none-linearity, turbulence and the interaction between propeller, hull and the flow field.

Co-Optimization of CO2 Storage and Enhanced Gas Recovery Using Carbonated Water and Supercritical CO2

CO2-based enhanced gas recovery (EGR) is an appealing method with the dual benefit of improving recovery from mature gas reservoirs and storing CO2 in the subsurface, thereby reducing net emissions. However, CO2 injection for EGR has the drawback of excessive mixing with the methane gas, therefore, reducing the quality of gas produced and leading to an early breakthrough of CO2. Although this issue has been identified as a major obstacle in CO2-based EGR, few strategies have been suggested to mitigate this problem. We propose a novel hybrid EGR method that involves the injection of a slug of carbonated water before beginning CO2 injection. While still ensuring CO2 storage, carbonated water hinders CO2-methane mixing and reduces CO2 mobility, therefore delaying breakthrough. We use reservoir simulation to assess the feasibility and benefit of the proposed method. Through a structured design of experiments (DoE) framework, we perform sensitivity analysis, uncertainty assessment, and optimization to identify the ideal operation and transition conditions. Results show that the proposed method only requires a small amount of carbonated water injected up to 3% pore volumes. This EGR scheme is mainly influenced by the heterogeneity of the reservoir, slug volume injected, and production rates. Through Monte Carlo simulations, we demonstrate that high recovery factors and storage ratios can be achieved while keeping recycled CO2 ratios low.