Permafrost - physical aspects and carbon cycling, databases and uncertainties

Permafrost is defined as ground that remains below 0°C for at least 2 consecutive years. About 24% of the northern hemisphere land area is underlain by permafrost. The thawing of permafrost has the potential to influence the climate system through the release of carbon (C) from northern high latitude terrestrial ecosystems, but there is substantial uncertainty about the sensitivity of the C cycle to thawing permafrost. Soil C can be mobilized from permafrost in response to changes in air temperature, directional changes in water balance, fire, thermokarst, and flooding. Observation networks need to be implemented to understand responses of permafrost and C at a range of temporal and spatial scales. The understanding gained from these observation networks needs to be integrated into modeling frameworks capable of representing how the responses of permafrost C will influence the trajectory of climate in the future

The impact of microphysical uncertainty conditional on initial and boundary condition uncertainty under varying synoptic control

The relative impact of individual and combined uncertainties of cloud condensation nuclei (CCN) concentration and the shape parameter of the cloud droplet size distribution (CDSD) in the presence of initial and boundary condition uncertainty (IBC) on convection forecasts is quantified using the convection-permitting model ICON-D2 (ICOsahedral Non-hydrostatic). We performed 180-member ensemble simulations for five real case studies representing different synoptic forcing situations over Germany and inspected the precipitation variability on different spatial and temporal scales. During weak synoptic control, the relative impact of combined microphysical uncertainty on daily area-averaged precipitation accounts for about one-third of the variability caused by operational IBC uncertainty. The effect of combined microphysical perturbations exceeds the impact of individual CCN or CDSD perturbations and is twice as large during weak control. The combination of IBC and microphysical uncertainty affects the extremes of daily spatially averaged rainfall of individual members by extending the tails of the forecast distribution by 5 % in weakly forced conditions. The responses are relatively insensitive in strong forcing situations. Visual inspection and objective analysis of the spatial variability in hourly rainfall rates reveal that IBC and microphysical uncertainties alter the spatial variability in precipitation forecasts differently. Microphysical perturbations slightly shift convective cells but affect precipitation intensities, while IBC perturbations scramble the location of convection during weak control. Cloud and rainwater contents are more sensitive to microphysical uncertainty than precipitation and less dependent on synoptic control

Fast assessment of uncertainty in buoyant fluid displacement using a connectivity-based proxy

It is crucial to estimate the uncertainty in flow characteristics of injected fluid. However, because a large suite of geological models is probable given sparse static data, it is impractical to conduct full physics flow simulations on the entire suite of models in order to quantify the uncertainty in fluid displacements. Thus a fast alternative to a full physics simulator is necessary to quickly predict the fluid displacements. Most of the proxies proposed thus far are inappropriate to approximate the buoyant flow of injected fluid for 3D heterogeneous rock during the injection period. In this dissertation, a new proxy will be proposed to quickly predict the buoyant flow of injected fluid during CO2 sequestration. The geological models are ranked based on the extent of the approximated CO2 plumes. By selecting a representative group of models among the ranked models, the uncertainty in the spatial and temporal characteristics of the CO2 plume migrations can be quickly quantified. About 90% of the computational cost of quantifying the uncertainty in the extent of CO2 plumes was saved using the proposed connectivity based proxy. In a geological carbon storage project, the spatial and temporal characteristics of CO2 plume migrations can be monitored by 4D seismic surveys. The images of CO2 plumes obtained from 4D seismic surveys are used as observed data to find subsurface models honoring the spatial and temporal characteristics of the observed CO2 plumes. However, because manually comparing an observed CO2 plume and prior CO2 plumes in a large suite of subsurface models is inefficient, an automatic measure to calculate the dissimilarity between the CO2 plumes is necessary. The most intuitive way to calculate the dissimilarity is the Euclidean distance between vectors representing CO2 plumes. However, this is inappropriate to measure the dissimilarity between CO2 plumes because it does not consider spatial relation between the elements of the vectors. The shape dissimilarity between the CO2 plumes that reflects the spatial relation can be calculated using the Hausdorff distance. The computational cost of calculating the shape dissimilarity between CO2 plumes is significantly reduced by calculating the Hausdorff distance between the representations of the CO2 plumes such as perimeter, surface, and skeleton instead of the original CO2 plumes. An appropriate representation should be chosen according to the spatial characteristics of CO2 plumes.Petroleum and Geosystems Engineerin

Time indeterminacy and spatio-temporal building transformations: an approach for architectural heritage understanding

Nowadays most digital reconstructions in architecture and archeology describe buildings heritage as awhole of static and unchangeable entities. However, historical sites can have a rich and complex history, sometimes full of evolutions, sometimes only partially known by means of documentary sources. Various aspects condition the analysis and the interpretation of cultural heritage. First of all, buildings are not inexorably constant in time: creation, destruction, union, division, annexation, partial demolition and change of function are the transformations that buildings can undergo over time. Moreover, other factors sometimes contradictory can condition the knowledge about an historical site, such as historical sources and uncertainty. On one hand, historical documentation concerning past states can be heterogeneous, dubious, incomplete and even contradictory. On the other hand, uncertainty is prevalent in cultural heritage in various forms: sometimes it is impossible to define the dating period, sometimes the building original shape or yet its spatial position. This paper proposes amodeling approach of the geometrical representation of buildings, taking into account the kind of transformations and the notion of temporal indetermination

Occurrence cubes : a new paradigm for aggregating species occurrence data

In this paper we describe a method of aggregating species occurrence data into what we coined “occurrence cubes”. The aggregated data can be perceived as a cube with three dimensions - taxonomic, temporal and geographic - and takes into account the spatial uncertainty of each occurrence. The aggregation level of each of the three dimensions can be adapted to the scope. Built on Open Science principles, the method is easily automated and reproducible, and can be used for species trend indicators, maps and distribution models. We are using the method to aggregate species occurrence data for Europe per taxon, year and 1km2 European reference grid, to feed indicators and risk mapping/modelling for the Tracking Invasive Alien Species (TrIAS) project

Tracking down the origin of NWP model uncertainty : coarse-graining studies

Ponencia presentada en: Workshop on representing model uncertainty and error in numerical weather and climate prediction celebrado del 20 al 24 de junio de 2011 en Reading, Inglaterra.Current implementations of the perturbed parametrization tendency method for representing uncertainty rely on ad hoc assumptions about its magnitude and its spatial and temporal correlation scales. Ideally one would use observational data to ascertain the statistical character of parametrization tendency errors and use the resulting probability distribution functions to devise and calibrate the perturbed tendency approach. The reality is that observations rarely have the coverage, representativity and accuracy to form a useful comparison with model data. A less satisfactory alternative is to use high resolution modelling to provide a ‘truth’ simulation and then compare this with an equivalent but lower resolution simulation. Tendency fields from both simulations are coarse-grained to a resolution compatible with the assumed horizontal correlation scale in the perturbed tendency method and the bias-corrected differences between them are used to quantify statistical uncertainty. Early results using the ECMWF IFS forecasts appear to show that the variance of the coarse-grained tendency differences is proportional to the tendency in the lower-resolution forecast. However the current perturbed parametrization tendency scheme at ECMWF assumes that the standard deviation of the perturbations is proportional to the tendency itself. Probability distribution functions of the high-resolution model tendency, sub-sampled by narrow ranges of the low-resolution model tendency, seem to be consistent with an underlying Poisson process

A semantic-based platform for the digital analysis of architectural heritage

This essay focuses on the fields of architectural documentation and digital representation. We present a research paper concerning the development of an information system at the scale of architecture, taking into account the relationships that can be established between the representation of buildings (shape, dimension, state of conservation, hypothetical restitution) and heterogeneous information about various fields (such as the technical, the documentary or still the historical one). The proposed approach aims to organize multiple representations (and associated information) around a semantic description model with the goal of defining a system for the multi-field analysis of buildings

Tracking uncertainty in a spatially explicit susceptible-infected epidemic model

In this paper we conceive an interval-valued continuous cellular automaton for describing the spatio-temporal dynamics of an epidemic, in which the magnitude of the initial outbreak and/or the epidemic properties are only imprecisely known. In contrast to well-established approaches that rely on probability distributions for keeping track of the uncertainty in spatio-temporal models, we resort to an interval representation of uncertainty. Such an approach lowers the amount of computing power that is needed to run model simulations, and reduces the need for data that are indispensable for constructing the probability distributions upon which other paradigms are based

Optimal measurement of visual motion across spatial and temporal scales

Sensory systems use limited resources to mediate the perception of a great variety of objects and events. Here a normative framework is presented for exploring how the problem of efficient allocation of resources can be solved in visual perception. Starting with a basic property of every measurement, captured by Gabor's uncertainty relation about the location and frequency content of signals, prescriptions are developed for optimal allocation of sensors for reliable perception of visual motion. This study reveals that a large-scale characteristic of human vision (the spatiotemporal contrast sensitivity function) is similar to the optimal prescription, and it suggests that some previously puzzling phenomena of visual sensitivity, adaptation, and perceptual organization have simple principled explanations.Comment: 28 pages, 10 figures, 2 appendices; in press in Favorskaya MN and Jain LC (Eds), Computer Vision in Advanced Control Systems using Conventional and Intelligent Paradigms, Intelligent Systems Reference Library, Springer-Verlag, Berli