Generalized extreme value shape parameter and its nature for extreme precipitation using long time series and the Bayesian approach

Assessing the probability of extreme precipitation events is consequential in civil planning. This requires an understanding of how return values change with return periods, which is essentially described by the generalized extreme value (GEV) shape parameter. Some works in the field suggest a constant shape parameter, while our analysis indicates a non-universal value. We re-analysed an older precipitation dataset (169 stations) extended by Norwegian data (71 stations). We showed that while each set seems to have a constant shape parameter, it differs between the two datasets, indicating regional differences. For a more comprehensive analysis of spatial effects, we examined a global dataset (1495 stations). We provided shape parameter maps for two models and found clear evidence that the shape parameter depends on elevation, while the effect of latitude remains uncertain. Our results confirm an explanation in terms of dominating precipitation systems based on a proxy derived from the Köppen-Geiger climate classification.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR not assigned     

Radiogenic trigger and driver for continental rifting and initial ocean spreading

Closure and opening of oceans on time‐scales of a few hundred million years is a fundamental tectonic process on Earth, typically referred to as a “Wilson cycle”. Subduction of oceanic and continental crust leading up to and during continent–continent collision can refertilize and enrich the orogenic continental lithospheric mantle in heat‐producing elements. The resulting thermal anomaly weakens the lithosphere and, along with structural weaknesses (e.g. sutures), make this orogenic lithosphere more prone to rifting given an extensional stress field. Thermal modelling shows that anomalously hot lithosphere can focus asthenospheric upwellings over time‐scales of a few hundred million years. Processes related to closure of oceans thus provide a mechanism for later localization of rifting and an extensional driving force.     

Method for generating parameterized ecotoxicity data of dispersed oil for use in environmental modelling

The aim of the work was to establish methodology for realistic laboratory-based test exposures of organisms to oil dispersions, specifically designed to generate parameterized toxicity data. Such data are needed to improve the value of numerical models used to predict fate and effects of oil spills and different oil spill responses. A method for continuous and predictable in-line production of oil dispersions with defined size distribution of different oil qualities was successfully established. The system enables simultaneous comparison between the effects of different concentrations of dispersion and their corresponding equilibrium water soluble fractions. Thus, net effects of the oil droplet fraction may be estimated. The method provides data for comparing the toxicity of oil dispersions generated both mechanically and with the use of chemical dispersions, incorporating the toxicity of both dissolved oil and droplets of oil.     

Pressure dependence of the lattice dynamics of diaspore, α-AlO(OH), from Raman spectroscopy and density functional perturbation theory

We have measured the pressure-induced change in the lattice dynamics of diaspore, α-AlO(OH), by in situ Raman spectroscopy up to 25 GPa. The spectra are evaluated by density functional perturbation theory-based atomistic model calculations. The assignment of calculated to experimentally observed Raman bands is based on the calculation of Raman intensities. We discuss the accuracy of the approach employed for these calculations and explain the relative magnitudes of mode Grüneisen parameters.     

Sampling error distribution for the ensemble Kalman filter update step

In recent years, data assimilation techniques have been applied to an increasingly wider specter of problems. Monte Carlo variants of the Kalman filter, in particular, the ensemble Kalman filter (EnKF), have gained significant popularity. EnKF is used for a wide variety of applications, among them for updating reservoir simulation models. EnKF is a Monte Carlo method, and its reliability depends on the actual size of the sample. In applications, a moderately sized sample (40–100 members) is used for computational convenience. Problems due to the resulting Monte Carlo effects require a more thorough analysis of the EnKF. Earlier we presented a method for the assessment of the error emerging at the EnKF update step (Kovalenko et al., SIAM J Matrix Anal Appl, in press). A particular energy norm of the EnKF error after a single update step was studied. The energy norm used to assess the error is hard to interpret. In this paper, we derive the distribution of the Euclidean norm of the sampling error under the same assumptions as before, namely normality of the forecast distribution and negligibility of the observation error. The distribution depends on the ensemble size, the number and spatial arrangement of the observations, and the prior covariance. The distribution is used to study the error propagation in a single update step on several synthetic examples. The examples illustrate the changes in reliability of the EnKF, when the parameters governing the error distribution vary.     

Assimilating spatially dense data for subsurface applications—balancing information and degrees of freedom

The degrees of freedom (DOF) in standard ensemble-based data assimilation is limited by the ensemble size. Successful assimilation of a data set with large information content (IC) therefore requires that the DOF is sufficiently large. A too small number of DOF with respect to the IC may result in ensemble collapse, or at least in unwarranted uncertainty reduction in the estimation results. In this situation, one has two options to restore a proper balance between the DOF and the IC: to increase the DOF or to decrease the IC. Spatially dense data sets typically have a large IC. Within subsurface applications, inverted time-lapse seismic data used for reservoir history matching is an example of a spatially dense data set. Such data are considered to have great potential due to their large IC, but they also contain errors that are challenging to characterize properly. The computational cost of running the forward simulations for reservoir history matching with any kind of data is large for field cases, such that a moderately large ensemble size is standard. Realization of the potential in seismic data for ensemble-based reservoir history matching is therefore not straightforward, not only because of the unknown character of the associated data errors, but also due to the imbalance between a large IC and a too small number of DOF. Distance-based localization is often applied to increase the DOF but is example specific and involves cumbersome implementation work. We consider methods to obtain a proper balance between the IC and the DOF when assimilating inverted seismic data for reservoir history matching. To decrease the IC, we consider three ways to reduce the influence of the data space; subspace pseudo inversion, data coarsening, and a novel way of performing front extraction. To increase the DOF, we consider coarse-scale simulation, which allows for an increase in the DOF by increasing the ensemble size without increasing the total computational cost. We also consider a combination of decreasing the IC and increasing the DOF by proposing a novel method consisting of a combination of data coarsening and coarse-scale simulation. The methods were compared on one small and one moderately large example with seismic bulk-velocity fields at four assimilation times as data. The size of the examples allows for calculation of a reference solution obtained with standard ensemble-based data assimilation methodology and an unrealistically large ensemble size. With the reference solution as the yardstick with which the quality of other methods are measured, we find that the novel method combining data coarsening and coarse-scale simulations gave the best results. With very restricted computational resources available, this was the only method that gave satisfactory results.     

The Taimyr fold belt, Arctic Siberia: timing of prefold remagnetisation and regional tectonics

Late Precambrian and Palaeozoic platform sediments from the Central–South Taimyr Peninsula (Arctic Siberia) are all remagnetised. The remagnetisation is prefold and is related to thermal remagnetisation caused by Taimyr Trap magmatism. The remagnetisation age is estimated to 220–230 Ma and, hence, is considerably younger than the ca. 251 Ma age for the main body of Siberian Trap flood basalts. The folding that affected the Taimyr region platform sediments also included the Taimyr “Traps,” hence, relegating Taimyr deformation to post-Mid Triassic time, and most probably, to a Late Triassic age. This shows that whilst thrusting terminated in the Urals during the Permian, crustal shortening continued in Taimyr, Novaya–Zemlya and the South Barents Sea, well into the Mesozoic.