Capelin migrations and climate change – a modelling analysis

The capelin is a small pelagic fish that performs long distance migrations. It is a key species in the Barents Sea ecosystem and its distribution is highly climate dependent. Here we use an individual based model to investigate consequences of global warming on capelin distribution and population dynamics. The model relies on input on physics and plankton from a biophysical ocean model, and the entire life cycle of capelin including spawning of eggs, larval drift and adult movement is simulated. Spawning day and adult movement strategies are adapted by a genetic algorithm. Spawning has to take place in designated near-shore spawning areas. The output generated by the model is capelin migration/distribution and population dynamics. We present simulations with present day climate and a future climate scenario. For the present climate the model evolves a spatial distribution resembling typical spatial dynamics of capelin with the coasts of Northern Norway and Murman as the main spawning areas. For the climate change simulation, the capelin is predicted to shift spawning eastwards and also utilize new spawning areas along Novaya Zemlya. There is also a shift in the adult distribution towards the north eastern part of the Barents Sea and earlier spawning associated with the warming.     

Timing and thermochemical constraints on multi-element mineralisation at the Nori/RA Cu–Mo–U prospect, Great Bear magmatic zone, Northwest Territories, Canada

The timing of Cu–Mo–U mineralisation at the Nori/RA prospect in the Paleoproterozoic Great Bear magmatic zone has been investigated using Re–Os molybdenite and ⁴⁰Ar–³⁹Ar biotite geochronology. The Re–Os molybdenite ages presented are the first robust sulphide mineralisation ages derived from the Great Bear magmatic zone. Cu–Mo–U mineralisation is hosted in early to syn-deformational hydrothermal veins consisting of quartz and K-feldspar or more commonly tourmaline-biotite-quartz-K-feldspar, with associated wall-rock alteration assemblages being predominantly biotite. Sulphide and oxide minerals consist of chalcopyrite, molybdenite and uraninite with lesser pyrite and magnetite. Elevated light rare earth elements and tungsten concentrations associated with the Cu–Mo–U mineralisation have also been reported at the prospect by previous workers. Molybdenite and uraninite occur intimately in dravitic tourmaline growth zones and at grain margins, attesting to their syngenetic nature (with respect to hydrothermal veining). Two molybdenite separates yield Re–Os model ages of 1,874.4 ± 8.7 (2σ) and 1,872.4 ± 8.8 Ma (2σ) with a weighted average model age of 1,873.4 ± 6.1 Ma (2σ). Laser step heating of biotite from the marginal alteration of the wall-rock adjacent to the veins yields a ⁴⁰Ar–³⁹Ar maximum cooling age of 1,875 ± 8 Ma (MSWD = 3.8; 2σ), indistinguishable from the Re–Os molybdenite model age and a previously dated ‘syn-tectonic’ aplitic dyke in the region. Dravitic tourmaline hosts abundant primary liquid–vapour–solid-bearing fluid inclusions. Analytical results indicate liquid–vapour homogenisation at >260°C constraining the minimum temperature of mineralisation. The solids, which are possibly trapped, did not homogenise with the liquid–vapour by 400°C. Salinities in the inclusions are variable. Raman spectra identify that at least some of the solids are calcite and anhydrite. Raman spectra also confirm the vapour phases contain some CO₂; whereas clathrates or CH₄ was not observed or detected. Quartz grains only host secondary fluid inclusions, which fluoresce under ultraviolet light, indicating trapped hydrocarbons. We speculate that these resulted from Phanerozoic fluid circulation through the Proterozoic basement. The collective interpretation of the age, hydrothermal character and associated metals, high temperature and variable salinity suggests that the Nori/RA Cu–Mo–U mineralisation can be linked with the earliest stages of plutonism in the Great Bear magmatic zone. From a regional perspective, the mineralisation may pre-date the extensive multi-element mineralisation now recognised as part of the iron oxide copper–gold (IOCG) spectrum of deposits. As IOCG provinces generally contain a variety of mineralisation styles, we interpret this as the earliest phase of the extensive mineralising system.     

Assimilation of ice concentration in a coupled ice–ocean model,using the Ensemble Kalman filter

An implementation of the Ensemble Kalman filter (EnKF) with a coupled ice–ocean model is presented. The model system consists of a dynamic–thermodynamic ice model using the elastic-viscous-plastic (EVP) rheology coupled with the HYbrid Coordinate Ocean Model (HYCOM). The observed variable is ice concentration from passive microwave sensor data (SSM/I). The assimilation of ice concentration has the desired effect of reducing the difference between observations and model. Comparison of the assimilation experiment with a free-run experiment shows that there are large differences, especially in summer. In winter the differences are relatively small, partly because the atmospheric forcing used to run the model depends upon SSM/I data. The assimilation has the strongest impact close to the ice edge, where it ensures a correct location of the ice edge throughout the simulation. An inspection of the model ensemble statistics reveals that the error estimates of the model are too small in winter, partly a result of too low model ice-concentration variance in the central ice pack. It is found that the ensemble covariance between ice concentration and sea-surface temperature in the same grid cell is of the same sign (negative) throughout the year. The ensemble covariance between ice concentration and salinity is more dependent upon the physical mechanisms involved, with ice transport and freeze/melt giving different signs of the covariances. The ice-transport and ice-melt mechanisms also impact the ice-concentration variance and the covariance between ice concentration and ice thickness. The ensemble statistics show a high degree of complexity, which to some extent merits the use of computationally expensive assimilation methods, such as the Ensemble Kalman filter. The present study focuses on the assimilation of ice concentration, but it is understood that assimilation of other datasets, such as sea-surface temperature, would be beneficial.Responsible Editor: Jin-Song von Storch     

Mesoscale modeling study of the oceanographic conditions off the southwest coast of India

A high resolution model, using the Miami Isopycnic Coordinate Ocean Model (MICOM), has been implemented for the first time to study the seasonal circulation and coastal upwelling off the southwest Indian coast during 1974. This model is part of a model and data assimilation system capable of describing the ocean circulation and variability in the Indian Ocean and its predictability in response to the monsoon system. Along the southwest coast of India the dominant coastal current is the reversing West Indian Coastal Current which is well simulated and described, in addition to the weaker undercurrent of the opposite direction. Upwelling of cold water, 4‡C lower than offshore temperatures appear in April. The upwelling intensifies with the southwest monsoon and is simulated in accordance within situ observations. Upwelling appears to be strongest off Cochin and Quilon, and the upwelling of cold water is seen together with a decrease in salinity in the model simulation.     

Frequency of maxima of non-narrow banded stochastic processes

In many engineering problems one deals with quantities that must be considered to be of a stochastic or random nature. This is true for the natural environment such as wind, waves, and earthquakes, which are the driving mechanism behind the loadings on a wide variety of land-based and offshore structures. From a design perspective it is important to determine the expected highest value of a stochastic process, and structural fatigue life. The relevant procedures are reasonably well established for processes that have narrow band spectra, but it is much less clear how to deal with non-narrow band cases. In this paper it is shown that the extremes of a Gaussian, non-narrow band process are asymptotically equal to the extremes calculated according to the narrow band formula. Also demonstrated is that fatigue estimates may, with good accuracy, be based on the narrow band formula unless the bandwidth becomes extremely large. These statements are illustrated by examples of a process with (1) a low pass box spectrum, and (2) a Pierson–Moskowitz wave amplitude spectrum. It is also shown that the bandwidth parameter may in some cases be counter-intuitive, as the Pierson–Moskowitz spectrum has a larger bandwidth parameter than the box spectrum, even when both cover the same frequency range.     

Iterated extended Kalman filter method for time-lapse seismic full-waveform inversion

Time-lapse seismic data is useful for identifying fluid movement and pressure and saturation changes in a petroleum reservoir and for monitoring of CO₂ injection. The focus of this paper is estimation of time-lapse changes with uncertainty quantification using full-waveform inversion. The purpose of also estimating the uncertainty in the inverted parameters is to be able to use the inverted seismic data quantitatively for updating reservoir models with ensemble-based methods. We perform Bayesian inversion of seismic waveform data in the frequency domain by combining an iterated extended Kalman filter with an explicit representation of the sensitivity matrix in terms of Green functions (acoustic approximation). Using this method, we test different strategies for inversion of the time-lapse seismic data with uncertainty. We compare the results from a sequential strategy (making a prior from the monitor survey using the inverted baseline survey) with a double difference strategy (inverting the difference between the monitor and baseline data). We apply the methods to a subset of the Marmousi2 P-velocity model. Both strategies performed well and relatively good estimates of the monitor velocities and the time-lapse differences were obtained. For the estimated time-lapse differences, the double difference strategy gave the lowest errors.     

Lysosomal and lipid-associated parameters in the livers of three species of arctic seabird chicks: species differences and relationships with contaminant levels

Lysosomal membrane stability, lipofuscin (LF), malondialdehyde (MDA), neutral lipid (NL) levels, as well as halogenated organic compounds (HOCs), Cr, Cd, Pb and Fe concentrations were analyzed in liver of black-legged kittiwake (BK), herring gull (HG), and northern fulmar (NF) chicks. There were significant species differences in the levels of NL, LF and lysosomal membrane stability. These parameters were not associated with the respective HOC concentrations. LF accumulation was associated with increasing Cr, Cd and Pb concentrations. HG presented the lowest lysosomal membrane stability and the highest.LF and NL levels, which indicated impaired lysosomes in HG compared to NF and BK. Lipid peroxidation was associated with HOC and Fe²⁺ levels. Specific HOCs showed positive and significant correlations with MDA levels in HG. The study indicates that contaminant exposure can affect lysosomal and lipid associated parameters in seabird chicks even at low exposure levels. These parameters may be suitable markers of contaminant induced stress in arctic seabirds.     

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.     

Conditioning reservoir models on rate data using ensemble smoothers

There are several issues to consider when we use ensemble smoothers to condition reservoir models on rate data. The values in a time series of rate data contain redundant information that may lead to poorly conditioned inversions and thereby influence the stability of the numerical computation of the update. A time series of rate data typically has correlated measurement errors in time, and negligence of the correlations leads to a too strong impact from conditioning on the rate data and possible ensemble collapse. The total number of rate data included in the smoother update will typically exceed the ensemble size, and special care needs to be taken to ensure numerically stable results. We force the reservoir model with production rate data derived from the observed production, and the further conditioning on the same rate data implies that we use the data twice. This paper discusses strategies for conditioning reservoir models on rate data using ensemble smoothers. In particular, a significant redundancy in the rate data makes it possible to subsample the rate data. The alternative to subsampling is to model the unknown measurement error correlations and specify the full measurement error covariance matrix. We demonstrate the proposed strategies using different ensemble smoothers with the Norne full-field reservoir model.