ALBERT

Description: This study has investigated serial (temporal) clustering of extra-tropical cyclones simulated by 17 climate models that participated in CMIP5. Clustering was estimated by calculating the dispersion (ratio of variance to mean) of 30 December-February counts of Atlantic storm tracks passing nearby each grid point. Results from single historical simulations of 1975-2005 were compared to those from historical ERA40 reanalyses from 1958-2001 ERA40 and single future model projections of 2069-2099 under the RCP4.5 climate change scenario. Models were generally able to capture the broad features in reanalyses reported previously: underdispersion/regularity (i.e. variance less than mean) in the western core of the Atlantic storm track surrounded by overdispersion/clustering (i.e. variance greater than mean) to the north and south and over western Europe. Regression of counts onto North Atlantic Oscillation (NAO) indices revealed that much of the overdispersion in the historical reanalyses and model simulations can be accounted for by NAO variability. Future changes in dispersion were generally found to be small and not consistent across models. The overdispersion statistic, for any 30 year sample, is prone to large amounts of sampling uncertainty that obscures the climate change signal. For example, the projected increase in dispersion for storm counts near London in the CNRMCM5 model is 0.1 compared to a standard deviation of 0.25. Projected changes in the mean and variance of NAO are insufficient to create changes in overdispersion that are discernible above natural sampling variations.

Description: The Journal of Organic Chemistry DOI: 10.1021/acs.joc.5b01373

Description: The Journal of Organic Chemistry DOI: 10.1021/acs.joc.6b00983

Description: This study investigates whether or not predictability always decreases for more extreme events. Predictability is measured by the Mean Squared Error (MSE), estimated here from the difference of pairs of ensemble forecasts, conditioned on one of the forecast variables (the “pseudo-observation”) exceeding a threshold. Using an exchangeable linear regression model for pairs of forecast variables, we show that the MSE can be decomposed into the sum of three terms: a threshold-independent constant, a mean term that always increases with threshold, and a variance term that can either increase, decrease, or stay constant with threshold. Using the Generalised Pareto Distribution to model wind speed excesses over a threshold, we show that MSE always increases with threshold at sufficiently high threshold. However, MSE can be a decreasing function of threshold at lower thresholds but only if the forecasts have finite upper bounds. The methods are illustrated by application to daily wind speed forecasts for London made using the 24 member Met Office Global and Regional Ensemble Prediction System from 1 Jan 2009 to 31 May 2011. For this example, the mean term increases faster than the variance term decreases with increasing threshold, and so predictability decreases for more extreme events.

Description: The Greater Caucasus and southern Crimean Mountains form part of a fold–thrust belt located on the northern margin of the Black Sea, south of the Precambrian craton of eastern Europe. Its southern limit is approximated by the Main Caucasus Thrust, which runs to the west from onshore Russia and Georgia along the whole of the northern margin of the Black Sea. The Main Caucasus Thrust is related to a zone of present-day seismicity along the southern Crimea–Caucasus coast of the Black Sea called the Crimea–Caucasus Seismic Zone. Thick continental crust north of the Main Caucasus Thrust lies adjacent to the thin ‘suboceanic' or transitional crust of the Black Sea Basin. A local seismic tomography study of this area in the vicinity of the Kerch and Taman peninsulas, which lie between the Azov Sea and the Black Sea, has been carried out based on 195 weak (m b ≤3) earthquakes occurring from 1975 to 2010 and recorded at four permanent and three temporary seismological stations on the Kerch and Taman peninsulas. The results, for a volume of about 200 x 100 km (east–west and north–south, respectively) and a depth of about 40 km, provide evidence for significant heterogeneity in the P-wave and S-wave velocities. Velocities inferred in the northern part of the model suggest that the continental crust underlying the Crimea–Azov region north of the Main Caucasus Thrust is of different tectonic affinity (cratonic) than that underlying the northeastern part of the Black Sea, south of the Main Caucasus Thrust (Neoproterozoic–Palaeozoic accretionary domain). In the southern part of the model, at depths of 25–40 km, the uppermost mantle below the thin quasi-oceanic crust of the Black Sea has anomalous low P-wave velocities with high P- to S-wave velocity ratios. This is tentatively interpreted as representing serpentinized upper mantle of continental lithosphere exhumed during Cretaceous rifting and lithospheric hyperextension of the eastern Black Sea. The transition between the continental domains and the crust underlain by anomalous upper mantle is closely related to the Crimea–Caucasus Seismic Zone, where earthquake foci deepen northwards, suggesting that the latter is being thrust under the former in this intra-plate setting.

Description: The DOBRE-2 wide-angle reflection and refraction profile was acquired in June 2007 as a direct, southwestwards prolongation of the 1999 DOBREfraction'99 that crossed the Donbas Foldbelt in eastern Ukraine. It crosses the Azov Massif of the East European Craton, the Azov Sea, the Kerch Peninsula (the easternmost part of Crimea) and the northern East Black Sea Basin, thus traversing the entire Crimea–Caucasus compressional zone centred on the Kerch Peninsula. The DOBRE-2 profile recorded a mix of onshore explosive sources as well as airguns at sea. A variety of single-component recorders were used on land and ocean bottom instruments were deployed offshore and recovered by ship. The DOBRE-2 datasets were degraded by a lack of shot-point reversal at the southwestern terminus and by some poor signal registration elsewhere, in particular in the Black Sea. Nevertheless, they allowed a robust velocity model of the upper crust to be constructed along the entire profile as well as through the entire crust beneath the Azov Massif. A less well constrained model was constructed for much of the crust beneath the Azov Sea and the Kerch Peninsula. The results showed that there is a significant change in the upper crustal lithology in the northern Azov Sea, expressed in the near surface as the Main Azov Fault; this boundary can be taken as the boundary between the East European Craton and the Scythian Platform. The upper crustal rocks of the Scythian Platform in this area probably consist of metasedimentary rocks. A narrow unit as shallow as about 5 km and characterized by velocities typical of the crystalline basement bounds the metasedimentary succession on its southern margin and also marks the northern margin of the northern foredeep and the underlying successions of the Crimea–Caucasus compressional zone in the southern part of the Azov Sea. A broader and somewhat deeper basement unit (about 11 km) with an antiformal shape lies beneath the northern East Black Sea Basin and forms the southern margin of the Crimea–Caucasus compressional zone. The depth of the underlying Moho discontinuity increases from 40 km beneath the Azov Massif to 47 km beneath the Crimea–Caucasus compressional zone.

Description: The Journal of Organic Chemistry DOI: 10.1021/jo300162c

Description: The Journal of Organic Chemistry DOI: 10.1021/jo502288q

Description: 〈p〉This review of the role of the mantle lithosphere in plate tectonic processes collates a wide range of recent studies from seismology and numerical modelling. A continually growing catalogue of deep geophysical imaging has illuminated the mantle lithosphere and generated new interpretations of how the lithosphere evolves. We review current ideas about the role of continental mantle lithosphere in plate tectonic processes. Evidence seems to be growing that scarring in the continental mantle lithosphere is ubiquitous, which implies a reassessment of the widely held view that it is the inheritance of crustal structure only (rather than the lithosphere as a whole) that is most important in the conventional theory of plate tectonics (e.g. the Wilson cycle). Recent studies have interpreted mantle lithosphere heterogeneities to be pre-existing structures and, as such, linked to the Wilson cycle and inheritance. We consider the current fundamental questions in the role of the mantle lithosphere in causing tectonic deformation, reviewing recent results and highlighting the potential of the deep lithosphere in infiltrating every aspect of plate tectonics processes.〈/p〉

Description: Ensemble forecasts are routinely used as a basis for probabilistic predictions. The skill of probabilistic predictions derived from ensemble forecasts depends on the number of ensemble members. We derive a new verification score, called the ensemble‐adjusted Ignorance Score, which can correct for the effect of limited ensemble size and therefore allows for a more robust comparison of forecasts based on different ensemble sizes. The unadjusted Ignorance Score (solid line) depends on the ensemble size m, assigning higher (worse) scores to smaller ensembles drawn from the same forecast distribution. The ensemble‐adjusted Ignorance Score (dashed line) proposed here does not depend on ensemble size and thus allows for a fair comparison of equivalent ensembles of different sizes. This study considers the application of the Ignorance Score (IS, also known as the Logarithmic Score) for ensemble verification. In particular, we consider the case where an ensemble forecast is transformed to a normal forecast distribution, and this distribution is evaluated by the IS. It is shown that the IS systematically depends on the ensemble size, such that larger ensembles yield better expected scores. An ensemble‐adjusted IS is proposed, which extrapolates the score of an m‐member ensemble to the score that the ensemble would achieve if it had fewer or more than m members. Using the ensemble adjustment, a fair version of the IS is derived, which is optimized if ensembles are statistically consistent with the observations. The benefit of the ensemble adjustment is illustrated by comparing ISs of ensembles of different sizes in a seasonal climate forecasting context and a medium‐range weather forecasting context. An ensemble‐adjusted score can be used for a fair comparison between ensembles of different sizes, and to accurately estimate the expected score of a large operational ensemble by running a much smaller hindcast ensemble.