Simulated response to inter-annual SST variations in the Gulf Stream region

Recent studies show that mid-latitude SST variations over the Kuroshio-Oyashio Extension influence the atmospheric circulation. However, the impact of variations in SST in the Gulf Stream region on the atmosphere has been less studied. Understanding the atmospheric response to such variability can improve the climate predictability in the North Atlantic Sector. Here we use a relatively high resolution (～1°) Atmospheric General Circulation Model to investigate the mechanisms linking observed 5-year low-pass filtered SST variability in the Gulf Stream region and atmospheric variability, with focus on precipitation. Our results indicate that up to 70 % of local convective precipitation variability on these timescales can be explained by Gulf Stream SST variations. In this region, SST and convective precipitation are strongly correlated in both summer (r = 0.73) and winter (r = 0.55). A sensitivity experiment with a prescribed local warm SST anomaly in the Gulf Stream region confirms that local SST drives most of the precipitation variability over the Gulf Stream. Increased evaporation connected to the anomalous warm SST plays a crucial role in both seasons. In summer there is an enhanced local SLP minimum, a concentrated band of low level convergence, deep upward motion and enhanced precipitation. In winter we also get enhanced precipitation, but a direct connection to deep vertical upward motion is not found. Nearly all of the anomalous precipitation in winter is connected to passing atmospheric fronts. In summer the connection between precipitation and atmospheric fronts is weaker, but still important.     

Story-by-story estimation of the stiffness parameters of laterally-torsionally coupled buildings using forced or ambient vibration data: I. Formulation and verification

A new parameter estimation algorithm is described for identifying the stiffness properties of torsionally coupled shear buildings from their linear response due to ambient excitations or during low-amplitude forced-vibration tests. The algorithm is based on the time-domain equations of motion, and yields estimates of the stiffness properties using a measure of the equilibrium of forces acting on each floor over a time interval. The banded structure of the stiffness matrix — a property intrinsic to torsion-shear buildings — is exploited to decompose the initial inverse problem into several problems of reduced size. This decomposition allows the identification of lateral and torsional stiffnesses of individual stories, independent of the others. The algorithm utilizes vibration data where input excitation is known/measured, which is typical for forced-vibration tests and earthquakes. If the ambient vibrations of the structure are adequately uncorrelated to the (unknown) external forces that induce such vibrations, then the algorithm can also be modified for output-only system identification. The proposed algorithm is verified — and its various attributes are investigated — using simulation data from the ‘Analytical Phase I’ of the IASC (International Association for Structural Control)-ASCE (American Society of Civil Engineers) benchmark studies. The companion article is devoted to the algorithm's application to experimental data, using data from the ‘Experimental Phase’ of the same benchmark studies. Copyright © 2011 John Wiley & Sons, Ltd.     

Formation and preservation of fresh lawsonite: Geothermobarometry of the North Makran Blueschists,southeast Iran

A low‐grade metamorphic “Coloured Mélange” in North Makran (SE Iran) contains lenses and a large klippe of low temperature, lawsonite‐bearing blueschists formed during the Cretaceous closure of the Tethys Ocean. The largest blueschist outcrop is a >1,000 m thick coherent unit with metagabbros overlain by interlayered metabasalts and metavolcanoclastic rocks. Blueschist metamorphism is only incipient in coarse‐grained rocks, whereas finer grained, foliated samples show thorough metamorphic recrystallization. The low‐variance blueschist peak assemblage is glaucophane, lawsonite, titanite, jadeite±phengitic mica. Investigated phase diagram sections of three blueschists with different protoliths yield peak conditions of ~300–380°C at 9–14 kbar. Magnesio‐hornblende and rutile cores indicate early amphibolite facies metamorphism at >460°C and 2–4 kbar. Later conditions at slightly higher pressures of 6–9 kbar at 350–450°C are recorded by barroisite, omphacite and rutile assemblages before entering into the blueschist facies and finally following a retrograde path through the pumpellyite–actinolite facies across the lawsonite stability field. Assuming that metamorphic pressure is lithostatic pressure, the corresponding counterclockwise P–T path is explained by burial along a warm geothermal gradient (~15°C/km) in a young subduction system, followed by exhumation along a cold gradient (~8°C/km); a specific setting that allows preservation of fresh undecomposed lawsonite in glaucophane‐bearing rocks.     

Optimal nudging strategies in regional climate modelling: investigation in a Big-Brother experiment over the European and Mediterranean regions

The objective of this work is to gain a general insight into the key mechanisms involved in the impact of nudging on the large scales and the small scales of a regional climate simulation. A “Big Brother experiment” (BBE) approach is used where a “reference atmosphere” is known, unlike when regional climate models are used in practice. The main focus is on the sensitivity to nudging time, but the BBE approach allows to go beyond a pure sensitivity study by providing a reference which model outputs try to approach, defining an optimal nudging time. Elaborating upon previous idealized studies, this work introduces key novel points. The BBE approach to optimal nudging is used with a realistic model, here the weather research and forecasting model over the European and Mediterranean regions. A winter simulation (1 December 1989–28 February 1990) and a summer simulation (1 June 1999–31 August 1999) with a 50 km horizontal mesh grid have been performed with initial and boundary conditions provided by the ERA-interim reanalysis of the European Center for Medium-range Weather Forecast to produce the “reference atmosphere”. The impacts of spectral and indiscriminate nudging are compared all others things being equal and as a function of nudging time. The impact of other numerical parameters, specifically the domain size and update frequency of the large-scale driving fields, on the sensitivity of the optimal nudging time is investigated. The nudged simulations are also compared to non-nudged simulations. Similarity between the reference and the simulations is evaluated for the surface temperature, surface wind and for rainfall, key variables for climate variability analysis and impact studies. These variables are located in the planetary boundary layer, which is not subject to nudging. Regarding the determination of a possible optimal nudging time, the conclusion is not the same for indiscriminate nudging (IN) and spectral nudging and depends on the update frequency of the driving large-scale fields τ _a . For IN, the optimal nudging time is around τ = 3 h for almost all cases. For spectral nudging, the best results are for the smallest value of τ used for the simulations (τ = 1 h) for frequent update of the driving large-scale fields (3 and 6 h). The optimal nudging time is 3 for 12 h interval between two consecutive driving large-scale fields due to time sampling errors. In terms of resemblance to the reference fields, the differences between the simulations performed with IN and spectral nudging are small. A possible reason for this very similar performance is that nudging is active only above the planetary boundary layer where small-scale features are less energetic. As expected from previous studies, the impact of nudging is weaker for a smaller domain size. However the optimal nudging time itself is not sensitive to domain size. The proposed strategy ensures a dynamical consistency between the driving field and the simulated small-scale field but it does not ensure the best “observed” fine scale field because of the possible impact of incorrect driving large-scale field. This type of downscaling provides an upper bound on the skill possible for recent historical past and twenty-first century projections. The optimal nudging strategy with respect to dynamic downscaling could add skill whenever the parent global model has some level of skill.     

Island-arc and Active Continental Margin Adakites from the Sabzevar Zone,Iran

Cretaceous to Eocene plutonic and volcanic rocks of the Sabzevar zone have an adakite characteristic with high Sr/Y ratio, depleted HFSE and enriched LILE features. Most of the Sabzevar adakites are high silica adakites with low Ni, Cr and Co contents. LREE/HREE ratio is high, while K₂O content is low to intermediate. Adakites in the Sabzevar zone are exposed in two areas, which are named southern and northern adakites here. The combination of Sr, Nd and Pb isotopic data with major and trace elements indicates that the adakitic rocks are formed by partial melting of the Sabzevar oceanic slab. Nb/Ta content of the samples indicates that the adakitic magmas were generated at different depth in the subduction system. Dy/Yb ratios of adakitic samples indicate positive, negative and roughly flat patterns for different samples, suggesting garnet and amphibole as residual phases during slab-derived adakitic magma formation. Sabzevar adakites emplaced during late to post-kinematic events. Sabzevar oceanic basin demised during a northward subduction by central Iranian micro-continents (CIM) and Eurasia plate convergence.     

The Sabzevar blueschists of the North-Central Iranian micro-continent as remnants of the Neotethys-related oceanic crust subduction

The Sabzevar ophiolites mark the Neotethys suture in east-north-central Iran. The Sabzevar metamorphic rocks, as part of the Cretaceous Sabzevar ophiolitic complex, consist of blueschist, amphibolite and greenschist. The Sabzevar blueschists contain sodic amphibole, epidote, phengite, calcite ± omphacite ± quartz. The epidote amphibolite is composed of sodic-calcic amphibole, epidote, albite, phengite, quartz ± omphacite, ilmenite and titanite. The greenschist contains chlorite, plagioclase and pyrite, as main minerals. Thermobarometry of a blueschist yields a pressure of 13–15.5 kbar at temperatures of 420–500 °C. Peak metamorphic temperature/depth ratios were low (~12 °C/km), consistent with metamorphism in a subduction zone. The presence of epidote in the blueschist shows that the rocks were metamorphosed entirely within the epidote stability field. Amphibole schist samples experienced pressures of 5–7 kbar and temperatures between 450 and 550 °C. The presence of chlorite, actinolite, biotite and titanite indicate greenschist facies metamorphism. Chlorite, albite and biotite replacing garnet or glaucophane suggests temperatures of >300 °C for greenschist facies. The formation of high-pressure metamorphic rocks is related to north-east-dipping subduction of the Neotethys oceanic crust and subsequent closure during lower Eocene between the Central Iranian Micro-continent and Eurasia (North Iran).     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Multi-label class assignment in land-use modelling

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.