The oxygen isotope fractionation behaviour of kyanite in experiment and nature

The oxygen isotope fractionation between kyanite and calcium carbonate has been investigated experimentally at four temperatures in the range between 625 and 775 °C at 13 kbar. Because of low exchange rates, the isotopic reaction was enhanced by polymorphic transformation of andalusite to kyanite. With this experimental modification a close approach to equilibrium was reached in all runs. The temperature dependence of the equilibrium fractionation is described by the equation 1000 ln _ky-cc=−2.62×10⁶/T ². Application of the experimental results to natural quartz-kyanite-garnet assemblages indicates the preservation of the oxygen isotope composition of kyanite acquired during its formation, reflecting its extremely low oxygen diffusivity. This refractory behaviour restricts the use of kyanite for thermometry but opens the possibility to use its O-isotope composition as an indicator for recognition of polymetamorphic rock histories and reconstruction of the prograde evolution of a metamorphic sequence. Received: 8 June 1998 / Accepted: 24 August 1998     

A New Framework for Solving the Spatial Network Problems Based on Line Graphs

In this article we define inverse line graphs of directed graphs as a new framework for solving some classical network analysis problems. The extraction method and theories of inverse line graphs are explained in this article. It is shown that by changing the analysis space from the original directed graph to the inverse line graph, complex problems can be changed into simpler problems. We show the usefulness of the proposed framework in two particular applications: shortest path computations and the more general route planning. Considering the implementation result, we expect that this framework could be used in many more network analysis problems.     

Do Cloud Properties in a Puerto Rican Tropical Montane Cloud Forest Depend on Occurrence of Long-Range Transported African Dust?

We investigated cloud properties of warm clouds in a tropical montane cloud forest at Pico del Este (1,051 m a.s.l.) in the northeastern part of Puerto Rico to address the question of whether cloud properties in the Caribbean could potentially be affected by African dust transported across the Atlantic Ocean. We analyzed data collected during 12 days in July 2011. Cloud droplet size spectra were measured using the FM-100 fog droplet spectrometer that measured droplet size distributions in the range from 2 to 49 µm, primarily during fog events. The droplet size spectra revealed a bimodal structure, with the first peak (D < 6 µm) being more pronounced in terms of droplet number concentrations, whereas the second peak (10 µm < D < 20 µm) was found to be the one relevant for total liquid water content (LWC) of the cloud. We identified three major clusters of characteristic droplet size spectra by means of hierarchical clustering. All clusters differed significantly from each other in droplet number concentration ( \(N_{\rm tot}\) ), effective diameter (ED), and median volume diameter (MVD). For the cluster comprising the largest droplets and the lowest droplet number concentrations, we found evidence of inhomogeneous mixing in the cloud. Contrastingly, the other two clusters revealed microphysical behavior, which could be expected under homogeneous mixing conditions. For those conditions, an increase in cloud condensation nuclei—e.g., from processed African dust transported to the site—is supposed to lead to an increased droplet concentration. In fact, one of these two clusters showed a clear shift of cloud droplet size spectra towards smaller droplet diameters. Since this cluster occurred during periods with strong evidence for the presence of long-range transported African dust, we hypothesize a link between the observed dust episodes and cloud characteristics in the Caribbean at our site, which is similar to the anthropogenic aerosol indirect effect.     

Combining climate model output via model correlations

In climate science, collections of climate model output, usually referred to as ensembles, are commonly used devices to study uncertainty in climate model experiments. The ensemble members may reflect variation in initial conditions, different physics implementations, or even entirely different climate models. However, there is a need to deliver a unified product based on the ensemble members that reflects the information contained in whole of the ensemble. We propose a technique for creating linear combinations of ensemble members where the weights are constructed from estimates of variation and correlation both within and between ensemble members. At the heart of this approach is a Bayesian hierarchical model that allows for estimation of the correlation between ensemble members as well as the study of the impact of uncertainty in the parameter estimates of the hierarchical model on the weights. The approach is demonstrated on an ensemble of regional climate model (RCM) output.     

A Straightforward Random Walk Model for Fast Push‐Pull Tracer Test Evaluation

In this article, we present a straightforward random walk model for fast evaluation of push‐pull tracer tests. By developing an adaptive algorithm, we overcome the problem of manually defining how many particles have to be used to simulate the transport problem. Beside this, we validate the random walk model by evaluating a push‐pull tracer test with drift phase and confirm the results with MT3DMS. The random walk model took less than 1% of computational time of MT3DMS, thus allowing a remarkable faster evaluation of push‐pull tracer tests.     

Changes of cloud microphysical properties during the transition from supercooled to mixed-phase conditions during CIME

A ground-based seeding experiment using carbon dioxide and propane sprayed from pressurized bottles was carried out under supercooled cloud conditions on a small spatial and short time scale. Water vapor deposition on the artificially generated dry ice and propane ice germs as the main ice formation process (nucleation and growth) is consistent with the experimental results. After nucleation, diffusional growth of the ice particles, partly at the expense of evaporating small droplets, was identified during the mixing of the seeding line with the ambient supercooled cloud. Within the seeding plume, ice water contents up to 80% of the total condensed water are observed, although the size of the formed ice particles did not exceed 25 μm. From the changes of the ice and supercooled liquid phase with time under mixed-phase conditions, liquid water content (LWC) evaporation, ice water content (IWC) formation, and ice crystal growth rates are estimated, which are not affected by the artificial nucleation process. Thus, these rates are assessed to be applicable for a growing ice phase of small ice particles in a young mixed-phase cloud, where other growth mechanisms, like riming or aggregation, are negligible.     

The Scaling Behaviour of a Turbulent Kinetic Energy Closure Model for Stably Stratified Conditions

We investigate the scaling behaviour of a turbulent kinetic energy (TKE) closure model for stably stratified conditions. The mixing length scale for stable stratification is proportional to the ratio of the square root of the TKE and the local Brunt–Väisälä frequency, which is a commonly applied formulation. We analyze the scaling behaviour of our model in terms of traditional Monin–Obukov Similarity Theory and local scaling. From the model equations, we derive expressions for the stable limit behaviour of the flux–gradient relations and other scaling quantities. It turns out that the scaling behaviour depends on only a few model parameters and that the results obey local scaling theory. The analytical findings are illustrated with model simulations for the second GABLS intercomparison study. We also investigate solutions for the case in which an empirical correction function is used to express the eddy diffusivity for momentum as a function of the Richardson number (i.e. an increasing turbulent Prandtl number with stability). In this case, it seems that for certain parameter combinations the model cannot generate a steady-state solution. At the same time, its scaling behaviour becomes unrealistic. This shows that the inclusion of empirical correction functions may have large and undesired consequences for the model behaviour.     

Scientific uncertainty and climate change: Part I. Uncertainty and unabated emissions

Uncertainty forms an integral part of climate science, and it is often used to argue against mitigative action. This article presents an analysis of uncertainty in climate sensitivity that is robust to a range of assumptions. We show that increasing uncertainty is necessarily associated with greater expected damages from warming, provided the function relating warming to damages is convex. This constraint is unaffected by subjective or cultural risk-perception factors, it is unlikely to be overcome by the discount rate, and it is independent of the presumed magnitude of climate sensitivity. The analysis also extends to “second-order” uncertainty; that is, situations in which experts disagree. Greater disagreement among experts increases the likelihood that the risk of exceeding a global temperature threshold is greater. Likewise, increasing uncertainty requires increasingly greater protective measures against sea level rise. This constraint derives directly from the statistical properties of extreme values. We conclude that any appeal to uncertainty compels a stronger, rather than weaker, concern about unabated warming than in the absence of uncertainty.     

Seismic strengthening of existing RC buildings with external cross-laminated timber (CLT) walls hosting an integrated energetic and architectural renovation

Bulletin of Earthquake Engineering - In this study a timber-based integrated solution is presented to solve at once common issues affecting typical reinforced concrete (RC) existing buildings, such...     

Predicting riverine dissolved silica fluxes to coastal zones from a hyperactive region and analysis of their first-order controls

Silicate weathering and resulting transport of dissolved matter influence the global carbon cycle in two ways. First by the uptake of atmospheric/soil CO₂ and second by providing the oceanic ecosystems via the fluvial systems with the nutrient dissolved silica (DSi). Previous work suggests that regions dominated by volcanics are hyperactive or even “hot spots” concerning DSi-mobilization. Here, we present a new approach for predicting DSi-fluxes to coastal zones, emphasizing “first-order” controlling factors (lithology, runoff, relief, land cover and temperature). This approach is applied to the Japanese Archipelago, a region characterized by a high percentage of volcanics (29.1% of surface area). The presented DSi-flux model is based on data of 516 catchments, covering approximately 56.7% of the area of the Japanese Archipelago. The spatial distribution of lithology—one of the most important first order controls—is taken from a new high resolution map of Japan. Results show that the Japanese Archipelago is a hyperactive region with a DSi-yield 6.6 times higher than the world average of 3.3 t SiO₂ km⁻² a⁻¹, but with large regional variations. Approximately 10% of its area exceeds 10 times the world average DSi-yield. Slope constitutes another important controlling factor on DSi-fluxes besides lithology and runoff, and can exceed the influence of runoff on DSi-yields. Even though the monitored area on the Japanese Archipelago stretches from about 31° to 46°N, temperature is not identified as a significant first-order model variable. This may be due to the fact that slope, runoff and lithology are correlated with temperature due to regional settings of the Archipelago, and temperature information is substituted to a certain extent by these factors. Land cover data also do not improve the prediction model. This may partly be attributed to misinterpreted land cover information from satellite images. Implications of results for Earth System and global carbon cycle modeling are discussed.