The determination of potential difference by the joint application of measured and synthetical gravity data: a case study in Hungary

In an elementary approach every geometrical height difference between the staff points of a levelling line should have a corresponding average g value for the determination of potential difference in the Earth’s gravity field. In practice this condition requires as many gravity data as the number of staff points if linear variation of g is assumed between them. Because of the expensive fieldwork, the necessary data should be supplied from different sources. This study proposes an alternative solution, which is proved at a test bed located in the Mecsek Mountains, Southwest Hungary, where a detailed gravity survey, as dense as the staff point density (~1 point/34 m), is available along a 4.3-km-long levelling line. In the first part of the paper the effect of point density of gravity data on the accuracy of potential difference is investigated. The average g value is simply derived from two neighbouring g measurements along the levelling line, which are incrementally decimated in the consecutive turns of processing. The results show that the error of the potential difference between the endpoints of the line exceeds 0.1 mm in terms of length unit if the sampling distance is greater than 2 km. Thereafter, a suitable method for the densification of the decimated g measurements is provided. It is based on forward gravity modelling utilising a high-resolution digital terrain model, the normal gravity and the complete Bouguer anomalies. The test shows that the error is only in the order of 10⁻³mm even if the sampling distance of g measurements is 4 km. As a component of the error sources of levelling, the ambiguity of the levelled height difference which is the Euclidean distance between the inclined equipotential surfaces is also investigated. Although its effect accumulated along the test line is almost zero, it reaches 0.15 mm in a 1-km-long intermediate section of the line.     

Assessment of RegCM4 simulated inter-annual variability and daily-scale statistics of temperature and precipitation over Mexico

The skill of a regional climate model (RegCM4) in capturing the mean patterns, interannual variability and extreme statistics of daily-scale temperature and precipitation events over Mexico is assessed through a comparison of observations and a 27-year long simulation driven by reanalyses of observations covering the Central America CORDEX domain. The analysis also includes the simulation of tropical cyclones. It is found that RegCM4 reproduces adequately the mean spatial patterns of seasonal precipitation and temperature, along with the associated interannual variability characteristics. The main model bias is an overestimation of precipitation in mountainous regions. The 5 and 95 percentiles of daily temperature, as well as the maximum dry spell length are realistically simulated. The simulated distribution of precipitation events as well as the 95 percentile of precipitation shows a wet bias in topographically complex regions. Based on a simple detection method, the model produces realistic tropical cyclone distributions even at its relatively coarse resolution (dx = 50 km), although the number of cyclone days is underestimated over the Pacific and somewhat overestimated over the Atlantic and Caribbean basins. Overall, it is assessed that the performance of RegCM4 over Mexico is of sufficient quality to study not only mean precipitation and temperature patterns, but also higher order climate statistics.     

Thermal conductivity measurements of road construction materials in frozen and unfrozen state

A series of thermal conductivity measurements for various materials was performed in a large climate chamber. The size of the chamber allowed the preparation of relatively large samples in a controlled thermal environment. Three types of thermal sensors were used: (1) two needle probes; (2) a grid of temperature sensors, evenly distributed inside the sample; (3) two additional thermal probes, which were simplified versions of an instrument originally developed for measuring thermal properties of the ice/dust mixture expected to exist at the surface of a comet nucleus. They consist of a series of individual temperature sensors integrated into a glass fibre rod. Each of these sensors can be operated in an active (heated) or passive (only temperature sensing) mode. The following sample materials were used: fine-grained reddish sand, coarse-grained moist sand, gravels with various grain size distributions from < 1 cm up to about 6 cm, and for comparison and calibration pure water (with convection suppressed by adding agar-agar), compact ice, and compact granite. Of particular interest are the measurements with composite samples, like stones embedded in an agar-agar matrix. We describe the evaluation methods and present the results of the thermal conductivity measurements.     

Climate and human health: synthesizing environmental complexity and uncertainty

Broad relationships between weather and human health have long been recognized, and there is currently a large body of research examining the impacts of climate change on human health. Much of the literature in this area examines climate–health relationships at global or regional levels, incorporating mostly generalized responses of pathogens and vectors to broad changes in climate. Far less research has been done to understand the direct and indirect climate-mediated processes involved at finer scales. Thus, some studies simplify the role of climate and may over- or under-estimate the potential response, while others have begun to highlight the subtle and complex role for climate that is contingent on other relevant processes occurring in natural and social environments. These fundamental processes need to be understood to determine the effects of past, current and future climate variation and change on human health. We summarize the principal climate variables and climate-dependent processes that are believed to impact human health across a representative set of diseases, along with key uncertainties in these relationships.     

Taming a “chaotic concept”: gentrification and segmented consumption in Brooklyn, 2002–2012

Public debate and prior scholarship tend to emphasize the link between gentrification and high-end chain retail, underscoring the importance of taste cultures tied to social class in making sense of gentrification’s impact on neighborhood identity. In this study, we present evidence from 10 years of change in the businesses of Brooklyn, New York City, a period of extensive gentrification across a wide variety of neighborhood and census-tract level contexts. Adopting census tracts clustered in neighborhoods as the units of analysis, we model the effects of institutional and demographic change on two separate outcomes: chain retail density and homogeneity in the types of goods and services available. We argue that changes in consumer culture embedded in broader processes of gentrification are neither “chaotic” nor “unitary” but are “segmented” according to local spatial and demographic context, taking two discrete forms: institutionally facilitated corporatization; lifestyle-driven homogenization.     

Changes in extremes and hydroclimatic regimes in the CREMA ensemble projections

We analyze changes of four extreme hydroclimatic indices in the RCP8.5 projections of the Phase I CREMA experiment, which includes 21st century projections over 5 CORDEX domains (Africa, Central America, South America, South Asia, Mediterranean) with the ICTP regional model RegCM4 driven by three CMIP5 global models. The indices are: Heat Wave Day Index (HWD), Maximum Consecutive Dry Day index (CDD), fraction of precipitation above the 95th intensity percentile (R95) and Hydroclimatic Intensity index (HY-INT). Comparison with coarse (GPCP) and high (TRMM) resolution daily precipitation data for the present day conditions shows that the precipitation intensity distributions from the GCMs are close to the GPCP data, while the RegCM4 ones are closer to TRMM, illustrating the added value of the increased resolution of the regional model. All global and regional model simulations project predominant increases in HWD, CDD, R95 and HY-INT, implying a regime shift towards more intense, less frequent rain events and increasing risk of heat wave, drought and flood with global warming. However, the magnitudes of the changes are generally larger in the global than the regional models, likely because of the relatively low “climate sensitivity” of the RegCM4, especially when using the CLM land surface scheme. In addition, pronounced regional differences in the change signals are found. The data from these simulations are available for use in impact assessment studies.     

Leveraging modern climatology to increase adaptive capacity across protected area networks

Human-driven changes in the global environment pose an increasingly urgent challenge for the management of ecosystems that is made all the more difficult by the uncertain future of both environmental conditions and ecological responses. Land managers need strategies to increase regional adaptive capacity, but relevant and rapid assessment approaches are lacking. To address this need, we developed a method to assess regional protected area networks across biophysically important climatic gradients often linked to biodiversity and ecosystem function. We plot the land of the southwestern United States across axes of historical climate space, and identify landscapes that may serve as strategic additions to current protected area portfolios. Considering climate space is straightforward, and it can be applied using a variety of relevant climate parameters across differing levels of land protection status. The resulting maps identify lands that are climatically distinct from existing protected areas, and may be utilized in combination with other ecological and socio-economic information essential to collaborative landscape-scale decision-making. Alongside other strategies intended to protect species of special concern, natural resources, and other ecosystem services, the methods presented herein provide another important hedging strategy intended to increase the adaptive capacity of protected area networks.     

Progression in sulfur isotopic compositions from coal to fly ash: Examples from single-source combustion in Indiana

Sulfur occurs in multiple mineral forms in coals, and its fate in coal combustion is still not well understood. The sulfur isotopic composition of coal from two coal mines in Indiana and fly ash from two power plants that use these coals were studied using geological and geochemical methods. The two coal beds are Middle Pennsylvanian in age; one seam is the low-sulfur (< 1%) Danville Coal Member of the Dugger Formation and the other is the high-sulfur (> 5%) Springfield Coal Member of the Petersburg Formation. Both seams have ash contents of approximately 11%. Fly-ash samples were collected at various points in the ash-collection system in the two plants. The results show notable difference in δ³⁴S for sulfur species within and between the low-sulfur and high-sulfur coal. The δ³⁴S values for all sulfur species are exclusively positive in the low-sulfur Danville coal, whereas the δ³⁴S values for sulfate, pyritic, and organic sulfur are both positive and negative in the high-sulfur Springfield coal. Each coal exhibits a distinct pattern of stratigraphic variation in sulfur isotopic composition. Overall, the δ³⁴S for sulfur species values increase up the section in the low-sulfur Danville coal, whereas they show a decrease up the vertical section in the high-sulfur Springfield coal. Based on the evolution of δ³⁴S for sulfur species, it is suggested that there was influence of seawater on peat swamp, with two marine incursions occurring during peat accumulation of the high-sulfur Springfield coal. Therefore, bacterial sulfate reduction played a key role in converting sulfate into hydrogen sulfide, sulfide minerals, and elemental sulfur. The differences in δ³⁴S between sulfate sulfur and pyritic sulfur is very small between individual benches of both coals, implying that some oxidation occurred during deposition or postdeposition.The δ³⁴S values for fly ash from the high-sulfur Springfield coal (averaging 9.7‰) are greatly enriched in ³⁴S relative to those in the parent coal (averaging 2.2‰). This indicates a fractionation of sulfur isotopes during high-sulfur coal combustion. By contrast, the δ³⁴S values for fly-ash samples from the low-sulfur Danville coal average 10.2‰, only slightly enriched in ³⁴S relative to those from the parent coal (average 7.5‰). The δ³⁴S values for bulk S determined directly from the fly-ash samples show close correspondence with the δ³⁴S values for SO₄^− 2 leached from the fly ash in the low-sulfur coal, suggesting that the transition from pyrite to sulfate occurred via high-temperature oxidation during coal combustion.     

Does the model regional bias affect the projected regional climate change? An analysis of global model projections

An analysis is presented of the dependence of the regional temperature and precipitation change signal on systematic regional biases in global climate change projections. The CMIP3 multi-model ensemble is analyzed over 26 land regions and for the A1B greenhouse gas emission scenario. For temperature, the model regional bias has a negligible effect on the projected regional change. For precipitation, a significant correlation between change and bias is found in about 30% of the seasonal/regional cases analyzed, covering a wide range of different climate regimes. For these cases, a performance-based selection of models in producing climate change scenarios can affect the resulting change estimate, and it is noted that a minimum of four to five models is needed to obtain robust precipitation change estimates. In a number of cases, models with largely different precipitation biases can still produce changes of consistent sign. Overall, it is assessed that in the present generation of models the regional bias does not appear to be a dominant factor in determining the simulated regional change in the majority of cases.