The Production of Investment Returns in Spatially Extensive Financial Markets

Investment returns are produced by combining financial assets with human capital, the decision-making protocols of investment houses, and the electronic infrastructure that supports the flow of information about investment. At the center of the production process stand senior managers; their power and authority in the production process is fundamental to the performance of investment organizations. This article provides a model of the production of investment returns in financial centers and spatially extensive financial markets. We begin with Coase's theory of the firm but go beyond models of the firm that represent commodity-producing industries. Having substantiated the model of investment management, it is applied to institutions that seek investment returns in geographically extensive financial markets. Operating in financial markets at a distance from home jurisdictions is an increasingly important aspect of investment management. Given recent turmoil in global financial markets, financial houses have sought higher rates of return from markets in which they have little direct experience. At issue is how different types of financial organizations (large and small, growing and declining) produce investment returns in this new environment.     

Climate change links fate of glaciers and an endemic alpine invertebrate

Climate warming in the mid- to high-latitudes and high-elevation mountainous regions is occurring more rapidly than anywhere else on Earth, causing extensive loss of glaciers and snowpack. However, little is known about the effects of climate change on alpine stream biota, especially invertebrates. Here, we show a strong linkage between regional climate change and the fundamental niche of a rare aquatic invertebrate—the meltwater stonefly Lednia tumana—endemic to Waterton-Glacier International Peace Park, Canada and USA. L. tumana has been petitioned for listing under the U.S. Endangered Species Act due to climate-change-induced glacier loss, yet little is known on specifically how climate impacts may threaten this rare species and many other enigmatic alpine aquatic species worldwide. During 14 years of research, we documented that L. tumana inhabits a narrow distribution, restricted to short sections (~500 m) of cold, alpine streams directly below glaciers, permanent snowfields, and springs. Our simulation models suggest that climate change threatens the potential future distribution of these sensitive habitats and the persistence of L. tumana through the loss of glaciers and snowfields. Mountaintop aquatic invertebrates are ideal early warning indicators of climate warming in mountain ecosystems. Research on alpine invertebrates is urgently needed to avoid extinctions and ecosystem change.     

Retrospective and prospective model simulations of sea level rise impacts on Gulf of Mexico coastal marshes and forests in Waccasassa Bay, Florida

The State of Florida (USA) is especially threatened by sea level rise due to extensive low elevation coastal habitats (approximately 8,000?km²?<?1?m above sea level) where the majority of the human population resides. We used the Sea Level Affecting Marshes Model (SLAMM) simulation to improve understanding of the magnitude and location of these changes for 58,000?ha of the Waccasassa Bay region of Florida??s central Gulf of Mexico coast. To assess how well SLAMM portrays changes in coastal wetland systems resulting from sea level rise, we conducted a hindcast in which we compared model results to 30?years of field plot data. Overall, the model showed the same pattern of coastal forest loss as observed. Prospective runs of SLAMM using 0.64?m, 1?m and 2?m sea level rise scenarios predict substantial changes over this century in the area covered by coastal wetland systems including net losses of coastal forests (69%, 83%, and 99%, respectively) and inland forests (33%, 50%, and 88%), but net gains of tidal flats (17%, 142%, and 3,837%). One implication of these findings at the site level is that undeveloped, unprotected lands inland from the coastal forest should be protected to accommodate upslope migration of this natural community in response to rising seas. At a broader scale, our results suggest that coastal wetland systems will be unevenly affected across the Gulf of Mexico as sea level rises. Species vulnerable to these anticipated changes will experience a net loss or even elimination.     

The reluctance of resource-users to adopt seasonal climate forecasts to enhance resilience to climate variability on the rangelands

As the impacts of climate-change on resource-dependent industries manifest, there is a commensurate effort to identify and implement strategies to reduce them. Yet, even when useful knowledge and tools exist, there can be poor adoption of adaptation strategies. We examine the reasons behind sub-optimal adoption of seasonal climate forecasts by graziers for managing climate variability. We surveyed 100 graziers in north-east Queensland, Australia and examined the influence of adaptive capacity, resource-dependency and forecast-perception on uptake. Technical perceptions were not important. Strategic skills, environmental awareness and social capital were. Results suggest that social factors (but not technical factors) are significant. These insights are important for adaptation planning and for maximising the resilience of communities and industries dependent on climate-sensitive resources.     

Contrasting isotopic signatures between anthropogenic and geogenic Zn and evidence for post-depositional fractionation processes in smelter-impacted soils from Northern France

Zinc isotopes have been studied along two smelter-impacted soil profiles sampled near one of the largest Pb and Zn processing plants in Europe located in northern France, about 50 km south of Lille. Mean δ⁶⁶Zn values along these two soil profiles range from +0.22 ± 0.17‰ (2σ) to +0.34 ± 0.17‰ (2σ) at the lowest horizons and from +0.38 ± 0.45‰ (2σ) to +0.76 ± 0.14‰ (2σ) near the surface. The δ⁶⁶Zn values in the lowest horizons of the soils are interpreted as being representative of the local geochemical background (mean value +0.31 ± 0.38‰), whereas heavier δ⁶⁶Zn values near the surface of the two soils are related to anthropogenic Zn. This anthropogenic Zn occurs in the form of franklinite (ZnFe₂O₄)-bearing slag grains originating from processing wastes at the smelter site and exhibiting δ⁶⁶Zn values of +0.81 ± 0.20‰ (2σ). The presence of franklinite is indicated by EXAFS analysis of the topsoil samples from both soil profiles as well as by micro-XANES analysis of the surface horizon of a third smelter-impacted soil from a distant site. These results indicate that naturally occurring Zn and smelter-derived Zn exhibit significantly different δ⁶⁶Zn values, which suggests that zinc isotopes can be used to distinguish between geogenic and anthropogenic sources of Zn in smelter-impacted soils. In addition to a possible influence of additional past sources of light Zn (likely Zn-sulfides and Zn-sulfates directly emitted by the smelter), the light δ⁶⁶Zn values in the surface horizons compared to smelter-derived slag materials are interpreted as resulting mainly from fractionation processes associated with biotic and/or abiotic pedological processes (Zn-bearing mineral precipitation, Zn complexation by organic matter, and plant uptake of Zn). This conclusion emphasizes the need for additional Zn isotopic studies before being able to use Zn isotopes to trace sources and pathways of this element in surface environments.     

Forecasting ocean wave energy: The ECMWF wave model and time series methods

Recently, the technology has been developed to make wave farms commercially viable. Since electricity is perishable, utilities will be interested in forecasting ocean wave energy. The horizons involved in short-term management of power grids range from as little as a few hours to as long as several days. In selecting a method, the forecaster has a choice between physics-based models and statistical techniques. A further idea is to combine both types of models. This paper analyzes the forecasting properties of a well-known physics-based model, the European Center for Medium-Range Weather Forecasts (ECMWF) Wave Model, and two statistical techniques, time-varying parameter regressions and neural networks. Thirteen data sets at locations in the Atlantic and Pacific Oceans and the Gulf of Mexico are tested. The quantities to be predicted are the significant wave height, the wave period, and the wave energy flux. In the initial tests, the ECMWF model and the statistical models are compared directly. The statistical models do better at short horizons, producing more accurate forecasts in the 1-5 h range. The ECMWF model is superior at longer horizons. The convergence point, at which the two methods achieve comparable degrees of accuracy, is in the area of 6 h. By implication, the physics-based model captures the underlying signals at lower frequencies, while the statistical models capture relationships over shorter intervals. Further tests are run in which the forecasts from the ECMWF model are used as inputs in regressions and neural networks. The combined models yield more accurate forecasts than either one individually.     

Fluid evolution in base‐metal sulphide mineral deposits in the metamorphic basement rocks of southwest Scotland and Northern Ireland

The Dalradian and Ordovician–Silurian metamorphic basement rocks of southwest Scotland and Northern Ireland host a number of base‐metal sulphide‐bearing vein deposits associated with kilometre‐scale fracture systems. Fluid inclusion microthermometric analysis reveals two distinct fluid types are present at more than half of these deposits. The first is an H₂O–CO₂–salt fluid, which was probably derived from devolatilization reactions during Caledonian metamorphism. This stage of mineralization in Dalradian rocks was associated with base‐metal deposition and occurred at temperatures between 220 and 360°C and pressures of between 1.6 and 1.9 kbar. Caledonian mineralization in Ordovician–Silurian metamorphic rocks occurred at temperatures between 300 and 360°C and pressures between 0.6 and 1.9 kbar. A later, probably Carboniferous, stage of mineralization was associated with base‐metal sulphide deposition and involved a low to moderate temperature (T_h 70 to 240°C), low to moderate salinity (0 to 20 wt% NaCl eq.), H₂O–salt fluid. The presence of both fluids at many of the deposits shows that the fractures hosting the deposits acted as long‐term controls for fluid migration and the location of Caledonian metalliferous fluids as well as Carboniferous metalliferous fluids. Copyright © 2004 John Wiley & Sons, Ltd.     

Asymmetric variability between maximum and minimum temperatures in Northeastern Tibetan Plateau: Evidence from tree rings

Ecological systems in the headwaters of the Yellow River, characterized by hash natural environmental conditions, are very vulnerable to climatic change. In the most recent decades, this area greatly attracted the public's attention for its more and more deteriorating environmental conditions. Based on tree-ring samples from the Xiqing Mountain and A'nyêmagên Mountains at the headwaters of the Yellow River in the Northeastern Tibetan Plateau, we reconstructed the minimum temperatures in the winter half year over the last 425 years and the maximum temperatures in the summer half year over the past 700 years in this region. The variation of minimum temperature in the winter half year during the time span of 1578―1940 was a relatively stable trend, which was followed by an abrupt warming trend since 1941. However, there is no significant warming trend for the maximum temperature in the summer half year over the 20th century. The asymmetric variation patterns between the minimum and maximum temperatures were observed in this study over the past 425 years. During the past 425 years, there are similar variation patterns between the minimum and maximum temperatures; however, the minimum temperatures vary about 25 years earlier compared to the maximum temperatures. If such a trend of variation patterns between the minimum and maximum temperatures over the past 425 years continues in the future 30 years, the maximum temperature in this region will increase significantly.     

Effects of substorms on the stormtime ring current index Dst

There has been some discussion in recent times regarding whether or not substorm expansive phase activity plays any role of importance in the formation of the stormtime ring current. I explore this question using the K_p index as a proxy for substorm expansive phase activity and the D_st index as a proxy for symmetric ring current strength. I find that increases in D_st are mildly related to the strength of substorm expansive phase activity during the development of the storm main phase. More surprisingly, I find that the strength of D_st during the storm recovery phase is positively correlated with the strength of substorm expansive phase activity. This result has an important bearing on the question of how much the Dst index reflects activity other than that of the stormtime symmetric ring current strength for which it is supposed to be a proxy.     

The seasonal cycle in coupled ocean-atmosphere general circulation models

We examine the seasonal cycle of near-surface air temperature simulated by 17 coupled ocean-atmosphere general circulation models participating in the Coupled Model Intercomparison Project (CMIP). Nine of the models use ad hoc “flux adjustment” at the ocean surface to bring model simulations close to observations of the present-day climate. We group flux-adjusted and non-flux-adjusted models separately and examine the behavior of each class. When averaged over all of the flux-adjusted model simulations, near-surface air temperature falls within 2?K of observed values over the oceans. The corresponding average over non-flux-adjusted models shows errors up to ～6?K in extensive ocean areas. Flux adjustments are not directly applied over land, and near-surface land temperature errors are substantial in the average over flux-adjusted models, which systematically underestimates (by ～5?K) temperature in areas of elevated terrain. The corresponding average over non-flux-adjusted models forms a similar error pattern (with somewhat increased amplitude) over land. We use the temperature difference between July and January to measure seasonal cycle amplitude. Zonal means of this quantity from the individual flux-adjusted models form a fairly tight cluster (all within ～30% of the mean) centered on the observed values. The non-flux-adjusted models perform nearly as well at most latitudes. In Southern Ocean mid-latitudes, however, the non-flux-adjusted models overestimate the magnitude of January-minus-July temperature differences by ～5?K due to an overestimate of summer (January) near-surface temperature. This error is common to five of the eight non-flux-adjusted models. Also, over Northern Hemisphere mid-latitude land areas, zonal mean differences between July and January temperatures simulated by the non-flux-adjusted models show a greater spread (positive and negative) about observed values than results from the flux-adjusted models. Elsewhere, differences between the two classes of models are less obvious. At no latitude is the zonal mean difference between averages over the two classes of models greater than the standard deviation over models. The ability of coupled GCMs to simulate a reasonable seasonal cycle is a necessary condition for confidence in their prediction of long-term climatic changes (such as global warming), but it is not a sufficient condition unless the seasonal cycle and long-term changes involve similar climatic processes. To test this possible connection, we compare seasonal cycle amplitude with equilibrium warming under doubled atmospheric carbon dioxide for the models in our data base. A small but positive correlation exists between these two quantities. This result is predicted by a simple conceptual model of the climate system, and it is consistent with other modeling experience, which indicates that the seasonal cycle depends only weakly on climate sensitivity.