Two-stage evolution of mantle peridotites from the Stalemate Fracture Zone, northwestern Pacific

This paper reports the results of a mineralogical study of 14 mantle peridotite samples dredged in 2009 from the eastern slope of the northwestern segment of the Stalemate Ridge in the northwestern Pacific during cruise SO201-KALMAR Leg 1b of the R/V Sonne. The sample collection included four serpentinized and silicified dunites and ten variably serpentinized lherzolites. The compositions of primary minerals (clinopyroxene, orthopyroxene, and spinel) change systematically from the lherzolites to dunites. Spinel from the lherzolites shows higher Mg# and lower Cr# values (0.65-0.68 and 0.26-0.33, respectively) compared with spinel from the dunites (Mg# = 0.56-0.64 and Cr# = 0.38-0.43). Clinopyroxene from the lherzolites is less magnesian (Mg# = 91.7-92.4) than clinopyroxene from dunite sample DR37-3 (Mg# = 93.7). Based on the obtained data, it was concluded that the lherzolites of the Stalemate Fracture Zone were derived by 10-12% near-fractional melting of a DMM-type depleted mantle reservoir beneath the Kula-Pacific spreading center. The dunites were produced by interaction of residual lherzolites with sodium- and titaniumrich melt and are probably fragments of a network of dunite channels in the shallow mantle. The moderately depleted composition of minerals clearly distinguishes the lherzolites from the strongly depleted peridotites of the East Pacific Rise and indicates the existence of slow-spreading mid-ocean ridges in the Pacific Ocean during the Cretaceous-Paleogene

Implications of climate change for agricultural productivity in the early twenty-first century

This paper reviews recent literature concerning a wide range of processes through which climate change could potentially impact global-scale agricultural productivity, and presents projections of changes in relevant meteorological, hydrological and plant physiological quantities from a climate model ensemble to illustrate key areas of uncertainty. Few global-scale assessments have been carried out, and these are limited in their ability to capture the uncertainty in climate projections, and omit potentially important aspects such as extreme events and changes in pests and diseases. There is a lack of clarity on how climate change impacts on drought are best quantified from an agricultural perspective, with different metrics giving very different impressions of future risk. The dependence of some regional agriculture on remote rainfall, snowmelt and glaciers adds to the complexity. Indirect impacts via sea-level rise, storms and diseases have not been quantified. Perhaps most seriously, there is high uncertainty in the extent to which the direct effects of CO2 rise on plant physiology will interact with climate change in affecting productivity. At present, the aggregate impacts of climate change on global-scale agricultural productivity cannot be reliably quantified

Psychometric Properties of an Assessment for Mental Health Recovery Programs

The concept of recovery can be operationalized from either the point of view of the consumer, or from the perspective of the agency providing services. The Milestones of Recovery Scale (MORS) was created to capture aspects of recovery from the agency perspective. Evidence establishing the psychometric properties of the MORS was obtained in three efforts: Inter-rater reliability using staff at The Village, a multi-service organization serving the homeless mentally ill in Long Beach, California; inter-rater reliability was also obtained from Vinfen Corporation, a large provider of housing services to mentally ill persons in Boston, Massachusetts. A test–retest reliability study was conducted using staff rating of clients at The Village, and evidence for validity was obtained using the Level of Care Utilization System (LOCUS) as a validity measure. The intra-class correlation coefficient for the inter-rater reliability study was r = .85 (CI .81, .89) for The Village and r = .86 (CI .80, .90) for Vinfen Corporation; test–retest reliability was r = .85 (CI .81, .87); and validity coefficients for the LOCUS were at or above r = .49 for all subscales except one. There is sufficient evidence for the reliability and validity of the MORS

A framework for the cross-sectoral integration of multi-model impact projections: land use decisions under climate impacts uncertainties

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impactmodel setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making

Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community

Multi-objective calibration of RothC using measured carbon stocks and auxiliary data of a long-term experiment in Switzerland

Interactions between model parameters and low spatiotemporal resolution of available data mean that conventional soil organic carbon (SOC) models are often affected by equifinality, with consequent uncertainty in SOC forecasts. Estimation of belowground C inputs is another major source of uncertainty in SOC modelling. Models are usually calibrated on SOC stocks and fluxes from long‐term experiments (LTEs), whereas other point data are not used for constraining the model parameters. We used data from an agricultural long‐term (> 65 years) fertilization experiment to test a multi‐objective parameter estimation approach on the RothC model, combining SOC data from different fertilization treatments with microbial biomass, basal respiration and Zimmermann’s fractions data. We also compared two methods to estimate the belowground C inputs: a conventional scaling of belowground biomass from crop harvest yield and an alternative approach based on constant belowground C for cereals measured experimentally in the field. The resulting posterior parameter distributions still suffered from some equifinality; the most stable C pool kinetic constants and composition of exogenous organic matter were the most sensitive parameters. The use of fixed belowground C inputs for cereals improved the model performance, reducing the importance of treatment‐specific parameters and processes. The introduction of microbial biomass and basal respiration data was effective for increasing determination of the calibration, but also suggested a change in the model structure: the microbial biomass pool, which is proportional to the C inputs in the traditional models, could be represented by different microbial physiology functions

Die Relation der Verhaltenstherapie zu systemischer Therapie und Synergetik

Fragt man Therapeutinnen und Therapeuten, was das Gemeinsame bzw. Trennende zwischen systemischer Therapie (im Weiteren: ST), Synergetik und Verhaltenstherapie (im Weiteren: VT) ist, bekommt man annähernd so viele Antworten, wie Therapeuten befragt werden. Das lässt sich auf den individuell unterschiedlichen Ausbildungsstand, auf persönliche Arbeitsstile, konkrete therapeutische Erfahrungen, heterogene Darstellungen in der einschlägigen Literatur zurückführen. Die folgende Abhandlung des Themas muss aus den gleichen Gründen subjektiv bleiben, auch wenn durch zahlreiche Bezüge auf die Literatur eine >>objektive<< Verankerung angestrebt wird. Die Frage wird zuweilen von Patienten und Ausbildungskandidaten gestellt - also sollte man auch versuchen, eine Antwort zu finden