http://www.sciencedirect.com/science/article/pii/S167498711400108X

We present three 3D numerical models of deep subduction where buoyant material from an oceanic plateau and a plume interact with the overriding plate to assess the influence on subduction dynamics,trench geometry,and mechanisms for plateau accretion and continental growth.Transient instabilities of the convergent margin are produced,resulting in:contorted trench geometry;trench migration parallel with the plate margin;folding of the subducting slab and orocline development at the convergent margin;and transfer of the plateau to the overriding plate.The presence of plume material beneath the oceanic plateau causes flat subduction above the plume,resulting in a "bowed" shaped subducting slab.In plateau-only models,plateau accretion at the edge of the overriding plate results in trench migration around the edge of the plateau before subduction is re-established directly behind the trailing edge of the plateau.The plateau shortens and some plateau material subducts.The presence of buoyant plume material beneath the oceanic plateau has a profound influence on the behaviour of the convergent margin.In the plateau + plume model,plateau accretion causes rapid trench advance.Plate convergence is accommodated by shearing at the base of the plateau and shortening in the overriding plate.The trench migrates around the edge of the plateau and subduction is re-established well behind the trailing edge of the plateau,effectively embedding the plateau into the overriding plate.A slab window forms beneath the accreted plateau and plume material is transferred from the subducting plate to the overriding plate through the window.In all of the models,the subduction zone maintains a relatively stable configuration away from the buoyancy anomalies within the downgoing plate.The models provide a dynamic context for plateau and plume accretion in Phanerozoic accretionary orogenic systems such as the East China Orogen and the Central Asian Orogen(Altiads),which are characterised by accreted ophiolite complexes with diverse geochemical affinities,and a protracted evolution of accretion of exotic terranes including oceanic plateau and terranes with plume origins.     

Towards a Better Understanding of Dynamic Interaction Metrics for Wildlife: a Null Model Approach

The ability to measure dynamic interactions, such as attraction or avoidance, is crucial to understanding socio‐spatial behaviors related to territoriality and mating as well as for exploring resource use and the potential spread of infectious epizootic diseases. In spite of the importance of measuring dynamic interactions, it has not been a main research focus in movement pattern analysis. With very few exceptions (see Benhamou et al. 2014), no new metrics have been developed in the past 20 years to accommodate the fundamental shift in the type of animal movement data now being collected and there have been few comparison or otherwise critical studies of existing dynamic interaction metrics (but see Long et al. 2014; Miller 2012). This research borrows from the null model approach commonly used in community ecology to compare six currently used dynamic interaction metrics using data on five brown hyena dyads in Northern Botswana. There was disconcerting variation among the dynamic interaction results depending on which metric and which null model was used, and these results highlight the need for more extensive research on measuring and interpreting dynamic interactions in order to avoid making potentially misleading inferences about socio‐spatial behaviors.     

Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.     

Confronting the challenge of integrated assessment of climate adaptation: a conceptual framework

Key limitations of integrated assessment models (IAMs) are their highly stylized and aggregated representation of climate damages and associated economic responses, as well as the omission of specific investments related to climate change adaptation. This paper proposes a framework for modeling climate impacts and adaptation that clarifies the relevant research issues and provides a template for making improvements. We identify five desirable characteristics of an ideal integrated assessment modeling platform, which we elaborate into a conceptual model that distinguishes three different classes of adaptation-related activities. Based on these elements we specify an impacts- and adaptation-centric IAM, whose optimality conditions are used to highlight the types of functional relationships necessary for realistic representations of adaptation-related decisions, the specific mechanisms by which these responses can be incorporated into IAMs, and the ways in which the inclusion of adaptation is likely to affect the simulations’ results.     

Evaluating a modified point-based method to downscale cell-based climate variable data to high-resolution grids

To address the demand for high spatial resolution gridded climate data, we have advanced the Daymet point-based interpolation algorithm for downscaling global, coarsely gridded data with additional output variables. The updated algorithm, High-Resolution Climate Downscaler (HRCD), performs very good downscaling of daily, global, historical reanalysis data from 1° input resolution to 2.5 arcmin output resolution for day length, downward longwave radiation, pressure, maximum and minimum temperature, and vapor pressure deficit. It gives good results for monthly and yearly cumulative precipitation and fair results for wind speed distributions and modeled downward shortwave radiation. Over complex terrain, 2.5 arcmin resolution is likely too low and aggregating it up to 15 arcmin preserves accuracy. HRCD performs comparably to existing daily and monthly US datasets but with a global extent for nine daily climate variables spanning 1948–2006. Furthermore, HRCD can readily be applied to other gridded climate datasets.     

Climate change and mental health: an exploratory case study from Rigolet, Nunatsiavut, Canada

As the impacts from anthropogenic climate change are increasing globally, people are experiencing dramatic shifts in weather, temperature, wildlife and vegetation patterns, and water and food quality and availability. These changes impact human health and well-being, and resultantly, climate change has been identified as the biggest global health threat of the 21st Century. Recently, research is beginning to indicate that changes in climate, and the subsequent disruption to the social, economic, and environmental determinants of health, may cause increased incidences and prevalence of mental health issues, emotional responses, and large-scale sociopsychological changes. Through a multi-year, community-led, exploratory case study conducted in Rigolet, Nunatsiavut, Labrador, Canada, this research qualitatively explores the impacts of climate change on mental health and well-being in an Inuit context. Drawing from 67 in-depth interviews conducted between January 2010 and October 2010 with community members and local and regional health professionals, participants reported that changes in weather, snow and ice stability and extent, and wildlife and vegetation patterns attributed to climate change were negatively impacting mental health and well-being due to disruptions in land-based activities and a loss of place-based solace and cultural identity. Participants reported that changes in climate and environment increased family stress, enhanced the possibility of increased drug and alcohol usage, amplified previous traumas and mental health stressors, and were implicated in increased potential for suicide ideation. While a preliminary case study, these exploratory findings indicate that climate change is becoming an additional mental health stressor for resource-dependent communities and provide a baseline for further research.     

Do people “personally experience” global warming,and if so how,and does it matter?

For most people, the direct and personally observable signals of climate change should be difficult to detect amid the variability of everyday weather. Yet, previous research has shown that some people believe they have personally experienced global warming. Through four related studies, our paper sheds light on what signals of global warming some people believe they are detecting, why, and whether or not it matters. These studies were conducted using population survey and climatic data from a single county in Michigan. Study 1 found that 27% of the county's adult residents felt that they had personally experienced global warming. Study 2 – based on content analysis of people's open-ended responses – found that the most frequently described personal experiences of global warming were changes in seasons (36%), weather (25%), lake levels (24%), animals and plants (20%), and snowfall (19%). Study 3 – based on NOAA climatic data – found that most, but not all, of these detected signals are borne out in the climatic record. Study 4 – using the survey data – found that personal experience of global warming matters in that it predicts perceptions of local risk of global warming, controlling for demographics, political affiliation, and cultural beliefs about national policy outcomes. We conclude that perceived personal experience of global warming appears to heighten people's perception of the risks, likely through some combination of direct experience, vicarious experience (e.g., news media stories), and social construction.