Mineralogical and chemical modification of components in CV3 chondrites: Nebular or asteroidal processing?

Abstract— Calcium- and aluminum-rich inclusions (CAIs), chondrules, dark inclusions and matrices in certain CV3 carbonaceous chondrites appear to have been modified by different degrees of late-stage alteration processes that caused significant variations in mineralogy and chemistry. Some chondrules and CAIs are rimmed with fayalitic olivine. Metal in all components may be oxidized and sulphidized to magnetite, Ni-rich metal and sulfides. Silicates in all components are aqueously altered to different degrees to phyllosilicates. Primary minerals in some CAIs experienced Fe-alkali-halogen metasomatism forming nepheline, sodalite, wollastonite, hedenbergite and other secondary minerals. In CV3 chondrites with metasomatized CAIs, nepheline, sodalite, etc. are also present in chondrule mesostases and in matrices. McSween's (1977b) reduced subgroup of CV3 chondrites generally shows minimal alteration of all components and may represent the unaltered precursors for the oxidized CV3 chondrites, which generally show major alteration. Most studies have been focused on specific components in CV3 chondrites and have not considered possible relationships between alteration processes. We infer from the correlated occurrences of the alteration features that they were closely related in time and space and review nebular and asteroidal models for their origins. We prefer an asteroidal model.     

COUNTER-URBANIZATION IN SOCIETAL CONTEXT: LONG-DISTANCE MIGRATION TO THE HIGHLANDS AND ISLANDS OF SCOTLAND*

Recent growth in long-distance migration by non-Scots to remote parts of the Scottish Highlands is evaluated in relation to key explanatory themes in counter-urbanization literature: rural-urban convergence, expansion of the affluent middle class, and changing residential preferences. A survey of in-migrants' motivations and livelihood adjustments reveals the primacy of quality of life considerations and a ‘satisficing’ approach to work, lifestyle and residential location. It is concluded that peripheral area counter-urbanization could be maintained without the context of societal affluence that spawned the movement in the 1960s.     

Dynamic adaptive pathways in downscaled climate change scenarios

The parallel scenario process enables characterization of climate-related risks and response options to climate change under different socio-economic futures and development prospects. The process is based on representative concentration pathways, shared socio-economic pathways, and shared policy assumptions. Although this scenario architecture is a powerful tool for evaluating the intersection of climate and society at the regional and global level, more specific context is needed to explore and understand risks, drivers, and enablers of change at the national and local level. We discuss the need for a stronger recognition of such national-scale characteristics to make climate change scenarios more relevant at the national and local scale, and propose ways to enrich the scenario architecture with locally relevant details that enhance salience, legitimacy, and credibility for stakeholders. Dynamic adaptive pathways are introduced as useful tools to draw out which elements of a potentially infinite scenario space connect with decision-relevant aspects of particular climate-related and non-climate-related risks and response options. Reviewing adaptation pathways for New Zealand case studies, we demonstrate how this approach could bring the global-scale scenario architecture within reach of local-scale decision-making. Such a process would enhance the utility of scenarios for mapping climate-related risks and adaptation options at the local scale, involving appropriate stakeholder involvement.     

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPI_JJA), Sep.–Nov. (SPI_SON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.     

Biogeographical drivers of ragweed pollen concentrations in Europe

Theoretical and Applied Climatology - The drivers of spatial variation in ragweed pollen concentrations, contributing to severe allergic rhinitis and asthma, are poorly quantified. We analysed the...     

The microscale cooling effects of water sensitive urban design and irrigation in a suburban environment

There is still considerable uncertainty about precipitation at high elevation in mountain terrain due to the relatively few in situ measurements available and to the particular variability of the parameter. In this study, several spatialization techniques were tested, some for climatological time scale and others for daily fields, for precipitation over the western Alps for the period of 1990–2012. The study domain and period were chosen for the quality of available in situ observations and density of the network. First, a weather-type classification was established with a technique based on canonical correlation analysis combining large- and regional-scale data. The spatialization techniques applied for the climatological time scale were adapted from the Aurelhy method which uses elevation and principal components of the topography as predictors. The spatialization techniques applied to daily fields were based on kriging of daily rain gauges and used the climatological fields as predictors. This study aims to validate the advantage of using the climatology of the weather type of the day as predictor for daily fields over a monthly climatology. The climatology of the weather type of the day seems to demonstrate some small improvement.Finally, annual means over the period of 1990–2012 were produced using several methods, including some from accumulation of daily fields and others from the spatialization of in situ station means. Precipitation at high elevations and vertical climatological gradients were particularly scrutinized. Annual means based on sums of daily fields seem to have better performances.This paper only presents results for precipitation but temperature was also analysed.     

Epistemic and ethical trade-offs in decision analytical modelling

Designing decision analytical models requires making choices that can involve a range of trade-offs and interactions between epistemic and ethical considerations. Such choices include determining the complexity of a model and deciding what types of risk will be assessed. Here, we demonstrate how model design choices can involve trade-offs between the epistemic benefits of representational completeness and simplicity, which interact with ethical considerations about fairness and human life. We illustrate this point by focusing on modeling studies that assess flood risks in New Orleans, Louisiana. Addressing the ethical and epistemic implications of model design choices can help clarify the scope of factors necessary to inform ethically sound and economically efficient decision-making.     

A warm-season comparison of WRF coupled to the CLM4.0, Noah-MP,and Bucket hydrology land surface schemes over the central USA

In climate modeling studies, there is a need to choose a suitable land surface model (LSM) while adhering to available resources. In this study, the viability of three LSM options (Community Land Model version 4.0 [CLM4.0], Noah-MP, and the five-layer thermal diffusion [Bucket] scheme) in the Weather Research and Forecasting model version 3.6 (WRF3.6) was examined for the warm season in a domain centered on the central USA. Model output was compared to Parameter-elevation Relationships on Independent Slopes Model (PRISM) data, a gridded observational dataset including mean monthly temperature and total monthly precipitation. Model output temperature, precipitation, latent heat (LH) flux, sensible heat (SH) flux, and soil water content (SWC) were compared to observations from sites in the Central and Southern Great Plains region. An overall warm bias was found in CLM4.0 and Noah-MP, with a cool bias of larger magnitude in the Bucket model. These three LSMs produced similar patterns of wet and dry biases. Model output of SWC and LH/SH fluxes were compared to observations, and did not show a consistent bias. Both sophisticated LSMs appear to be viable options for simulating the effects of land use change in the central USA.     

Different Asian Monsoon Rainfall Responses to Idealized Orography Sensitivity Experiments in the HadGEM3-GA6 and FGOALS-FAMIL Global Climate Models

Recent work has shown the dominance of the Himalaya in supporting the Indian summer monsoon(ISM),perhaps by surface sensible heating along its southern slope and by mechanical blocking acting to separate moist tropical flow from drier midlatitude air.Previous studies have also shown that Indian summer rainfall is largely unaffected in sensitivity experiments that remove only the Tibetan Plateau.However,given the large biases in simulating the monsoon in CMIP5 models,such results may be model dependent.This study investigates the impact of orographic forcing from the Tibetan Plateau,Himalaya and Iranian Plateau on the ISM and East Asian summer monsoon(EASM) in the UK Met Office's Had GEM3-GA6 and China's Institute of Atmospheric Physics FGOALS-FAMIL global climate models.The models chosen feature oppositesigned biases in their simulation of the ISM rainfall and circulation climatology.The changes to ISM and EASM circulation across the sensitivity experiments are similar in both models and consistent with previous studies.However,considerable differences exist in the rainfall responses over India and China,and in the detailed aspects such as onset and retreat dates.In particular,the models show opposing changes in Indian monsoon rainfall when the Himalaya and Tibetan Plateau orography are removed.Our results show that a multi-model approach,as suggested in the forthcoming Global Monsoon Model Intercomparison Project(GMMIP) associated with CMIP6,is needed to clarify the impact of orographic forcing on the Asian monsoon and to fully understand the implications of model systematic error.