Simple uncertainty frameworks for selecting weighting schemes and interpreting multimodel ensemble climate change experiments

Future climate change projections are often derived from ensembles of simulations from multiple global circulation models using heuristic weighting schemes. This study provides a more rigorous justification for this by introducing a nested family of three simple analysis of variance frameworks. Statistical frameworks are essential in order to quantify the uncertainty associated with the estimate of the mean climate change response. The most general framework yields the “one model, one vote” weighting scheme often used in climate projection. However, a simpler additive framework is found to be preferable when the climate change response is not strongly model dependent. In such situations, the weighted multimodel mean may be interpreted as an estimate of the actual climate response, even in the presence of shared model biases. Statistical significance tests are derived to choose the most appropriate framework for specific multimodel ensemble data. The framework assumptions are explicit and can be checked using simple tests and graphical techniques. The frameworks can be used to test for evidence of nonzero climate response and to construct confidence intervals for the size of the response. The methodology is illustrated by application to North Atlantic storm track data from the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble. Despite large variations in the historical storm tracks, the cyclone frequency climate change response is not found to be model dependent over most of the region. This gives high confidence in the response estimates. Statistically significant decreases in cyclone frequency are found on the flanks of the North Atlantic storm track and in the Mediterranean basin.

Overview and response to reviewers of The Singing Neanderthals

Why are humans musical? Why do people in all cultures sing or play instruments? Why do we appear to have specialized neurological apparatus for hearing and interpreting music as distinct from other sounds? And how does our musicality relate to language and to our evolutionary history? Anthropologists and archaeologists have paid little attention to the origin of music and musicality — far less than for either language or ‘art’. While art has been seen as an index of cognitive complexity and language as an essential tool of communication, music has suffered from our perception that it is an epiphenomenal ‘leisure activity’, and archaeologically inaccessible to boot. Nothing could be further from the truth, according to Steven Mithen; music is integral to human social life, he argues, and we can investigate its ancestry with the same rich range of analyses — neurological, physiological, ethnographic, linguistic, ethological and even archaeological — which have been deployed to study language. In The Singing Neanderthals Steven Mithen poses these questions and proposes a bold hypothesis to answer them. Mithen argues that musicality is a fundamental part of being human, that this capacity is of great antiquity, and that a holistic protolanguage of musical emotive expression predates language and was an essential precursor to it. This is an argument with implications which extend far beyond the mere origins of music itself into the very motives of human origins. Any argument of such range is bound to attract discussion and critique; we here present commentaries by archaeologists Clive Gamble and Iain Morley and linguists Alison Wray and Maggie Tallerman, along with Mithen's response to them. Whether right or wrong, Mithen has raised fascinating and important issues. And it adds a great deal of charm to the time-honoured, perhaps shopworn image of the Neanderthals shambling ineffectively through the pages of Pleistocene prehistory to imagine them humming, crooning or belting out a cappella harmonies as they went.

Soils and their distribution on Bambouto volcanic mountain, West Cameroon highland, Central Africa

Morphological, physical and chemical studies were carried out on soils of Mount Bambouto, a volcanic mountain of the West Cameroon highland. These studies show that the soils of this region can be divided into seven groups according to Soils Taxonomy USA [Soil taxonomy: a basic system of soil classification for making and interpreting soils surveys: USDA Agriculture Handbook 436: Washington, DC, US Government Pronting Office, 1975, 754]: lithic dystrandept soils, typical dystrandept soils, oxic dystrandept soils, typical haplohumox soils, typical kandiudox soils, tropopsamment soils and umbriaquox soils. A soils map of this region at scale 1:50,000 has been drawn up, using the seven soils groups above as soil cartography units. These soils are organised into of three main categories: soils with andic characteristics in the upper region of the mountain (lithic dystrandept soils, typical dystrandept soils and oxic dystrandept soils); ferrallitic soils in the lower part of the mountain (typical haplohumox soils and typical kandiudox soils) and imperfectly developed soils (tropopsamment soils and umbraquox soils).

Theory of mind and attentional bias to facial emotional expressions: A preliminary study

Theory of mind ability has been associated with performance in interpersonal interactions and has been found to influence aspects such as emotion recognition, social competence, and social anxiety. Being able to attribute mental states to others requires attention to subtle communication cues such as facial emotional expressions. Decoding and interpreting emotions expressed by the face, especially those with negative valence, are essential skills to successful social interaction. The current study explored the association between theory of mind skills and attentional bias to facial emotional expressions. According to the study hypothesis, individuals with poor theory of mind skills showed preferential attention to negative faces over both non-negative faces and neutral objects. Tentative explanations for the findings are offered emphasizing the potential adaptive role of vigilance for threat as a way of allocating a limited capacity to interpret others’ mental states to obtain as much information as possible about potential danger in the social environment.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Participatory scenarios as a tool to link science and policy on food security under climate change in East Africa

How effective are multi-stakeholder scenarios building processes to bring diverse actors together and create a policy-making tool to support sustainable development and promote food security in the developing world under climate change? The effectiveness of a participatory scenario development process highlights the importance of ‘boundary work’ that links actors and organizations involved in generating knowledge on the one hand, and practitioners and policymakers who take actions based on that knowledge on the other. This study reports on the application of criteria for effective boundary work to a multi-stakeholder scenarios process in East Africa that brought together a range of regional agriculture and food systems actors. This analysis has enabled us to evaluate the extent to which these scenarios were seen by the different actors as credible, legitimate and salient, and thus more likely to be useful. The analysis has shown gaps and opportunities for improvement on these criteria, such as the quantification of scenarios, attention to translating and communicating the results through various channels and new approaches to enable a more inclusive and diverse group of participants. We conclude that applying boundary work criteria to multi-stakeholder scenarios processes can do much to increase the likelihood of developing sustainable development and food security policies that are more appropriate.

Design, implementation and interpretation of in vitro batchculture experiments to assess enteric methane mitigation in ruminants—a review

In vitro fermentation techniques (IVFT) have been widely used to evaluate the nutritivevalue of feeds for ruminants and in the last decade to assess the effect of different nutritionalstrategies on methane (CH4) production. However, many technical factors may influencethe results obtained. The present review has been prepared by the ‘Global Network’ FACCE-JPI international research consortium to provide a critical evaluation of the main factorsthat need to be considered when designing, conducting and interpreting IVFT experimentsthat investigate nutritional strategies to mitigate CH4emission from ruminants. Given theincreasing and wide-scale use of IVFT, there is a need to critically review reports in the lit-erature and establish what criteria are essential to the establishment and implementationof in vitro techniques. Key aspects considered include: i) donor animal species and numberof animal used, ii) diet fed to donor animals, iii) collection and processing of rumen fluidas inoculum, iv) choice of substrate and incubation buffer, v) incubation procedures andCH4measurements, vi) headspace gas composition and vii) comparability of in vitro andin vivo measurements. Based on an evaluation of experimental evidence, a set of techni-cal recommendations are presented to harmonize IVFT for feed evaluation, assessment ofrumen function and CH4production.

Effect of ENSO phase on large-scale snow water equivalent distribution in a GCM

Understanding links between the El Nino-Southern Oscillation (ENSO) and snow would be useful for seasonal forecasting, but also for understanding natural variability and interpreting climate change predictions. Here, a 545-year run of the general circulation model HadCM3, with prescribed external forcings and fixed greenhouse gas concentrations, is used to explore the impact of ENSO on snow water equivalent (SWE) anomalies. In North America, positive ENSO events reduce the mean SWE and skew the distribution towards lower values, and vice versa during negative ENSO events. This is associated with a dipole SWE anomaly structure, with anomalies of opposite sign centered in western Canada and the central United States. In Eurasia, warm episodes lead to a more positively skewed distribution and the mean SWE is raised. Again, the opposite effect is seen during cold episodes. In Eurasia the largest anomalies are concentrated in the Himalayas. These correlations with February SWE distribution are seen to exist from the previous June-July-August (JJA) ENSO index onwards, and are weakly detected in 50-year subsections of the control run, but only a shifted North American response can be detected in the anaylsis of 40 years of ERA40 reanalysis data. The ENSO signal in SWE from the long run could still contribute to regional predictions although it would be a weak indicator only

Ballet-performing normality in the face of crisis

This paper contextualises the framework and methodology for producing the video performance Ballet, by Szuper Gallery (Susanne Clausen & Pavlo Kerestey), which was initiated through an encounter with an archive of rural information and propaganda films from the Museum of English Rural Life [MERL] in Reading, UK. This project looked at ways of extrapolating filmed gestures from the MERL films to choreograph a large-scale performance film and to consider how this practice-led research could instigate a new way of engaging with and interpreting the MERL film collection. The resulting video was produced in 2009 and was first exhibited at MERL, where it became part of the archive. This was followed by a series of international screenings. I will set out the surrounding research in and around the archive propaganda films, focusing on the performances by rural extras (background actors) in these films, while looking at the way one could understand the relation between a future-past, or tradition and accident in these films (Massumi, 1993). I will pair this with a reflection on the cultural reading of the extras (Didi-Huberman, 2009) and the notion of social choreography (Hewitt, 2005) in this context. I will then lay out reflections on artistic methods for the final performance, a Crash Choreography, based on calculated, but spontaneous encounters.

Convective quasi–equilibrium

The concept of convective quasi–equilibrium (CQE) is a key ingredient in order to understand the role of deep moist convection in the atmosphere. It has been used as a guiding principle to develop almost all convective parameterizations and provides a basic theoretical framework for large–scale tropical dynamics. The CQE concept as originally proposed by Arakawa and Schubert [1974] is systematically reviewed from wider perspectives. Various interpretations and extensions of Arakawa and Schubert’s CQE are considered in terms of both a thermodynamic analogy and as a dynamical balance. The thermodynamic interpretations can be more emphatically embraced as a homeostasis. The dynamic balance interpretations can be best understood by analogy with the slow manifold. Various criticisms of CQE can be avoided by taking the dynamic balance interpretation. Possible limits of CQE are also discussed, including the importance of triggering in many convective situations, as well as the possible self–organized criticality of tropical convection. However, the most intriguing aspect of the CQE concept is that, in spite of many observational tests supporting and interpreting it in many different senses, it has 1never been established in a robust manner based on a systematic analysis of the cloud–work function budget by observations as was originally defined.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.