Visual attention and attribute attendance in multi-attribute choice experiments

Decision strategies in multi-attribute Choice Experiments are investigated using eye-tracking. The visual attention towards, and attendance of, attributes is examined. Stated attendance is found to diverge substantively from visual attendance of attributes. However, stated and visual attendance are shown to be informative, non-overlapping sources of information about respondent utility functions when incorporated into model estimation. Eye-tracking also reveals systematic nonattendance of attributes only by a minority of respondents. Most respondents visually attend most attributes most of the time. We find no compelling evidence that the level of attention is related to respondent certainty, or that higher or lower value attributes receive more or less attention

Using attribute importance rankings within discrete choice experiments: an application to valuing bread attributes

We present a new Bayesian econometric specification for a hypothetical Discrete Choice Experiment (DCE) incorporating respondent ranking information about attribute importance. Our results indicate that a DCE debriefing question that asks respondents to rank the importance of attributes helps to explain the resulting choices. We also examine how mode of survey delivery (online and mail) impacts model performance, finding that results are not substantively a§ected by the mode of survey delivery. We conclude that the ranking data is a complementary source of information about respondent utility functions within hypothetical DCEs

An examination of climate sensitivity for idealised climate change experiments in an intermediate general circulation model

Radiative forcing and climate sensitivity have been widely used as concepts to understand climate change. This work performs climate change experiments with an intermediate general circulation model (IGCM) to examine the robustness of the radiative forcing concept for carbon dioxide and solar constant changes. This IGCM has been specifically developed as a computationally fast model, but one that allows an interaction between physical processes and large-scale dynamics; the model allows many long integrations to be performed relatively quickly. It employs a fast and accurate radiative transfer scheme, as well as simple convection and surface schemes, and a slab ocean, to model the effects of climate change mechanisms on the atmospheric temperatures and dynamics with a reasonable degree of complexity. The climatology of the IGCM run at T-21 resolution with 22 levels is compared to European Centre for Medium Range Weather Forecasting Reanalysis data. The response of the model to changes in carbon dioxide and solar output are examined when these changes are applied globally and when constrained geographically (e.g. over land only). The CO2 experiments have a roughly 17% higher climate sensitivity than the solar experiments. It is also found that a forcing at high latitudes causes a 40% higher climate sensitivity than a forcing only applied at low latitudes. It is found that, despite differences in the model feedbacks, climate sensitivity is roughly constant over a range of distributions of CO2 and solar forcings. Hence, in the IGCM at least, the radiative forcing concept is capable of predicting global surface temperature changes to within 30%, for the perturbations described here. It is concluded that radiative forcing remains a useful tool for assessing the natural and anthropogenic impact of climate change mechanisms on surface temperature.

Causal evidence for a privileged working memory state in early visual cortex

Emerging evidence suggests that items held in working memory(WM)might not all be in the same representational state. One item might be privileged over others, making it more accessible and thereby recalled with greater precision. Here, using transcranial magnetic stimulation (TMS), we provide causal evidence in human participants that items inWMare differentially susceptible to disruptive TMS, depending on their state, determined either by task relevance or serial position. Across two experiments, we applied TMS to area MT during the WM retention of two motion directions. In Experiment 1, we used an “incidental cue” to bring one of the two targets into a privileged state. In Experiment 2, we presented the targets sequentially so that the last item was in a privileged state by virtue of recency. In both experiments, recall precision of motion direction was differentially affected by TMS, depending on the state of the memory target at the time of disruption. Privileged items were recalled with less precision, whereas nonprivileged items were recalled with higher precision. Thus, only the privileged item was susceptible to disruptive TMS over MT�. By contrast, precision of the nonprivileged item improved either directly because of facilitation by TMS or indirectly through reduced interference from the privileged item. Our results provide a unique line of evidence, as revealed by TMS over a posterior sensory brain region, for at least two different states of item representation in WM.

Results of PMIP2 coupled simulations of the mid-Holocene and Last Glacial Maximum, Part 1: experiments and large-scale features

A set of coupled ocean-atmosphere simulations using state of the art climate models is now available for the Last Glacial Maximum and the Mid-Holocene through the second phase of the Paleoclimate Modeling Intercomparison Project (PMIP2). This study presents the large-scale features of the simulated climates and compares the new model results to those of the atmospheric models from the first phase of the PMIP, for which sea surface temperature was prescribed or computed using simple slab ocean formulations. We consider the large-scale features of the climate change, pointing out some of the major differences between the different sets of experiments. We show in particular that systematic differences between PMIP1 and PMIP2 simulations are due to the interactive ocean, such as the amplification of the African monsoon at the Mid-Holocene or the change in precipitation in mid-latitudes at the LGM. Also the PMIP2 simulations are in general in better agreement with data than PMIP1 simulations.

The depression of tropical snow lines at the Last Glacial Maximum: what can we learn from climate and model experiments?

Analyses of simulations of the last glacial maximum (LGM) made with 17 atmospheric general circulation models (AGCMs) participating in the Paleoclimate Modelling Intercomparison Project, and a high-resolution (T106) version of one of the models (CCSR1), show that changes in the elevation of tropical snowlines (as estimated by the depression of the maximum altitude of the 0 °C isotherm) are primarily controlled by changes in sea-surface temperatures (SSTs). The correlation between the two variables, averaged for the tropics as a whole, is 95%, and remains >80% even at a regional scale. The reduction of tropical SSTs at the LGM results in a drier atmosphere and hence steeper lapse rates. Changes in atmospheric circulation patterns, particularly the weakening of the Asian monsoon system and related atmospheric humidity changes, amplify the reduction in snowline elevation in the northern tropics. Colder conditions over the tropical oceans combined with a weakened Asian monsoon could produce snowline lowering of up to 1000 m in certain regions, comparable to the changes shown by observations. Nevertheless, such large changes are not typical of all regions of the tropics. Analysis of the higher resolution CCSR1 simulation shows that differences between the free atmospheric and along-slope lapse rate can be large, and may provide an additional factor to explain regional variations in observed snowline changes.

A systematic approach to identify the sources of tropical SST errors in coupled models using the adjustment of initialised experiments

Understanding the sources of systematic errors in climate models is challenging because of coupled feedbacks and errors compensation. The developing seamless approach proposes that the identification and the correction of short term climate model errors have the potential to improve the modeled climate on longer time scales. In previous studies, initialised atmospheric simulations of a few days have been used to compare fast physics processes (convection, cloud processes) among models. The present study explores how initialised seasonal to decadal hindcasts (re-forecasts) relate transient week-to-month errors of the ocean and atmospheric components to the coupled model long-term pervasive SST errors. A protocol is designed to attribute the SST biases to the source processes. It includes five steps: (1) identify and describe biases in a coupled stabilized simulation, (2) determine the time scale of the advent of the bias and its propagation, (3) find the geographical origin of the bias, (4) evaluate the degree of coupling in the development of the bias, (5) find the field responsible for the bias. This strategy has been implemented with a set of experiments based on the initial adjustment of initialised simulations and exploring various degrees of coupling. In particular, hindcasts give the time scale of biases advent, regionally restored experiments show the geographical origin and ocean-only simulations isolate the field responsible for the bias and evaluate the degree of coupling in the bias development. This strategy is applied to four prominent SST biases of the IPSLCM5A-LR coupled model in the tropical Pacific, that are largely shared by other coupled models, including the Southeast Pacific warm bias and the equatorial cold tongue bias. Using the proposed protocol, we demonstrate that the East Pacific warm bias appears in a few months and is caused by a lack of upwelling due to too weak meridional coastal winds off Peru. The cold equatorial bias, which surprisingly takes 30 years to develop, is the result of an equatorward advection of midlatitude cold SST errors. Despite large development efforts, the current generation of coupled models shows only little improvement. The strategy proposed in this study is a further step to move from the current random ad hoc approach, to a bias-targeted, priority setting, systematic model development approach.