Forecasted attribution of the human influence on Hurricane Florence.

Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm's core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones

Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections

Seasonal extreme daily precipitation is analyzed in the ensemble of NARCAPP regional climate models. Significant variation in these models' abilities to reproduce observed precipitation extremes over the contiguous United States is found. Model performance metrics are introduced to characterize overall biases, seasonality, spatial extent and the shape of the precipitation distribution. Comparison of the models to gridded observations that include an elevation correction is found to be better than to gridded observations without this correction. A complicated model weighting scheme based on model performance in simulating observations is found to cause significant improvements in ensemble mean skill only if some of the models are poorly performing outliers. The effect of lateral boundary conditions are explored by comparing the integrations driven by reanalysis to those driven by global climate models. Projected mid-century future changes in seasonal precipitation means and extremes are presented and discussions of the sources of uncertainty and the mechanisms causing these changes are presented. © 2012 The Author(s)

Insect-Inspired Navigation Algorithm for an Aerial Agent Using Satellite Imagery

Humans have long marveled at the ability of animals to navigate swiftly, accurately, and across long distances. Many mechanisms have been proposed for how animals acquire, store, and retrace learned routes, yet many of these hypotheses appear incongruent with behavioral observations and the animals’ neural constraints. The “Navigation by Scene Familiarity Hypothesis” proposed originally for insect navigation offers an elegantly simple solution for retracing previously experienced routes without the need for complex neural architectures and memory retrieval mechanisms. This hypothesis proposes that an animal can return to a target location by simply moving toward the most familiar scene at any given point. Proof of concept simulations have used computer-generated ant’s-eye views of the world, but here we test the ability of scene familiarity algorithms to navigate training routes across satellite images extracted from Google Maps. We find that Google satellite images are so rich in visual information that familiarity algorithms can be used to retrace even tortuous routes with low-resolution sensors. We discuss the implications of these findings not only for animal navigation but also for the potential development of visual augmentation systems and robot guidance algorithms.Ye

Using an insect mushroom body circuit to encode route memory in complex natural environments

Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster) olfactory association, can also account for the ability of desert ants (Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images

Effects of Alcohol on the Acquisition and Expression of Fear Potentiated Startle in Mouse Lines Selectively Bred for High and Low Alcohol Preference

Rationale: Anxiety disorders and alcohol-use disorders frequently co-occur in humans perhaps because alcohol relieves anxiety. Studies in humans and rats indicate that alcohol may have greater anxiolytic effects in organisms with increased genetic propensity for high alcohol consumption. Objectives and Methods: The purpose of this study was to investigate the effects of moderate doses of alcohol (0.5, 1.0, 1.5 g/kg) on the acquisition and expression of anxiety-related behavior using a fear-potentiated startle (FPS) procedure. Experiments were conducted in two replicate pairs of mouse lines selectively bred for high- (HAP1 and HAP2) and low- (LAP1 and LAP2) alcohol preference; these lines have previously shown a genetic correlation between alcohol preference and FPS (HAP\u3eLAP; Barrenha and Chester 2007). In a control experiment, the effect of diazepam (4.0 mg/kg) on the expression of FPS was tested in HAP2 and LAP2 mice. Results: The 1.5 g/kg alcohol dose moderately decreased the expression of FPS in both HAP lines but not LAP lines. Alcohol had no effect on the acquisition of FPS in any line. Diazepam reduced FPS to a similar extent in both HAP2 and LAP2 mice. Conclusions: HAP mice may be more sensitive to the anxiolytic effects of alcohol than LAP mice when alcohol is given prior to the expression of FPS. These data collected in two pairs of HAP/LAP mouse lines suggest that the anxiolytic response to alcohol in HAP mice may be genetically correlated with their propensity toward high alcohol preference and robust FPS