Unravelling the role of tropical cyclones in shaping present species distributions.

Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored.

Continental United States climate projections based on thermodynamic modification of historical weather.

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals).

Global tropical cyclone precipitation scaling with sea surface temperature.

Understanding the relationship between tropical cyclone (TC) precipitation and sea surface temperature (SST) is essential for both TC hazard forecasting and projecting how these hazards will change in the future due to climate change. This work untangles how global TC precipitation is impacted by present-day SST variability (known as apparent scaling) and by long-term changes in SST caused by climate change (known as climate scaling). A variety of datasets are used including precipitation and SST observations, realistic climate model simulations, and idealized climate model simulations.

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.

Development of a supported self-management intervention for adults with type 2 diabetes and a learning disability.

Background: Although supported self-management is a well-recognised part of chronic disease management, it has not been routinely used as part of healthcare for adults with a learning disability. We developed an intervention for adults with a mild or moderate learning disability and type 2 diabetes, building on the principles of supported self-management with reasonable adjustments made for the target population.

Methods: In five steps, we:Clarified the principles of supported self-management as reported in the published literatureIdentified the barriers to effective self-management of type 2 diabetes in adults with a learning disabilityReviewed existing materials that aim to support self-management of diabetes for people with a learning disabilitySynthesised the outputs from the first three phases and identified elements of supported self-management that were (a) most relevant to the needs of our target population and (b) most likely to be acceptable and useful to themImplemented and field tested the intervention.