Regularly varying probability densities

The convolution of regularly varying probability densities is proved asymptotic to their sum, and hence is also regularly varying. Extensions to rapid variation, O-regular variation, and other types of asymptotic decay are also given

Electrothermal Model of Kinetic Inductance Detectors

An electrothermal model of Kinetic Inductance Detectors (KIDs) is described. The non-equilibrium state of the resonator's quasiparticle system is characterized by an effective temperature, which because of readout-power heating is higher than that of the bath. By balancing the flow of energy into the quasiparticle system, it is possible to calculate the steady-state large-signal, small-signal and noise behaviour. Resonance-curve distortion and hysteretic switching appear naturally within the framework. It is shown that an electrothermal feedback process exists, which affects all aspects of behaviour. It is also shown that generation-recombination noise can be interpreted in terms of the thermal fluctuation noise in the effective thermal conductance that links the quasiparticle and phonon systems of the resonator. Because the scheme is based on electrothermal considerations, multiple elements can be added to simulate the behaviour of complex devices, such as resonators on membranes, again taking into account readout power heating.Comment: This is an author-created, un-copyedited version of an article published in Superconductor Science and Technology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at 10.1088/0953-2048/28/4/045012. Consult Version of Record for changes in response to reviewer

Australian climate extremes in the 21st century according to a regional climate model ensemble: Implications for health and agriculture

The negative impacts of climate extremes on socioeconomic sectors in Australia makes understanding their behaviour under future climate change necessary for regional planning. Providing robust and actionable climate information at regional scales relies on the downscaling of global climate model data and its translation into impact-relevant information. The New South Wales/Australian Capital Territory Regional Climate Modelling (NARCliM) project contains downscaled climate data over all of Australia at a 50 km resolution, with ensembles of simulations for the recent past (1990–2009), near future (2020–2039) and far future (2060–2079). Here we calculate and examine sector-relevant indices of climate extremes recommended by the Expert Team on Sector-specific Climate Indices (ET-SCI). We demonstrate the utility of NARCliM and the ET-SCI indices in understanding how future changes in climate extremes could impact aspects of the health and agricultural sectors in Australia. Consistent with previous climate projections, our results indicate that increases in heat and drought related extremes throughout the 21st century will occur. In the far future, maximum day time temperatures are projected to increase by up to 3.5 °C depending on season and location. The number of heatwaves and the duration of the most intense heatwaves will increase significantly in the near and far future, with greater increases in the north than south. All capital cities are projected to experience at least a tripling of heatwave days each year by the far future, compared to the recent past. Applying published heat-health relationships to projected changes in temperature shows that increases in mortality due to high temperatures for all cities examined would occur if projected future climates occurred today. Drought and the number of days above 30 °C are also projected to increase over the major wheat-growing regions of the country, particularly during spring when sensitivity of wheat to heat stress is greatest. Assuming no adaptation or acclimatisation, published statistical relationships between drought and national wheat yield suggest that national yields will have a less than one quarter chance of exceeding the annual historical average under far future precipitation change (excluding impacts of future temperature change and CO2 fertilization). The NARCliM data examined here, along with the ET-SCI indices calculated, provide a powerful and publicly available dataset for regional planning against future changes in climate extremes